Source: https://nirx.net/publications/
Timestamp: 2019-04-25 01:04:44+00:00

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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.
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.
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.
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.
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, 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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, 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.
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.
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.
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.
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.
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.
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. 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. 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. 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.
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.
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. 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.
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.
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.
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.
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.
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. B. Balardin et al., “Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments,” Frontiers in Human Neuroscience, vol. 11, May 2017.
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.
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.
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.
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.
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.
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 and M. E. Vanutelli, “When Cooperation Was Efficient or Inefficient. Functional Near-Infrared Spectroscopy Evidence,” Frontiers in Systems Neuroscience, vol. 11, May 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, “Competition in the Brain. The Contribution of EEG and fNIRS Modulation and Personality Effects in Social Ranking,” Front. Psychol., p. 1587, 2016.
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.
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.
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.
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.
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.
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.
C. Issard and J. Gervain, “Adult-like processing of time-compressed speech by newborns: A NIRS study,” Developmental Cognitive Neuroscience. Oct. 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.
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. 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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, 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.
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.
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.
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.
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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