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.

 

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.
 

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

U. Chaudhary, B. Xia, S. Silvoni, L. G. Cohen, and N. Birbaumer, “Brain–Computer Interface–Based Communication in the Completely Locked-In State,” PLOS Biology, vol. 15, no. 1, p. e1002593, Jan. 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.

 

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.
 

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.
 

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.

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

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. 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.

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. 
 

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.

 

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.

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.

 

 

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.

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. 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.
 

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. 

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.

 

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.
 

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

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. Galderisi, S. Brigadoi, S. Cutini, S.B. Moro, E. Lolli, F. Meconi, S. Benavides-Varela, E. Baraldi, P. Amodio, C. 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.

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.

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. 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.

S. R. Jadcherla, J. F. Pakiraih, K. A. Hasenstab, I. Dar, X. Gao, D. G. Bates, and N. H. Kashou, “Esophageal reflexes modulate frontoparietal response in neonates: Novel application of concurrent NIRS and provocative esophageal manometry,” American Journal of Physiology - Gastrointestinal and Liver Physiology, vol. 307, no. 1, pp. G41–G49, Jul. 2014.

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.
 

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.

 

U. Chaudhary, B. Xia, S. Silvoni, L. G. Cohen, and N. Birbaumer, “Brain–Computer Interface–Based Communication in the Completely Locked-In State,” PLOS Biology, vol. 15, no. 1, p. e1002593, Jan. 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.

 

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 

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.
 

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.
 

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.


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. 
 

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.
 

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.
 

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.

 

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 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.
 

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.

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.
 

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.
 

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.