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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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