NIRx NIRS Publications

There have been many peer-reviewed publications over the past 30 years using near-infrared specstrocopy in neuroimaging.  This is a small sample of some recent publications by NIRx end-users, organized by application.

Auditory System

As fNIRS measurements are characterized by silent operations, innumerous possibilities of studies intended to explore cortical activation in the presence of controlled sounds can be achieved. Besides a better understanding of auditory processes in the brain, this may facilitate critical improvements on current solutions for cochlear implants.

Alemi, R., Wolfe, J., Neumann, S., Manning, J., Hanna, L., Towler, W., ... & Deroche, M. (2023). Motor Processing in Children With Cochlear Implants as Assessed by Functional Near-Infrared Spectroscopy. Perceptual and Motor Skills, 00315125231213167.

Yaman, H., Yılmaz, O., Hanoğlu, L., & Bayazıt, Y. (2023). fNIRS‐based evaluation of the impact of SARS‐CoV‐2 infection central auditory processing. Brain and Behavior, e3303.

Wang, Y., Wu, M., Wu, K., Liu, H., Wu, S., Zhang, Z., ... & Liu, Y. (2022). Differential auditory cortical development in left and right cochlear implanted children. Cerebral Cortex, 32(23), 5438-5454.

Steinmetzger, K., Meinhardt, B., Praetorius, M., Andermann, M., & Rupp, A. (2022). A direct comparison of voice pitch processing in acoustic and electric hearing. NeuroImage: Clinical, 36, 103188.

Zhang, Y. F., Lasfargues-Delannoy, A., & Berry, I. (2022). Adaptation of stimulation duration to enhance auditory response in fNIRS block design. Hearing Research, 424, 108593.

Coëz, A., Loundon, N., Rouillon, I., Parodi, M., Blanchard, M., Achard, S., ... & Gervain, J. (2022). The recognition of human voice in deaf and hearing infants. Hearing, Balance and Communication, 20(3), 179-185.

Luke, R., Innes-Brown, H., Undurraga, J., & McAlpine, D. (2022). Human Cortical Processing of Interaural Coherence. iScience, 104181.

Zhou, X., Planalp, E. M., Heinrich, L., Pletcher, C., DiPiero, M., Alexander, A. L., ... & III, D. C. D. (2022). Inhibitory Control in Children 4–10 Years of Age: Evidence From Functional Near-Infrared Spectroscopy Task-Based Observations. Frontiers in Human Neuroscience.

Laurent, S., Paire-Ficout, L., Boucheix, J. M., Argon, S., & Hidalgo-Muñoz, A. R. (2021). Cortical Activity Linked to Clocking in Deaf Adults: fNIRS Insights with Static and Animated Stimuli Presentation. Brain Sciences, 11(2), 196.

Steinmetzger, K., Shen, Z., Riedel, H., & Rupp, A. (2020). Auditory cortex activity measured using functional near-infrared spectroscopy (fNIRS) appears to be susceptible to masking by cortical blood stealing. Hearing Research, 108069.

H. Bortfeld, “Functional near‐infrared spectroscopy as a tool for assessing speech and spoken language processing in pediatric and adult cochlear implant users,” Developmental Psychobiology, Dec. 2018.

X. Zhou et al., “Cortical Speech Processing in Postlingually Deaf Adult Cochlear Implant Users, as Revealed by Functional Near-Infrared Spectroscopy,” Trends Hear, vol. 22, p. 2331216518786850, Dec. 2018.

S. Weder, X. Zhou, M. Shoushtarian, H. Innes-Brown, and C. McKay, “Cortical Processing Related to Intensity of a Modulated Noise Stimulus—a Functional Near-Infrared Study,” JARO, vol. 19, no. 3, pp. 273–286, Jun. 2018.

H. Chuang, Z. Cao, J.-T. King, B.-S. Wu, Y.-K. Wang, and C.-T. Lin, “Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving,” Front. Neurosci., vol. 12, 2018.

D. Farkas, S. L. Denham, and I. Winkler, “Functional brain networks underlying idiosyncratic switching patterns in multi-stable auditory perception,” Neuropsychologia, vol. 108, pp. 82–91, Jan. 2018.

X. Zhou, H. Innes-Brown, and C. McKay, “Using fNIRS to study audio-visual speech integration in post-lingually deafened cochlear implant users,” Proceedings of the International Symposium on Auditory and Audiological Research, vol. 6, pp. 55–62, Dec. 2017.

R. Gabbard, M. Fendley, I. A. Dar, R. Warren, and N. H. Kashou, “Utilizing functional near-infrared spectroscopy for prediction of cognitive workload in noisy work environments,” Neurophotonics, vol. 4, no. 04, p. 1, Aug. 2017.

D. Zhang, Y. Zhou, X. Hou, Y. Cui, and C. Zhou, “Discrimination of emotional prosodies in human neonates: A pilot fNIRS study,” Neuroscience Letters, vol. 658, pp. 62–66, Sep. 2017. 

C. Olds et al., “Cortical Activation Patterns Correlate with Speech Understanding After Cochlear Implantation,” Ear Hear, vol. 37, no. 3, pp. e160-172, Jun. 2016.

C. Issard and J. Gervain, “Adult-like processing of time-compressed speech by newborns: A NIRS study,” Developmental Cognitive Neuroscience. Oct. 2017.

K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hearing Research, vol. 333, pp. 157–166, Mar. 2016.

L.-C. Chen, M. Stropahl, M. Schönwiesner, and S. Debener, “Enhanced visual adaptation in cochlear implant users revealed by concurrent EEG-fNIRS,” Neuroimage, Sep. 2016.

L.-C. Chen, P. Sandmann, J. D. Thorne, M. G. Bleichner, and S. Debener, “Cross-Modal Functional Reorganization of Visual and Auditory Cortex in Adult Cochlear Implant Users Identified with fNIRS,” Neural Plast, vol. 2016, 2016.

N. Altvater-Mackensen and T. Grossmann, “The role of left inferior frontal cortex during audiovisual speech perception in infants,” NeuroImage, vol. 133, pp. 14–20, Jun. 2016.

N. Abboub, T. Nazzi, and J. Gervain, “Prosodic grouping at birth,” Brain Lang, vol. 162, pp. 46–59, Aug. 2016.

L.-C. Chen, P. Sandmann, J. D. Thorne, C. S. Herrmann, and S. Debener, “Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli,” Brain Topogr, vol. 28, no. 5, pp. 710–725, Sep. 2015.

C. Bouchon, T. Nazzi, and J. Gervain, “Hemispheric Asymmetries in Repetition Enhancement and Suppression Effects in the Newborn Brain,” PLOS ONE, vol. 10, no. 10, p. e0140160, Oct. 2015.

H. Santosa, M. J. Hong, and K.-S. Hong, “Lateralization of music processing with noises in the auditory cortex: an fNIRS study,” Front Behav Neurosci, vol. 8, p. 418, 2014.

L. Pollonini, C. Olds, H. Abaya, H. Bortfeld, M. S. Beauchamp, and J. S. Oghalai, “Auditory cortex activation to natural speech and simulated cochlear implant speech measured with functional near-infrared spectroscopy,” Hear. Res., vol. 309, pp. 84–93, Mar. 2014.

T. T. Brink et al., “The role of orbitofrontal cortex in processing empathy stories in 4- to 8-year-old children,” Front Psychol, vol. 2, p. 80, 2011.

For latest updates on health information pertaining to hearing, balance, taste, smell, and speech and language development, please visit: 

http://www.nidcd.nih.gov/Pages/default.aspx

Brain-Computer Interface (BCI)

Given its great performance in the presence of muscle movements and the possibility of setting up measurements in realistic environments, fNIRS presents itself as an ideal candidate for the acquisition of cortical signals as reliable and representative inputs for Brain-Computer Interface investigations.

Clemente, L., La Rocca, M., Paparella, G., Delussi, M., Tancredi, G., Ricci, K., ... & de Tommaso, M. (2024). Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain—Computer Interface Study. Sensors, 24(7), 2329.

Pyarelal, A., Duong, E., Shibu, C. J., Soares, P., Boyd, S., Khosla, P., ... & Barnard, K. (2023, November). The ToMCAT Dataset. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://tomcat.ivilab.org/

Vorreuther, A., Bastian, L., Benitez Andonegui, A., Evenblij, D., Riecke, L., Lührs, M., & Sorger, B. (2023). It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain–computer interface communication. Neurophotonics, 10(4), 045005-045005.

Lingelbach, K., Diers, D., Bui, M., and Vukelic, M. (2023) Investigating Feature Set Decisions for Mental State Decoding in Virtual Reality Based Learning Environments. Neuroergonomics and Cognitice Engineering, Vol 102, 141-151

Isaev, M. R., & Bobrov, P. D. (2023). Effects of Selection of the Learning Set Formation Strategy and Filtration Method on the Effectiveness of a BCI Based on Near Infrared Spectrometry. Neuroscience and Behavioral Physiology, 53(3), 373-380.

Zhang, Y., Qiu, S., & He, H. (2023). Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion. Journal of Neural Engineering, 20(2), 026009.

Subramanian, A., Shamsi, F., & Najafizadeh, L. (2023). Investigation of the Performance of fNIRS-based BCIs for Assistive Systems in the Presence of Acute Pain. In Signal Processing in Medicine and Biology: Innovations in Big Data Processing (pp. 61-85). Cham: Springer International Publishing.

Khalil, K., Asgher, U., & Ayaz, Y. (2022). Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain–computer interface. Scientific Reports, 12(1), 1-12.

Orihuela-Espina, F., Rojas-Cisneros, M., Montero-Hernández, S. A., García-Salinas, J. S., Cuervo-Soto, B., & Herrera-Vega, J. (2022). Physics augmented classification of fNIRS signals. In Biosignal Processing and Classification Using Computational Learning and Intelligence (pp. 375-405). Academic Press.

Sattar, N. Y., Kausar, Z., Usama, S. A., Farooq, U., Shah, M. F., Muhammad, S., ... & Badran, M. (2022). fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees. Sensors, 22(3), 726.

Nagels-Coune, L., Riecke, L., Benitez-Andonegui, A., Klinkhammer, S., Goebel, R., De Weerd, P., ... & Sorger, B. (2021). See, Hear, or Feel–to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions. Frontiers in Human Neuroscience, 15.

Ghaffar, M. S. B. A., Khan, U. S., Iqbal, J., Rashid, N., Hamza, A., Qureshi, W. S., ... & Izhar, U. (2021). Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC). Infrared Physics & Technology, 112, 103589.

Moslehi, A. H., & Davies, T. C. (2021, May). Comparison of Classification Accuracies Between Different Brain Areas During a Two-Class Motor Imagery in a fNIRS Based BCI. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 702-705). IEEE.

Shamsi, F., & Najafizadeh, L. (2021). Multi-class fNIRS Classification of Motor Execution Tasks with Application to Brain-Computer Interfaces. Biomedical Signal Processing: Innovation and Applications, 1-32.

Geissler, C. F., Schneider, J., & Frings, C. (2021). Shedding light on the prefrontal correlates of mental workload in simulated driving: a functional near-infrared spectroscopy study. Scientific reports, 11(1), 1-10.

Lyu, B., Pham, T., Blaney, G., Haga, Z., Sassaroli, A., Fantini, S., & Aeron, S. (2021). Domain adaptation for robust workload level alignment between sessions and subjects using fNIRS. Journal of Biomedical Optics, 26(2), 022908.

Ortega, P., Zhao, T., & Faisal, A. A. (2020). HYGRIP: Full-Stack Characterization of Neurobehavioral Signals (fNIRS, EEG, EMG, Force, and Breathing) During a Bimanual Grip Force Control Task. Frontiers in Neuroscience, 14.

Chhabra, H., Shajil, N., & Venkatasubramanian, G. (2020). Investigation of deep convolutional neural network for classification of motor imagery fNIRS signals for BCI applications. Biomedical Signal Processing and Control, 62, 102133.

Almulla, L., Al-Naib, I., & Althobaiti, M. (2020). Hemodynamic Responses During Standing and Sitting Activities: A Study Toward fNIRS-BCI. Biomedical Physics & Engineering Express

Aslan, O., Kurtoğlu, K. K., Yeşilalan, K., & Erdoğan, S. B. (2020, April). Machine Learning Based Prediction of Motor Imagery and Motor Execution Tasks from Functional Near Infrared Spectroscopy Signals. In Optics and the Brain (pp. BM4C-2). Optical Society of America.

Padmavathy, T. V., Kumar, M. P., Shakunthala, M., Kumar, M. V., & Saravanan, S. (2020). A Novel Deep Learning Classifier and Genetic Algorithm based Feature Selection for Hybrid EEG-fNIRS Brain-Computer Interface. NeuroQuantology, 18(9), 125.

Sun, Z., Huang, Z., Duan, F., & Liu, Y. (2020). A Novel Multimodal Approach for Hybrid Brain–Computer Interface. IEEE Access, 8, 89909-89918.

K. Li et al., “Functional Near-Infrared Spectroscopy (fNIRS) informed neurofeedback: regional-specific modulation of lateral orbitofrontal activation and cognitive flexibility,” bioRxiv, p. 511824, Jan. 2019.

L. R. Trambaiolli, C. E. Biazoli, A. M. Cravo, T. H. Falk, and J. R. Sato, “Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles,” NPh, vol. 5, no. 3, p. 035009, Sep. 2018.

Dehais, F., Dupres, A., Di Flumeri, G., Verdiere, K., Borghini, G., Babiloni, F., & Roy, R. (2018, October). Monitoring pilot's cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI. In 2018 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 544-549). IEEE.A.

Janani and M. Sasikala, “Evaluation of classification performance of functional near infrared spectroscopy signals during movement execution for developing a brain-computer interface application using optimal channels,” J. Near Infrared Spectrosc., JNIRS, vol. 26, no. 4, pp. 209–221, Aug. 2018.

S. E. Kober, V. Hinterleitner, G. Bauernfeind, C. Neuper, and G. Wood, “Trainability of hemodynamic parameters: A near-infrared spectroscopy based neurofeedback study,” Biological Psychology, vol. 136, pp. 168–180, Jul. 2018.

J. Shin, D.-W. Kim, K.-R. Müller, and H.-J. Hwang, “Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses,” Sensors (Basel), vol. 18, no. 6, Jun. 2018.

J. Shin, K.-R. Müller, and H.-J. Hwang, “Eyes-closed hybrid brain-computer interface employing frontal brain activation,” PLOS ONE, vol. 13, no. 5, p. e0196359, May 2018.

R. A. Khan, N. Naseer, N. K. Qureshi, F. M. Noori, H. Nazeer, and M. U. Khan, “fNIRS-based Neurorobotic Interface for gait rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, p. 7, Feb. 2018.

A. Janani and M. Sasikala, “Classification of fNIRS Signals for Decoding Right- and Left-Arm Movement Execution Using SVM for BCI Applications,” in Computational Signal Processing and Analysis, 2018, pp. 315–323.

F. Dehais et al., “Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI,” in IEEE SMC, 2018, pp. 1–6.

K. J. Verdière, R. N. Roy, and F. Dehais, “Detecting Pilot’s Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario,” Frontiers in Human Neuroscience, vol. 12, Jan. 2018.

K. Pollmann, D. Ziegler, M. Peissner, and M. Vukelić, “A New Experimental Paradigm for Affective Research in Neuro-adaptive Technologies,” 2017, pp. 1–8.

H. Banville, R. Gupta, and T. H. Falk, “Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–24, 2017.

M. Lührs and R. Goebel, “Turbo-Satori: a neurofeedback and brain–computer interface toolbox for real-time functional near-infrared spectroscopy,” Neurophotonics, vol. 4, no. 04, p. 1, Oct. 2017.

H. Aghajani, M. Garbey, and A. Omurtag, “Measuring Mental Workload with EEG+fNIRS,” Frontiers in Human Neuroscience, vol. 11, Jul. 2017.

N. K. Qureshi, N. Naseer, F. M. Noori, H. Nazeer, R. A. Khan, and S. Saleem, “Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients,” Frontiers in Neurorobotics, vol. 11, Jul. 2017.

A. Omurtag, H. Aghajani, and H. O. Keles, “Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance,” Journal of Neural Engineering, Jul. 2017.

F. M. Noori, N. Naseer, N. K. Qureshi, H. Nazeer, and R. A. Khan, “Optimal feature selection from fNIRS signals using genetic algorithms for BCI,” Neuroscience Letters, vol. 647, pp. 61–66, Apr. 2017.

M. Abtahi, A. Amiri, D. Byrd, and K. Mankodiya, “Hand Motion Detection in fNIRS Neuroimaging Data,” Healthcare, vol. 5, no. 2, p. 20, Apr. 2017.

J. Shin et al., “Open Access Dataset for EEG+NIRS Single-Trial Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. PP, no. 99, pp. 1–1, 2016.

J. Shin, K.-R. Müller, and H.-J. Hwang, “Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic,” Scientific Reports, vol. 6, p. 36203, Nov. 2016.

H. Aghajani and A. Omurtag, “Assessment of mental workload by EEG+FNIRS,” 2016, pp. 3773–3776.

N. Naseer, F. M. Noori, N. K. Qureshi, and K.-S. Hong, “Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application,” Front. Hum. Neurosci, p. 237, 2016.

K.-S. Hong and H. Santosa, “Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy,” Hearing Research, vol. 333, pp. 157–166, Mar. 2016.

A. P. Buccino, H. O. Keles, and A. Omurtag, “Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks,” PLOS ONE, vol. 11, no. 1, p. e0146610, Jan. 2016.

K. Tumanov, R. Goebel, R. Möckel, B. Sorger, and G. Weiss, “fNIRS-based BCI for Robot Control,” in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, Richland, SC, 2015, pp. 1953–1954.

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc, vol. 23, no. 1, pp. 23–31, 2015.

M.-H. Lee, S. Fazli, J. Mehnert, and S.-W. Lee, “Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI,” Pattern Recognition, vol. 48, no. 8, pp. 2725–2737, Aug. 2015.

M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed Opt Express, vol. 6, no. 10, pp. 4063–4078, Oct. 2015.

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI,” Neuroscience Letters, vol. 587, pp. 87–92, Feb. 2015.

R. K. Almajidy, Y. Boudria, U. G. Hofmann, W. Besio, and K. Mankodiya, “Multimodal 2D Brain Computer Interface,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 1067–1070.

W. Guo, P. Yao, X. Sheng, H. Liu, and X. Zhu, “A wireless wearable sEMG and NIRS acquisition system for an enhanced human-computer interface,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014, pp. 2192–2197.

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front Hum Neurosci, vol. 8, p. 244, 2014.

C.-H. Chen, M.-S. Ho, K.-K. Shyu, K.-C. Hsu, K.-W. Wang, and P.-L. Lee, “A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses,” Neuroscience Letters, vol. 580, pp. 22–26, Sep. 2014.

X. Shu, L. Yao, X. Sheng, D. Zhang, and X. Zhu, “A hybrid BCI study: Temporal optimization for EEG single-trial classification by exploring hemodynamics from the simultaneously measured NIRS data,” in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014, pp. 914–918.

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface,” Exp Brain Res, vol. 232, no. 2, pp. 555–564, Nov. 2013.

M. M. DiStasio and J. T. Francis, “Use of frontal lobe hemodynamics as reinforcement signals to an adaptive controller,” PLoS ONE, vol. 8, no. 7, p. e69541, 2013.

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain?computer interface,” Neuroscience Letters, vol. 553, pp. 84–89, Oct. 2013.

S. Waldert, L. Tüshaus, C. P. Kaller, A. Aertsen, and C. Mehring, “fNIRS Exhibits Weak Tuning to Hand Movement Direction,” PLOS ONE, vol. 7, no. 11, p. e49266, Nov. 2012.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J Neural Eng, vol. 9, no. 2, p. 26012, Apr. 2012.

C. Herff, F. Putze, D. Heger, C. Guan, and T. Schultz, “Speaking mode recognition from functional Near Infrared Spectroscopy,” Conf Proc IEEE Eng Med Biol Soc, vol. 2012, pp. 1715–1718, 2012.

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K.-R. Müller, and B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage, vol. 59, no. 1, pp. 519–529, Jan. 2012.

S. Fazli, J. Mehnert, J. Steinbrink, and B. Blankertz, “Using NIRS as a predictor for EEG-based BCI performance,” Conf Proc IEEE Eng Med Biol Soc, vol. 2012, pp. 4911–4914, 2012.

K. K. Ang, J. Yu, and C. Guan, “Extracting effective features from high density nirs-based BCI for assessing numerical cognition,” 2012, pp. 2233–2236.

V. Gottemukkula and R. Derakhshani, “Classification-guided feature selection for NIRS-based BCI,” in 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), 2011, pp. 72–75.

For latest updates on NIH and DARPA funded efforts for BCI funded research, please visit:

http://www.nibib.nih.gov/news-events/newsroom/brain-computer-interfaces-come-home;  
http://www.nidcd.nih.gov/funding/programs/npp/Pages/workshop_bci_summary.aspx. 

 Brain Perfusion

Brain perfusion assessment in clinical environments has mostly been performed by techniques that cannot accomplish constant monitoring of the brain. Due to its intrinsic capability of constant monitoring as well as the unique portability, fNIRS has clear potential for intensive care unit applications.

Bjerkan, J., Lancaster, G., Meglič, B., Kobal, J., Crawford, T. J., McClintock, P. V., & Stefanovska, A. (2023). Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity. Brain Research Bulletin, 201, 110704.

Wang, L., Sang, L., Cui, Y., Li, P., Qiao, L., Wang, Q., ... & Chen, J. (2022). Effects of acute high‐altitude exposure on working memory: A functional near‐infrared spectroscopy study. Brain and Behavior, e2776.

Owens, C. D., Mukli, P., Csipo, T., Lipecz, A., Silva-Palacios, F., Dasari, T. W., ... & Yabluchanskiy, A. (2022). Microvascular dysfunction and neurovascular uncoupling are exacerbated in peripheral artery disease, increasing the risk of cognitive decline in older adults. American Journal of Physiology-Heart and Circulatory Physiology.

Broscheid, K. C., Hamacher, D., Lamprecht, J., Sailer, M., & Schega, L. (2020). Inter-Session Reliability of Functional Near-Infrared Spectroscopy at the Prefrontal Cortex While Walking in Multiple Sclerosis. Brain Sciences, 10(9), 643.

Holmes, E., Barrett, D. W., Saucedo, C. L., O’Connor, P., Liu, H., & Gonzalez-Lima, F. (2019). Cognitive enhancement by transcranial photobiomodulation is associated with cerebrovascular oxygenation of the prefrontal cortex. Frontiers in Neuroscience, 1129.

M. Tessari, A. M. Malagoni, M. E. Vannini, and P. Zamboni, “A novel device for non-invasive cerebral perfusion assessment,” Veins and Lymphatics, vol. 4, no. 1, Mar. 2015.

J. Stojanovic-Radic, G. Wylie, G. Voelbel, N. Chiaravalloti, and J. DeLuca, “Neuroimaging and cognition using functional near infrared spectroscopy (fNIRS) in multiple sclerosis,” Brain Imaging Behav, vol. 9, no. 2, pp. 302–311, Jun. 2015.

C. Habermehl, C. Schmitz, S. P. Koch, J. Mehnert, and J. Steinbrink, “Investigating hemodynamics in scalp and brain using high-resolution diffuse optical tomography in humans,” 2012, p. BSu2A.2.

C. Habermehl, C. H. Schmitz, and J. Steinbrink, “Contrast enhanced high-resolution diffuse optical tomography of the human brain using ICG,” Opt Express, vol. 19, no. 19, pp. 18636–18644, Sep. 2011.

For updates on the latest announcements on the NIH brain initiative: Brain Research through Advancing Innovative Neurotechnologies® (BRAIN), please visit:

http://braininitiative.nih.gov

  Clinical Neurology

With the capabilities of constant monitoring of oxygenation, perfusion and autoregulation, fNIRS has a high potential for diagnoses of cerebrovascular disease and severe brain injury. Other clinical neurology methodologies, including epileptic disorders and central nervous system tumors, may benefit from the technique on the preoperative function localization.

Xiao, F., Liu, M., Wang, Y., Zhou, L., Luo, J., Chen, C., & Chen, W. (2024). Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study. Bioengineering, 11(4), 389.

Jezierska, K., Lietz-Kijak, D., Gronwald, H., Oleksy, B., Gronwald, B. J., & Podraza, W. (2023). Taste dysfunction after COVID-19: Analysis with functional near-infrared spectroscopy. Polish Journal of Otolaryngology, 78(1), 14-19.

Singh, S., Singh, J., Shrivastava, N. P., & Verma, R. (2023). Can Quantitative Electroencephalography and Functional Near-Infrared Spectroscopy be a Good Guide in Kleine–Levin Syndrome?. Neurology India, 71(6), 1250-1253.

Stephens, J. A., Press, D., Atkins, J., Duffy, J. R., Thomas, M. L., Weaver, J. A., & Schmid, A. A. (2023). Feasibility of Acquiring Neuroimaging Data from Adults with Acquired Brain Injuries before and after a Yoga Intervention. Brain Sciences, 13(10), 1413.

Zarantonello, L., Mangini, C., Erminelli, D., Fasolato, S., Angeli, P., Amodio, P., & Montagnese, S. (2023). Working Memory in Patients with Varying Degree of Hepatic Encephalopathy (HE): A Pilot EEG-fNIRS Study. Neurochemical Research, 1-12.

Yoo, M., Chun, M. H., Hong, G. R., Lee, C., Lee, J. K., & Lee, A. (2023). Effects of Training with a Powered Exoskeleton on Cortical Activity Modulation in Hemiparetic Chronic Stroke Patients: A Randomized Controlled Pilot Trial. Archives of Physical Medicine and Rehabilitation.

Ranchet, M., Hoang, I., Derollepot, R., & Paire-Ficout, L. (2023). Between-sessions test-retest reliability of prefrontal cortical activity during usual walking in patients with Parkinson’s Disease: A fNIRS study. Gait & Posture, 103, 99-105.

Gorniak, S. L., Wagner, V. E., Vaughn, K., Perry, J., Cox, L. G., Hibino, H., ... & Pollonini, L. (2023). Functional near infrared spectroscopy detects cortical activation changes concurrent with memory loss in postmenopausal women with Type II Diabetes. Experimental Brain Research, 241(6), 1555-1567.

Kaligal, C., Kanthi, A., Vidyashree, M., Krishna, D., Raghuram, N., Hongasandra Ramarao, N., & Deepeshwar, S. (2023). Prefrontal oxygenation and working memory in patients with type 2 diabetes mellitus following integrated yoga: a randomized controlled trial. Acta Diabetologica, 1-11. 

Si, J., Yang, Y., Xu, L., Xu, T., Liu, H., Zhang, Y., ... & He, J. (2023). Evaluation of residual cognition in patients with disorders of consciousness based on functional near-infrared spectroscopy. Neurophotonics, 10(2), 025003.

Guo, L., Huang, C., Lu, J., Wu, X., Shan, H., Chen, T., ... & Zhao, M. (2023). Decreased inter-brain synchronization in the right middle frontal cortex in alcohol use disorder during social interaction: An fNIRS hyperscanning study. Journal of Affective Disorders, 329, 573-580.

Li, R., Bruno, J. L., Lee, C. H., Bartholomay, K. L., Sundstrom, J., Piccirilli, A., ... & Reiss, A. L. (2022). Aberrant brain network and eye gaze patterns during natural social interaction predict multi-domain social-cognitive behaviors in girls with fragile X syndrome. Molecular Psychiatry, 1-9.

Quiroga, A., Novi, S., Martins, G., Bortoletto, L. F., Avelar, W., Guillaumon, A. T., ... & Mesquita, R. C. (2022). Quantification of the Tissue Oxygenation Delay Induced by Breath-Holding in Patients with Carotid Atherosclerosis. Metabolites, 12(11), 1156.

Soroush, A., Adingupu, D. D., Evans, T., Jarvis, S., Brown, L., & Dunn, J. F. (2022). NIRS Studies Show Reduced Interhemispheric Functional Connectivity in Individuals with Multiple Sclerosis That Exhibit Cortical Hypoxia. In Oxygen Transport to Tissue XLIII (pp. 145-149). Springer, Cham.

Park, M., Park, S., Lee, S. H., Hur, W., & Yoo, H. (2022). A Case Report of a Patient with Parkinson’s Disease Treated with Acupuncture and Exercise Therapy. The Journal of Internal Korean Medicine, 43(5), 1018-1028

La Rocca, M., Clemente, L., Gentile, E., Ricci, K., Delussi, M., & de Tommaso, M. (2022). Effect of Single Session of Anodal M1 Transcranial Direct Current Stimulation—TDCS—On Cortical Hemodynamic Activity: A Pilot Study in Fibromyalgia. Brain Sciences, 12(11), 1569.

Pelicioni, P. H. S., Lord, S. R., Okubo, Y., & Menant, J. C. (2022). Cortical activation during gait adaptability in people with Parkinson’s disease. Gait & Posture, 91, 247-253.

Kuo, H. T., Yeh, N. C., Yang, Y. R., Hsu, W. C., Liao, Y. Y., & Wang, R. Y. (2022). Effects of different dual task training on dual task walking and responding brain activation in older adults with mild cognitive impairment. Scientific reports, 12(1), 1-11.

de Tommaso, M., La Rocca, M., Quitadamo, S. G., Ricci, K., Tancredi, G., Clemente, L., ... & Delussi, M. (2022). Central effects of galcanezumab in migraine: a pilot study on Steady State Visual Evoked Potentials and occipital hemodynamic response in migraine patients. The Journal of Headache and Pain, 23(1), 1-14.

Walia, Pushpinder, et al. "Portable Neuroimaging-Guided Noninvasive Brain Stimulation of the Cortico-Cerebello-Thalamo-Cortical Loop—Hypothesis and Theory in Cannabis Use Disorder." Brain Sciences 12.4 (2022): 445.

Jezierska, K., Sękowska-Namiotko, A., Pala, B., Lietz-Kijak, D., Gronwald, H., & Podraza, W. (2022). Searching for the Mechanism of Action of Extremely Low Frequency Electromagnetic Field—The Pilot fNIRS Research. International Journal of Environmental Research and Public Health, 19(7), 4012.

Ćurčić-Blake, B., Kos, C., & Aleman, A. (2022). Causal connectivity from right DLPFC to IPL in schizophrenia patients: a pilot study. Schizophrenia, 8(1), 1-9.

Rotgans, J. I. (2021). Learning to diagnose X-rays: a neuroscientific study of practice-related activation changes in the prefrontal cortex. Diagnosis, 9(2), 255-264.

Mao, D., Wunderlich, J., Savkovic, B., Jeffreys, E., Nicholls, N., Lee, O. W., ... & McKay, C. M. (2021). Speech token detection and discrimination in individual infants using functional near-infrared spectroscopy. Scientific Reports, 11(1), 1-14.

Kumar, V., Nichenmetla, S., Chhabra, H., Sreeraj, V. S., Rao, N. P., Kesavan, M., ... & Gangadhar, B. N. (2021). Prefrontal cortex activation during working memory task in schizophrenia: A fNIRS study. Asian Journal of Psychiatry, 56, 102507.

Segar, R., Chhabra, H., Sreeraj, V. S., Parlikar, R., Kumar, V., Ganesan, V., & Kesavan, M. (2021). fNIRS study of prefrontal activation during emotion recognition–A Potential endophenotype for bipolar I disorder?. Journal of Affective Disorders, 282, 869-875.

Sato, J. R., Junior, C. E. B., de Araújo, E. L. M., de Souza Rodrigues, J., & Andrade, S. M. (2021). A guide for the use of fNIRS in microcephaly associated to congenital Zika virus infection. Scientific Reports, 11(1), 1-13.

Caumo, W., Franco, Á. O., Fernandes, C., Vicunha, P., Bandeira, J., Aratanha, M. A., ... & Fregni, F. (2021). Hyper-connectivity between the left motor cortex and prefrontal cortex is associated with the severity of dysfunction of the descending pain modulatory system in fibromyalgia. bioRxiv.

Shoushtarian M, Alizadehsani R, Khosravi A, Acevedo N, McKay CM, et al. (2020) Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning. PLOS ONE 15(11): e0241695.

Gilman, J. M., Yücel, M. A., Pachas, G. N., Potter, K., Levar, N., Broos, H., ... & Evins, A. E. (2019). “Delta-9-tetrahydrocannabinol intoxication is associated with increased prefrontal activation as assessed with functional near-infrared spectroscopy: A report of a potential biomarker of intoxication”. NeuroImage, 197, 575-585.

Sagiv, S. K., Bruno, J. L., Baker, J. M., Palzes, V., Kogut, K., Rauch, S., ... & Eskenazi, B. (2019). “Prenatal exposure to organophosphate pesticides and functional neuroimaging in adolescents living in proximity to pesticide application.” Proceedings of the National Academy of Sciences, 116(37), 18347-18356.

Bigelow, H. B. (2020). Understanding the Effects of Physical Activity on Executive Functioning and Psycho-Emotional Well-Being in Children with ADHD.

Grazioli, S., Crippa, A., Mauri, M., Piazza, C., Bacchetta, A., Salandi, A., ... & Nobile, M. (2019). “Association between fatty acids profile and cerebral blood flow: An exploratory fNIRS study on children with and without ADHD”. Nutrients, 11(10), 2414.

R. Li, T. Nguyen, T. Potter, and Y. Zhang, “Dynamic cortical connectivity alterations associated with Alzheimer’s disease: An EEG and fNIRS integration study,” NeuroImage: Clinical, Dec. 2018.

F. Colledge, S. Ludyga, M. Mücke, U. Pühse, and M. Gerber, “The effects of an acute bout of exercise on neural activity in alcohol and cocaine craving: study protocol for a randomised controlled trial,” Trials, vol. 19, no. 1, p. 713, Dec. 2018.

O. Klempíř et al., “P 024 - Near-infrared spectroscopy patterns of cortical activity during gait in Parkinson’s disease patients treated with DBS STN,” Gait & Posture, vol. 65, pp. 273–275, Sep. 2018.

J. Eun-Sun et al., “Effect of acupuncture on patients with mild cognitive impairment assessed using functional near-infrared spectroscopy on week 12 (close-out): a pilot study protocol,” Integrative Medicine Research, vol. 7, no. 3, pp. 287–295, Sep. 2018.

A. Lee et al., “Slow oscillations of cerebral hemodynamics changes during low-level light therapy in the elderly with and without mild cognitive impairment: An fNIRS study,” Annals of Physical and Rehabilitation Medicine, vol. 61, p. e256, Jul. 2018.

J.-H. Jang, J. Lee, I. Jung, and H. Yoo, “Efficacy of Yokukansankachimpihange on sleep disturbance in Parkinson’s disease,” Medicine (Baltimore), vol. 97, no. 26, Jun. 2018.

C.-T. Li, C.-F. Lu, Y.-T. Wu, S.-H. Lee, R.-W. Chu, and T.-P. Su, “Attenuated Motor Cortical Responsiveness to Motor and Cognitive Tasks in Generalized Anxiety Disorder,” vol. 8, no. 3, pp. 843–853, May 2018.

C. S. H. Ho, R. C. M. Ho, and A. M. L. Quek, “Chronic Manganese Toxicity Associated with Voltage-Gated Potassium Channel Complex Antibodies in a Relapsing Neuropsychiatric Disorder,” International Journal of Environmental Research and Public Health, vol. 15, no. 4, p. 783, Apr. 2018.

O. Klempíř, R. Krupička, and R. Jech, “MEDIAN METHOD FOR DETERMINING CORTICAL BRAIN ACTIVITY IN A NEAR INFRARED SPECTROSCOPY IMAGE,” Lékař a technika - Clinician and Technology, vol. 48, no. 1, pp. 11–16, Mar. 2018.

M. Balconi, C. Siri, N. Meucci, G. Pezzoli, and L. Angioletti, “Personality Traits and Cortical Activity Affect Gambling Behavior in Parkinson’s Disease,” Journal of Parkinson’s Disease, vol. 8, no. 2, pp. 341–352, Jan. 2018.

R. Li, G. Rui, W. Chen, S. Li, P. E. Schulz, and Y. Zhang, “Early Detection of Alzheimer’s Disease Using Non-invasive Near-Infrared Spectroscopy,” Front. Aging Neurosci., vol. 10, 2018.

Z. Liang et al., “Design of multichannel functional near-infrared spectroscopy system with application to propofol and sevoflurane anesthesia monitoring,” NPh, NEUROW, vol. 3, no. 4, p. 045001, Oct. 2016.

A. M. Kempny et al., “Functional near infrared spectroscopy as a probe of brain function in people with prolonged disorders of consciousness,” NeuroImage: Clinical, vol. 12, pp. 312–319, Feb. 2016.

S. E. Kober, G. Bauernfeind, C. Woller, M. Sampl, P. Grieshofer, C. Neuper, and G. Wood, “Hemodynamic Signal Changes Accompanying Execution and Imagery of Swallowing in Patients with Dysphagia: A Multiple Single-Case Near-Infrared Spectroscopy Study,” Front Neurol, vol. 6, Jul. 2015.

H. Obrig, “NIRS in clinical neurology - a ‘promising’ tool?,” Neuroimage, vol. 85 Pt 1, pp. 535–546, Jan. 2014.

For the latest listing of clinical trials involving brain disorders, please visit:

http://www.ninds.nih.gov/disorders/clinical_trials/index.htm

Cognitive States

fNIRS adds another dimension to studies investigating cognitive functions and mental states, since it is a portable technique not too sensitive to motion artifacts. Attention processes, inhibition mechanisms, and working memory, as well as other cognitive states, may be studied in natural environments with a fast setup preparation.

Meier, J. K., & Schwabe, L. (2024). Consistently increased dorsolateral prefrontal cortex activity during the exposure to acute stressors. Cerebral Cortex, 34(4), bhae159.

Toyofuku, K., Hiwa, S., Tanioka, K., Hiroyasu, T., & Takeda, M. (2024, March). Hemispheric Lateralization in Older Adults Who Habitually Play Darts: A Cross-Sectional Study Using Functional Near-Infrared Spectroscopy. In Healthcare (Vol. 12, No. 7, p. 734).

Kang, K., Antonenko, D., Glöckner, F., Flöel, A., & Li, S. C. (2024). Neural correlates of home-based intervention effects on value-based sequential decision-making in healthy older adults. Aging Brain, 5, 100109.

Mukli, P., Pinto, C. B., Owens, C. D., Csipo, T., Lipecz, A., Szarvas, Z., ... & Yabluchanskiy, A. (2023). Impaired Neurovascular Coupling and Increased Functional Connectivity in the Frontal Cortex Predict Age‐Related Cognitive Dysfunction. Advanced Science, 2303516.

Schmaderer, L. F., Meyer, M., Reer, R., & Schumacher, N. (2023). What happens in the prefrontal cortex? Cognitive processing of novel and familiar stimuli in soccer: An exploratory fNIRS study. European Journal of Sport Science, 1-11.

Kanatschnig, T., Rominger, C., Fink, A., Wood, G., & Kober, S. E. (2023). Sensorimotor cortex activity during basketball dribbling and its relation to creativity. Plos one, 18(4), e0284122.

Shoaib, Z., Akbar, A., Kim, E. S., Kamran, M. A., Kim, J. H., & Jeong, M. Y. (2023). Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation. Scientific Reports, 13(1), 6465.

L, Y., Chen, J., Zheng, X., Liu, J., Peng, C., & Liao, Y. (2023). Functional Near-Infrared Spectroscopy Evidence of Prefrontal Regulati

Ayman, S. U., Arrafuzzaman, A., & Rahman, M. A. (2023, January). Subject Dependent Cognitive Load Level Classification from fNIRS Signal Using Support Vector Machine. In Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022 (pp. 365-377). Singapore: Springer Nature Singapore.

Keleş, Özge, and Erol Yıldırım. "Depression affects working memory performance a functional near infrared spectroscopy (fNIRS) study." Psychiatry Research: Neuroimaging (2022): 111581.

Yu, M., Xu, S., Hu, H., Li, S., & Yang, G. (2022). Differences in right hemisphere fNIRS activation associated with executive network during performance of the lateralized attention network tast by elite, expert and novice ice hockey athletes. Behavioural Brain Research, 114209.

Liu, Y., Lu, S., Liu, J., Zhao, M., Chao, Y., & Kang, P. (2022). A Characterization of Brain Area Activation in Orienteers with Different Map-Recognition Memory Ability Task Levels—Based on fNIRS Evidence. Brain Sciences, 12(11), 1561.

Mora, A. M., Baker, J. M., Hyland, C., Rodríguez-Zamora, M. G., Rojas-Valverde, D., Winkler, M. S., ... & Sagiv, S. K. (2022). Pesticide exposure and cortical brain activation among farmworkers in Costa Rica. Neurotoxicology, 93, 200-210.

Karmakar, S., Kamilya, S., Dey, P., Guhathakurta, P. K., Dalui, M., Bera, T. K., ... & Basu, A. (2023). Real time detection of cognitive load using fNIRS: A deep learning approach. Biomedical Signal Processing and Control, 80, 104227.

Jing, J., Qi, M., & Gao, H. (2022). A functional near-infrared spectroscopy investigation of item-method directed forgetting. Neuroscience Research. 

Karthikeyan, R., Carrizales, J., Johnson, C., & Mehta, R. K. (2022). A window into the tired brain: neurophysiological dynamics of visuospatial working memory under fatigue. Human factors, 00187208221094900.

Li, Y., Chen, J., Zheng, X., Liu, J., Peng, C., Liao, Y., & Liu, Y. (2022). Cognitive deficit in adults with ADHD lies in the cognitive state disorder rather than the working memory deficit: A functional near-infrared spectroscopy study. Journal of Psychiatric Research, 154, 332-340.

Oku, A. Y. A., & Sato, J. R. (2021). Predicting student performance using machine learning in fNIRS data. Frontiers in Human Neuroscience, 15, 622224.

Park, S. Y., & Schott, N. (2022). The Immediate and Sustained Effects of Exercise-Induced Hemodynamic Response on Executive Function During Fine Motor-Cognitive Tasks Using Functional Near-Infrared Spectroscopy. Journal of Integrative Neuroscience, 21(3), 98.

Balconi, M., & Angioletti, L. (2022). Interoceptive Attentiveness Induces Significantly More PFC Activation during a Synchronized Linguistic Task Compared to a Motor Task as Revealed by Functional Near-Infrared Spectroscopy. Brain Sciences, 12(3), 301.

Gilman, J. M., Schmitt, W. A., Potter, K., Kendzior, B., Pachas, G. N., Hickey, S., ... & Evins, A. E. (2022). Identification of∆ 9-tetrahydrocannabinol (THC) impairment using functional brain imaging. Neuropsychopharmacology, 1-9.

Csipo, T., Lipecz, A., Owens, C., Mukli, P., Perry, J. W., Tarantini, S., ... & Yabluchanskiy, A. (2021). Sleep deprivation impairs cognitive performance, alters task-associated cerebral blood flow and decreases cortical neurovascular coupling-related hemodynamic responses. Scientific reports, 11(1), 1-13.

Abujelala, M., Karthikeyan, R., Tyagi, O., Du, J., & Mehta, R. K. (2021). Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics. Brain Sciences, 11(7), 885.

König, N., Steber, S., Borowski, A., Bliem, H. R., & Rossi, S. (2021). Neural Processing of Cognitive Control in an Emotionally Neutral Context in Anxiety Patients. Brain Sciences, 11(5), 543.

Yuan, Y., Li, G., Ren, H., & Chen, W. (2021). Effect of Light on Cognitive Function During a Stroop Task Using Functional Near-Infrared Spectroscopy. Phenomics, 1-8.

Valdés, B. A., Lajoie, K., Marigold, D. S., & Menon, C. (2021). Cortical Effects of Noisy Galvanic Vestibular Stimulation Using Functional Near-Infrared Spectroscopy. Sensors, 21(4), 1476.

Herold, F., Behrendt, T., Törpel, A., Hamacher, D., Müller, N. G., & Schega, L. (2021). Cortical hemodynamics as a function of handgrip strength and cognitive performance: a cross-sectional fNIRS study in younger adults. BMC neuroscience, 22(1), 1-16.

Oku, A. Y. A., & Sato, J. R. (2021). Predicting student performance using machine learning in fNIRS data. Frontiers in Human Neuroscience.

Yuan, Y., Li, G., Ren, H., & Chen, W. (2020). Caffeine Effect on Cognitive Function during a Stroop Task: fNIRS Study. Neural Plasticity, 2020.

Kalbe, F., Bange, S., Lutz, A., & Schwabe, L. (2020). Expectancy Violation Drives Memory Boost for Stressful Events. Psychological Science, 0956797620958650.

Cuesta, U., Niño, J. I., Martinez, L., & Paredes, B. (2020). The Neurosciences of Health Communication: An fNIRS Analysis of Prefrontal Cortex and Porn Consumption in Young Women for the Development of Prevention Health Programs. Frontiers of Psychology (11).

Meidenbauer, K. L., Choe, K. W., Cardenas-Iniguez, C., Huppert, T. J., & Berman, M. G. (2020). Load-Dependent Relationships between Frontal fNIRS Activity and Performance: A Data-Driven PLS Approach. bioRxiv.

Geissler, C. F., Domes, G., & Frings, C. (2020). Shedding light on the frontal hemodynamics of spatial working memory using functional near-infrared spectroscopy. Neuropsychologia, 107570.

Lee, G., Park, J. S., Ortiz, M. L. B., Hong, J. Y., Paik, S. H., Lee, S. H., ... & Jung, Y. J. (2020). Hemodynamic Activity and Connectivity of the Prefrontal Cortex by Using Functional Near-Infrared Spectroscopy during Color-Word Interference Test in Korean and English Language. Brain Sciences, 10(8), 484.

Stadler, K. M., Wolff, W., & Schüler, J. (2020). On Your Mark, Get Set, Self-Control, Go: A Differentiated View on the Cortical Hemodynamics of Self-Control during Sprint Start. Brain Sciences, 10(8), 494.

Cataldo, I., Neoh, M. J. Y., Chew, W. F., Foo, J. N., Lepri, B., & Esposito, G. (2020). Oxytocin receptor gene and parental bonding modulate prefrontal responses to cries: a NIRS Study. Scientific Reports, 10(1), 1-11.

Badarin, A. A., Skazkina, V. V., & Grubov, V. V. (2020, April). Studying of human’s mental state during visual information processing with combined EEG and fNIRS. In Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions (Vol. 11459, p. 114590D). International Society for Optics and Photonics.

Huang, T., Gu, Q., Deng, Z., Tsai, C., Xue, Y., Zhang, J., ... & Wang, K. (2019). “Executive Function Performance in Young Adults When Cycling at an Active Workstation: An fNIRS Study.” International journal of environmental research and public health, 16(7), 1119.

Tanveer, M. A., Khan, M. J., Qureshi, M. J., Naseer, N., & Hong, K. S. (2019). “Enhanced Drowsiness Detection Using Deep Learning: An fNIRS Study”. IEEE Access, 7, 137920-137929.

F. Colledge, S. Ludyga, M. Mücke, U. Pühse, and M. Gerber, “The effects of an acute bout of exercise on neural activity in alcohol and cocaine craving: study protocol for a randomised controlled trial,” Trials, vol. 19, no. 1, p. 713, Dec. 2018.

F. A. Fishburn, C. O. Hlutkowsky, L. M. Bemis, T. J. Huppert, L. S. Wakschlag, and S. B. Perlman, “Irritability uniquely predicts prefrontal cortex activation during preschool inhibitory control among all temperament domains: A LASSO approach,” NeuroImage, vol. 184, pp. 68–77, Jan. 2019.

L. Zhu, S. Li, Y. Li, M. Wang, Y. Li, and J. Yao, “Study on driver’s braking intention identification based on functional near-infrared spectroscopy,” Journal of Intelligent and Connected Vehicles, Dec. 2018.

S. Peci and F. Peci, “Hemoglobin (Hb) - Oxyhemoglobin (HbO) Variation in Rehabilitation Processes Involving Prefrontal Cortex,” Prefrontal Cortex, Nov. 2018.

A. Landowska, D. Roberts, P. Eachus, and A. Barrett, “Within- and Between-Session Prefrontal Cortex Response to Virtual Reality Exposure Therapy for Acrophobia,” Front Hum Neurosci, vol. 12, Nov. 2018.

S. C. Wriessnegger, G. Bauernfeind, E.-M. Kurz, P. Raggam, and G. R. Müller-Putz, “Imagine squeezing a cactus: Cortical activation during affective motor imagery measured by functional near-infrared spectroscopy,” Brain and Cognition, vol. 126, pp. 13–22, Oct. 2018.

G. C. Costa et al., “Tactical Knowledge, Decision-Making, and Brain Activation Among Volleyball Coaches of Varied Experience,” Percept Mot Skills, vol. 125, no. 5, pp. 951–965, Oct. 2018.

J. L. Bruno et al., “Mind over motor mapping: Driver response to changing vehicle dynamics,” Human Brain Mapping, vol. 39, no. 10, pp. 3915–3927, Oct. 2018.

S. Woo, “Classification of stress and non-stress condition using functional near-infrared spectroscopy,” in 2018 18th International Conference on Control, Automation and Systems (ICCAS), 2018, pp. 1147–1151.

J. I. Rotgans et al., “Evidence supporting dual‐process theory of medical diagnosis: a functional near‐infrared spectroscopy study,” Medical Education, Sep. 2018.

K. Ihme, A. Unni, M. Zhang, J. W. Rieger, and M. Jipp, “Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy,” Front Hum Neurosci, vol. 12, Aug. 2018. 

L.-S. Giboin, M. Gruber, J. Schüler, and W. Wolff, “The cognitive control of a strenuous physical task,” Aug. 2018.

J.-I. Byun et al., “Bright light exposure before bedtime impairs response inhibition the following morning: a non-randomized crossover study,” Chronobiology International, vol. 35, no. 8, pp. 1035–1044, Aug. 2018.

J. M. Baker, J. L. Bruno, A. Gundran, S. M. H. Hosseini, and A. L. Reiss, “fNIRS measurement of cortical activation and functional connectivity during a visuospatial working memory task,” PLOS ONE, vol. 13, no. 8, p. e0201486, Aug. 2018.

E.-M. Kurz et al., “Towards using fNIRS recordings of mental arithmetic for the detection of residual cognitive activity in patients with disorders of consciousness (DOC),” Brain and Cognition, vol. 125, pp. 78–87, Aug. 2018.

D. Crivelli, M. D. Sabogal Rueda, and M. Balconi, “Linguistic and motor representations of everyday complex actions: an fNIRS investigation,” Brain Struct Funct, vol. 223, no. 6, pp. 2989–2997, Jul. 2018.

Y. Chen, Y. Yu, R. Niu, and Y. Liu, “Selective Effects of Postural Control on Spatial vs. Nonspatial Working Memory: A Functional Near-Infrared Spectral Imaging Study,” Front Hum Neurosci, vol. 12, Jun. 2018.

M. Balconi, M. E. Vanutelli, and L. Gatti, “Functional brain connectivity when cooperation fails,” Brain and Cognition, vol. 123, pp. 65–73, Jun. 2018.

C.-T. Li, C.-F. Lu, Y.-T. Wu, S.-H. Lee, R.-W. Chu, and T.-P. Su, “Attenuated Motor Cortical Responsiveness to Motor and Cognitive Tasks in Generalized Anxiety Disorder,” vol. 8, no. 3, pp. 843–853, May 2018.

E. Vassena, R. Gerrits, J. Demanet, T. Verguts, and R. Siugzdaite, “Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study,” Neuropsychologia, Apr. 2018.

J. Shin, A. von Lühmann, D.-W. Kim, J. Mehnert, H.-J. Hwang, and K.-R. Müller, “Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset,” Scientific Data, vol. 5, p. 180003, Feb. 2018.

M. Balconi, C. Siri, N. Meucci, G. Pezzoli, and L. Angioletti, “Personality Traits and Cortical Activity Affect Gambling Behavior in Parkinson’s Disease,” Journal of Parkinson’s Disease, vol. 8, no. 2, pp. 341–352, Jan. 2018.

A. Unni, K. Ihme, M. Jipp, and J. W. Rieger, “Chapter 37 - Estimating Cognitive Workload Levels While Driving Using Functional Near-Infrared Spectroscopy (fNIRS),” in Neuroergonomics, H. Ayaz and F. Dehais, Eds. Academic Press, 2018, pp. 205–206.

F. Dehais et al., “Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI,” in IEEE SMC, 2018, pp. 1–6.

M. Balconi, L. Gatti, and M. E. Vanutelli, “When cooperation goes wrong: brain and behavioural correlates of ineffective joint strategies in dyads,” International Journal of Neuroscience, vol. 128, no. 2, pp. 155–166, Feb. 2018.

K. J. Verdière, R. N. Roy, and F. Dehais, “Detecting Pilot’s Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario,” Frontiers in Human Neuroscience, vol. 12, Jan. 2018.

A. Landowska, S. Royle, P. Eachus, and D. Roberts, “Testing the Potential of Combining Functional Near-Infrared Spectroscopy with Different Virtual Reality Displays—Oculus Rift and oCtAVE,” in Augmented Reality and Virtual Reality, Springer, Cham, 2018, pp. 309–321.

S. M. H. Hosseini et al., “Neural, physiological, and behavioral correlates of visuomotor cognitive load,” Scientific Reports, vol. 7, no. 1, Dec. 2017.

H. O. Keles, M. Radoman, G. N. Pachas, A. E. Evins, and J. M. Gilman, “Using Functional Near-Infrared Spectroscopy to Measure Effects of Delta 9-Tetrahydrocannabinol on Prefrontal Activity and Working Memory in Cannabis Users,” Frontiers in Human Neuroscience, vol. 11, Oct. 2017.

R. Gabbard, M. Fendley, I. A. Dar, R. Warren, and N. H. Kashou, “Utilizing functional near-infrared spectroscopy for prediction of cognitive workload in noisy work environments,” Neurophotonics, vol. 4, no. 04, p. 1, Aug. 2017.

M. Balconi and M. E. Vanutelli, “Brains in Competition: Improved Cognitive Performance and Inter-Brain Coupling by Hyperscanning Paradigm with Functional Near-Infrared Spectroscopy,” Frontiers in Behavioral Neuroscience, vol. 11, Aug. 2017.

H. Aghajani, M. Garbey, and A. Omurtag, “Measuring Mental Workload with EEG+fNIRS,” Frontiers in Human Neuroscience, vol. 11, Jul. 2017.

H. Banville, R. Gupta, and T. H. Falk, “Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–24, 2017.

A. Omurtag, H. Aghajani, and H. O. Keles, “Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance,” Journal of Neural Engineering, Jul. 2017.

K. N. de Winkel, A. Nesti, H. Ayaz, and H. H. Bülthoff, “Neural correlates of decision making on whole body yaw rotation: An fNIRS study,” Neuroscience Letters, vol. 654, no. Supplement C, pp. 56–62, Jul. 2017.

M. Mücke, C. Andrä, M. Gerber, U. Pühse, and S. Ludyga, “Moderate-to-vigorous physical activity, executive functions and prefrontal brain oxygenation in children: A functional near-infrared spectroscopy study,” Journal of Sports Sciences, pp. 1–7, May 2017.

A. Unni, K. Ihme, M. Jipp, and J. W. Rieger, “Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study,” Frontiers in Human Neuroscience, vol. 11, Apr. 2017.

L. Holper, L. D. Van Brussel, L. Schmidt, S. Schulthess, C. J. Burke, K. Louie, E. Seifritz and P. N. Tobler, “Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load,” eNeuro, vol. 4, no. 2, p. ENEURO.0365-17.2017, Mar. 2017.

Z. Deng, Q. Huang, J. Huang, W. Zhang, C. Qi, and X. Xu, “Association between central obesity and executive function as assessed by Stroop task performance: A functional near-infrared spectroscopy study,” Journal of Innovative Optical Health Sciences, p. 1750010, Mar. 2017.

X. Xu, Z.-Y. Deng, Q. Huang, W.-X. Zhang, C. Qi, and J.-A. Huang, “Prefrontal cortex-mediated executive function as assessed by Stroop task performance associates with weight loss among overweight and obese adolescents and young adults,” Behavioural Brain Research, vol. 321, pp. 240–248, Mar. 2017.

H. Aghajani and A. Omurtag, “Assessment of mental workload by EEG+FNIRS,” 2016, pp. 3773–3776.

J. Stojanovic-Radic, G. Wylie, G. Voelbel, N. Chiaravalloti, and J. DeLuca, “Neuroimaging and cognition using functional near infrared spectroscopy (fNIRS) in multiple sclerosis,” Brain Imaging Behav, vol. 9, no. 2, pp. 302–311, Jun. 2015.

N. Naseer and K.-S. Hong, “Decoding answers to four-choice questions using functional near infrared spectroscopy,” J. Near Infrared Spectrosc, vol. 23, no. 1, pp. 23–31, 2015.

M. J. Khan and K.-S. Hong, “Passive BCI based on drowsiness detection: an fNIRS study,” Biomed Opt Express, vol. 6, no. 10, pp. 4063–4078, Oct. 2015.

K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI,” Neuroscience Letters, vol. 587, pp. 87–92, Feb. 2015.

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front Hum Neurosci, vol. 8, p. 244, 2014.

M. A. Kamran and K.-S. Hong, “Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding,” Neurosci. Lett., vol. 580, pp. 130–136, Sep. 2014.

C. Bogler, J. Mehnert, J. Steinbrink, and J.-D. Haynes, “Decoding vigilance with NIRS,” PLoS ONE, vol. 9, no. 7, p. e101729, 2014.

J. Bahnmueller, T. Dresler, A.-C. Ehlis, U. Cress, and H.-C. Nuerk, “NIRS in motion—unraveling the neurocognitive underpinnings of embodied numerical cognition,” Front. Psychol, vol. 5, p. 743, 2014.

N. Naseer, M. J. Hong, and K.-S. Hong, “Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface,” Exp Brain Res, vol. 232, no. 2, pp. 555–564, Nov. 2013.

M. M. DiStasio and J. T. Francis, “Use of frontal lobe hemodynamics as reinforcement signals to an adaptive controller,” PLoS ONE, vol. 8, no. 7, p. e69541, 2013.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “fNIRS-based online deception decoding,” J Neural Eng, vol. 9, no. 2, p. 26012, Apr. 2012.

For the latest description on NIH’s intramural efforts to explore cognition and its influences on mental health, please visit:

http://www.nimh.nih.gov/labs-at-nimh/research-areas/clinics-and-labs/lbc/index.shtml

Complementary and Integrative Medicine

Acupuncture, interactions of herbal medicines with conventional drugs, pain management, meditation, Yoga, Tai Chi and Qi Gong are among other alternative therapies whose serious inquiry is well supported by fNIRS. NIRx experts can help you plan experimental strategies best suited to explore nontraditional yet promising methods.

Li, X., Zhou, Y., Zhang, C., Wang, H., & Wang, X. (2024). Neural correlates of breath work, mental imagery of yoga postures, and meditation in yoga practitioners: a functional near-infrared spectroscopy study. Frontiers in Neuroscience, 18, 1322071.

Xiao, R., Xu, P., Liang, X. L., Zou, Z., Zhong, J. G., Xiang, M. Q., & Hou, X. H. (2023). Effects of the special olympics unified sports soccer training program on executive function in adolescents with intellectual disabilities. Journal of Exercise Science & Fitness.

Chen, Y., Wang, X., & Zhou, C. (2023). Effects of different exercise patterns on drug craving in female methamphetamine-dependent patients: Evidence from behavior and fNIRS. Mental Health and Physical Activity, 100534.

Al-Shargie, F., Katmah, R., Tariq, U., Babiloni, F., Al-Mughairbi, F., & Al-Nashash, H. (2022). Stress management using fNIRS and binaural beats stimulation. Biomedical Optics Express, 13(6), 3552-3575.

Olszewska-Guizzo, A., Fogel, A., Escoffier, N., Sia, A., Nakazawa, K., Kumagai, A., ... & Ho, R. (2022). Therapeutic Garden With Contemplative Features Induces Desirable Changes in Mood and Brain Activity in Depressed Adults. Front. Psychiatry, 13, 757056. 

Khan, M. A., Ghafoor, U., Yoo, H. R., & Hong, K. S. (2022). Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study. Neural Regeneration Research, 17(8), 1850.

Wong, Y. K., Wu, J. M., Zhou, G., Zhu, F., Zhang, Q., Yang, X. J., ... & Zhang, Z. J. (2021). Antidepressant Monotherapy and Combination Therapy with Acupuncture in Depressed Patients: A Resting-State Functional Near-Infrared Spectroscopy (fNIRS) Study. Neurotherapeutics, 1-13.

Schega, L., Kaps, B., Broscheid, K. C., Bielitzki, R., Behrens, M., Meiler, K., ... & Franke, J. (2021). Effects of a multimodal exercise intervention on physical and cognitive functions in patients with chronic low back pain (MultiMove): study protocol for a randomized controlled trial. BMC geriatrics, 21(1), 1-13.

Gorniak, S. L., Wagner, V. E., Vaughn, K., Perry, J., Cox, L. G., Hernandez, A. E., & Pollonini, L. (2020). Functional neuroimaging of sensorimotor cortices in postmenopausal women with type II diabetes. Neurophotonics, 7(3), 035007.

W. W. N. Tsang, K. K. Chan, C. N. Cheng, F. S. F. Hu, C. T. K. Mak, and J. W. C. Wong, “Tai Chi practice on prefrontal oxygenation levels in older adults: A pilot study,” Complementary Therapies in Medicine, vol. 42, pp. 132–136, Feb. 2019.

W. Wolff, J. L. Thürmer, K.-M. Stadler, and J. Schüler, “Ready, set, go: Cortical hemodynamics during self-controlled sprint starts,” Psychology of Sport and Exercise, vol. 41, pp. 21–28, Mar. 2019.

J.-H. Jang, J. Lee, I. Jung, and H. Yoo, “Efficacy of Yokukansankachimpihange on sleep disturbance in Parkinson’s disease,” Medicine (Baltimore), vol. 97, no. 26, Jun. 2018.

J.-H. Jang, H. Kim, I. Jung, and H. Yoo, “Acupuncture for improving gait disturbance in Parkinson’s disease: A study protocol for a pilot randomized controlled trial,” European Journal of Integrative Medicine, vol. 20, pp. 16–21, Jun. 2018.

G. Litscher, G. Bauernfeind, X. Gao, G. Mueller-Putz, L. Wang, W. Anderle, I. Gaischek, D. Litscher, C. Neuper, and R. C. Niemtzow, “Battlefield Acupuncture and Near-Infrared Spectroscopy–Miniaturized Computer-Triggered Electrical Stimulation of Battlefield Ear Acupuncture Points and 50-Channel Near-Infrared Spectroscopic Mapping,” Medical Acupuncture, vol. 23, no. 4, pp. 263–270, Dec. 2011.

For latest updates on complementary and integrative health strategies, please visit

https://nccih.nih.gov

Connectivity 

fNIRS brings connectivity studies to a new level. The hyperscanning modality enables both online feedback as well as offline analysis regarding within- and between-subjects connectivity. In addition to that, fNIRS fast sampling rate for hemodynamic states allows for a quick update rate of connectivity feedback, resulting into enhanced subject engagement.

Gorniak, S. L., Wagner, V. E., Vaughn, K., Perry, J., Cox, L. G., Hibino, H., ... & Pollonini, L. (2023). Functional near infrared spectroscopy detects cortical activation changes concurrent with memory loss in postmenopausal women with Type II Diabetes. Experimental Brain Research, 241(6), 1555-1567.

Open access dataset: Blanco, B., Molnar, M., Carreiras, M., & Caballero-Gaudes, C. (2022). Open access dataset of task-free hemodynamic activity in 4-month-old infants during sleep using fNIRS. Scientific Data, 9(1), 1-5.

Mukli, P., Csipo, T., Lipecz, A., Stylianou, O., Racz, F. S., Owens, C. D., ... & Yabluchanskiy, A. (2021). Sleep deprivation alters task‐related changes in functional connectivity of the frontal cortex: A near‐infrared spectroscopy study. Brain and Behavior, e02135.

Blanco, B., Molnar, M., Carreiras, M., Collins-Jones, L. H., Rosas, E. E. V., Cooper, R. J., & Caballero-Gaudes, C. (2021). Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants. Neurophotonics, 8(2), 025011.

Ávila-Sansores, S. M., Rodríguez-Gómez, G., Tachtsidis, I., & Orihuela-Espina, F. (2020). Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level. Neurophotonics, 7(4), 045009.

Nguyen, T., Schleihauf, H., Kayhan, E., Matthes, D., Vrtička, P., & Hoehl, S. (2020). Neural synchrony in mother-child conversation: Exploring the role of conversation patterns. Social Cognitive and Affective Neuroscience.

Balconi, M., & Fronda, G. (2020). The “gift effect” on functional brain connectivity. Inter-brain synchronization when prosocial behavior is in action. Scientific Reports, 10(1), 1–10. https://doi.org/10.1038/s41598-020-62421-0

Fronda, G., & Balconi, M. (2020). The effect of interbrain synchronization in gesture observation: A fNIRS study. Brain and Behavior, April, 1–13. https://doi.org/10.1002/brb3.1663

Nguyen, T., Schleihauf, H., Kayhan, E., Matthes, D., Vrticka, P., & Hoehl, S. (2020). The effects of interaction quality on neural synchrony during mother-child problem solving. Cortex (under Review), 4. https://doi.org/10.1016/j.cortex.2019.11.020

de Souza Rodrigues, J., Ribeiro, F. L., Sato, J. R., Mesquita, R. C., & Júnior, C. E. B. (2019). Identifying individuals using fNIRS-based cortical connectomes. Biomedical Optics Express, 10(6), 2889-2897.

D. Farkas, S. L. Denham, and I. Winkler, “Functional brain networks underlying idiosyncratic switching patterns in multi-stable auditory perception,” Neuropsychologia, vol. 108, pp. 82–91, Jan. 2018.

S. Tak, A. M. Kempny, K. J. Friston, A. P. Leff, and W. D. Penny, “Dynamic causal modelling for functional near-infrared spectroscopy,” Neuroimage, vol. 111, pp. 338–349, May 2015.

L. Holper, F. Scholkmann, and E. Seifritz, “Time-frequency dynamics of the sum of intra- and extracerebral hemodynamic functional connectivity during resting-state and respiratory challenges assessed by multimodal functional near-infrared spectroscopy,” Neuroimage, vol. 120, pp. 481–492, Oct. 2015.

H. Niu and Y. He, “Resting-State Functional Brain Connectivity: Lessons from Functional Near-Infrared Spectroscopy,” The Neuroscientist, vol. 20, no. 2, pp. 173–188, Apr. 2014.

J. Mehnert, A. Akhrif, S. Telkemeyer, S. Rossi, C. H. Schmitz, J. Steinbrink, I. Wartenburger, H. Obrig, and S. Neufang, “Developmental changes in brain activation and functional connectivity during response inhibition in the early childhood brain,” Brain Dev., vol. 35, no. 10, pp. 894–904, Nov. 2013.

R. L. Barbour, H. L. Graber, Y. Xu, Y. Pei, C. H. Schmitz, D. S. Pfeil, A. Tyagi, R. Andronica, D. C. Lee, S.-L. S. Barbour, J. D. Nichols, and M. E. Pflieger, “A programmable laboratory testbed in support of evaluation of functional brain activation and connectivity,” IEEE Trans Neural Syst Rehabil Eng, vol. 20, no. 2, pp. 170–183, Mar. 2012.

H. Niu, S. Khadka, F. Tian, Z.-J. Lin, C. Lu, C. Zhu, and H. Liu, “Resting-state functional connectivity assessed with two diffuse optical tomographic systems,” J Biomed Opt, vol. 16, no. 4, p. 46006, Apr. 2011.

J. Mehnert, C. Schmitz, H. E. Möller, H. Obrig, and K. Mueller, Simultaneous optical tomography (OT) and fMRI with and without task activation. 2010.

For a description of the Human Connectome Project, please visit:

http://www.neuroscienceblueprint.nih.gov/connectome/

Developmental Changes

The portability of fNIRS, its performance in presence of general movements and the feasibility it offers in exploring cortical responses in social environments, represent the greatest advantages for studies on brain functional changes during development of infants and children.

Guo, X., Xu, C., Chen, J., Wu, Z., Hou, S., & Wei, Z. (2024). Disrupted cognitive and affective empathy network interactions in autistic children viewing social animation. Social Cognitive and Affective Neuroscience, nsae028.

Provost, S., Fourdain, S., Vannasing, P., Tremblay, J., Roger, K., Caron-Desrochers, L., ... & Gallagher, A. (2024). Language brain responses and neurodevelopmental outcome in preschoolers with congenital heart disease: A fNIRS study. Neuropsychologia, 108843.

Ludyga, S., Gerber, M., Herold, F., Schwarz, A., Looser, V. N., & Hanke, M. (2024). Cortical hemodynamics and inhibitory processing in preadolescent children with low and high physical activity. International Journal of Clinical and Health Psychology, 24(1), 100438.

Sagiv, S. K., Baker, J. M., Rauch, S., Gao, Y., Gunier, R. B., Mora, A. M., ... & Reiss, A. L. (2023). Prenatal and childhood exposure to organophosphate pesticides and functional brain imaging in young adults. Environmental Research, 117756.

Alemi, R., Wolfe, J., Neumann, S., Manning, J., Towler, W., Koirala, N., ... & Deroche, M. (2023). Audiovisual integration in children with cochlear implants revealed through EEG and fNIRS. Brain Research Bulletin, 110817.

Peng, X. R., Bundil, I., Schulreich, S., & Li, S. C. (2023). Neural correlates of valence-dependent belief and value updating during uncertainty reduction: An fNIRS study. NeuroImage, 279, 120327.

Fraser, K., Kuhn, M., Swanson, R., Coulter, D. W., Copeland, C., & Zuniga, J. M. (2023). Low Motor Dexterity and Significant Behaviors Following Hospitalized Isolation in Children. Children, 10(8), 1287.

Friedman, L. M., Eckrich, S. J., Rapport, M. D., Bohil, C. J., & Calub, C. (2023). Working and short-term memory in children with ADHD: an examination of prefrontal cortical functioning using functional Near-Infrared Spectroscopy (fNIRS). Child Neuropsychology, 1-24.

Scaffei, E., Mazziotti, R., Conti, E., Costanzo, V., Calderoni, S., Stoccoro, A., ... & Battini, R. (2023). A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers. Brain Sciences, 13(6), 951.

Sato, J. R., Pereira, T. D., Martins, C. M. D. L., Bezerra, T. A., Queiroz, M. E., Costa, L. P., ... & Biazoli, C. E. (2023). A Novel Exploratory Graph-Based Analytical Tool for Functional Near-Infrared Spectroscopy in Naturalistic Experiments: An Illustrative Application in Typically Developing Children. Brain Sciences, 13(6), 905.

Hou, S., Chen, S., Huang, Z., Yin, X., Zhao, K., & Zou, J. (2023). Mapping the neural mechanisms of creativity by convergent and divergent thinking in school-aged children: A functional near-infrared spectroscopy study. Thinking Skills and Creativity, 101300.

Miller, J. G., Hyat, M., Perlman, S. B., Wong, R. J., Shaw, G. M., Stevenson, D. K., & Gotlib, I. H. (2023). Prefrontal activation in preschool children is associated with maternal adversity and child temperament: A preliminary fNIRS study of inhibitory control. Developmental Psychobiology, 65(1), e22351.

Mark, C. A., & Poltavski, D. V. (2023). Functional near-infrared spectroscopy is a sensitive marker of neurophysiological deficits on executive function tasks in young adults with a history of child abuse. Applied Neuropsychology: Adult, 1-14.

Davidson, C., Shing, Y. L., McKay, C., Rafetseder, E., & Wijeakumar, S. (2023). The first year in formal schooling improves working memory and academic abilities. Developmental Cognitive Neuroscience, 60, 101205.

Paranawithana, I., Mao, D., McKay, C. M., & Wong, Y. T. (2023). Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting state fNIRS. Journal of Neural Engineering.

Machado, A. C. C. D. P., Magalhães, L. D. C., de Oliveira, S. R., Novi, S. L., Mesquita, R. C., de Miranda, D. M., & Bouzada, M. C. F. (2022). Can tactile reactivity in preterm born infants be explained by an immature cortical response to tactile stimulation in the first year? A pilot study. Journal of Perinatology, 1-7

Hou, S., Liu, N., Zou, J., Yin, X., Liu, X., Zhang, S., ... & Wei, Z. (2022). Young children with autism show atypical prefrontal cortical responses to humanoid robots: An fNIRS study. International Journal of Psychophysiology, 181, 23-32.

Wang, S., Tzeng, O. J., & Aslin, R. N. (2022). Predictive brain signals mediate association between shared reading and expressive vocabulary in infants. PloS one, 17(8), e0272438.

Azhari, A., Bizzego, A., Balagtas, J. P. M., Leng, K. S. H., & Esposito, G. (2022). Asymmetric Prefrontal Cortex Activation Associated with Mutual Gaze of Mothers and Children during Shared Play. Symmetry, 14(5), 998.

Grabell, A. S., Santana, A. M., Thomsen, K. N., Gonzalez, K., Zhang, Z., Bivins, Z., & Rahman, T. (2022). Prefrontal modulation of frustration-related physiology in preschool children ranging from low to severe irritability. Developmental Cognitive Neuroscience, 101112.

Heiland, E. G., Kjellenberg, K., Tarassova, O., Fernström, M., Nyberg, G., Ekblom, M. M., ... & Ekblom, Ö. (2022). ABBaH teens: Activity Breaks for Brain Health in adolescents: study protocol for a randomized crossover trial. Trials, 23(1), 1-11.

Ding, K., Li, C., Li, Y., Wang, H., & Yu, D. (2021). The Effect of Socioeconomic Disparities on Prefrontal Activation in Initiating Joint Attention: A Functional Near-Infrared Spectroscopy Evidence From Two Socioeconomic Status Groups. Frontiers in Human Neuroscience.

Mayseless, N., & Reiss, A. L. (2021). The neurodevelopmental basis of humor appreciation: A fNIRS study of young children. PloS one, 16(12), e0259422.

Ding, K., Li, C., Jia, H., Zhang, M., & Yu, D. (2021). Is left-behind a real reason for children’s social cognition deficit? An fNIRS study on the effect of social interaction on left-behind preschooler’s prefrontal activation. PloS one, 16(9), e0254010.

Li, X., Lipschutz, R., Hernandez, S. M., Biekman, B., Shen, S., Montgomery, D. A., ... & Bick, J. (2021). Links between socioeconomic disadvantage, neural function, and working memory in early childhood. Developmental Psychobiology.

Burgess, A., Hernandez, S. M., Li, X., Bick, J., & Pollonini, L. (2021, April). Correlation between socioeconomic disadvantage in preschool children and brain organization: a functional NIRS connectivity study. In Optics and the Brain (pp. BW4B-5). Optical Society of America.

McKay, C. A., Shing, Y. L., Rafetseder, E., & Wijeakumar, S. (2021). Home assessment of visual working memory in pre‐schoolers reveals associations between behaviour, brain activation and parent reports of life stress. Developmental Science, e13094.

Hoyniak, C. P., Quiñones-Camacho, L. E., Camacho, M. C., Chin, J. H., Williams, E. M., Wakschlag, L. S., & Perlman, S. B. (2021). Adversity is Linked with Decreased Parent-Child Behavioral and Neural Synchrony. Developmental Cognitive Neuroscience, 100937.

Camacho, M. C., Williams, E. M., Ding, K., & Perlman, S. B. (2021). Multimodal Examination of Emotion Processing Systems Associated with Negative Affectivity across Early Childhood. Developmental Cognitive Neuroscience, 100917.

Hashmi, S., Vanderwert, R. E., Price, H. A., & Gerson, S. A. (2020). Exploring the Benefits of Doll Play Through Neuroscience. Frontiers in human neuroscience, 14, 413.

N. Altvater-Mackensen and T. Grossmann, “Modality-independent recruitment of inferior frontal cortex during speech processing in human infants,” Developmental Cognitive Neuroscience, vol. 34, pp. 130–138, Nov. 2018. 

T. Grossmann, M. Missana, and K. M. Krol, “The neurodevelopmental precursors of altruistic behavior in infancy,” PLOS Biology, vol. 16, no. 9, p. e2005281, Sep. 2018.

C. Issard and J. Gervain, “Adult-like processing of time-compressed speech by newborns: A NIRS study,” Developmental Cognitive Neuroscience. Oct. 2017.

M. Mücke, C. Andrä, M. Gerber, U. Pühse, and S. Ludyga, “Moderate-to-vigorous physical activity, executive functions and prefrontal brain oxygenation in children: A functional near-infrared spectroscopy study,” Journal of Sports Sciences, pp. 1–7, May 2017.

Benavides-Varela, S., & Gervain, J. (2017). Learning word order at birth: A NIRS study. Developmental cognitive neuroscience, 25, 198-208.

H. Obrig, J. Mock, F. Stephan, M. Richter, M. Vignotto, and S. Rossi, “Impact of associative word learning on phonotactic processing in 6-month-old infants: A combined EEG and fNIRS study,” Developmental Cognitive Neuroscience. Sep. 2016.

C. Bouchon, T. Nazzi, and J. Gervain, “Hemispheric Asymmetries in Repetition Enhancement and Suppression Effects in the Newborn Brain,” PLOS ONE, vol. 10, no. 10, p. e0140160, Oct. 2015.

J. Mehnert et al., “Developmental changes in brain activation and functional connectivity during response inhibition in the early childhood brain,” Brain Dev., vol. 35, no. 10, pp. 894–904, Nov. 2013.

T. T. Brink et al., “The role of orbitofrontal cortex in processing empathy stories in 4- to 8-year-old children,” Front Psychol, vol. 2, p. 80, 2011.

For updates from Dr. Catherine Spong, acting director of NICHD, on new program initiatives including Learning Disabilities Innovation Hubs, Precision Medicine Initiative, Intellectual and Developmental Disabilities Research Centers, please visit: 

https://www.nichd.nih.gov/about/overview/directors_corner/Pages/default.aspx

Emotions

Near-infrared spectroscopy is non-invasive and particularly well suited for evaluating activity in the prefrontal cortex, one of the regions involved in emotional processing. More specific areas related to emotional processing, such as the frontopolar cortex, are easily accessible for measurements by NIRS, making the technique particularly suited to explore the emotional domain.

Zhu, S., Liu, Q., Zhang, X., Zhou, M., Zhou, X., Ding, F., ... & Zhao, W. (2024). Transcutaneous auricular vagus nerve stimulation enhanced emotional inhibitory control via increasing intrinsic prefrontal couplings. International Journal of Clinical and Health Psychology, 24(2), 100462.

Ersöz, S., Nissen, A., & Schütte, R. (2023). Risk, Trust, and Emotion in Online Pharmacy Medication Purchases: Multimethod Approach Incorporating Customer Self-Reports, Facial Expressions, and Neural Activation. JMIR Formative Research, 7(1), e48850.

Cheng, C., & Yang, Y. (2023). Food stimuli decrease activation in regions of the prefrontal cortex related to executive function: an fNIRS study. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 28(1), 96.

Zhang, W., Qiu, L., Tang, F., & Sun, H. J. (2023). Gender differences in cognitive and affective interpersonal emotion regulation in couples: an fNIRS hyperscanning. Social Cognitive and Affective Neuroscience, 18(1), nsad057.

Yin, J., Deng, M., Zhao, Z., Bao, W., & Luo, J. (2023). Maintaining her image: A social comparative evaluation of the particularity of mothers in the Chinese cultural context. Brain and Cognition, 169, 105995.

Kalanadhabhatta, M., Santana, A. M., Zhang, Z., Ganesan, D., Grabell, A. S., & Rahman, T. (2022). EarlyScreen: Multi-scale Instance Fusion for Predicting Neural Activation and Psychopathology in Preschool Children. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(2), 1-39.

Deng, Z., Guo, J., Wang, D., Huang, T., & Chen, Z. (2022). Effectiveness of the world anti-doping agency's e-learning programme for anti-doping education on knowledge of, explicit and implicit attitudes towards, and likelihood of doping among Chinese college athletes and non-athletes. Substance Abuse Treatment, Prevention, and Policy, 17(1), 1-13.

Balters, S., Li, R., Espil, F., Piccirilli, A., Liu, N., Gundran, A., ... & Reiss, A. L. (2021). Functional near-infrared spectroscopy brain imaging predicts symptom severity in youth exposed to traumatic stress. Journal of Psychiatric Research.

Mehlhose, C.; Risius, A. Assessing Label Frames and Emotional Primes in the Context of Animal Rearing—Response of an Explorative fNIRS Study. Sustainability 2021, 13, 5275. 

Mehlhose, C., & Risius, A. (2020). Signs of Warning: Do Health Warning Messages on Sweets Affect the Neural Prefrontal Cortex Activity?. Nutrients, 12(12), 3903.

Krol, K. M., Puglia, M. H., Morris, J. P., Connelly, J. J., & Grossmann, T. (2019). “Epigenetic modification of the oxytocin receptor gene is associated with emotion processing in the infant brain.” Developmental cognitive neuroscience, 37, 100648.

F. A. Fishburn, C. O. Hlutkowsky, L. M. Bemis, T. J. Huppert, L. S. Wakschlag, and S. B. Perlman, “Irritability uniquely predicts prefrontal cortex activation during preschool inhibitory control among all temperament domains: A LASSO approach,” NeuroImage, vol. 184, pp. 68–77, Jan. 2019.

A. R. Sonkaya and Z. Z. Bayazit, “A Neurolinguistic Investigation of Emotional Prosody and Verbal Components of Speech,” NeuroQuantology, vol. 16, no. 12, Nov. 2018.

M. Balconi, A. Frezza, and M. E. Vanutelli, “Emotion Regulation in Schizophrenia: A Pilot Clinical Intervention as Assessed by EEG and Optical Imaging (Functional Near-Infrared Spectroscopy),” Front Hum Neurosci, vol. 12, Oct. 2018.

G. E. Giles et al., “Cognitive reappraisal reduces perceived exertion during endurance exercise,” Motiv Emot, vol. 42, no. 4, pp. 482–496, Aug. 2018.

M. Balconi et al., “Emotion regulation in Schizophrenia: A comparison between implicit (EEG and fNIRS) and explicit (valence) measures: Preliminary observations,” Asian Journal of Psychiatry, vol. 34, pp. 12–13, Apr. 2018.

L. R. Trambaiolli, C. E. Biazoli, A. M. Cravo, and J. R. Sato, “Predicting affective valence using cortical hemodynamic signals,” Scientific Reports, vol. 8, no. 1, p. 5406, Mar. 2018.

D. Zhang, Y. Zhou, and J. Yuan, “Speech Prosodies of Different Emotional Categories Activate Different Brain Regions in Adult Cortex: an fNIRS Study,” Scientific Reports, vol. 8, no. 1, p. 218, Jan. 2018.

G. E. Giles et al., “Endurance Exercise Enhances Emotional Valence and Emotion Regulation,” Front. Hum. Neurosci., vol. 12, 2018.

A. van der Kant, S. Biro, C. Levelt, and S. Huijbregts, “Negative affect is related to reduced differential neural responses to social and non-social stimuli in 5-to-8-month-old infants: A functional near-infrared spectroscopy-study,” Developmental Cognitive Neuroscience, vol. 30, pp. 23–30, Apr. 2018.

A. Landowska, S. Royle, P. Eachus, and D. Roberts, “Testing the Potential of Combining Functional Near-Infrared Spectroscopy with Different Virtual Reality Displays—Oculus Rift and oCtAVE,” in Augmented Reality and Virtual Reality, Springer, Cham, 2018, pp. 309–321. 

L. Trambaiolli, J. Tossato, A. Cravo, C. Biazoli, and J. Sato, “Decoding affective states across databases using functional near-infrared spectroscopy,”, Dec. 2017.

J. Yu, K. K. Ang, S. H. Ho, A. Sia, and R. Ho, “Prefrontal cortical activation while viewing urban and garden scenes: A pilot fNIRS study,” 2017, pp. 2546–2549.

K. Pollmann, D. Ziegler, M. Peissner, and M. Vukelić, “A New Experimental Paradigm for Affective Research in Neuro-adaptive Technologies,” 2017, pp. 1–8.

D. Zhang, Y. Zhou, X. Hou, Y. Cui, and C. Zhou, “Discrimination of emotional prosodies in human neonates: A pilot fNIRS study,” Neuroscience Letters, vol. 658, pp. 62–66, Sep. 2017.

M. Balconi, M. E. Vanutelli, and E. Grippa, “Resting state and personality component (BIS/BAS) predict the brain activity (EEG and fNIRS measure) in response to emotional cues,” Brain Behav, p. n/a-n/a, Mar. 2017.

M. E. Vanutelli and M. Balconi, “Perceiving emotions in human-human and human-animal interactions: Hemodynamic prefrontal activity (fNIRS) and empathic concern,” Neurosci. Lett., vol. 605, pp. 1–6, Sep. 2015.

M. Balconi and M. E. Vanutelli, “Emotions and BIS/BAS components affect brain activity (ERPs and fNIRS) in observing intra-species and inter-species interactions,” Brain Imaging and Behavior, vol. 10, no. 3, pp. 750–760, Aug. 2015.

M. Balconi, E. Grippa, and M. E. Vanutelli, “What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing,” Brain Cogn, vol. 95, pp. 67–76, Apr. 2015.

M. Balconi, E. Grippa, and M. E. Vanutelli, “Resting lateralized activity predicts the cortical response and appraisal of emotions: an fNIRS study,” Soc Cogn Affect Neurosci, vol. 10, no. 12, pp. 1607–1614, Dec. 2015.

Event-Related Optical Signal

fNIRS is potentially the only imaging method that may be capable to measure both hemodynamics and neuronal activity. The Event-Related Optical Signal, caused by changes in light scattering from activated neurons, is observable when employing high frequency sampling with fNIRS.

McLinden, J., Borgheai, S. B., Hosni, S., Kumar, C., Rahimi, N., Shao, M., ... & Shahriari, Y. (2023). Individual-specific characterization of event-related hemodynamic responses during an auditory task: An exploratory study. Behavioural Brain Research, 436, 114074.

de Tommaso, M., La Rocca, M., Quitadamo, S. G., Ricci, K., Tancredi, G., Clemente, L., ... & Delussi, M. (2022). Central effects of galcanezumab in migraine: a pilot study on Steady State Visual Evoked Potentials and occipital hemodynamic response in migraine patients. The Journal of Headache and Pain, 23(1), 1-14.

Csipo, T., Lipecz, A., Mukli, P., Bahadli, D., Abdulhussein, O., Owens, C. D., ... & Yabluchanskiy, A. (2021). Increased cognitive workload evokes greater neurovascular coupling responses in healthy young adults. Plos one, 16(5), e0250043.

X.-S. Hu, K.-S. Hong, and S. S. Ge, “Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series,” Neurosci. Lett., vol. 504, no. 2, pp. 115–120, Oct. 2011.

A. V. Medvedev, J. Kainerstorfer, S. V. Borisov, R. L. Barbour, and J. VanMeter, “Event-related fast optical signal in a rapid object recognition task: improving detection by the independent component analysis,” Brain Res., vol. 1236, pp. 145–158, Oct. 2008.

For an informative discussion on the various strategies of optical imaging techniques, please visit: 

http://www.nibib.nih.gov/science-education/science-topics/optical-imaging;
http://www.report.nih.gov/nihfactsheets/ViewFactSheet.aspx?csid=105.

Infant Monitoring

Infant monitoring is based on continuous measurements of cortical activity within a population that may be characterized by its constant movement. The low sensitivity of fNIRS to motion artifacts make this technique an ideal choice for studies intended to explore the many unknown features of the infant brain.

Hunter, S., Flaten, E., Petersen, C., Gervain, J., Werker, J. F., Trainor, L. J., & Finlay, B. B. (2023). Babies, bugs and brains: How the early microbiome associates with infant brain and behavior development. PLoS One, 18(8), e0288689.

de la Cruz-Pavía, I., & Gervain, J. (2023). Six-month-old infants' perception of structural regularities in speech. Cognition, 238, 105526.

Kebaya, L. M., Stubbs, K., Lo, M., Al-Saoud, S., Karat, B., St Lawrence, K., ... & Duerden, E. G. (2023). Three-dimensional cranial ultrasound and functional near-infrared spectroscopy for bedside monitoring of intraventricular hemorrhage in preterm neonates. Scientific Reports, 13(1), 3730.

Lee, O. W., Mao, D., Savkovic, B., Wunderlich, J., Nicholls, N., Jeffreys, E., ... & McKay, C. M. (2023). The Use of Heart Rate Responses Extracted From Functional Near-Infrared Spectroscopy Data as a Measure of Speech Discrimination Ability in Sleeping Infants. Ear and Hearing, 10-1097.

Martinez-Alvarez, A., Gervain, J., Koulaguina, E., Pons, F., & de Diego-Balaguer, R. (2023). Prosodic cues enhance infants’ sensitivity to nonadjacent regularities. Science Advances, 9(15), eade4083.

Lee, O. W., Mao, D., Wunderlich, J., Balasubramanian, G., Haneman, M., Korneev, M., & McKay, C. (2023). Two independent response mechanisms to auditory stimuli measured with fNIRS in sleeping infants

Filippetti, M. L., Andreu-Perez, J., De Klerk, C., Richmond, C., & Rigato, S. (2023). Are advanced methods necessary to improve infant fNIRS data analysis? An assessment of baseline-corrected averaging, general linear model (GLM) and multivariate pattern analysis (MVPA) based approaches. Neuroimage, 265(119756)

Arredondo, M. M., Aslin, R. N., Zhang, M., & Werker, J. F. (2022). Attentional orienting abilities in bilinguals: Evidence from a large infant sample. Infant Behavior and Development, 66, 101683.

Wang, S., Tzeng, O. J., & Aslin, R. N. (2022). Predictive brain signals mediate association between shared reading and expressive vocabulary in infants. PloS one, 17(8), e0272438.

Martinez‐Alvarez, A., Benavides‐Varela, S., Lapillonne, A., & Gervain, J. (2022). Newborns discriminate utterance‐level prosodic contours. Developmental Science, e13304.

Coëz, A., Loundon, N., Rouillon, I., Parodi, M., Blanchard, M., Achard, S., ... & Gervain, J. (2022). The recognition of human voice in deaf and hearing infants. Hearing, Balance and Communication, 20(3), 179-185.

Chajes, J. R., Stern, J. A., Kelsey,C. M., & Grossmann, T. Examining the Role of Socioeconomic Status and Maternal Sensitivity in Predicting Functional Brain Network Connectivity in 5-Month-Old Infants.

Farris, K., Kelsey, C. M., Krol, K. M., Thiele, M., Hepach, R., Haun, D. B., & Grossmann, T. (2022). Processing third-party social interactions in the human infant brain. Infant Behavior and Development, 68, 101727.

Wang, S., Zhang, X., Hong, T., Tzeng, O. J., & Aslin, R. (2022). Top-down sensory prediction in the infant brain at 6 months is correlated with language development at 12 and 18 months. Brain and Language, 230, 105129.

Forgács, B., Tauzin, T., Gergely, G., & Gervain, J. (2022). The newborn brain is sensitive to the communicative function of language. Scientific Reports, 12(1), 1-6.

Nguyen, T., Hoehl, S., Bertenthal, B. I., & Abney, D. H. (2021). Coupling between Prefrontal Brain Activity and Respiratory Sinus Arrhythmia in Infants and Adults. Developmental Cognitive Neuroscience, 101047.

Mao, D., Wunderlich, J., Savkovic, B., Jeffreys, E., Nicholls, N., Lee, O. W., ... & McKay, C. M. (2021). Speech token detection and discrimination in individual infants using functional near-infrared spectroscopy. Scientific Reports, 11(1), 1-14.

Andreu-Perez, J., Emberson, L. L., Kiani, M., Filippetti, M. L., Hagras, H., & Rigato, S. (2021). Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience. Communications biology, 4(1), 1-13.

Richter, M., Vignotto, M., Mock, J., Obrig, H., & Rossi, S. (2021). Different word-learning contexts alter phonotactic rule learning in 6-month-olds. Language, Cognition and Neuroscience, 1-24.

Nguyen, T., Abney, D. H., Salamander, D., Bertenthal, B., & Hoehl, S. (2021). Social touch is associated with neural but not physiological synchrony in naturalistic mother-infant interactions. BioRxiv.

Arredondo, M. M., Aslin, R. N., & Werker, J. F. (2021). Bilingualism alters infants’ cortical organization for attentional orienting mechanisms. Developmental Science.

Krol, K. M., Namaky, N., Monakhov, M., San Lai, P., Ebstein, R., & Grossmann, T. (2021). Genetic variation in the oxytocin system and its link to social motivation in human infants. Psychoneuroendocrinology, 131, 105290 https://doi.org/10.1016/j.psyneuen.2021.105290

Cabrera, L., & Gervain, J. (2020). Speech perception at birth: The brain encodes fast and slow temporal information. Science Advances, 6(30), eaba7830.

Mazaika, P. K., Marzelli, M., Tong, G., Foland‐Ross, L. C., Buckingham, B. A., Aye, T., & Reiss, A. L. (2020). Functional near‐infrared spectroscopy detects increased activation of the brain frontal‐parietal network in youth with type 1 diabetes. Pediatric Diabetes, 21(3), 515-523.

Krol, K. M., & Grossmann, T. (2020). Friend or foe? Impression formation in the human infant brain.

Altvater-Mackensen, N., & Grossmann, T. (2016). The role of left inferior frontal cortex during audiovisual speech perception in infants. https://doi.org/10.1016/j.neuroimage.2016.02.061

de Oliveira, S. R., Machado, A. C. C. P., de Paula, J. J., Novi, S. L., Mesquita, R. C., de Miranda, D. M., & Bouzada, M. C. F. (2019). Changes of functional response in sensorimotor cortex of preterm and full-term infants during the first year: An fNIRS study. Early Human Development, 133, 23–28. https://doi.org/10.1016/j.earlhumdev.2019.04.007

Obrig, H., Mock, J., Stephan, F., Richter, M., Vignotto, M., & Rossi, S. (2017). Impact of associative word learning on phonotactic processing in 6-month-old infants: A combined EEG and fNIRS study. Developmental Cognitive Neuroscience, 25, 185–197. https://doi.org/10.1016/j.dcn.2016.09.001

McNiel, D. B., Barbour, R. L., Lee, D. C., & Kim, R. M. (n.d.). Examination of necrotizing enterocolitis in preterm infants using diffuse optical tomography.

de Oliveira, S. R., de Paula Machado, A. C. C., de Paula, J. J., de Moraes, P. H. P., Nahin, M. J. S., Magalhães, L. de C., Novi, S. L., Mesquita, R. C., de Miranda, D. M., & Bouzada, M. C. F. (2017). Association between hemodynamic activity and motor performance in six-month-old full-term and preterm infants: a functional near-infrared spectroscopy study. Neurophotonics, 5(01), 1. https://doi.org/10.1117/1.nph.5.1.011016

van der Kant, A., Biro, S., Levelt, C., & Huijbregts, S. (2018). Negative affect is related to reduced differential neural responses to social and non-social stimuli in 5-to-8-month-old infants: A functional near-infrared spectroscopy-study. Developmental Cognitive Neuroscience, 30, 23–30. https://doi.org/10.1016/j.dcn.2017.12.003

Kelsey, C. M., Krol, K. M., Kret, M. E., & Grossmann, T. (2019). Infants’ brain responses to pupillary changes in others are affected by race. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-40661-z

Altvater-Mackensen, N., & Grossmann, T. (2018). Modality-independent recruitment of inferior frontal cortex during speech processing in human infants. Developmental Cognitive Neuroscience, 34, 130–138. https://doi.org/10.1016/j.dcn.2018.10.002

Kelsey, C., & Dreisbach, C. (2017). Mothers, Infants, Microbiome and Mental Health (MIMMs): A social neuroscience perspective of maternal-child health to unlock the potential of biobanked data An Application to the Presidential Fellowship in Data Science.

Krol, K. M., Puglia, M. H., Morris, J. P., Connelly, J. J., & Grossmann, T. (2019). Epigenetic modification of the oxytocin receptor gene is associated with emotion processing in the infant brain. Developmental Cognitive Neuroscience, 37. https://doi.org/10.1016/j.dcn.2019.100648

Zores, C., Marchal, A., Davy, M., Pebayle, T., Astruc, D., Dufour, A., & Kuhn, P. (2019). Development of Cortical Integration of Visual Stimuli in Very Preterm Infants. Developmental Observer, 16–17.

Cabrera, L., & Gervain, J. (n.d.). How infants use the envelope of the speech signal to perceive phonetic contrasts at birth.

Frie, J. (2018). Ontogeny of perception of maternal and nosocomial stimuli in infants. Inst för kvinnors och barns hälsa/Dept of Women's and Children's Health.

Vassena, E., Gerrits, R., Demanet, J., Verguts, T., & Siugzdaite, R. (2019). Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study. Neuropsychologia, 123, 106–115. https://doi.org/10.1016/j.neuropsychologia.2018.04.033

Blanco, B., Molnar, M., & Caballero-Gaudes, C. (2018). Effect of prewhitening in resting-state functional near-infrared spectroscopy data. Neurophotonics, 5(04), 1. https://doi.org/10.1117/1.nph.5.4.040401

Benavides-Varela, S., & Gervain, J. (2017). Learning word order at birth: A NIRS study. Developmental Cognitive Neuroscience, 25, 198–208. https://doi.org/10.1016/j.dcn.2017.03.003

Grossmann, T., Missana, M., & Krol, K. M. (2018). The neurodevelopmental precursors of altruistic behavior in infancy. PLoS Biology, 16(9). https://doi.org/10.1371/journal.pbio.2005281

Steber, S., & Rossi, S. (2020). So young, yet so mature? Electrophysiological and vascular correlates of phonotactic processing in 18-month-olds. Developmental Cognitive Neuroscience, 43, 100784. https://doi.org/10.1016/j.dcn.2020.100784

Liang, Z., Minagawa, Y., Yang, H. C., Tian, H., Cheng, L., Arimitsu, T., Takahashi, T., & Tong, Y. (2018). Symbolic time series analysis of fNIRS signals in brain development assessment. Journal of Neural Engineering, 15(6). https://doi.org/10.1088/1741-2552/aae0c9

Zhang, D., Zhou, Y., Hou, X., Cui, Y., & Zhou, C. (2017). Discrimination of emotional prosodies in human neonates: A pilot fNIRS study. Neuroscience Letters, 658, 62–66. https://doi.org/10.1016/j.neulet.2017.08.047

Issard, C., & Gervain, J. (2017). Adult-like processing of time-compressed speech by newborns: A NIRS study. Developmental Cognitive Neuroscience, 25, 176–184. https://doi.org/10.1016/j.dcn.2016.10.006

Grazioli, S., Crippa, A., Mauri, M., Piazza, C., Bacchetta, A., Salandi, A., Trabattoni, S., Agostoni, C., Molteni, M., & Nobile, M. (2019). Association between fatty acids profile and cerebral blood flow: An exploratory fNIRS study on children with and without ADHD. Nutrients, 11(10). https://doi.org/10.3390/nu11102414

Azhari, A., Leck, W. Q., Gabrieli, G., Bizzego, A., Rigo, P., Setoh, P., Bornstein, M. H., & Esposito, G. (2019). Parenting Stress Undermines Mother-Child Brain-to-Brain Synchrony: A Hyperscanning Study. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-47810-4

Caron-Desrochers, L., Noiseux, C., Vannasing, P., Tremblay, J., Kraimeche, S., Constantin, I. M., Paquette, N., Roger, K., Provost, S., Taillefer, C., Boucoiran, I., & Gallagher, A. (n.d.). Brain activation patterns in newborns: The influence of prenatal exposure to a foreign language.

Jadcherla, S. R., Pakiraih, J. F., Hasenstab, K. A., Dar, I., Gao, X., Gregory Bates, D., & Kashou, N. H. (2014). Esophageal reflexes modulate frontoparietal response in neonates: Novel application of concurrent NIRS and provocative esophageal manometry. American Journal of Physiology - Gastrointestinal and Liver Physiology, 307(1). https://doi.org/10.1152/ajpgi.00350.2013

Kashou, N. H., Dar, I. A., Hasenstab, K. A., Nahhas, R. W., & Jadcherla, S. R. (2016). Somatic stimulation causes frontoparietal cortical changes in neonates: a functional near-infrared spectroscopy study. Neurophotonics, 4(1), 011004. https://doi.org/10.1117/1.nph.4.1.011004

Kim, M. R., Graber, H. L., Barbour, R. L., Nidhi, N., Rawal, R., Pradeep, P., Siwach, S., Devaraj, D., & Sambalingam, S. (n.d.). The Determination of Neonatal Brain Oxygenation Status by Near The Determination of Neonatal Brain Oxygenation Status by Near Infrared Technology Infrared Technology.

Bouchon, C., Nazzi, T., & Gervain, J. (2015). Hemispheric Asymmetries in Repetition Enhancement and Suppression Effects in the Newborn Brain. PLOS ONE, 10(10), e0140160. https://doi.org/10.1371/journal.pone.0140160

Groba, A., De Houwer, A., Obrig, H., & Rossi, S. (2019). Bilingual and monolingual first language acquisition experience differentially shapes children’s property term learning: Evidence from behavioral and neurophysiological measures. Brain Sciences, 9(2). https://doi.org/10.3390/brainsci9020040

Mehnert, J., Akhrif, A., Telkemeyer, S., Rossi, S., Schmitz, C. H., Steinbrink, J., Wartenburger, I., Obrig, H., & Neufang, S. (2013). Developmental changes in brain activation and functional connectivity during response inhibition in the early childhood brain. Brain and Development, 35(10), 894–904. https://doi.org/10.1016/j.braindev.2012.11.006

Groba, A., De Houwer, A., Mehnert, J., Rossi, S., & Obrig, H. (2018). Bilingual and monolingual children process pragmatic cues differently when learning novel adjectives. Bilingualism, 21(2), 384–402. https://doi.org/10.1017/S1366728917000232

Nguyen, T., Schleihauf, H., Kayhan, E., Matthes, D., Vrticka, P., & Hoehl, S. (2020). The effects of interaction quality on neural synchrony during mother-child problem solving. Cortex (under Review), 4. https://doi.org/10.1016/j.cortex.2019.11.020

Frie, J., Bartocci, M., Lagercrantz, H., & Kuhn, P. (2018). Cortical Responses to Alien Odors in Newborns: An fNIRS Study. Cerebral Cortex, 28(9), 3229–3240. https://doi.org/10.1093/cercor/bhx194

Quiñones-Camacho, L. E., Fishburn, F. A., Camacho, M. C., Wakschlag, L. S., & Perlman, S. B. (2019). Cognitive flexibility-related prefrontal activation in preschoolers: A biological approach to temperamental effortful control. Developmental Cognitive Neuroscience, 38. https://doi.org/10.1016/j.dcn.2019.100651

Fishburn, F. A., Hlutkowsky, C. O., Bemis, L. M., Huppert, T. J., Wakschlag, L. S., & Perlman, S. B.(2019), “Irritability uniquely predicts prefrontal cortex activation during preschool inhibitory control among all temperament domains: A LASSO approach”, NeuroImage, 2019, 184, 68-77.

Gervain, J., “Plasticity in early language acquisition: the effects of prenatal and early childhood experience,” Curr. Opin. Neurobiol., vol. 35, pp. 13–20, Dec. 2015.

Galderisi, A., Brigadoi, S., Cutini, S., Moro, S.B., Lolli, E., Meconi, F., Benavides-Varela, S., Baraldi, E., Amodio, P., Cobelli, C., et al., “Long-term continuous monitoring of the preterm brain with diffuse optical tomography and electroencephalography: a technical note on cap manufacturing,” Neurophoton, vol. 3, no. 4, pp. 045009–045009, 2016.

Altvater-Mackensen, N., and Grossmann, T., “The role of left inferior frontal cortex during audiovisual speech perception in infants,” NeuroImage, vol. 133, pp. 14–20, Jun. 2016.

 For an informative summary of timelines for sensory, motor and psychosocial development in infants and young children, please visit: 

https://www.nlm.nih.gov/medlineplus/infantandnewborndevelopment.html

Motor Execution 

Motor execution and fine movements depend on coordinated action of brain function and peripheral muscles. Its portability, ease of use in natural environments, and compatibility with bioelectric measures make fNIRS an optimal choice for studies investigating motor execution.

Yeo, S. S., Park, S. Y., & Yun, S. H. (2024). Investigating cortical activity during cybersickness by fNIRS. Scientific Reports, 14(1), 8093.

Venckunas, T., Satas, A., Brazaitis, M., Eimantas, N., Sipaviciene, S., & Kamandulis, S. (2024). Near-InfraRed Spectroscopy Provides a Reproducible Estimate of Muscle Aerobic Capacity, but Not Whole-Body Aerobic Power. Sensors, 24(7), 2277.

Yeo, S. S., Park, S. Y., & Yun, S. H. (2024). Investigating cortical activity during cybersickness by fNIRS. Scientific Reports, 14(1), 8093.

Helmich, I., & Gemmerich, R. (2024). Neuronal Control of Posture in Blind Individuals. Brain Topography, 1-13.

Healey, R., Goldsworthy, M., Salomoni, S., Weber, S., Kemp, S., Hinder, M. R., & St George, R. J. (2024). Impaired motor inhibition during perceptual inhibition in older, but not younger adults: a psychophysiological study. Scientific Reports, 14(1), 2023.

Tung, C., Lord, S. R., Pelicioni, P. H. S., Sturnieks, D. L., & Menant, J. C. C. (2023). Prefrontal and Motor Planning Cortical Activity during Stepping Tasks Is Related to Task Complexity but Not Concern about Falling in Older People: A fNIRS Study. Brain Sciences, 13(12), 1675.

Von Au, S., Helmich, I., Kieffer, S., & Lausberg, H. (2023). Phasic and Repetitive Self-Touch differ in Hemodynamic Response in the prefrontal cortex-A fNIRS study. Frontiers in Neuroergonomics, 4, 1266439.

Yun, S. H., Jang, T. S., & Kwon, J. W. (2024). Cortical activity and spatiotemporal parameters during gait termination and walking: A preliminary study. Behavioural Brain Research, 456, 114701.

Sánchez-González, J. L., Díez-Villoria, E., Pérez-Robledo, F., Sanz-Esteban, I., Llamas-Ramos, I., Llamas-Ramos, R., ... & Martín-Nogueras, A. M. (2023). Synergy of Muscle and Cortical Activation through Vojta Reflex Locomotion Therapy in Young Healthy Adults: A Pilot Randomized Controlled Trial. Biomedicines, 11(12), 3203.

de Rond, V., D’Cruz, N., Hulzinga, F., McCrum, C., Verschueren, S., de Xivry, J. J. O., & Nieuwboer, A. (2023). Neural correlates of weight-shift training in older adults: a randomized controlled study. Scientific Reports, 13(1), 19609.

Li, F., Bi, J., Liang, Z., Li, L., Liu, Y., & Huang, L. (2023). Functional Near-Infrared Spectroscopy-Based Evidence of the Cerebral Oxygenation and Network Characteristics of Upper Limb Fatigue. Bioengineering, 10(10), 1112.

Gentile, E., Brunetti, A., Ricci, K., Vecchio, E., Santoro, C., Sibilano, E., ... & de Tommaso, M. (2023). Effects of movement congruence on motor resonance in early Parkinson’s disease. Scientific Reports, 13(1), 14887.

Bae, S., & Park, H. S. (2023). Development of immersive virtual reality-based hand rehabilitation system using a gesture-controlled rhythm game with vibrotactile feedback: an fNIRS pilot study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 31.

Bonzano, L., Biggio, M., Brigadoi, S., Pedullà, L., Pagliai, M., Iester, C., ... & Bove, M. (2023). Don't plan, just do it: Cognitive and sensorimotor contributions to manual dexterity. NeuroImage, 280, 120348

Nguyen, T., Behrens, M., Broscheid, K. C., Bielitzki, R., Weber, S., Libnow, S., ... & Schega, L. (2023). Associations between gait performance and pain intensity, psychosocial factors, executive functions as well as prefrontal cortex activity in chronic low back pain patients: A cross-sectional fNIRS study. Frontiers in Medicine, 10, 1147907.

Fu, Y., Walia, P., Schwaitzberg, S. D., Intes, X., De, S., Dutta, A., & Cavuoto, L. (2023). Changes in functional neuroimaging measures as novices gain proficiency on the fundamentals of laparoscopic surgery suturing task. Neurophotonics, 10(2), 023521-023521.

Carius, D., Herold, F., Clauß, M., Kaminski, E., Wagemann, F., Sterl, C., & Ragert, P. (2023). Increased Cortical Activity in Novices Compared to Experts During Table Tennis: A Whole-Brain fNIRS Study Using Threshold-Free Cluster Enhancement Analysis. Brain Topography, 1-17.

Stojan, R., Mack, M., Bock, O., & Voelcker-Rehage, C. (2023). Inefficient frontal and parietal brain activation during dual-task walking in a virtual environment in older adults. NeuroImage, 273, 120070.

Khan, H., Qureshi, N. K., Yazidi, A., Engell, H., & Mirtaheri, P. (2023). Single-leg stance on a challenging surface can enhance cortical activation in the right hemisphere-A case study. Heliyon.

Tao, P., Shao, X., Dong, Y., Adams, R., Preston, E., Liu, Y., & Han, J. (2023). Functional near-infrared spectroscopy measures of frontal hemodynamic responses in Parkinson's patients and controls performing the Timed-Up-and-Go test. Behavioural Brain Research, 438, 114219.

 Walia, P., Fu, Y., Schwaitzberg, S. D., Intes, X., De, S., Dutta, A., & Cavuoto, L. (2022). Portable neuroimaging differentiates novices from those with experience for the Fundamentals of Laparoscopic Surgery (FLS) suturing with intracorporeal knot tying task. Surgical Endoscopy, 1-7.

Lin, C. C., Bair, W. N., & Willson, J. (2022). Age differences in brain activity in dorsolateral prefrontal cortex and supplementary motor areas during three different walking speed tasks. Human Movement Science, 85, 102982.

Bao, S., Liu, J., & Liu, Y. (2022). Shedding Light on the Effects of Orienteering Exercise on Spatial Memory Performance in College Students of Different Genders: An fNIRS Study. Brain sciences, 12(7), 852.

Almulla, L., Al-Naib, I., Ateeq, I. S., & Althobaiti, M. (2022). Observation and motor imagery balance tasks evaluation: An fNIRS feasibility study. Plos one, 17(3), e0265898.

Lehmann, N., Kuhn, Y. A., Keller, M., Aye, N., Herold, F., Draganski, B., ... & Taubert, M. (2022) Brain Activation During Active Balancing and Its Behavioral Relevance in Younger and Older Adults: A Functional Near-Infrared Spectroscopy (fNIRS) Study. Front. Aging Neurosci. 14

Gentile, E., Brunetti, A., Ricci, K., Bevilacqua, V., Craighero, L., & de Tommaso, M. (2022). Movement observation activates motor cortex in fibromyalgia patients: a fNIRS study. Scientific Reports, 12(1), 1-14.

Hoang, I., Paire-Ficout, L., Derollepot, R., Perrey, S., Devos, H., & Ranchet, M. (2022). Increased prefrontal activity during usual walking in aging. International Journal of Psychophysiology.

Hoang, I., Ranchet, M., Cheminon, M., Derollepot, R., Devos, H., Perrey, S., ... & Paire-Ficout, L. (2021). An intensive exercise-based training program reduces prefrontal activity during usual walking in patients with parkinson’s disease. Clinical Parkinsonism & Related Disorders, 100128.

Krahnen, L. S., Bauernfeind, G., Leiber, P., & Jipp, M. (2022). Evaluation of two short-term stress interventions in the context of mobility. Transportation Research Part F: Traffic Psychology and Behaviour, 84, 155-164.

Kim, J., Lee, G., Lee, J., & Kim, Y. H. (2021). Changes in Cortical Activity during Preferred and Fast Speed Walking under Single-and Dual-Tasks in the Young-Old and Old-Old Elderly. Brain Sciences, 11(12), 1551.

Carius, D., Kenville, R., Maudrich, D., Riechel, J., Lenz, H., & Ragert, P. (2021). Cortical processing during table tennis-an fNIRS study in experts and novices. European Journal of Sport Science, 1-11.

Kurkin, S., Badarin, A., Grubov, V., Maksimenko, V., & Hramov, A. (2021). The oxygen saturation in the primary motor cortex during a single hand movement: functional near-infrared spectroscopy (fnirs) study. The European Physical Journal Plus, 136(5), 548.

Zhu, Y., Weston, E. B., Mehta, R. K., & Marras, W. S. (2021). Neural and biomechanical tradeoffs associated with human-exoskeleton interactions. Applied ergonomics, 96, 103494.

Prôa, R., Balardin, J., de Faria, D. D., Paulo, A. M., Sato, J. R., Baltazar, C. A., ... & de Carvalho Aguiar, P. (2021). Motor Cortex Activation During Writing in Focal Upper-Limb Dystonia: An fNIRS Study. Neurorehabilitation and Neural Repair, 15459683211019341.

Borrell, J. A., Copeland, C., Lukaszek, J. L., Fraser, K., & Zuniga, J. M. (2021). Use-Dependent Prosthesis Training Strengthens Contralateral Hemodynamic Brain Responses in a Young Adult With Upper Limb Reduction Deficiency: A Case Report. Frontiers in Neuroscience, 15.

Lamberti, N., Manfredini, F., Baroni, A., Crepaldi, A., Lavezzi, S., Basaglia, N., & Straudi, S. (2021). Motor Cortical Activation Assessment in Progressive Multiple Sclerosis Patients Enrolled in Gait Rehabilitation: A Secondary Analysis of the RAGTIME Trial Assisted by Functional Near-Infrared Spectroscopy. Diagnostics, 11(6), 1068.

Seidel-Marzi, O., Hähner, S., Ragert, P., & Carius, D. (2021). Task-Related Hemodynamic Response Alterations During Slacklining: An fNIRS Study in Advanced Slackliners. Frontiers in Neuroergonomics, 2, 4.

Ranchet, M., Hoang, I., Cheminon, M., Derollepot, R., Devos, H., Perrey, S., ... & Paire-Ficout, L. (2020). Changes in Prefrontal Cortical Activity During Walking and Cognitive Functions Among Patients With Parkinson's Disease. Frontiers in Neurology, 11, 1658.

St George, R. J., Hinder, M. R., Puri, R., Walker, E., & Callisaya, M. L. (2020). Functional near-infrared spectroscopy reveals the compensatory potential of pre-frontal cortical activity for standing balance in young and older adults. Neuroscience.

Pelicioni, P. H., Lord, S. R., Okubo, Y., Sturnieks, D. L., & Menant, J. C. (2020). People With Parkinson’s Disease Exhibit Reduced Cognitive and Motor Cortical Activity When Undertaking Complex Stepping Tasks Requiring Inhibitory Control. Neurorehabilitation and Neural Repair, 1545968320969943.

Jang, J. H., Park, S., An, J., Choi, J. D., Seol, I. C., Park, G., ... & Cha, J. Y. (2020). Gait Disturbance Improvement and Cerebral Cortex Rearrangement by Acupuncture in Parkinson’s Disease: A Pilot Assessor-Blinded, Randomized, Controlled, Parallel-Group Trial. Neurorehabilitation and Neural Repair, 1545968320969942.

Carius, D., Seidel-Marzi, O., Kaminski, E., Lisson, N., & Ragert, P. (2020). Characterizing hemodynamic response alterations during basketball dribbling. PloS one, 15(9), e0238318.

Peters, S., Lim, S. B., Louie, D. R., Yang, C. L., & Eng, J. J. (2020). Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task. Journal of NeuroEngineering and Rehabilitation, 17(1), 1-12.

Sylcott, B., Hinderaker, M., Smith, M., Willson, J., & Lin, C. C. (2020, July). Prefrontal and Vestibular Cortex Activation During Overground and Treadmill Walking. In International Conference on Applied Human Factors and Ergonomics (pp. 225-230). Springer, Cham.

Helmich, I., Coenen, J., Henckert, S., Pardalis, E., Schupp, S., & Lausberg, H. (2020). Reduced frontopolar brain activation characterizes concussed athletes with balance deficits. Neuroimage: clinical, 25, 102164.

Yu, M., & Liu, Y. (2020). Differences in executive function of the attention network between athletes from interceptive and strategic sports. Journal of Motor Behavior, 1-12.

Heiland, E. G., Ekblom, Ö., Tarassova, O., Fernström, M., English, C., & Ekblom, M. M. (2020). ABBaH: Activity Breaks for Brain Health. A protocol for a randomised crossover trial. Frontiers in Human Neuroscience, 14, 273.

Khan, A. F., Zhang, F., Yuan, H., & Ding, L. (2019, July). Dynamic Activation Patterns of the Motor Brain Revealed by Diffuse Optical Tomography. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6028-6031). IEEE.

Ludyga, S., Mücke, M., Colledge, F. M. A., Pühse, U., & Gerber, M. (2019). A Combined EEG-fNIRS Study Investigating Mechanisms Underlying the Association between Aerobic Fitness and Inhibitory Control in Young Adults. Neuroscience, 419, 23-33.

Seidel, O., Carius, D., Roediger, J., Rumpf, S., & Ragert, P. (2019). Changes in neurovascular coupling during cycling exercise measured by multi-distance fNIRS: a comparison between endurance athletes and physically active controls. Experimental brain research, 237(11), 2957-2972.

Heinze, R. A., Vanzella, P., Morais, G. A. Z., & Sato, J. R. “Hand motor learning in a musical context and prefrontal cortex hemodynamic response: a functional near-infrared spectroscopy (fNIRS) study”. Cognitive processing, 1-7

Huang, T., Gu, Q., Deng, Z., Tsai, C., Xue, Y., Zhang, J., ... & Wang, K. (2019). “Executive Function Performance in Young Adults When Cycling at an Active Workstation: An fNIRS Study.” International journal of environmental research and public health, 16(7), 1119.

L. Zhu, S. Li, Y. Li, M. Wang, Y. Li, and J. Yao, “Study on driver’s braking intention identification based on functional near-infrared spectroscopy,” Journal of Intelligent and Connected Vehicles, Dec. 2018.

Y. Liu, Y. Yang, Y. Tsai, R. Wang, and C. Lu, “Brain Activation and Gait Alteration During Cognitive and Motor Dual Task Walking in Stroke—A Functional Near-Infrared Spectroscopy Study,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 12, pp. 2416–2423, Dec. 2018.

W. Wolff, M. Bieleke, A. Hirsch, C. Wienbruch, P. M. Gollwitzer, and J. Schüler, “Increase in prefrontal cortex oxygenation during static muscular endurance performance is modulated by self-regulation strategies,” Scientific Reports, vol. 8, no. 1, p. 15756, Oct. 2018.

S. C. Wriessnegger, G. Bauernfeind, E.-M. Kurz, P. Raggam, and G. R. Müller-Putz, “Imagine squeezing a cactus: Cortical activation during affective motor imagery measured by functional near-infrared spectroscopy,” Brain and Cognition, vol. 126, pp. 13–22, Oct. 2018.

U. Ghafoor, A. Zafar, and K. Hong, “Cortical activation during voluntary and passive movement of human index finger,” in 2018 18th International Conference on Control, Automation and Systems (ICCAS), 2018, pp. 1129–1134.

O. Klempíř et al., “P 024 - Near-infrared spectroscopy patterns of cortical activity during gait in Parkinson’s disease patients treated with DBS STN,” Gait & Posture, vol. 65, pp. 273–275, Sep. 2018.

Y. H. Kim et al., “Long-term intensive locomotion training with wearable hip-assist robot in elderly adults: A preliminary study,” Annals of Physical and Rehabilitation Medicine, vol. 61, p. e340, Jul. 2018. 

Y. H. Kim et al., “Cerebral oxygenation patterns during walking with wearable hip-assist robot in elderly adults: A fNIRS study,” Annals of Physical and Rehabilitation Medicine, vol. 61, p. e340, Jul. 2018.

A. Berger, N. H. Pixa, F. Steinberg, and M. Doppelmayr, “Brain Oscillatory and Hemodynamic Activity in a Bimanual Coordination Task Following Transcranial Alternating Current Stimulation (tACS): A Combined EEG-fNIRS Study,” Front Behav Neurosci, vol. 12, Apr. 2018.

R. A. Khan, N. Naseer, N. K. Qureshi, F. M. Noori, H. Nazeer, and M. U. Khan, “fNIRS-based Neurorobotic Interface for gait rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, p. 7, Feb. 2018.

S. Perry et al., “Getting to the Root of Fine Motor Skill Performance in Dentistry: Brain Activity During Dental Tasks in a Virtual Reality Haptic Simulation,” J Med Internet Res, vol. 19, no. 12, Dec. 2017.

O. Seidel, D. Carius, R. Kenville, and P. Ragert, “Motor learning in a complex balance task and associated neuroplasticity: a comparison between endurance athletes and nonathletes,” Journal of Neurophysiology, vol. 118, no. 3, pp. 1849–1860, Sep. 2017.

S. M. H. Hosseini et al., “Neural, physiological, and behavioral correlates of visuomotor cognitive load,” Scientific Reports, vol. 7, no. 1, Dec. 2017.

R. Kenville, T. Maudrich, D. Carius, and P. Ragert, “Hemodynamic Response Alterations in Sensorimotor Areas as a Function of Barbell Load Levels during Squatting: An fNIRS Study,” Front Hum Neurosci, vol. 11, May 2017.

K. N. de Winkel, A. Nesti, H. Ayaz, and H. H. Bülthoff, “Neural correlates of decision making on whole body yaw rotation: An fNIRS study,” Neuroscience Letters, vol. 654, no. Supplement C, pp. 56–62, Jul. 2017.

A. C. de Lima-Pardini et al., “Measuring cortical motor hemodynamics during assisted stepping ? An fNIRS feasibility study of using a walker,” Gait & Posture, vol. 56, pp. 112–118, Jul. 2017.

M. Balconi, D. Crivelli, and L. Cortesi, “Transitive Versus Intransitive Complex Gesture Representation: A Comparison Between Execution, Observation and Imagination by fNIRS,” Applied Psychophysiology and Biofeedback, Jun. 2017.

M. Abtahi, A. Amiri, D. Byrd, and K. Mankodiya, “Hand Motion Detection in fNIRS Neuroimaging Data,” Healthcare, vol. 5, no. 2, p. 20, Apr. 2017.

J. B. Balardin, G. A. Z. Morais, R. A. Furucho, L. R. Trambaiolli, and J. R. Sato, “Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows,” J. Biomed. Opt, vol. 22, no. 4, pp. 046010–046010, 2017.

M. Balconi, L. Cortesi, and D. Crivelli, “Motor planning and performance in transitive and intransitive gesture execution and imagination: Does EEG (RP) activity predict hemodynamic (fNIRS) response?,” Neuroscience Letters, vol. 648, pp. 59–65, May 2017.

M. Abtahi, A. M. Amiri, D. Byrd, and K. Mankodiya, “Hand Motion Detection in fNIRS Neuroimaging Data,” Healthcare, vol. 5, no. 2, p. 20, Apr. 2017.

N. H. Kashou, B. M. Giacherio, R. W. Nahhas, and S. R. Jadcherla, “Hand-grasping and finger tapping induced similar functional near-infrared spectroscopy cortical responses,” Neurophotonics, vol. 3, no. 2, p. 25006, Apr. 2016.

A. M. Kempny et al., “Functional near infrared spectroscopy as a probe of brain function in people with prolonged disorders of consciousness,” NeuroImage: Clinical, vol. 12, pp. 312–319, Feb. 2016.

M.-H. Lee, B.-J. Kim, and S.-W. Lee, “Quantifying movement intentions with multimodal neuroimaging for functional electrical stimulation-based rehabilitation,” Neuroreport, vol. 27, no. 2, pp. 61–66, Jan. 2016.

D. Carius, C. Andrä, M. Clauß, P. Ragert, M. Bunk, and J. Mehnert, “Hemodynamic Response Alteration As a Function of Task Complexity and Expertise—An fNIRS Study in Jugglers,” Front. Hum. Neurosci, p. 126, 2016.

A. P. Buccino, H. O. Keles, and A. Omurtag, “Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks,” PLOS ONE, vol. 11, no. 1, p. e0146610, Jan. 2016.

M. Balconi and L. Cortesi, “Brain Activity (fNIRS) in Control State Differs from the Execution and Observation of Object-Related and Object-Unrelated Actions,” J Mot Behav, vol. 48, no. 4, pp. 289–296, Aug. 2016.

S. Tak, A. M. Kempny, K. J. Friston, A. P. Leff, and W. D. Penny, “Dynamic causal modelling for functional near-infrared spectroscopy,” Neuroimage, vol. 111, pp. 338–349, May 2015.

C.-F. Lu, Y.-C. Liu, Y.-R. Yang, Y.-T. Wu, and R.-Y. Wang, “Maintaining Gait Performance by Cortical Activation during Dual-Task Interference: A Functional Near-Infrared Spectroscopy Study,” PLOS ONE, vol. 10, no. 6, p. e0129390, Jun. 2015.

S. E. Kober, G. Bauernfeind, C. Woller, M. Sampl, P. Grieshofer, C. Neuper, and G. Wood, “Hemodynamic Signal Changes Accompanying Execution and Imagery of Swallowing in Patients with Dysphagia: A Multiple Single-Case Near-Infrared Spectroscopy Study,” Front Neurol, vol. 6, Jul. 2015.

I. Helmich, H. Holle, R. Rein, and H. Lausberg, “Brain oxygenation patterns during the execution of tool use demonstration, tool use pantomime, and body-part-as-object tool use,” Int J Psychophysiol, vol. 96, no. 1, pp. 1–7, Apr. 2015.

M. Brunetti, N. Morkisch, C. Fritzsch, J. Mehnert, J. Steinbrink, M. Niedeggen, and C. Dohle, “Potential determinants of efficacy of mirror therapy in stroke patients--A pilot study,” Restor. Neurol. Neurosci., vol. 33, no. 4, pp. 421–434, 2015.

W. Guo, P. Yao, X. Sheng, H. Liu, and X. Zhu, “A wireless wearable sEMG and NIRS acquisition system for an enhanced human-computer interface,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014, pp. 2192–2197.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage, vol. 85 Pt 1, pp. 64–71, Jan. 2014.

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front Hum Neurosci, vol. 8, p. 244, 2014.

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed Opt Express, vol. 5, no. 6, pp. 1778–1798, May 2014.

R. Beurskens, I. Helmich, R. Rein, and O. Bock, “Age-related changes in prefrontal activity during walking in dual-task situations: A fNIRS study,” International Journal of Psychophysiology, vol. 92, no. 3, pp. 122–128, Jun. 2014.

N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain?computer interface,” Neuroscience Letters, vol. 553, pp. 84–89, Oct. 2013.

I. Helmich, R. Rein, N. Niermann, and H. Lausberg, “Hemispheric differences of motor execution: a near-infrared spectroscopy study,” Adv. Exp. Med. Biol., vol. 789, pp. 59–64, 2013.

S. Waldert, L. Tüshaus, C. P. Kaller, A. Aertsen, and C. Mehring, “fNIRS Exhibits Weak Tuning to Hand Movement Direction,” PLOS ONE, vol. 7, no. 11, p. e49266, Nov. 2012.

For an informative discussion on health information related to movement disorders, please visit:

https://www.nlm.nih.gov/medlineplus/movementdisorders.html

Multi-modal

In order to render measurements more robust, information may be provided by different modalities. Many groups appreciate multi-modal applications with fNIRS. Typical combinations are fNIRS and EEG, Eye-Tracking or fMRI, but tDCS and TMS have also been applied to concurrently modulate brain activity.

Clemente, L., La Rocca, M., Paparella, G., Delussi, M., Tancredi, G., Ricci, K., ... & de Tommaso, M. (2024). Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain—Computer Interface Study. Sensors, 24(7), 2329.

Borgheai, S. B., Zisk, A. H., McLinden, J., Mcintyre, J., Sadjadi, R., & Shahriari, Y. (2024). Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme. Computers in Biology and Medicine, 168, 107658.

Gao, Y., Jia, B., Houston, M., & Zhang, Y. (2023). Hybrid EEG-fNIRS Brain Computer Interface based on Common Spatial Pattern by using EEG-informed General Linear Model. IEEE Transactions on Instrumentation and Measurement.

Cao, J., Bulger, E., Shinn-Cunningham, B., Grover, P., & Kainerstorfer, J. M. (2023). Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex. NeuroImage, 120210.

Shoaib, Z., Akbar, A., Kim, E. S., Kamran, M. A., Kim, J. H., & Jeong, M. Y. (2023). Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation. Scientific Reports, 13(1), 6465.

Hamann, A., & Carstengerdes, N. (2023). Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement. Scientific Reports, 13(1), 4738

Guglielmini, S., Bopp, G., Marcar, V. L., Scholkmann, F., & Wolf, M. (2022). Machine Learning Distinguishes Familiar from Unfamiliar Pairs of Subjects Performing an Eye Contact Task: A Systemic Physiology Augmented Functional Near-Infrared Spectroscopy Hyperscanning Study. In Oxygen Transport to Tissue XLIII (pp. 177-182). Cham: Springer International Publishing.

Novi, S. L., Carvalho, A. C., Forti, R. M., Cendes, F., Yasuda, C. L., & Mesquita, R. C. (2023). Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study. Neurophotonics, 10(1), 013510.

Hucke, C. I., Heinen, R. M., Wascher, E., & van Thriel, C. (2023). Trigeminal stimulation is required for neural representations of bimodal odor localization: A time-resolved multivariate EEG and fNIRS study. NeuroImage, 119903.

Guglielmini, S., Bopp, G., Marcar, V. L., Scholkmann, F., & Wolf, M. (2022). Cross-Frequency Coupling Between Brain and Body Biosignals: A Systemic Physiology Augmented Functional Near-Infrared Spectroscopy Hyperscanning Study. In Oxygen Transport to Tissue XLIII (pp. 171-176). Springer, Cham.

Steinmetzger, K., Meinhardt, B., Praetorius, M., Andermann, M., & Rupp, A. (2022). A direct comparison of voice pitch processing in acoustic and electric hearing. NeuroImage: Clinical, 36, 103188.

Hosni, S. M., Borgheai, S., McLinden, J., Zhu, S., Huang, X., Ostadabbas, S., & Shahriari, Y. (2022). A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI. Neuroinformatics, 1-21.

Hamann, A., & Carstengerdes, N. (2022). Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights. Scientific Reports, 12(1), 1-12.

Klein, F., Debener, S., Witt, K., & Kranczioch, C. (2022). fMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery. Scientific Reports, 12(1), 1-20.

dos Santos, F. R., Bazán, P. R., Balardin, J. B., de Aratanha, M. A., Rodrigues, M., Lacerda, S., ... & Kozasa, E. H. (2021). Changes in Prefrontal fNIRS Activation and Heart Rate Variability During Self-Compassionate Thinking Related to Stressful Memories. Mindfulness, 1-13.

San Miguel, P. O., & Faisal, A. A. (2021). Deep Learning multimodal fNIRS and EEG signals for bimanual grip force decoding. Journal of Neural Engineering.

Aryadoust, V., Foo, S., & Ng, L. Y. (2021). What can gaze behaviors, neuroimaging data, and test scores tell us about test method effects and cognitive load in listening assessments?. Language Testing, 02655322211026876

Mukli, P., Yabluchanskiy, A., & Csipo, T. (2021). Mental workload during n-back task captured by TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) monitoring (version 1.0). PhysioNet.

Deligani, R. J., Borgheai, S. B., McLinden, J., & Shahriari, Y. (2021). Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework. Biomedical Optics Express, 12(3), 1635-1650.

Chen, Y., Tang, J., Chen, Y., Farrand, J., Craft, M. A., Carlson, B. W., & Yuan, H. (2020). Amplitude of fNIRS Resting-State Global Signal Is Related to EEG Vigilance Measures: A Simultaneous fNIRS and EEG Study. Frontiers in Neuroscience, 14.

Gao, Y., Cavuoto, L., Dutta, A., Kruger, U., Yan, P., Nemani, A., ... & Intes, X. (2020). Decreasing the Surgical Errors by Neurostimulation of Primary Motor Cortex and the Associated Brain Activation via Neuroimaging

Badarin, A. A., Skazkina, V. V., & Grubov, V. V. (2020, April). Studying of human’s mental state during visual information processing with combined EEG and fNIRS. In Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions (Vol. 11459, p. 114590D). International Society for Optics and Photonics.

Grubov, V. V., Badarin, A. A., Frolov, N. S., & Pitsik, E. N. (2020, April). Analysis of real and imaginary motor activity with combined EEG and fNIRS. In Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions (Vol. 11459, p. 114590B). International Society for Optics and Photonics.

Gao, Y., Cavuoto, L., Yan, P., Kruger, U., Silvestri, J., Schwaitzberg, S., ... & De, S. (2020). Monitoring the effect of transcranial Electric current Stimulation (tES) during a bimanual motor task via functional Near-InfraRed Spectroscopy (fNIRS). In Clinical and Translational Biophotonics (pp. JTh2A-29). Optical Society of America.

Zhang, F., Cheong, D., Chen, Y., Khan, A., Ding, L., & Yuan, H. (2019, July). Superficial Fluctuations in Functional Near-Infrared Spectroscopy. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4779-4782). IEEE.

Yaqub, M. A., Woo, S. W., & Hong, K. S. (2018). Effects of HD-tDCS on resting-state functional connectivity in the prefrontal cortex: An fNIRS study. Complexity, 2018. https://doi.org/10.1155/2018/1613402

Verma, R., Jha, A., & Singh, S. (2019). Functional near-infrared spectroscopy to probe tdcs-induced cortical functioning changes in tinnitus. Journal of International Advanced Otology, 15(2), 321–325. https://doi.org/10.5152/iao.2019.6022

Li, H., Zhu, N., Klomparens, E. A., Xu, S., Wang, M., Wang, Q., Wang, J., & Song, L. (2019). Application of functional near-infrared spectroscopy to explore the neural mechanism of transcranial direct current stimulation for post-stroke depression. Neurological Research, 41(8), 714–721. https://doi.org/10.1080/01616412.2019.1612539

Berger, A., Pixa, N. H., & Doppelmayr, M. (2017). Frequency-specific after-effects of transcranial alternating current stimulation (tACS) on motor learning: Preliminary data of a simultaneous tACS-EEG-NIRS study. Brain Stimulation, 10(2), 412. https://doi.org/10.1016/j.brs.2017.01.220

Taylor, S. T., Chhabra, H., Sreeraj, V. S., Shivakumar, V., Kalmady, S. V., & Venkatasubramanian, G. (2017). Neural effects of transcranial direct current stimulation in schizophrenia: A case study using functional near-infrared spectroscopy. Indian Journal of Psychological Medicine, 39(5), 691–694. https://doi.org/10.4103/IJPSYM.IJPSYM_238_17

Richardson, J., Quinn, D., Arenas, R. M., Dalton, S. G., Larsen, N., von Hoyningen-Huene, S., Duhigg, C. C., & Pinchotti, D. (2019). Abstract #86: Optical Measurement of Cerebral Blood Flow to Investigate Brain Stimulation and Task Engagement. Brain Stimulation, 12(2), e30. https://doi.org/10.1016/j.brs.2018.12.093

Choe, J., Coffman, B. A., Bergstedt, D. T., Ziegler, M. D., & Phillips, M. E. (2016). Transcranial direct current stimulation modulates neuronal activity and learning in pilot training. Frontiers in Human Neuroscience, 10(FEB2016). https://doi.org/10.3389/fnhum.2016.00034

Gao, Y., Cavuoto, L., Yan, P., Kruger, U., & Silvestri, J. (2020). Monitoring the effect of transcranial Electric current Stimulation ( tES ) during a bimanual motor task via functional Near-InfraRed Spectroscopy ( fNIRS ). 2020, 3–4.

Ludyga, S., Mücke, M., Colledge, F. M. A., Pühse, U., & Gerber, M. (2019). “A Combined EEG-fNIRS Study Investigating Mechanisms Underlying the Association between Aerobic Fitness and Inhibitory Control in Young Adults”. Neuroscience, 419, 23-33

Vassena, E., Gerrits, R., Demanet, J., Verguts, T., & Siugzdaite, R. (2019). “Anticipation of a mentally effortful task recruits Dorsolateral Prefrontal Cortex: An fNIRS validation study”. Neuropsychologia, 123, 106-115.

R. Li, T. Nguyen, T. Potter, and Y. Zhang, “Dynamic cortical connectivity alterations associated with Alzheimer’s disease: An EEG and fNIRS integration study,” NeuroImage: Clinical, Dec. 2018.

A. Landowska, D. Roberts, P. Eachus, and A. Barrett, “Within- and Between-Session Prefrontal Cortex Response to Virtual Reality Exposure Therapy for Acrophobia,” Front Hum Neurosci, vol. 12, Nov. 2018.

M. Balconi, A. Frezza, and M. E. Vanutelli, “Emotion Regulation in Schizophrenia: A Pilot Clinical Intervention as Assessed by EEG and Optical Imaging (Functional Near-Infrared Spectroscopy),” Front Hum Neurosci, vol. 12, Oct. 2018.

O. Klempíř et al., “P 024 - Near-infrared spectroscopy patterns of cortical activity during gait in Parkinson’s disease patients treated with DBS STN,” Gait & Posture, vol. 65, pp. 273–275, Sep. 2018.

A. Lee et al., “Slow oscillations of cerebral hemodynamics changes during low-level light therapy in the elderly with and without mild cognitive impairment: An fNIRS study,” Annals of Physical and Rehabilitation Medicine, vol. 61, p. e256, Jul. 2018.

J. Shin, D.-W. Kim, K.-R. Müller, and H.-J. Hwang, “Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses,” Sensors (Basel), vol. 18, no. 6, Jun. 2018

A. Berger, N. H. Pixa, F. Steinberg, and M. Doppelmayr, “Brain Oscillatory and Hemodynamic Activity in a Bimanual Coordination Task Following Transcranial Alternating Current Stimulation (tACS): A Combined EEG-fNIRS Study,” Front Behav Neurosci, vol. 12, Apr. 2018.

M. Balconi et al., “Emotion regulation in Schizophrenia: A comparison between implicit (EEG and fNIRS) and explicit (valence) measures: Preliminary observations,” Asian Journal of Psychiatry, vol. 34, pp. 12–13, Apr. 2018.

K. Arun, K. Smitha, P. Rajesh, and C. Kesavadas, “Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas,” Neuroradiol J, vol. 31, no. 2, pp. 133–141, Apr. 2018.

J. Shin, A. von Lühmann, D.-W. Kim, J. Mehnert, H.-J. Hwang, and K.-R. Müller, “Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset,” Scientific Data, vol. 5, p. 180003, Feb. 2018.

M. A. Yaqub, S.-W. Woo, and K.-S. Hong, “Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study,” Complexity, 2018. 

A. Landowska, S. Royle, P. Eachus, and D. Roberts, “Testing the Potential of Combining Functional Near-Infrared Spectroscopy with Different Virtual Reality Displays—Oculus Rift and oCtAVE,” in Augmented Reality and Virtual Reality: Empowering Human, Place and Business, T. Jung and M. C. tom Dieck, Eds. Cham: Springer International Publishing, 2018, pp. 309–321.

F. Dehais et al., “Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI,” in IEEE SMC, 2018, pp. 1–6.

D. Farkas, S. L. Denham, and I. Winkler, “Functional brain networks underlying idiosyncratic switching patterns in multi-stable auditory perception,” Neuropsychologia, vol. 108, pp. 82–91, Jan. 2018.

A. Landowska, S. Royle, P. Eachus, and D. Roberts, “Testing the Potential of Combining Functional Near-Infrared Spectroscopy with Different Virtual Reality Displays—Oculus Rift and oCtAVE,” in Augmented Reality and Virtual Reality, Springer, Cham, 2018, pp. 309–321. 

S. Perry et al., “Getting to the Root of Fine Motor Skill Performance in Dentistry: Brain Activity During Dental Tasks in a Virtual Reality Haptic Simulation,” J Med Internet Res, vol. 19, no. 12, Dec. 2017.

T.-J. Kim et al., “The effect of dim light at night on cerebral hemodynamic oscillations during sleep: A near-infrared spectroscopy study,” Chronobiology International, vol. 34, no. 10, pp. 1325–1338, Nov. 2017.

K. Pollmann, D. Ziegler, M. Peissner, and M. Vukelić, “A New Experimental Paradigm for Affective Research in Neuro-adaptive Technologies,” 2017, pp. 1–8.

H. Banville, R. Gupta, and T. H. Falk, “Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–24, 2017.

O. Seidel, D. Carius, R. Kenville, and P. Ragert, “Motor learning in a complex balance task and associated neuroplasticity: a comparison between endurance athletes and nonathletes,” Journal of Neurophysiology, vol. 118, no. 3, pp. 1849–1860, Sep. 2017.

R. Gabbard, M. Fendley, I. A. Dar, R. Warren, and N. H. Kashou, “Utilizing functional near-infrared spectroscopy for prediction of cognitive workload in noisy work environments,” Neurophotonics, vol. 4, no. 04, p. 1, Aug. 2017.

A. Omurtag, H. Aghajani, and H. O. Keles, “Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance,” Journal of Neural Engineering, Jul. 2017.

L. Holper, F. Scholkmann, and E. Seifritz, “Prefrontal hemodynamic after-effects caused by rebreathing may predict affective states – A multimodal functional near-infrared spectroscopy study,” Brain Imaging and Behavior, vol. 11, no. 2, pp. 461–472, Apr. 2017.

J. Shin et al., “Open Access Dataset for EEG+NIRS Single-Trial Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. PP, no. 99, pp. 1–1, 2016.

J. Choe, B. A. Coffman, D. T. Bergstedt, M. D. Ziegler, and M. E. Phillips, “Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training,” Front. Hum. Neurosci., vol. 10, 2016.

H. Obrig, J. Mock, F. Stephan, M. Richter, M. Vignotto, and S. Rossi, “Impact of associative word learning on phonotactic processing in 6-month-old infants: A combined EEG and fNIRS study,” Developmental Cognitive Neuroscience.

L.-C. Chen, M. Stropahl, M. Schönwiesner, and S. Debener, “Enhanced visual adaptation in cochlear implant users revealed by concurrent EEG-fNIRS,” Neuroimage, Sep. 2016.

L. Zhu, A. E. Haddad, T. Zeng, Y. Wang, and L. Najafizadeh, “Assessing Optimal Electrode/Optode Arrangement in EEG-fNIRS Multi-Modal Imaging,” in Biomedical Optics 2016, 2016, p. paper–JW3A.

M.-H. Lee, B.-J. Kim, and S.-W. Lee, “Quantifying movement intentions with multimodal neuroimaging for functional electrical stimulation-based rehabilitation,” Neuroreport, vol. 27, no. 2, pp. 61–66, Jan. 2016.

H. O. Keles, R. L. Barbour, and A. Omurtag, “Hemodynamic correlates of spontaneous neural activity measured by human whole-head resting state EEG+fNIRS,” Neuroimage, vol. 138, pp. 76–87, Sep. 2016.

L. Holper, F. Scholkmann, and E. Seifritz, “Prefrontal hemodynamic after-effects caused by rebreathing may predict affective states - A multimodal functional near-infrared spectroscopy study,” Brain Imaging Behav, Mar. 2016.

T. Geall, “Could new ‘Matrix’ hat mean we can learn new skills in no time at all?,” mirror, 29-Feb-2016. [Online]. Available: http://www.mirror.co.uk/news/technology-science/science/scientists-develop-matrix-style-technique-7463286.

D. Carius, C. Andrä, M. Clauß, P. Ragert, M. Bunk, and J. Mehnert, “Hemodynamic Response Alteration As a Function of Task Complexity and Expertise—An fNIRS Study in Jugglers,” Front. Hum. Neurosci, p. 126, 2016.

A. P. Buccino, H. O. Keles, and A. Omurtag, “Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks,” PLOS ONE, vol. 11, no. 1, p. e0146610, Jan. 2016.

N. Altvater-Mackensen and T. Grossmann, “The role of left inferior frontal cortex during audiovisual speech perception in infants,” NeuroImage, vol. 133, pp. 14–20, Jun. 2016.

A. D. Zaidi et al., “Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates,” Neuroimage, vol. 120, pp. 394–399, Oct. 2015.

E. Maggioni et al., “Investigation of negative BOLD responses in human brain through NIRS technique. A visual stimulation study,” NeuroImage, vol. 108, pp. 410–422, Mar. 2015.

M.-H. Lee, S. Fazli, J. Mehnert, and S.-W. Lee, “Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI,” Pattern Recognition, vol. 48, no. 8, pp. 2725–2737, Aug. 2015.

L.-C. Chen, P. Sandmann, J. D. Thorne, C. S. Herrmann, and S. Debener, “Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli,” Brain Topogr, vol. 28, no. 5, pp. 710–725, Sep. 2015.

M. Brunetti et al., “Potential determinants of efficacy of mirror therapy in stroke patients--A pilot study,” Restor. Neurol. Neurosci., vol. 33, no. 4, pp. 421–434, 2015.

M. Balconi and M. E. Vanutelli, “Emotions and BIS/BAS components affect brain activity (ERPs and fNIRS) in observing intra-species and inter-species interactions,” Brain Imaging and Behavior, vol. 10, no. 3, pp. 750–760, Aug. 2015.

M. Balconi, E. Grippa, and M. E. Vanutelli, “What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing,” Brain Cogn, vol. 95, pp. 67–76, Apr. 2015.

R. K. Almajidy, Y. Boudria, U. G. Hofmann, W. Besio, and K. Mankodiya, “Multimodal 2D Brain Computer Interface,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 1067–1070.

V. V. Nikulin et al., “Monochromatic ultra-slow (~0.1 Hz) oscillations in the human electroencephalogram and their relation to hemodynamics,” Neuroimage, vol. 97, pp. 71–80, Aug. 2014.

I. M. Kopton and P. Kenning, “Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research,” Front Hum Neurosci, vol. 8, Aug. 2014.

M. J. Khan, M. J. Hong, and K.-S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface,” Front Hum Neurosci, vol. 8, p. 244, 2014.

E. Maggioni et al., “Coupling of fMRI and NIRS measurements in the study of negative BOLD response to intermittent photic stimulation,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, pp. 1378–1381.

S. Dähne, F. Bießmann, F. C. Meinecke, J. Mehnert, S. Fazli, and K. R. Müller, “Integration of Multivariate Data Streams With Bandpower Signals,” IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1001–1013, Aug. 2013.

S. Fazli et al., “Enhanced performance by a hybrid NIRS-EEG brain computer interface,” Neuroimage, vol. 59, no. 1, pp. 519–529, Jan. 2012.

S. Fazli, J. Mehnert, J. Steinbrink, and B. Blankertz, “Using NIRS as a predictor for EEG-based BCI performance,” Conf Proc IEEE Eng Med Biol Soc, vol. 2012, pp. 4911–4914, 2012.

R. L. Barbour et al., “A programmable laboratory testbed in support of evaluation of functional brain activation and connectivity,” IEEE Trans Neural Syst Rehabil Eng, vol. 20, no. 2, pp. 170–183, Mar. 2012.

Naturalistic Environment 

With the advent of portable and wearable solutions, in addition to its intrinsic performance in the presence of movements, fNIRS is currently the ideal solution for studies that intend to evaluate cortical actitiy within naturalistic environments.

Schmaderer, L. F., Meyer, M., Reer, R., & Schumacher, N. (2023). What happens in the prefrontal cortex? Cognitive processing of novel and familiar stimuli in soccer: An exploratory fNIRS study. European Journal of Sport Science, 1-11.

Kvist, A., Bezuidenhout, L., Johansson, H., Albrecht, F., Ekman, U., Conradsson, D. M., & Franzén, E. (2023). Using functional near‐infrared spectroscopy to measure prefrontal cortex activity during dual‐task walking and navigated walking: A feasibility study. Brain and Behavior, e2948.

Ogihara, T., Tanioka, K., Hiroyasu, T., & Hiwa, S. (2022). Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study. Front. Neuroergonomics.Sec. Augmented and Synthetic Neuroergonomics

Parker, S. M., Andreasen, S. C., Ricks, B., Kaipust, M. S., Zuniga, J., & Knarr, B. A. (2022). Comparison of brain activation and functional outcomes between physical and virtual reality box and block test: a case study. Disability and Rehabilitation: Assistive Technology, 1-8.

Dybvik, H., & Steinert, M. (2021). Real-World fNIRS Brain Activity Measurements during Ashtanga Vinyasa Yoga. Brain Sciences, 11(6), 742.

Stojan, R., & Voelcker-Rehage, C. (2020). Neurophysiological correlates of age differences in driving behavior during concurrent subtask performance. NeuroImage, 117492.

Barreto, C. D. S. F., Morais, G. A. Z., Vanzella, P., & Sato, J. R. (2020). Combining the intersubject correlation analysis and the multivariate distance matrix regression to evaluate associations between fNIRS signals and behavioral data from ecological experiments. Experimental Brain Research, 1-10.

Scheunemann, J., Unni, A., Ihme, K., Jipp, M., & Rieger, J. W. (2019). Demonstrating brain-level interactions between visuospatial attentional demands and working memory load while driving using functional near-infrared spectroscopy. Frontiers in human neuroscience, 12, 542.

Ihme, K., Unni, A., Zhang, M., Rieger, J. W., & Jipp, M. (2018). Recognizing frustration of drivers from face video recordings and brain activation measurements with functional near-infrared spectroscopy. Frontiers in human neuroscience, 12, 327.

Sagiv, S. K., Bruno, J. L., Baker, J. M., Palzes, V., Kogut, K., Rauch, S., ... & Eskenazi, B. (2019). Prenatal exposure to organophosphate pesticides and functional neuroimaging in adolescents living in proximity to pesticide application. Proceedings of the National Academy of Sciences, 116(37), 18347-18356.

W. W. N. Tsang, K. K. Chan, C. N. Cheng, F. S. F. Hu, C. T. K. Mak, and J. W. C. Wong, “Tai Chi practice on prefrontal oxygenation levels in older adults: A pilot study,” Complementary Therapies in Medicine, vol. 42, pp. 132–136, Feb. 2019.

W. Wolff, J. L. Thürmer, K.-M. Stadler, and J. Schüler, “Ready, set, go: Cortical hemodynamics during self-controlled sprint starts,” Psychology of Sport and Exercise, vol. 41, pp. 21–28, Mar. 2019.

L. Zhu, S. Li, Y. Li, M. Wang, Y. Li, and J. Yao, “Study on driver’s braking intention identification based on functional near-infrared spectroscopy,” Journal of Intelligent and Connected Vehicles, Dec. 2018.

J. L. Bruno et al., “Mind over motor mapping: Driver response to changing vehicle dynamics,” Human Brain Mapping, vol. 39, no. 10, pp. 3915–3927, Oct. 2018.

K. Ihme, A. Unni, M. Zhang, J. W. Rieger, and M. Jipp, “Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy,” Front Hum Neurosci, vol. 12, Aug. 2018.

K. Ihme, A. Unni, J. W. Rieger, and M. Jipp, “Chapter 42 - Assessing Driver Frustration Using Functional Near-Infrared Spectroscopy (fNIRS),” in Neuroergonomics, H. Ayaz and F. Dehais, Eds. Academic Press, 2018, pp. 215–216.

J. M. Baker et al., “Portable Functional Neuroimaging as an Environmental Epidemiology Tool: A How-To Guide for the Use of fNIRS in Field Studies,” Environmental Health Perspectives, vol. 125, no. 9, Sep. 2017.

J. B. Balardin et al., “Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments,” Frontiers in Human Neuroscience, vol. 11, May 2017.

A. Unni, K. Ihme, M. Jipp, and J. W. Rieger, “Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study,” Frontiers in Human Neuroscience, vol. 11, Apr. 2017.

D. Carius, C. Andrä, M. Clauß, P. Ragert, M. Bunk, and J. Mehnert, “Hemodynamic Response Alteration As a Function of Task Complexity and Expertise—An fNIRS Study in Jugglers,” Front. Hum. Neurosci, p. 126, 2016.

C.-F. Lu, Y.-C. Liu, Y.-R. Yang, Y.-T. Wu, and R.-Y. Wang, “Maintaining Gait Performance by Cortical Activation during Dual-Task Interference: A Functional Near-Infrared Spectroscopy Study,” PLOS ONE, vol. 10, no. 6, p. e0129390, Jun. 2015.

I. Helmich, H. Holle, R. Rein, and H. Lausberg, “Brain oxygenation patterns during the execution of tool use demonstration, tool use pantomime, and body-part-as-object tool use,” Int J Psychophysiol, vol. 96, no. 1, pp. 1–7, Apr. 2015.

M. Brunetti, N. Morkisch, C. Fritzsch, J. Mehnert, J. Steinbrink, M. Niedeggen, and C. Dohle, “Potential determinants of efficacy of mirror therapy in stroke patients--A pilot study,” Restor. Neurol. Neurosci., vol. 33, no. 4, pp. 421–434, 2015.

S. K. Piper, A. Krueger, S. P. Koch, J. Mehnert, C. Habermehl, J. Steinbrink, H. Obrig, and C. H. Schmitz, “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” Neuroimage, vol. 85 Pt 1, pp. 64–71, Jan. 2014.

J. Bahnmueller, T. Dresler, A.-C. Ehlis, U. Cress, and H.-C. Nuerk, “NIRS in motion—unraveling the neurocognitive underpinnings of embodied numerical cognition,” Front. Psychol, vol. 5, p. 743, 2014.

Neuroeconomics and Human Performance

A key interest of neuroeconomics research is value-based decision making, in which the prefrontal lobe is an important player. Although prefrontal activity has been explored with fMRI, the restricted environment does impose a limit to the number of applications that can be explored. fNIRS may represent a conspicuous improvement to the field, as it enables outdoor measurements that can be combined with simultaneous Eye-Tracking.

Nissen, A., Riedl, R., & Schuette, R. (2024). Users’ reactions to website designs: A neuroimaging study based on evolutionary psychology with a focus on color and button shape. Computers in Human Behavior, 108168.

Kamat, A., Eastmond, C., Gao, Y., Nemani, A., Yanik, E., Cavuoto, L., ... & Intes, X. (2023). Assessment of Surgical Tasks Using Neuroimaging Dataset (ASTaUND). Scientific Data, 10(1), 699.

Perello-March, J., Burns, C. G., Woodman, R., Birrell, S., & Elliott, M. T. (2023). How Do Drivers Perceive Risks During Automated Driving Scenarios? An fNIRS Neuroimaging Study. Human Factors, 00187208231185705.

León, J. J., Fernández-Martin, P., González-Rodríguez, A., Rodríguez-Herrera, R., García-Pinteño, J., Pérez-Fernández, C., ... & Flores, P. (2023). Decision-making and frontoparietal resting-state functional connectivity among impulsive-compulsive diagnoses. Insights from a Bayesian approach. Addictive Behaviors, 143, 107683.

Xiang, M., Li, G., Ye, J., Wu, M., Xu, R., & Hu, M. (2023). Effects of combined physical and cognitive training on executive function of adolescent shooting athletes: A functional near-infrared spectroscopy study. Sports Medicine and Health Science.

Fan, S., Blanco-Davis, E., Fairclough, S., Zhang, J., Yan, X., Wang, J., & Yang, Z. (2023). Incorporation of seafarer psychological factors into maritime safety assessment. Ocean & Coastal Management, 237, 106515.

Niu, B., Li, Y., Ding, X., Shi, C., Zhou, B., & Gong, J. (2023). Neural correlates of bribe-taking decision dilemma: An fNIRS study. Brain and Cognition, 166, 105951.

Fan, S., & Yang, Z. (2023). Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method. Reliability Engineering & System Safety, 109103.

Shi, Y., Johnson, C., Xia, P., Kang, J., Tyagi, O., Mehta, R. K., & Du, J. (2022). Neural Basis Analysis of Firefighters’ Wayfinding Performance via Functional Near-Infrared Spectroscopy. Journal of Computing in Civil Engineering, 36(4), 04022016.

X. Liu, C.-S. Kim, and K.-S. Hong, “An fNIRS-based investigation of visual merchandising displays for fashion stores,” PLOS ONE, vol. 13, no. 12, p. e0208843, Dec. 2018.

S. G. H. Meyerding and C. M. Mehlhose, “Can neuromarketing add value to the traditional marketing research? An exemplary experiment with functional near-infrared spectroscopy (fNIRS),” Journal of Business Research, Oct. 2018.

C. Krampe, E. Strelow, A. Haas, and P. Kenning, “The application of mobile fNIRS to ‘shopper neuroscience’ – first insights from a merchandising communication study,” European Journal of Marketing, Jan. 2018.

X. Liu and K.-S. Hong, “Investigate the visual merchandising of a fashion store using fNIRS,” 2017, pp. 11488–11493.

L. Holper, L. D. Van Brussel, L. Schmidt, S. Schulthess, C. J. Burke, K. Louie, E. Seifritz and P. N. Tobler, “Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load,” eNeuro, vol. 4, no. 2, p. ENEURO.0365-17.2017, Mar. 2017.

I. M. Kopton and P. Kenning, “Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research,” Front Hum Neurosci, vol. 8, Aug. 2014.

M. M. DiStasio and J. T. Francis, “Use of frontal lobe hemodynamics as reinforcement signals to an adaptive controller,” PLoS ONE, vol. 8, no. 7, p. e69541, 2013.

Pain Research

Obtaining pain indicators from brain activity can be particularly interesting when the efficiency of pain treatments is to be evaluated, or when determining pain levels from people that may not be able to verbally communicate. fNIRS in particular, is a promising tool for this area giving its portability and noninvasiveness.

de Oliveira Franco, Á., de Oliveira Venturini, G., da Silveira Alves, C. F., Alves, R. L., Vicuña, P., Ramalho, L., ... & Caumo, W. (2022). Functional connectivity response to acute pain assessed by fNIRS is associated with BDNF genotype in fibromyalgia: an exploratory study. Scientific Reports, 12(1), 1-13

Balconi, M., & Angioletti, L. (2022). Aching face and hand: the interoceptive attentiveness and social context in relation to empathy for pain. Journal of integrative neuroscience, 21(1), 34.

Alter, B., Santosa, H., Nguyen, Q. H., Huppert, T. J., & Wasan, A. D. (2022). Offset analgesia is associated with opposing modulation of medial versus dorsolateral prefrontal cortex activations: a functional near-infrared spectroscopy study. Molecular Pain, 17448069221074991.

Donadel, D.G., Zortea, M., Torres, I.L.S. et al. The mapping of cortical activation by near-infrared spectroscopy might be a biomarker related to the severity of fibromyalgia symptoms. Sci Rep 11, 15754 (2021). 

Öztürk, Ö., Algun, Z. C., Bombacı, H., & Erdoğan, S. B. (2021). Changes in prefrontal cortex activation with exercise in knee osteoarthritis patients with chronic pain: An fNIRS study. Journal of Clinical Neuroscience, 90, 144-151.

Balconi, M., & Angioletti, L. (2021). Interoception as a social alarm amplification system. What multimethod (EEG-fNIRS) integrated measures can tell us about interoception and empathy for pain? Neuropsychological Trends, 29, 39–64.

T. Zeng, D. Peru, V. P. Maloney, and L. Najafizadeh, “Cortical activity changes as related to oral irritation-an fNIRS study,” 2017, pp. 2558–2561.

A. Vrana, M. L. Meier, S. Hotz-Boendermaker, B. K. Humphreys, and F. Scholkmann, “Cortical Sensorimotor Processing of Painful Pressure in Patients with Chronic Lower Back Pain—An Optical Neuroimaging Study using fNIRS,” Front. Hum. Neurosci., vol. 10, 2016.

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed Opt Express, vol. 5, no. 6, pp. 1778–1798, May 2014.

J.-W. He, F. Tian, H. Liu, and Y. B. Peng, “Cerebrovascular responses of the rat brain to noxious stimuli as examined by functional near-infrared whole brain imaging,” J. Neurophysiol., vol. 107, no. 10, pp. 2853–2865, May 2012.

Satori

We are very happy to offer you the Satori fNIRS analysis software. This software was developed in collaboration with our partners at Brain Innovation and distributed exclusively by NIRx. We offer Satori, an easy-to-use and powerful fNIRS analysis software. It includes the current standard fNIRS processing methods and statistical tools. We also offer Turbo-Satori which is an advanced real-time analysis software for fNIRS data. It allows for fast, result-driven high-quality fNIRS research.

Von Au, S., Helmich, I., Kieffer, S., & Lausberg, H. (2023). Phasic and Repetitive Self-Touch differ in Hemodynamic Response in the prefrontal cortex-A fNIRS study. Frontiers in Neuroergonomics, 4, 1266439.

Vorreuther, A., Bastian, L., Benitez Andonegui, A., Evenblij, D., Riecke, L., Lührs, M., & Sorger, B. (2023). It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain–computer interface communication. Neurophotonics, 10(4), 045005-045005.

Pereira, J., Direito, B., Lührs, M. et al. Multimodal assessment of the spatial correspondence between fNIRS and fMRI hemodynamic responses in motor tasks. Sci Rep 13, 2244 (2023).

Social Interaction

The ability of fNIRS to measure two or more subjects simultaneously, enables researchers to study cortical activity in response to social interaction. This way, a new dimension is added to studies investigating topics such as empathy, competitive and cooperative tasks, mother-child interactions and truth telling.

Guo, X., Xu, C., Chen, J., Wu, Z., Hou, S., & Wei, Z. (2024). Disrupted cognitive and affective empathy network interactions in autistic children viewing social animation. Social Cognitive and Affective Neuroscience, nsae028.

Grossmann, T., & Allison, O. (2024). Dorso-medial prefrontal cortex responses to social smiles predict sociability in early human development. Imaging Neuroscience. 2: 1–8.

Zhang, M., Yin, Z., Zhang, X., Zhang, H., Bao, M., & Xuan, B. (2024). Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study. NeuroImage, 120587.

Liu, Q., Zhu, S., Zhou, X., Liu, F., Becker, B., Kendrick, K. M., & Zhao, W. (2024). Mothers and fathers show different neural synchrony with their children during shared experiences. NeuroImage, 120529.

Lim, M., Carollo, A., Bizzego, A., Chen, S. A., & Esposito, G. (2023). Decreased activation in left prefrontal cortex during role-play: an fNIRS study of the psychodrama sociocognitive model. The Arts in Psychotherapy, 102098.

Gvirts Provolovski, H. Z., Sharma, M., Gutman, I., Dahan, A., Pan, Y., Stotler, J., & Wilcox, T. (2023). New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies. J. Vis. Exp.

Balconi, M., & Angioletti, L. (2023). Hemodynamic and Electrophysiological Biomarkers of Interpersonal Tuning during Interoceptive Synchronization. Information, 14(5), 289.

Eloy, L., Spencer, C., Doherty, E., & Hirshfield, L. (2023). Capturing the Dynamics of Trust and Team Processes in Human-Human-Agent Teams via Multidimensional Neural Recurrence Analyses. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1-23.

Zhang, Q., Liu, Z., Qian, H., Hu, Y., & Gao, X. (2023). Interpersonal Competition in Elderly Couples: A Functional Near-Infrared Spectroscopy Hyperscanning Study. Brain Sciences, 13(4), 600.

Neoh, M. J. Y., Bizzego, A., Teng, J. H., Gabrieli, G., & Esposito, G. (2023). Neural Processing of Sexist Comments: Associations between Perceptions of Sexism and Prefrontal Activity. Brain Sciences, 13(4), 529.

Azhari, A., Bizzego, A., & Esposito, G. (2023). Parent–child dyads with greater parenting stress exhibit less synchrony in posterior areas and more synchrony in frontal areas of the prefrontal cortex during shared play. Social Neuroscience, 1-12.

Balters, S., Miller, J. G., & Reiss, A. L. (2023). Expressing appreciation is linked to interpersonal closeness and inter-brain coherence, both in person and over Zoom. Cerebral Cortex, bhad032.

Zhao, H., Zhang, T., Cheng, T., Chen, C., Zhai, Y., Liang, X., ... & Lu, C. (2022). Neurocomputational mechanisms of young children’s observational learning of delayed gratification. Cerebral Cortex.

Bizzego, A., Gabrieli, G., Azhari, A., Lim, M., & Esposito, G. (2022). Dataset of parent-child hyperscanning functional near-infrared spectroscopy recordings. Scientific Data, 9(1), 1-8.

Chen, L., Qu, Y., Cao, J., Liu, T., Gong, Y., Tian, Z., ... & Zeng, F. (2022). The increased inter-brain neural synchronization in prefrontal cortex between simulated patient and acupuncturist during acupuncture stimulation: Evidence from functional near-infrared spectroscopy hyperscanning. Human Brain Mapping.

Fronda, G., & Balconi, M. (2022). What hyperscanning and brain connectivity for hemodynamic (fNIRS), electrophysiological (EEG) and behavioral measures can tell us about prosocial behavior. Psychology & Neuroscience, 15(2), 147.

Wang, S., Lu, J., Yu, M., Wang, X., & Shangguan, C. (2022). “I'm listening, did it make any difference to your negative emotions?” Evidence from hyperscanning. Neuroscience Letters, 788, 136865.

Kayhan, E., Nguyen, T., Matthes, D., Langeloh, M., Michel, C., Jiang, J., & Hoehl, S. (2022). Interpersonal neural synchrony when predicting others’ actions during a game of rock-paper-scissors. Scientific reports, 12(1), 1-11.

Liang, Z., Li, S., Zhou, S., Chen, S., Li, Y., Chen, Y., ... & Zhou, Z. (2022). Increased or decreased? Interpersonal neural synchronization in group creation. NeuroImage, 260, 119448.

Zhou, S., Zhang, Y., Fu, Y., Wu, L., Li, X., Zhu, N., ... & Zhang, M. (2022). The Effect of Task Performance and Partnership on Interpersonal Brain Synchrony during Cooperation. Brain Sciences, 12(5), 635.

Guglielmini, S., Bopp, G., Marcar, V. L., Scholkmann, F., & Wolf, M. (2022). Systemic physiology augmented functional near-infrared spectroscopy hyperscanning: a first evaluation investigating entrainment of spontaneous activity of brain and body physiology between subjects. Neurophotonics, 9(2), 026601. 

Bizzego, A., Azhari, A., & Esposito, G. (2021). Assessing Computational Methods to Quantify Mother-Child Brain Synchrony in Naturalistic Settings Based on fNIRS Signals. Neuroinformatics, 1-10.

Barreto, C., Bruneri, G. D. A., Brockington, G., Ayaz, H., & Sato, J. R. (2021). A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s. Frontiers in human neuroscience, 15, 181.

Li, R., Mayseless, N., Balters, S., & Reiss, A. L. (2021). Dynamic Inter-Brain Synchrony in Real-life Inter-Personal Cooperation: A Functional Near-infrared Spectroscopy Hyperscanning Study. NeuroImage, 118263

Zhao, H., Cheng, T., Zhai, Y., Long, Y., Wang, Z., & Lu, C. (2021). How Mother–Child Interactions are Associated with a Child’s Compliance. Cerebral Cortex.

Tyagi, O., Hopko, S., Kang, J., Shi, Y., Du, J., & Mehta, R. K. (2021). Modeling brain dynamics during virtual reality-based emergency response learning under stress. Human factors, 00187208211054894.

Quiñones‐Camacho, L. E., Fishburn, F. A., Belardi, K., Williams, D. L., Huppert, T. J., & Perlman, S. B. (2021). Dysfunction in interpersonal neural synchronization as a mechanism for social impairment in autism spectrum disorder. Autism Research.

Balconi, M., & Fronda, G. (2020). Morality and management: an oxymoron? fNIRS and neuromanagement perspective explain us why things are not like this. Cognitive, Affective, & Behavioral Neuroscience, 1-13.

Balconi, M., Kopis, N., & Angioletti, L. (2020). Does aesthetic judgment on face attractiveness affect neural correlates of empathy for pain? A fNIRS study. Experimental Brain Research, 1-10.

Azhari, A., Lim, M., Bizzego, A., Gabrieli, G., Bornstein, M. H., & Esposito, G. (2020). Physical presence of spouse enhances brain-to-brain synchrony in co-parenting couples. Scientific reports, 10(1), 1-11.

Mayseless, N., Hawthorne, G., & Reiss, A. L. (2019). “Real-life creative problem solving in teams: fNIRS based hyperscanning study. NeuroImage, 203, 116161.

M. Balconi, M. E. Vanutelli, and L. Gatti, “Functional brain connectivity when cooperation fails,” Brain and Cognition, vol. 123, pp. 65–73, Jun. 2018.

L. Holper, C. J. Burke, C. Fausch, E. Seifritz, and P. N. Tobler, “Inequality signals in dorsolateral prefrontal cortex inform social preference models,” Soc Cogn Affect Neurosci, vol. 13, no. 5, pp. 513–524, May 2018.

M. Balconi, L. Gatti, and M. E. Vanutelli, “When cooperation goes wrong: brain and behavioural correlates of ineffective joint strategies in dyads,” International Journal of Neuroscience, vol. 128, no. 2, pp. 155–166, Feb. 2018.

M. Balconi, L. Pezard, J.-L. Nandrino, and M. E. Vanutelli, “Two is better than one: The effects of strategic cooperation on intra- and inter-brain connectivity by fNIRS,” PLOS ONE, vol. 12, no. 11, p. e0187652, Nov. 2017.

M. Balconi and M. E. Vanutelli, “Brains in Competition: Improved Cognitive Performance and Inter-Brain Coupling by Hyperscanning Paradigm with Functional Near-Infrared Spectroscopy,” Frontiers in Behavioral Neuroscience, vol. 11, Aug. 2017.

J. B. Balardin et al., “Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments,” Frontiers in Human Neuroscience, vol. 11, May 2017.

M. Balconi and M. E. Vanutelli, “When Cooperation Was Efficient or Inefficient. Functional Near-Infrared Spectroscopy Evidence,” Frontiers in Systems Neuroscience, vol. 11, May 2017.

J. B. Balardin, G. A. Z. Morais, R. A. Furucho, L. R. Trambaiolli, and J. R. Sato, “Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows,” J. Biomed. Opt, vol. 22, no. 4, pp. 046010–046010, 2017.

M. Balconi and M.E. Vanutelli, "Empathy in Negative and Positive Interpersonal Interactions. What is the Relationship Between Central (EEG, fNIRS) and Peripheral (Autonomic) Neurophysiological Responses?", Advances in Cognitive Psychology, vol 13, issue 1, pp 1-120, 31 March 2017.

M. Balconi and M. E. Vanutelli, “Interbrains cooperation: Hyperscanning and self-perception in joint actions,” Journal of Clinical and Experimental Neuropsychology, vol. 0, no. 0, pp. 1–14, Nov. 2016.

M. Balconi and M. E. Vanutelli, “Competition in the Brain. The Contribution of EEG and fNIRS Modulation and Personality Effects in Social Ranking,” Front. Psychol., p. 1587, 2016.

M. E. Vanutelli and M. Balconi, “Perceiving emotions in human-human and human-animal interactions: Hemodynamic prefrontal activity (fNIRS) and empathic concern,” Neurosci. Lett., vol. 605, pp. 1–6, Sep. 2015.

M. Balconi and M. E. Vanutelli, “Emotions and BIS/BAS components affect brain activity (ERPs and fNIRS) in observing intra-species and inter-species interactions,” Brain Imaging and Behavior, vol. 10, no. 3, pp. 750–760, Aug. 2015.

Somatosensory

fNIRS determines changes in hemoglobin oxygenation in the human head non-invasively, and has the advantage of being more robust to motion artifacts than fMRI. In addition, the application of fNIRS is more convenient for somatosensory research, especially when measuring patients with chronic pain, as measurements can take place on a more comfortable bench compared to the MR scanner bench.

Rahimpour Jounghani, A., Lanka, P., Pollonini, L., Proksch, S., Balasubramaniam, R., & Bortfeld, H. (2023). Multiple levels of contextual influence on action-based timing behavior and cortical activation. Scientific Reports, 13(1), 7154.

Chen, Y., Zou, H., Hou, X., Lan, C., Wang, J., Qing, Y., ... & Kendrick, K. M. (2023). Oxytocin administration enhances pleasantness and neural responses to gentle stroking but not moderate pressure social touch by increasing peripheral concentrations. Elife, 12, e85847.

Marschallek, B. E., Löw, A., & Jacobsen, T. (2023). You can touch this! Brain correlates of aesthetic processing of active fingertip exploration of material surfaces. Neuropsychologia, 182, 108520.

Zhou, Y., Oustric, P., Li, X., Zhu, H., Finlayson, G., & Zhou, C. (2023). Neurobehavioral markers of food preference and reward in fasted and fed states and their association with eating behaviors in young Chinese adults. Food Quality and Preference, 103, 104689.

Jensen, A. M., Andersen, J. Q., Quisth, L., & Ramstrand, N. (2020). Finger orthoses for management of joint hypermobility disorders: Relative effects on hand function and cognitive load. Prosthetics and orthotics international, 0309364620956866.

N. H. Kashou, I. A. Dar, K. A. Hasenstab, R. W. Nahhas, and S. R. Jadcherla, “Somatic stimulation causes frontoparietal cortical changes in neonates: a functional near-infrared spectroscopy study,” Neurophotonics, vol. 4, no. 1, p. 11004, Jan. 2017.

A. Vrana, M. L. Meier, S. Hotz-Boendermaker, B. K. Humphreys, and F. Scholkmann, “Cortical Sensorimotor Processing of Painful Pressure in Patients with Chronic Lower Back Pain—An Optical Neuroimaging Study using fNIRS,” Front. Hum. Neurosci., vol. 10, 2016.

A. Vrana, M. L. Meier, S. Hotz-Boendermaker, B. K. Humphreys, and F. Scholkmann, “Different mechanosensory stimulations of the lower back elicit specific changes in hemodynamics and oxygenation in cortical sensorimotor areas—A fNIRS study,” Brain Behav, p. n/a-n/a, Sep. 2016.

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed Opt Express, vol. 5, no. 6, pp. 1778–1798, May 2014.

C. Habermehl et al., “Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography,” Neuroimage, vol. 59, no. 4, pp. 3201–3211, Feb. 2012.

S. P. Koch, “High-resolution optical functional mapping of the human somatosensory cortex,” Frontiers in Neuroenergetics, 2010.

Speech and Language 

Realistic experiments involve verbalized speech. As such, they should account for the muscle movements that are required for this process, and the eventual artifacts that these may cause. The robustness of fNIRS in the presence of muscle movements as well as its portability in comparison to other imaging techniques, render this technology a very promising tool for studying speech and language under a great variety of conditions.

Kou, J. W., Fan, L. Y., Chen, H. C., Chen, S. Y., Hu, X., Zhang, K., ... & Chou, T. L. (2024). Neural Substrates of L2-L1 Transfer Effects on Phonological Awareness in Young Chinese-English Bilingual Children. NeuroImage, 120592.

Dewey, D. P., Green, J. J., Nuckolls, J., Nygaard, A., & Swanson, T. D. (2024). Neurological evidence for the context-independent multisensorial semantics of ideophones in Pastaza Kichwa: an fNIRS study in the Ecuadorian Amazon. Language and Cognition, 1-28.

Gao, F., Hua, L., He, Y., Xu, J., Li, D., Zhang, J., & Yuan, Z. (2023). Word structure tunes electrophysiological and hemodynamic responses in the frontal cortex. Bioengineering, 10(3), 288.

Moffat, R., Başkent, D., Luke, R., McAlpine, D., & Van Yper, L. (2022). Cortical haemodynamic responses predict individual ability to recognise vocal emotions with uninformative pitch cues but do not distinguish different emotions. Human Brain Mapping.

Lanzilotti, C., Andéol, G., Micheyl, C., & Scannella, S. (2022). Cocktail party training induces increased speech intelligibility and decreased cortical activity in bilateral inferior frontal gyri. A functional near-infrared study. Plos one, 17(12), e0277801.

Zhang, F., Gervain, J., & Roeyers, H. (2022). Developmental changes in the brain response to speech during the first year of life: A near-infrared spectroscopy study of dutch-learning infants. Infant Behavior and Development, 67, 101724.

Butera, I. M., Larson, E. D., DeFreese, A. J., Lee, A. K., Gifford, R. H., & Wallace, M. T. (2022). Functional localization of audiovisual speech using near infrared spectroscopy. Brain topography, 35(4), 416-430.

Gao, F., Wang, R., Armada-da-Silva, P., Wang, M. Y., Lu, H., Leong, C., & Yuan, Z. (2022). How the brain encodes morphological constraints during Chinese word reading: An EEG-fNIRS study. cortex, 154, 184-196.

Zhou, X., Sobczak, G. S., McKay, C. M., & Litovsky, R. Y. (2022). Effects of degraded speech processing and binaural unmasking investigated using functional near-infrared spectroscopy (fNIRS). Plos one, 17(4), e0267588.

Berent, I., de la Cruz-Pavía, I., Brentari, D., & Gervain, J. (2021). Infants differentially extract rules from language. Scientific reports, 11(1), 1-10.

Steber, S., & Rossi, S. (2021). The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach. Plos one, 16(2), e0246421.

Stephan, F., Saalbach, H., & Rossi, S. (2020). Inner versus Overt Speech Production: Does This Make a Difference in the Developing Brain?. Brain Sciences, 10(12), 939.

Hitomi, T., Gerrits, R., & Hartsuiker, R. J. (2020). Using functional near-infrared spectroscopy to study word production in the brain: A picture-word interference study. Journal of Neurolinguistics, 57, 2021, 100957.

H. Bortfeld, “Functional near‐infrared spectroscopy as a tool for assessing speech and spoken language processing in pediatric and adult cochlear implant users,” Developmental Psychobiology, Dec. 2018.

A. R. Sonkaya and Z. Z. Bayazit, “A Neurolinguistic Investigation of Emotional Prosody and Verbal Components of Speech,” NeuroQuantology, vol. 16, no. 12, Nov. 2018.

N. Altvater-Mackensen and T. Grossmann, “Modality-independent recruitment of inferior frontal cortex during speech processing in human infants,” Developmental Cognitive Neuroscience, vol. 34, pp. 130–138, Nov. 2018.

R. Gupta, A. Avila, and T. H. Falk, “Towards a Neuro-Inspired No-Reference Instrumental Quality Measure for Text-to-Speech Systems,” in 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), 2018, pp. 1–6.

K. Arun, K. Smitha, P. Rajesh, and C. Kesavadas, “Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas,” Neuroradiol J, vol. 31, no. 2, pp. 133–141, Apr. 2018.

A. Groba, A. De Houwer, J. Mehnert, S. Rossi, and H. Obrig, “Bilingual and monolingual children process pragmatic cues differently when learning novel adjectives,” Bilingualism: Language and Cognition, pp. 1–19, May 2017.

S. Benavides-Varela and J. Gervain, “Learning word order at birth: A NIRS study,” Developmental Cognitive Neuroscience. Mar. 2017.

H. Obrig, J. Mock, F. Stephan, M. Richter, M. Vignotto, and S. Rossi, “Impact of associative word learning on phonotactic processing in 6-month-old infants: A combined EEG and fNIRS study,” Developmental Cognitive Neuroscience. Sep. 2016.

C. Issard and J. Gervain, “Adult-like processing of time-compressed speech by newborns: A NIRS study,” Developmental Cognitive Neuroscience. Oct. 2016.

N. Altvater-Mackensen and T. Grossmann, “The role of left inferior frontal cortex during audiovisual speech perception in infants,” NeuroImage, vol. 133, pp. 14–20, Jun. 2016.

N. Abboub, T. Nazzi, and J. Gervain, “Prosodic grouping at birth,” Brain Lang, vol. 162, pp. 46–59, Aug. 2016.

G. Tellis and C. Tellis, “Using functional near infrared spectroscopy with fluent speakers to determine haemoglobin changes in the brain during speech and non-speech tasks,” NIR news, vol. 27, no. 3, p. 4, 2016.

C. Vitale, T. Murgallis, G. Tellis, and D. Anson, “Near-Infrared Spectroscopy Technology in Typically Fluent Speakers and Persons who Stutter,” Procedia - Social and Behavioral Sciences, vol. 193, p. 354, Jun. 2015.

J. Gervain, “Plasticity in early language acquisition: the effects of prenatal and early childhood experience,” Curr. Opin. Neurobiol., vol. 35, pp. 13–20, Dec. 2015.

C. Herff, F. Putze, D. Heger, C. Guan, and T. Schultz, “Speaking mode recognition from functional Near Infrared Spectroscopy,” Conf Proc IEEE Eng Med Biol Soc, vol. 2012, pp. 1715–1718, 2012.

Stroke Rehabilitation 

In addition to advantages towards brain perfusion monitoring, stroke rehabilitation studies may benefit from fNIRS because of its portability and ease of application. These features allow for assessment during whole-body movements as well as neurofeedback methods that are indicative of brain function, which may be of particular interest for rehabilitation strategies that take place at home.

Lim, S. B., Peters, S., Yang, C. L., Boyd, L. A., Liu-Ambrose, T., & Eng, J. J. (2023). Premotor and Posterior Parietal Cortex Activity is Increased for Slow, as well as Fast Walking Poststroke: An fNIRS Study. Neural Plasticity, 2023.

Senthilvelan, S., Kannath, S. K., Arun, K. M., Menon, R., & Kesavadas, C. (2022). Non-invasive assessment of cerebral hemoglobin parameters in intracranial dural arteriovenous fistula using functional near-infrared spectroscopy—A feasibility study. Frontiers in Neuroscience, 16.

Laves, K., Mehlhose, C., & Risius, A. (2022). Sensory Measurements of Taste: Aiming to Visualize Sensory Differences in Taste Perception by Consumers—An Experiential fNIRS Approach. Journal of International Food & Agribusiness Marketing, 1-21.

Lu, Y. H., Wu, C. W., Pi-Shan, S. U. N. G., Lin, C. C. K., Lin, P. Y., Wang, S. M. S., ... & Chen, J. J. J. (2022). Evaluating Interhemispheric Synchronization and Cortical Activity in Acute Stroke Patients Using Optical Hemodynamic Oscillations. Journal of Neural Engineering.

Kim, H., Kim, J., Lee, G., Lee, J., & Kim, Y. H. (2022). Task-Related Hemodynamic Changes Induced by High-Definition Transcranial Direct Current Stimulation in Chronic Stroke Patients: An Uncontrolled Pilot fNIRS Study. Brain Sciences, 12(4), 453.

Lee, G., Lee, J., Kim, J., Kim, H., Chang, W. H., & Kim, Y. H. (2022). Whole Brain Hemodynamic Response Based on Synchrony Analysis of Brain Signals for Effective Application of HD-tDCS in Stroke Patients: An fNIRS Study. Journal of Personalized Medicine, 12(3), 432.

Cho, S., Chang, W. K., Park, J., Lee, S. H., Lee, J., Han, C. E., ... & Kim, W. S. (2022). Feasibility study of immersive virtual prism adaptation therapy with depth-sensing camera using functional near-infrared spectroscopy in healthy adults. Scientific Reports, 12(1), 1-12.
Chang, P. W., Lu, C. F., Chang, S. T., & Tsai, P. Y. (2021). Functional Near-Infrared Spectroscopy as a Target Navigator for rTMS Modulation in Patients with Hemiplegia: A Randomized Control Study. Neurology and Therapy, 1-19.

Li, R., Li, S., Roh, J., Wang, C., & Zhang, Y. (2020). Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment: An Exploratory Study. Neurorehabilitation and Neural Repair. 1545968320969937.

Arun, K. M., Smitha, K. A., Sylaja, P. N., & Kesavadas, C. (2020). Identifying Resting-State Functional Connectivity Changes in the Motor Cortex Using fNIRS During Recovery from Stroke. Brain Topography, 1-10.

Li, H., Zhu, N., Klomparens, E. A., Xu, S., Wang, M., Wang, Q., ... & Song, L. (2019). “Application of functional near-infrared spectroscopy to explore the neural mechanism of transcranial direct current stimulation for post-stroke depression”. Neurological research, 1-8.

Y. Liu, Y. Yang, Y. Tsai, R. Wang, and C. Lu, “Brain Activation and Gait Alteration During Cognitive and Motor Dual Task Walking in Stroke—A Functional Near-Infrared Spectroscopy Study,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 12, pp. 2416–2423, Dec. 2018.

S. Peci and F. Peci, “Hemoglobin (Hb) - Oxyhemoglobin (HbO) Variation in Rehabilitation Processes Involving Prefrontal Cortex,” Prefrontal Cortex, Nov. 2018.

C. Lo, P. Lin, Z. Hoe, and J. J. Chen, “Near Infrared Spectroscopy Study of Cortical Excitability During Electrical Stimulation-Assisted Cycling for Neurorehabilitation of Stroke Patients,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 6, pp. 1292–1300, Jun. 2018.

C.-C. Lo and J.-J. J. Chen, “Design of Neurorehabilitation Device and Program for Stroke Patients Under Electrical Stimulation Assisted Cycling Using Near Infrared Spectroscopy1,” J. Med. Devices, vol. 9, no. 3, pp. 030916–030916, Sep. 2015.

S. E. Kober, G. Bauernfeind, C. Woller, M. Sampl, P. Grieshofer, C. Neuper, and G. Wood, “Hemodynamic Signal Changes Accompanying Execution and Imagery of Swallowing in Patients with Dysphagia: A Multiple Single-Case Near-Infrared Spectroscopy Study,” Front Neurol, vol. 6, Jul. 2015.

Z.-J. Lin, M. Ren, L. Li, Y. Liu, J. Su, S.-H. Yang, and H. Liu, “Interleaved imaging of cerebral hemodynamics and blood flow index to monitor ischemic stroke and treatment in rat by volumetric diffuse optical tomography,” Neuroimage, vol. 85 Pt 1, pp. 566–582, Jan. 2014.

J. Mehnert, M. Brunetti, J. Steinbrink, M. Niedeggen, and C. Dohle, “Effect of a mirror-like illusion on activation in the precuneus assessed with functional near-infrared spectroscopy,” J Biomed Opt, vol. 18, no. 6, p. 66001, Jun. 2013.

H. Obrig and J. Steinbrink, “Non-invasive optical imaging of stroke,” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 369, no. 1955, pp. 4470–4494, Nov. 2011.

C. Habermehl, C. H. Schmitz, and J. Steinbrink, “Contrast enhanced high-resolution diffuse optical tomography of the human brain using ICG,” Opt Express, vol. 19, no. 19, pp. 18636–18644, Sep. 2011.

Technological/Analytical Advances

Frequently, research is limited by the technologies available. Efforts towards overcoming current limits, by design of new hardware and software solutions, is therefore much appreciated. Research aiming for technological advance constantly pushes forward and creates a wide range of new possibilities to be explored by the whole scientific community.

Caulier-Cisterna, R., Appelgren-Gonzáles, J. P., Oyarzún, J. E., Valenzuela, F., Sitaram, R., Eblen-Zajjur, A., & Uribe, S. (2024). Comparison of LED-and LASER-based fNIRS technologies to record the human peri-spinal cord neurovascular response. Medical Engineering & Physics, 104170.

Wickramasuriya, D. S., Khazaei, S., Kiani, R., & Faghih, R. T. (2023). A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations. IEEE Access, 11, 137204-137247.

Gvirts Provolovski, H. Z., Sharma, M., Gutman, I., Dahan, A., Pan, Y., Stotler, J., & Wilcox, T. (2023). New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies. J. Vis. Exp.

Schwab, S. M., Cooper, D., Carver, N. S., Doren, S., & Boyne, P. (2023). Motivation-related influences on fNIRS signals during walking exercise: a permutation entropy approach. Experimental Brain Research, 1-9.

Jahromi, L. M., Yang, L., Grosenick, D., & von Lühmann, A. (2023, August). Accuracy of tissue oxygen saturation measurement with multidistance CW fNIRS: a phantom study. In Diffuse Optical Spectroscopy and Imaging IX (Vol. 12628, pp. 180-182). SPIE.

Gao, Y., Rogers, D. J., von Lühmann, A., Ortega-Martinez, A., Boas, D. A., & Yücel, M. A. (2023). Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy. Neurophotonics, 10(2), 025007-025007.

de Rond, V., Gilat, M., D’Cruz, N., Hulzinga, F., Orban de Xivry, J. J., & Nieuwboer, A. (2023). Test-retest reliability of functional near-infrared spectroscopy during a finger-tapping and postural task in healthy older adults. Neurophotonics, 10(2), 025010-025010.

Kim, J. H., Lee, J. Y., Kim, E. S., & Jeong, M. Y. (2023). Electric field enhancement of coupled plasmonic nanostructures for optical amplification. PhotoniX, 4(1), 8.

Donaldson, A., Andrade, D., & Das, M. (2023, March). Sensitivity of functional near-infrared spectroscopy to optical properties in brain imaging. In Multiscale Imaging and Spectroscopy IV (Vol. 12363, pp. 19-24). SPIE.

Bizzego, A., & Esposito, G. (2023). Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals. Sensors, 23(7), 3668.

Cakar, S., & Yavuz, F. G. (2023). Hybrid statistical and machine learning modeling of cognitive neuroscience data. Journal of Applied Statistics, 1-22.

Zhai, X., Santosa, H., Krafty, R. T., & Huppert, T. J. (2023). Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model. Neurophotonics, 10(2), 023516.

Zhang, F., Reid, A., Schroeder, A., Ding, L., & Yuan, H. (2023). Controlling Jaw-Related Motion Artifacts in Functional Near-Infrared Spectroscopy. Journal of Neuroscience Methods, 109810.

Kang, K., Rosenkranz, R., Karan, K., Altinsoy, E., & Li, S. C. (2022). Congruence-based contextual plausibility modulates cortical activity during vibrotactile perception in virtual multisensory environments. Communications Biology, 5(1), 1-13.

Cakar, S., & Yavuz, F. G. (2022). Nested and Robust Modeling Techniques for fNIRS Data with Demographics and Experiment Related Factors in n-back Task. Neuroscience Research.

Klein, F., Lührs, M., Benitez-Andonegui, A., Roehn, P., & Kranczioch, C. (2022). Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels. Neurophotonics, 10(1), 013503.

Wang, Y., Chen, C., & Chen, W. (2022). Nonlinear directed information flow estimation for fNIRS brain network analysis based on the modified multivariate transfer entropy. Biomedical Signal Processing and Control, 74, 103422.

Zhou, X., Burg, E., Kan, A., & Litovsky, R. Y. (2022). Investigating effortful speech perception using fNIRS and pupillometry measures. Current Research in Neurobiology, 3, 100052.

Lanka, P., Bortfeld, H., & Huppert, T. J. (2022). Correction of global physiology in resting-state functional near-infrared spectroscopy. Neurophotonics, 9(3), 035003.

Ortega-Martinez, A., Von Lühmann, A., Farzam, P., Mugler, E. M., Boas, D. A., & Yücel, M. A. (2022). Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data. Neurophotonics, 9(2), 025003.

Paranawithana, I., Mao, D., Wong, Y. T., & McKay, C. M. (2022). Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study. Neurophotonics, 9(1), 015001.

Wang, Y., Zhao, X., Zhou, W., Chen, C., & Chen, W. (2021). Dynamic weighted “small-world” graphical network establishment for fNIRS time-varying brain function analysis. Biomedical Signal Processing and Control, 69, 102902.

Khan, A. F., Zhang, F., Yuan, H., & Ding, L. (2021). Brain-wide functional diffuse optical tomography of resting state networks. Journal of Neural Engineering, 18(4), 046069.

Wu, S. T., Silva, J. A. I. R., Novi, S. L., de Souza, N. G. S., Forero, E. J., & Mesquita, R. C. (2020). Accurate Image-guided (Re) Placement of NIRS Probes. Computer Methods and Programs in Biomedicine, 105844.

Zhou, X., Sobczak, G., McKay, C. M., & Litovsky, R. Y. (2020). Comparing fNIRS signal qualities between approaches with and without short channels. PLOS ONE, 15(12), e0244186.

Wyser, D., Mattille, M., Wolf, M., Lambercy, O., Scholkmann, F., & Gassert, R. (2020). Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics. Neurophotonics, 7(3), 035011.

Santosa, H., Zhai, X., Fishburn, F., Sparto, P. J., & Huppert, T. J. (2020). Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies. Neurophotonics, 7(3), 035009.

Novi, S. L., Forero, E. J., Rubianes Silva, J. A. I., de Souza, N. G. S., Martins, G. G., Quiroga, A., ... & Mesquita, R. C. (2020). Integration of Spatial Information Increases Reproducibility in Functional Near-Infrared Spectroscopy. Frontiers in Neuroscience.

Lyu, B., Pham, T., Blaney, G., Haga, Z., Fantini, S., & Aeron, S. (2020). Domain Adaptation for Robust Workload Classification using fNIRS. arXiv preprint arXiv:2007.06706

Yu, C. L., Chen, H. C., Yang, Z. Y., & Chou, T. L. (2020). Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy. Behavior Research Methods, 1-14.

R. A. Khan, N. Naseer, and M. J. Khan, “Chapter 13 - Drowsiness Detection During a Driving Task Using fNIRS,” in Neuroergonomics, H. Ayaz and F. Dehais, Eds. Academic Press, 2019, pp. 79–85.

B. Wortelen, A. Unni, J. W. Rieger, A. Lüdtke, and J.-P. Osterloh, “Monte Carlo Methods for Real-Time Driver Workload Estimation Using a Cognitive Architecture,” in Cognitive Infocommunications, Theory and Applications, R. Klempous, J. Nikodem, and P. Z. Baranyi, Eds. Cham: Springer International Publishing, 2019, pp. 25–48.

B. Blanco, M. Molnar, and C. Caballero-Gaudes, “Effect of prewhitening in resting-state functional near-infrared spectroscopy data,” NPh, vol. 5, no. 4, p. 040401, Oct. 2018.

A. Janani and M. Sasikala, “Evaluation of classification performance of functional near infrared spectroscopy signals during movement execution for developing a brain-computer interface application using optimal channels,” J. Near Infrared Spectrosc., JNIRS, vol. 26, no. 4, pp. 209–221, Aug. 2018. 

L. Duan, Z. Zhao, Y. Lin, X. Wu, Y. Luo, and P. Xu, “Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy,” Biomed. Opt. Express, BOE, vol. 9, no. 8, pp. 3805–3820, Aug. 2018.

S. E. Kober, V. Hinterleitner, G. Bauernfeind, C. Neuper, and G. Wood, “Trainability of hemodynamic parameters: A near-infrared spectroscopy based neurofeedback study,” Biological Psychology, vol. 136, pp. 168–180, Jul. 2018.

L. M. Hocke, I. K. Oni, C. C. Duszynski, A. V. Corrigan, B. deB Frederick, and J. F. Dunn, “Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences,” Algorithms, vol. 11, no. 5, p. 67, May 2018.

R. Gupta, A. Avila, and T. H. Falk, “Towards a Neuro-Inspired No-Reference Instrumental Quality Measure for Text-to-Speech Systems,” in 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), 2018, pp. 1–6.

L. Zhu, S. Haghani, and L. Najafizadeh, “Spatiotemporal Characterization of Brain Function Via Multiplex Visibility Graph,” in Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS) (2018), paper JTh3A.54, 2018, p. JTh3A.54.

O. Klempíř, R. Krupička, and R. Jech, “MEDIAN METHOD FOR DETERMINING CORTICAL BRAIN ACTIVITY IN A NEAR INFRARED SPECTROSCOPY IMAGE,” Lékař a technika - Clinician and Technology, vol. 48, no. 1, pp. 11–16, Mar. 2018.

L. R. Trambaiolli, C. E. Biazoli, A. M. Cravo, and J. R. Sato, “Predicting affective valence using cortical hemodynamic signals,” Scientific Reports, vol. 8, no. 1, p. 5406, Mar. 2018.

M. D. Pfeifer, F. Scholkmann, and R. Labruyère, “Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different Statistical Results,” Front. Hum. Neurosci., vol. 11, 2018.

M. A. Kamran, M. M. Naeem Mannan, and M.-Y. Jeong, “Initial-Dip Existence and Estimation in Relation to DPF and Data Drift,” Front. Neuroinform., vol. 12, 2018.

A. Janani and M. Sasikala, “Classification of fNIRS Signals for Decoding Right- and Left-Arm Movement Execution Using SVM for BCI Applications,” in Computational Signal Processing and Analysis, 2018, pp. 315–323.

J. Gemignani, E. Middell, R. L. Barbour, H. L. Graber, and B. Blankertz, “Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation,” J. Neural Eng., vol. 15, no. 4, p. 045001, 2018.

G. A. Zimeo Morais, J. B. Balardin, and J. R. Sato, “fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest,” Scientific Reports, vol. 8, no. 1, Dec. 2018.

G. A. Zimeo Morais et al., “Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals,” Neurophotonics, vol. 5, no. 01, p. 1, Aug. 2017.

J. B. Balardin, G. A. Z. Morais, R. A. Furucho, L. R. Trambaiolli, and J. R. Sato, “Impact of communicative head movements on the quality of functional near-infrared spectroscopy signals: negligible effects for affirmative and negative gestures and consistent artifacts related to raising eyebrows,” J. Biomed. Opt, vol. 22, no. 4, pp. 046010–046010, 2017.

N. K. Qureshi, N. Naseer, F. M. Noori, H. Nazeer, R. A. Khan, and S. Saleem, “Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients,” Frontiers in Neurorobotics, vol. 11, Jul. 2017.

L. Pollonini, H. Bortfeld, and J. S. Oghalai, “PHOEBE: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy,” Biomed. Opt. Express, BOE, vol. 7, no. 12, pp. 5104–5119, Dec. 2016.

H.-D. Nguyen and K.-S. Hong, “Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy,” Biomed Opt Express, vol. 7, no. 9, pp. 3491–3507, Aug. 2016.

L. Holper, E. Seifritz, and F. Scholkmann, “Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography,” J Biomed Opt, vol. 21, no. 9, p. 91308, Sep. 2016.

J. Yao, F. Tian, Y. Rakvongthai, S. Oraintara, and H. Liu, “Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography,” Biomed Opt Express, vol. 6, no. 8, pp. 2961–2979, Aug. 2015.

D. Piao, R. L. Barbour, H. L. Graber, and D. C. Lee, “On the geometry dependence of differential pathlength factor for near-infrared spectroscopy. I. Steady-state with homogeneous medium,” J. Biomed. Opt, vol. 20, no. 10, pp. 105005–105005, 2015.

H. D. Nguyen and K. S. Hong, “Multiple optodes configuration for measuring the absolute hemodynamic response using spatially resolved spectroscopy method: An fNIRS study,” in 2015 15th International Conference on Control, Automation and Systems (ICCAS), 2015, pp. 1827–1832.

M. A. Kamran, M. Y. Jeong, and M. M. N. Mannan, “Optimal hemodynamic response model for functional near-infrared spectroscopy,” Front Behav Neurosci, vol. 9, Jun. 2015.

E. E. Vidal-Rosas et al., “Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography,” J Biomed Opt, vol. 19, no. 2, p. 26008, Feb. 2014.

I. W. Selesnick, H. L. Graber, D. S. Pfeil, and R. L. Barbour, “Simultaneous Low-Pass Filtering and Total Variation Denoising,” IEEE Transactions on Signal Processing, vol. 62, no. 5, pp. 1109–1124, Mar. 2014.

M. A. Kamran and K.-S. Hong, “Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding,” Neurosci. Lett., vol. 580, pp. 130–136, Sep. 2014.

N. Hemmati Berivanlou, S. K. Setarehdan, and H. Ahmadi Noubari, “Evoked hemodynamic response estimation using ensemble empirical mode decomposition based adaptive algorithm applied to dual channel functional near infrared spectroscopy (fNIRS),” Journal of Neuroscience Methods, vol. 224, pp. 13–25, Mar. 2014.

C. Habermehl, J. Steinbrink, K.-R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J Biomed Opt, vol. 19, no. 9, p. 96006, Sep. 2014.

V. C. Kavuri, Z.-J. Lin, F. Tian, and H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed Opt Express, vol. 3, no. 5, pp. 943–957, Apr. 2012.

C. Habermehl et al., “Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography,” Neuroimage, vol. 59, no. 4, pp. 3201–3211, Feb. 2012.

M. Aqil, K.-S. Hong, M.-Y. Jeong, and S. S. Ge, “Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity,” Neuroimage, vol. 63, no. 1, pp. 553–568, Oct. 2012.

X.-S. Hu, K.-S. Hong, S. S. Ge, and M.-Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed Eng Online, vol. 9, p. 82, 2010.

A. Bluestone, G. Abdoulaev, C. Schmitz, R. Barbour, and A. Hielscher, “Three-dimensional optical tomography of hemodynamics in the human head,” Opt Express, vol. 9, no. 6, pp. 272–286, Sep. 2001.

R. L. Barbour, H. L. Graber, J. Chang, S.-L. S. Barbour, P. C. Koo, and R. Aronson, “MRI-Guided Optical Tomography: Prospects and Computation for a New Imaging Method,” IEEE Comput. Sci. Eng., vol. 2, no. 4, pp. 63–77, Dec. 1995.

Traumatic Brain Injury (TBI)

fNIRS offers a practical, portable, and relatively inexpensive alternative to assess correlates of brain oxygenation. Moreover, it allows to coregister other neurophysiological and behavioral data in a “near natural” environment. Because of this, the technique is promising for the field of clinical neurology, and indeed fNIRS has been used to detect changes in cerebral hemodynamics after severe TBI.

Straudi, S., Antonioni, A., Baroni, A., Bonsangue, V., Lavezzi, S., Koch, G., ... & Lamberti, N. (2023). Anti-Inflammatory and Cortical Responses after Transcranial Direct Current Stimulation in Disorders of Consciousness: An Exploratory Study. Journal of Clinical Medicine, 13(1), 108.

Parker, S. M., Ricks, B., Zuniga, J., & Knarr, B. A. (2023). Comparison of virtual reality to physical box and blocks on cortical an neuromuscualar activations in young adults. Scientific Reports, 13(1), 16567.

Stephens, J. A., Mingils, S., & Orlandi, S. (2023). Evaluating Dual Task Neurological Costs with Functional Near-Infrared Spectroscopy: A Preliminary Report in Healthy Athletes. Journal of Integrative Neuroscience, 22(5), 133.

 Si, J., Yang, Y., Xu, L., Xu, T., Liu, H., Zhang, Y., ... & He, J. (2023). Evaluation of residual cognition in patients with disorders of consciousness based on functional near-infrared spectroscopy. Neurophotonics, 10(2), 025003.

Lapointe, A. P., Ware, A. L., Duszynski, C. C., Stang, A., Yeates, K. O., & Dunn, J. F. (2023). Cerebral Hemodynamics and Microvasculature Changes in Relation to White Matter Microstructure After Pediatric Mild Traumatic Brain Injury: An A-CAP Pilot Study. Neurotrauma Reports, 4(1), 64-70.

Karunakaran, K. K., Nisenson, D. M., & Nolan, K. J. (2020, July). Alterations in Cortical Activity due to Robotic Gait Training in Traumatic Brain Injury. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3224-3227). IEEE.

I. Helmich, A. Berger, and H. Lausberg, “Neural Control of Posture in Individuals with Persisting Postconcussion Symptoms,” Med Sci Sports Exerc, Jul. 2016.

I. Helmich, R. S. Saluja, H. Lausberg, M. Kempe, P. Furley, A. Berger, J.-K. Chen, and A. Ptito, “Persistent Postconcussive Symptoms Are Accompanied by Decreased Functional Brain Oxygenation,” J Neuropsychiatry Clin Neurosci, vol. 27, no. 4, pp. 287–298, 2015.

Visual Stimulation 

fNIRS techniques have become increasingly popular because of their easy and safe operation, cost-efficiency, good temporal resolution, and the clear and robust results they deliver in real time. As such, fNIRS is ideal to explore visual stimulation, and indeed vision-related fNIRS research is very active.

Roelke, A., Vorstius, C., Radach, R., & Hofmann, M. J. (2020). Fixation-related NIRS indexes retinotopic occipital processing of parafoveal preview during natural reading. NeuroImage, 116823.

X. Liu and K.-S. Hong, “Detection of primary RGB colors projected on a screen using fNIRS,” Journal of Innovative Optical Health Sciences, Jan. 2017.

N. H. Kashou and B. M. Giacherio, “Stimulus and optode placement effects on functional near-infrared spectroscopy of visual cortex,” Neurophotonics, vol. 3, no. 2, p. 25005, Apr. 2016.

L.-C. Chen, P. Sandmann, J. D. Thorne, M. G. Bleichner, and S. Debener, “Cross-Modal Functional Reorganization of Visual and Auditory Cortex in Adult Cochlear Implant Users Identified with fNIRS,” Neural Plast, vol. 2016, 2016.

A. D. Zaidi et al., “Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates,” Neuroimage, vol. 120, pp. 394–399, Oct. 2015.

E. Maggioni et al., “Investigation of negative BOLD responses in human brain through NIRS technique. A visual stimulation study,” NeuroImage, vol. 108, pp. 410–422, Mar. 2015.

X. Liu and K. S. Hong, “fNIRS based color detection from human visual cortex,” in Society of Instrument and Control Engineers of Japan (SICE), 2015 54th Annual Conference of the, 2015, pp. 1156–1161.

L.-C. Chen, P. Sandmann, J. D. Thorne, C. S. Herrmann, and S. Debener, “Association of Concurrent fNIRS and EEG Signatures in Response to Auditory and Visual Stimuli,” Brain Topogr, vol. 28, no. 5, pp. 710–725, Sep. 2015.

K.-S. Hong and H.-D. Nguyen, “State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices,” Biomed Opt Express, vol. 5, no. 6, pp. 1778–1798, May 2014.

C.-H. Chen, M.-S. Ho, K.-K. Shyu, K.-C. Hsu, K.-W. Wang, and P.-L. Lee, “A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses,” Neuroscience Letters, vol. 580, pp. 22–26, Sep. 2014.

E. Maggioni et al., “Coupling of fMRI and NIRS measurements in the study of negative BOLD response to intermittent photic stimulation,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, pp. 1378–1381.

G. R. Wylie et al., “Using co-variations in the Hb signal to detect visual activation: A near infrared spectroscopic imaging study,” NeuroImage, vol. 47, no. 2, pp. 473–481, Aug. 2009.

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About NIRS and NIRx

- fNIRS and NIRx Overview
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Applications

- fMRI-concurrent fNIRS
- EEG-concurrent fNIRS
- TMS-concurrent fNIRS
- Customized Real-time Analysis in NIRS: BCI / Neurofeedback
- Combining eye-tracking with fNIRS
- Using NIRS to conduct child and infant measurements

Products

- NIRScout NIRS Imaging System for Multi-Modal Integration
- NIRSport Portable NIRS Imaging System for Multi-Modal Integration
- NIRS Caps & NIRS Optodes (Probes)
- NIRS software