Treffer: Advances in Functional Near-Infrared Spectroscopy: Physical Principles and Expanding Applications in Neuroscience.
Title:
Advances in Functional Near-Infrared Spectroscopy: Physical Principles and Expanding Applications in Neuroscience.
Authors:
Chu M; The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China.; School of Information Sciences and Technology, Northwest University, Xi'an, China., Ma Y; The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China., Tian R; Henan University of Chinese Medicine, Zhengzhou, China., Liu S; The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China., Yu J; School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China., He X; The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China.; School of Information Sciences and Technology, Northwest University, Xi'an, China., Guo H; The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China.; School of Information Sciences and Technology, Northwest University, Xi'an, China.
Source:
Journal of biophotonics [J Biophotonics] 2025 Nov; Vol. 18 (11), pp. e202500147. Date of Electronic Publication: 2025 Aug 12.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Weinheim : Wiley-VCH
MeSH Terms:
References:
L. Luo , “Architectures of Neuronal Circuits,” Science 373, no. 6559 (2021): eabg7285.
A. A. Phillips , F. H. N. Chan , M. M. Z. Zheng , A. V. Krassioukov , and P. N. Ainslie , “Neurovascular Coupling in Humans: Physiology, Methodological Advances and Clinical Implications,” Journal of Cerebral Blood Flow & Metabolism 36, no. 4 (2016): 647–664.
V. Quaresima and M. Ferrari , “Functional Near‐Infrared Spectroscopy (Fnirs) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review,” Organizational Research Methods 22, no. 1 (2019): 46–68.
A. Mishra , C. N. Hall , C. Howarth , and R. D. Freeman , “Key Relationships Between Non‐Invasive Functional Neuroimaging and the Underlying Neuronal Activity,” Philosophical Transactions of the Royal Society B 2021, no. 376 (1815): 20190622.
T. Liu , L. Duan , R. Dai , M. Pelowski , and C. Zhu , “Team‐Work, Team‐Brain: Exploring Synchrony and Team Interdependence in a Nine‐Person Drumming Task via Multiparticipant Hyperscanning and Inter‐Brain Network Topology With Fnirs,” NeuroImage 237 (2021): 118147.
K. A. Johnson , N. C. Fox , R. A. Sperling , and W. E. Klunk , “Brain Imaging in Alzheimer Disease,” Cold Spring Harbor Perspectives in Medicine 2, no. 4 (2012): a006213.
N. Tournier , C. Comtat , V. Lebon , and J.‐L. Gennisson , “Challenges and Perspectives of the Hybridization of Pet With Functional Mri or Ultrasound for Neuroimaging,” Neuroscience 474 (2021): 80–93.
A. Shmuel and A. Maier , “Locally Measured Neuronal Correlates of Functional Mri Signals,” in EEG‐fMRI: Physiological Basis, Technique, and Applications (Springer, 2023), 79–104.
E. Gibson , N. J. Lobaugh , S. Joordens , and A. R. McIntosh , “Eeg Variability: Task‐Driven or Subject‐Driven Signal of Interest?,” NeuroImage 252 (2022): 119034.
X. Hou , Z. Zhang , C. Zhao , et al., “Nirs‐Kit: A Matlab Toolbox for Both Resting‐State and Task Fnirs Data Analysis,” Neurophotonics 8, no. 1 (2021): 010802–010802.
P. Pinti , I. Tachtsidis , A. Hamilton , et al., “The Present and Future Use of Functional Near‐Infrared Spectroscopy (fNIRS) for Cognitive Neuroscience,” Annals of the new York Academy of Sciences 1464, no. 1 (2020): 5–29.
M. K. Yeung and J. Lin , “Probing Depression, Schizophrenia, and Other Psychiatric Disorders Using fNIRS and the Verbal Fluency Test: A Systematic Review and Meta‐Analysis,” Journal of Psychiatric Research 140 (2021): 416–435.
E. Poliakova , A. L. Conrad , K. M. Schieltz , and M. J. O’Brien , “Using fNIRS to Evaluate ADHD Medication Effects on Neuronal Activity: A Systematic Literature Review,” Frontiers in Neuroimaging 2 (2023): 1083036.
M. K. Yeung and A. S. Chan , “A Systematic Review of the Application of Functional Near‐Infrared Spectroscopy to the Study of Cerebral Hemodynamics in Healthy Aging,” Neuropsychology Review 31, no. 1 (2021): 139–166.
M. Sousani , R. F. Rojas , E. Preston , and M. Ghahramani , “Towards a Multi‐Modal Brain‐Body Assessment in Parkinson's Disease: A Systematic Review in fNIRS (February 2023),” IEEE Journal of Biomedical and Health Informatics (2023).
C. Huo , X. Gongcheng , W. Li , et al., “A Review on Functional Near‐Infrared Spectroscopy and Application in Stroke Rehabilitation,” Medicine in Novel Technology and Devices 11 (2021): 100064.
Z. Zhan , Q. Yang , L. Luo , and X. Zhang , “Applying Functional Near‐Infrared Spectroscopy (fNIRS) in Educational Research: A Systematic Review,” Current Psychology 43, no. 11 (2024): 9676–9691.
R. Li , D. Yang , F. Fang , K.‐S. Hong , A. L. Reiss , and Y. Zhang , “Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology‐Focused Review,” Sensors 22, no. 15 (2022): 5865.
J. Chen , Y. Xia , X. Zhou , et al., “fNIRS‐EEG BCIs for Motor Rehabilitation: A Review,” Bioengineering 10, no. 12 (2023): 1393.
K. Peng , Z. Moussavi , K. D. Karunakaran , D. Borsook , F. Lesage , and D. K. Nguyen , “iVR‐fNIRS: Studying Brain Functions in a Fully Immersive Virtual Environment,” Neurophotonics 11, no. 2 (2024): 020601–020601.
A. Curtin , S. Tong , J. Sun , J. Wang , B. Onaral , and H. Ayaz , “A Systematic Review of Integrated Functional Near‐Infrared Spectroscopy (fNIRS) and Transcranial Magnetic Stimulation (TMS) Studies,” Frontiers in Neuroscience 13 (2019): 84.
V. Tuchin , “Tissue Optics and Photonics: Light‐Tissue Interaction II,” Journal of Biomedical Photonics & Engineering 2, no. 3 (2016): 030201.
N. E. Karishma Shaik , B. Widdicombe , D. Sun , et al., “Longwave Infrared Multispectral Image Sensor System Using Aluminum‐Germanium Plasmonic Filter Arrays,” Nano Research 16, no. 7 (2023): 10018–10025.
E. B. Wang , R. Kaur , M. Fierro , E. Austin , L. R. Jones , and J. Jagdeo , “Safety and Penetration of Light Into the Brain,” in Photobiomodulation in the Brain (Elsevier, 2019), 49–66.
L. Zhu , L. Lu , S. Wang , et al., “Oral Absorption Basics: Pathways and Physicochemical and Biological Factors Affecting Absorption,” in Developing Solid Oral Dosage Forms (Elsevier, 2017), 297–329.
V. O. Korhonen , T. S. Myllylä , M. Y. Kirillin , et al., “Light Propagation in NIR Spectroscopy of the Human Brain,” IEEE Journal of Selected Topics in Quantum Electronics 20, no. 2 (2013): 289–298.
Y. Zhu , T. Jiang , Y. Zhou , and L. Zhao , “Discriminative Analysis of Functional Near‐Infrared Spectroscopy Signals for Development of Neuroimaging Biomarkers of Elderly Depression,” Journal of Innovative Optical Health Sciences 3, no. 01 (2010): 69–74.
D. Rosenbaum , K. Hagen , S. Deppermann , et al., “State‐Dependent Altered Connectivity in Late‐Life Depression: A Functional Near‐Infrared Spectroscopy Study,” Neurobiology of Aging 39 (2016): 57–68.
Y. Tong , Q. Wang , X. Wang , et al., “A Scoping Review of Functional Near‐Infrared Spectroscopy Biomarkers in Late‐Life Depression: Depressive Symptoms, Cognitive Functioning, and Social Functioning,” Psychiatry Research: Neuroimaging (2024): 111810.
H. Lyu , M. Zhao , X. Pengfeng , et al., “Gender Differences in Brain Region Activation During Verbal Fluency Task as Detected by fNIRS in Patients With Depression,” World Journal of Biological Psychiatry 25, no. 2 (2024): 141–150.
R. Briggs , D. Carey , P. Claffey , et al., “The Association Between Frontal Lobe Perfusion and Depressive Symptoms in Later Life,” British Journal of Psychiatry 214, no. 4 (2019): 230–236.
P.‐H. Chou , W.‐C. Liu , W.‐H. Lin , C.‐W. Hsu , S.‐C. Wang , and S. Kuan‐Pin , “NIRS‐Aided Differential Diagnosis Among Patients With Major Depressive Disorder, Bipolar Disorder, and Schizophrenia,” Journal of Affective Disorders 341 (2023): 366–373.
W. Huifen , L. Baoquan , Y. Zhang , and T. Li , “Differences in Prefrontal Cortex Activation in Chinese College Students With Different Severities of Depressive Symptoms: A Large Sample of Functional Near‐Infrared Spectroscopy (fNIRS) Findings,” Journal of Affective Disorders 350 (2024): 521–530.
J. E. Shin , Y. S. Lee , S. Y. Park , et al., “The Relationship Between Depression Severity and Prefrontal Hemodynamic Changes in Adolescents With Major Depression Disorder: A Functional Near‐Infrared Spectroscopy Study,” Clinical Psychopharmacology and Neuroscience 22, no. 1 (2024): 118.
G. Li , K. Ma , K. Rossbach , et al., “Cortical Activation for Adolescent‐Onset Minor Depression and Major Depressive Disorder: An fNIRS Study,” Annals of General Psychiatry 23, no. 1 (2024): 17.
X. Liang , D. Qiao , T. Ren , et al., “Neural Association Between Cognitive Function and Anhedonia in Adolescents With Melancholic Major Depressive Disorder: A fNIRS Study,” Journal of Affective Disorders 338 (2023): 305–311.
X. Liu , Q. Chen , F. Cheng , et al., “The Abnormal Brain Activation Pattern of Adolescents With Major Depressive Disorder Based on Working Memory Tasks: A fNIRS Study,” Journal of Psychiatric Research 169 (2024): 31–37.
Y. Zhang , X. Li , Y. Guo , et al., “Dorsolateral Prefrontal Activation in Emotional Autobiographical Task in Depressed and Anxious College Students: An fNIRS Study,” International Journal of Environmental Research and Public Health 19, no. 21 (2022): 14335.
M. Zheng , H. Da , X. Pan , et al., “Dorsolateral Prefrontal Activation in Depressed Young Adults With and Without Suicidal Ideation During an Emotional Autobiographical Memory Task: A fNIRS Study,” Journal of Affective Disorders 326 (2023): 216–224.
X. Liu , F. Cheng , H. Shasha , et al., “Cortical Activation and Functional Connectivity During the Verbal Fluency Task for Adolescent‐Onset Depression: A Multi‐Channel NIRS Study,” Journal of Psychiatric Research 147 (2022): 254–261.
Z. Song , Y. Zhou , Y. Zheng , et al., “Factors Influencing Functional Near‐Infrared Spectroscopy in Postpartum Depression: A Cross‐Sectional Study,” International Journal of Gynecology & Obstetrics 161, no. 3 (2023): 1046–1052.
S. Wang , C. Ding , C. Dou , et al., “Associations Between Maternal Prenatal Depression and Neonatal Behavior and Brain Function—Evidence From the Functional Near‐Infrared Spectroscopy,” Psychoneuroendocrinology 146 (2022): 105896.
J. A. Porto , J. Bick , K. L. Perdue , J. E. Richards , M. L. Nunes , and C. A. Nelson , “The Influence of Maternal Anxiety and Depression Symptoms on fNIRS Brain Responses to Emotional Faces in 5‐and 7‐Month‐Old Infants,” Infant Behavior and Development 59 (2020): 101447.
W. Huifen , T. Li , C. Peng , et al., “The Right Prefrontal Cortex (PFC) can Distinguish Anxious Depression From Non‐Anxious Depression: A Promising Functional Near Infrared Spectroscopy Study (fNIRS),” Journal of Affective Disorders 317 (2022): 319–328.
J. Chao , S. Zheng , W. Hongtong , et al., “fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 2211–2221.
Y. Zhu , W. Quan , H. Wang , et al., “Prefrontal Activation During a Working Memory Task Differs Between Patients With Unipolar and Bipolar Depression: A Preliminary Exploratory Study,” Journal of Affective Disorders 225 (2018): 64–70.
M. K. Yeung , T. L. Lee , and A. S. Chan , “Depressive and Anxiety Symptoms Are Related to Decreased Lateral Prefrontal Cortex Functioning During Cognitive Control in Older People,” Biological Psychology 166 (2021): 108224.
K. Uemura , H. Shimada , T. Doi , H. Makizako , H. Park , and T. Suzuki , “Depressive Symptoms in Older Adults Are Associated With Decreased Cerebral Oxygenation of the Prefrontal Cortex During a Trail‐Making Test,” Archives of Gerontology and Geriatrics 59, no. 2 (2014): 422–428.
D. Rosenbaum , I. Int‐Veen , H. Laicher , et al., “Insights From a Laboratory and Naturalistic Investigation on Stress, Rumination and Frontal Brain Functioning in MDD: An fNIRS Study,” Neurobiology of Stress 15 (2021): 100344.
X. You , Y. Wang , H. Nannan , et al., “Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting‐State fNIRS Study,” Brain Sciences 12, no. 11 (2022): 1562.
Y. Zhu , J. K. Jayagopal , R. K. Mehta , et al., “Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 4 (2020): 961–969.
T. Ma , H. Lyu , J. Liu , et al., “Distinguishing Bipolar Depression From Major Depressive Disorder Using Fnirs and Deep Neural Network,” Progress in Electromagnetics Research 169 (2020): 73–86.
Y. Kawabata , S.‐i. Imazu , K. Matsumoto , et al., “rTMS Therapy Reduces Hypofrontality in Patients With Depression as Measured by fNIRS,” Frontiers in Psychiatry 13 (2022): 814611.
K. Feng , C.‐Y. Shen , X.‐Y. Ma , et al., “Effects of Music Therapy on Major Depressive Disorder: A Study of Prefrontal Hemodynamic Functions Using fNIRS,” Psychiatry Research 275 (2019): 86–93.
T. Yang , H. Wang , H. Dai , et al., “The fNIRS Evaluation of Frontal and Temporal Lobe Cortical Activation in Chinese First‐Episode Medication‐Naïve and Recurrent Depression During a Verbal Fluency Task,” Frontiers in Psychiatry 14 (2023): 1132666.
A. S. Huhn , M. M. Sweeney , R. K. Brooner , et al., “Prefrontal Cortex Response to Drug Cues, Craving, and Current Depressive Symptoms Are Associated With Treatment Outcomes in Methadone‐Maintained Patients,” Neuropsychopharmacology 44, no. 4 (2019): 826–833.
C. Gao , H. Zhou , J. Liu , et al., “Characteristics of Frontal Activity Relevant to Cognitive Function in Bipolar Depression: An fNIRS Study,” Biomedical Optics Express 13, no. 3 (2022): 1551–1563.
S. F. Husain , T.‐B. Tang , W. W. Tam , B. X. Tran , C. S. Ho , and R. C. Ho , “Cortical Haemodynamic Response During the Verbal Fluency Task in Patients With Bipolar Disorder and Borderline Personality Disorder: A Preliminary Functional Near‐Infrared Spectroscopy Study,” BMC Psychiatry 21 (2021): 1–10.
M. Mao , G. Chen , K. Feng , et al., “Characteristics of Prefrontal Activity During Emotional and Cognitive Processing in Patients With Bipolar Disorder: A Multi‐Channel Functional Near‐Infrared Spectroscopy Study,” Frontiers in Neuroscience 16 (2022): 946543.
A. Manelis , T. J. Huppert , E. Rodgers , H. A. Swartz , and M. L. Phillips , “The Role of the Right Prefrontal Cortex in Recognition of Facial Emotional Expressions in Depressed Individuals: fNIRS Study,” Journal of Affective Disorders 258 (2019): 151–158.
R. Segar , H. Chhabra , V. S. Sreeraj , et al., “fNIRS Study of Prefrontal Activation During Emotion Recognition–a Potential Endophenotype for Bipolar I Disorder?,” Journal of Affective Disorders 282 (2021): 869–875.
T. Matsubara , K. Matsuo , M. Nakashima , et al., “Prefrontal Activation in Response to Emotional Words in Patients With Bipolar Disorder and Major Depressive Disorder,” NeuroImage 85 (2014): 489–497.
T. Matsubara , C. Chen , M. Hirotsu , et al., “Prefrontal Cortex Activities During Verbal Fluency and Emotional Words Tasks in Major Depressive, Adjustment, and Bipolar Disorders With Depressive States,” Journal of Affective Disorders 316 (2022): 109–117.
Y. H. Alici , H. D. Özgüven , E. Kale , I. Yenihayat , and B. Baskak , “Prefrontal Activity Measured by Functional Near Infrared Spectroscopy During Divergent and Convergent Thinking in Bipolar Disorder,” Archives of Neuropsychiatry 56, no. 2 (2019): 86.
A. Aleksandrowicz , F. Hagenmuller , H. Haker , et al., “Frontal Brain Activity in Individuals at Risk for Schizophrenic Psychosis and Bipolar Disorder During the Emotional Stroop Task–An fNIRS Study,” NeuroImage: Clinical 26 (2020): 102232.
S. Hassel , J. R. C. Almeida , N. Kerr , et al., “Elevated Striatal and Decreased Dorsolateral Prefrontal Cortical Activity in Response to Emotional Stimuli in Euthymic Bipolar Disorder: No Associations With Psychotropic Medication Load,” Bipolar Disorders 10, no. 8 (2008): 916–927.
Y. Ren , G. Cui , X. Zhang , K. Feng , Y. Chenchao , and P. Liu , “The Promising fNIRS: Uncovering the Function of Prefrontal Working Memory Networks Based on Multi‐Cognitive Tasks,” Frontiers in Psychiatry 13 (2022): 985076.
H. Zhu , X. Jie , J. Li , et al., “Decreased Functional Connectivity and Disrupted Neural Network in the Prefrontal Cortex of Affective Disorders: A Resting‐State fNIRS Study,” Journal of Affective Disorders 221 (2017): 132–144.
Y. Monden , H. Dan , M. Nagashima , et al., “Clinically‐Oriented Monitoring of Acute Effects of Methylphenidate on Cerebral Hemodynamics in ADHD Children Using fNIRS,” Clinical Neurophysiology 123, no. 6 (2012): 1147–1157.
S. Jang , J. K. Choi , O. Jooyoung , et al., “Use of Virtual Reality Working Memory Task and Functional Near‐Infrared Spectroscopy to Assess Brain Hemodynamic Responses to Methylphenidate in ADHD Children,” Frontiers in Psychiatry 11 (2021): 564618.
S. Grazioli , E. Rosi , M. Mauri , et al., “Patterns of Response to Methylphenidate Administration in Children With ADHD: A Personalized Medicine Approach Through Clustering Analysis,” Children 8, no. 11 (2021): 1008.
S. B. Wigal , C. M. Polzonetti , A. Stehli , and E. Gratton , “Phase Synchronization of Oxygenation Waves in the Frontal Areas of Children With Attention‐Deficit Hyperactivity Disorder Detected by Optical Diffusion Spectroscopy Correlates With Medication,” Journal of Biomedical Optics 17, no. 12 (2012): 127002–127002.
J. Li , W.‐J. Yan , W. Yan , X.‐X. Tian , and Y.‐W. Zhang , “Synaptosomal‐Associated Protein 25 Gene Polymorphisms Affect Treatment Efficiency of Methylphenidate in Children With Attention‐Deficit Hyperactivity Disorder: An fNIRS Study,” Frontiers in Behavioral Neuroscience 15 (2022): 793643.
A. Ishii‐Takahashi , R. Takizawa , Y. Nishimura , et al., “Neuroimaging‐Aided Prediction of the Effect of Methylphenidate in Children With Attention‐Deficit Hyperactivity Disorder: A Randomized Controlled Trial,” Neuropsychopharmacology 40, no. 12 (2015): 2676–2685.
Y. Nakanishi , T. Ota , J. Iida , et al., “Differential Therapeutic Effects of Atomoxetine and Methylphenidate in Childhood Attention Deficit/Hyperactivity Disorder as Measured by Near‐Infrared Spectroscopy,” Child and Adolescent Psychiatry and Mental Health 11 (2017): 1–11.
N. Dolu , M. Altınkaynak , A. Güven , et al., “Effects of Methylphenidate Treatment in Children With ADHD: A Multimodal EEG/fNIRS Approach,” Psychiatry and Clinical Psychopharmacology 29, no. 3 (2019): 285–292.
A. Araki , M. Ikegami , A. Okayama , et al., “Improved Prefrontal Activity in AD/HD Children Treated With Atomoxetine: A NIRS Study,” Brain and Development 37, no. 1 (2015): 76–87.
T. Ota , J. Iida , Y. Nakanishi , et al., “Increased Prefrontal Hemodynamic Change After Atomoxetine Administration in Pediatric Attention‐Deficit/Hyperactivity Disorder as Measured by Near‐Infrared Spectroscopy,” Psychiatry and Clinical Neurosciences 69, no. 3 (2015): 161–170.
M. Nagashima , Y. Monden , I. Dan , et al., “Neuropharmacological Effect of Atomoxetine on Attention Network in Children With Attention Deficit Hyperactivity Disorder During Oddball Paradigms as Assessed Using Functional Near‐Infrared Spectroscopy,” Neurophotonics 1, no. 2 (2014): 025007–025007.
M. Nagashima , Y. Monden , I. Dan , et al., “Neuropharmacological Effect of Methylphenidate on Attention Network in Children With Attention Deficit Hyperactivity Disorder During Oddball Paradigms as Assessed Using Functional Near‐Infrared Spectroscopy,” Neurophotonics 1, no. 1 (2014): 015001–015001.
D. B. Clemow and C. J. Bushe , “Atomoxetine in Patients With ADHD: A Clinical and Pharmacological Review of the Onset, Trajectory, Duration of Response and Implications for Patients,” Journal of Psychopharmacology 29, no. 12 (2015): 1221–1230.
T. Ikeda , A. Inoue , D. Tanaka , et al., “Visualizing Neuropharmacological Effects of Guanfacine Extended Release in Attention Deficit Hyperactivity Disorder Using Functional Near‐Infrared Spectroscopy,” Frontiers in Neuroergonomics 2 (2021): 657657.
C. Kawai , K. Mori , T. Tanioka , et al., “Usefulness of Near‐Infrared Spectroscopy (NIRS) for Evaluating Drug Effects and Improvements in Medication Adherence in Children With Attention Deficit Hyperactivity Disorder (ADHD),” Journal of Medical Investigation 68, no. 1.2 (2021): 53–58.
F. Makedon , R. Zhang , G. Alexandrakis , C. B. Owen , H. Huang , and A. J. Saykin , “An Interactive User Interface System for Alzheimer's Intervention,” in Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments (2010), 1–5.
B. P. Acevedo , N. Dattatri , J. Le , C. Lappinga , and N. L. Collins , “Cognitive Training With Neurofeedback Using fNIRS Improves Cognitive Function in Older Adults,” International Journal of Environmental Research and Public Health 19, no. 9 (2022): 5531.
K. Sato , A. Ochi , K. Watanabe , and K. Yamada , “Effects of Dance Video Game Training on Cognitive Functions of Community‐Dwelling Older Adults With Mild Cognitive Impairment,” Aging Clinical and Experimental Research 35, no. 5 (2023): 987–994.
P. Eggenberger , M. Wolf , M. Schumann , and E. D. De Bruin , “Exergame and Balance Training Modulate Prefrontal Brain Activity During Walking and Enhance Executive Function in Older Adults,” Frontiers in Aging Neuroscience 8 (2016): 66.
Y.‐Y. Liao , I.‐H. Chen , W.‐C. Hsu , H.‐Y. Tseng , and R.‐Y. Wang , “Effect of Exergaming Versus Combined Exercise on Cognitive Function and Brain Activation in Frail Older Adults: A Randomised Controlled Trial,” Annals of Physical and Rehabilitation Medicine 64, no. 5 (2021): 101492.
Y. Jiulong , X. Zhang , J. Yang , et al., “A Functional Near‐Infrared Spectroscopy Study of the Effects of Video Game‐Based Bilateral Upper Limb Training on Brain Cortical Activation and Functional Connectivity,” Experimental Gerontology 169 (2022): 111962.
P. C. Thumm , I. Maidan , M. Brozgol , et al., “Treadmill Walking Reduces Pre‐Frontal Activation in Patients With Parkinson's Disease,” Gait & Posture 62 (2018): 384–387.
N. R. Conceição , L. T. B. Gobbi , P. Nóbrega‐Sousa , et al., “Aerobic Exercise Combined With Transcranial Direct Current Stimulation Over the Prefrontal Cortex in Parkinson Disease: Effects on Cortical Activity, Gait, and Cognition,” Neurorehabilitation and Neural Repair 35, no. 8 (2021): 717–728.
I. Maidan , R. Hacham , I. Galperin , et al., “Neural Variability in the Prefrontal Cortex as a Reflection of Neural Flexibility and Stability in Patients With Parkinson Disease,” Neurology 98, no. 8 (2022): e839–e847.
D. Orcioli‐Silva , R. Vitório , P. NóbregaSousa , et al., “Cortical Activity Underlying Gait Improvements Achieved With Dopaminergic Medication During Usual Walking and Obstacle Avoidance in Parkinson Disease,” Neurorehabilitation and Neural Repair 35, no. 5 (2021): 406–418.
M. Dagan , T. Herman , H. Bernad‐Elazari , et al., “Dopaminergic Therapy and Prefrontal Activation During Walking in Individuals With Parkinson's Disease: Does the Levodopa Overdose Hypothesis Extend to Gait?,” Journal of Neurology 268 (2021): 658–668.
T. Sharon , I. Kurz , H. Bernad‐Elazari , et al., “Which Obstacle Attributes Place Additional Demands on Higher‐Level Cognitive Function in Patients With Parkinson's Disease?,” Parkinsonism & Related Disorders 78 (2020): 178–183.
F. Nieuwhof , M. F. Reelick , I. Maidan , et al., “Measuring Prefrontal Cortical Activity During Dual Task Walking in Patients With Parkinson's Disease: Feasibility of Using a New Portable fNIRS Device,” Pilot and Feasibility Studies 2 (2016): 1–11.
D. Orcioli‐Silva , R. Vitório , P. NóbregaSousa , et al., “Levodopa Facilitates Prefrontal Cortex Activation During Dual Task Walking in Parkinson Disease,” Neurorehabilitation and Neural Repair 34, no. 7 (2020): 589–599.
I. Maidan , F. Nieuwhof , H. Bernad‐Elazari , et al., “The Role of the Frontal Lobe in Complex Walking Among Patients With Parkinson's Disease and Healthy Older Adults: An fNIRS Study,” Neurorehabilitation and Neural Repair 30, no. 10 (2016): 963–971.
I. Maidan , F. Nieuwhof , H. Bernad‐Elazari , et al., “Evidence for Differential Effects of 2 Forms of Exercise on Prefrontal Plasticity During Walking in Parkinson's Disease,” Neurorehabilitation and Neural Repair 32, no. 3 (2018): 200–208.
S. Stuart and M. Mancini , “Prefrontal Cortical Activation With Open and Closed‐Loop Tactile Cueing When Walking and Turning in Parkinson Disease: A Pilot Study,” Journal of Neurologic Physical Therapy 44, no. 2 (2020): 121–131.
V. Belluscio , S. Stuart , E. Bergamini , G. Vannozzi , and M. Mancini , “The Association Between Prefrontal Cortex Activity and Turning Behavior in People With and Without Freezing of Gait,” Neuroscience 416 (2019): 168–176.
I. Maidan , H. Bernad‐Elazari , N. Giladi , J. M. Hausdorff , and A. Mirelman , “When Is Higher Level Cognitive Control Needed for Locomotor Tasks Among Patients With Parkinson's Disease?,” Brain Topography 30 (2017): 531–538.
I. Maidan , H. Bernad‐Elazari , E. Gazit , N. Giladi , J. M. Hausdorff , and A. Mirelman , “Changes in Oxygenated Hemoglobin Link Freezing of Gait to Frontal Activation in Patients With Parkinson Disease: An fNIRS Study of Transient Motor‐Cognitive Failures,” Journal of Neurology 262 (2015): 899–908.
R. Vitorio , S. Stuart , and M. Mancini , “Executive Control of Walking in People With Parkinson's Disease With Freezing of Gait,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1138–1149.
A. Shumway‐Cook , Motor Control: Translating Research Into Clinical Practice ( Lippincoot Williams & Wilkins , 2007).
P. H. S. Pelicioni , S. R. Lord , Y. Okubo , D. L. Sturnieks , and J. C. Menant , “People With Parkinson's Disease Exhibit Reduced Cognitive and Motor Cortical Activity When Undertaking Complex Stepping Tasks Requiring Inhibitory Control,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1088–1098.
S. S. Kantak , J. W. Stinear , E. R. Buch , and L. G. Cohen , “Rewiring the Brain: Potential Role of the Premotor Cortex in Motor Control, Learning, and Recovery of Function Following Brain Injury,” Neurorehabilitation and Neural Repair 26, no. 3 (2012): 282–292.
C. H. Kasess , C. Windischberger , R. Cunnington , R. Lanzenberger , L. Pezawas , and E. Moser , “The Suppressive Influence of SMA on M1 in Motor Imagery Revealed by fMRI and Dynamic Causal Modeling,” NeuroImage 40, no. 2 (2008): 828–837.
J.‐H. Jang , S. Park , J. An , et al., “Gait Disturbance Improvement and Cerebral Cortex Rearrangement by Acupuncture in Parkinson's Disease: A Pilot Assessor‐Blinded, Randomized, Controlled, Parallel‐Group Trial,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1111–1123.
O. Klempir , R. Krupicka , J. Mehnert , et al., “Reshaping Cortical Activity With Subthalamic Stimulation in Parkinson's Disease During Finger Tapping and Gait Mapped by Near Infrared Spectroscopy,” Journal of Applied Biomedicine 17 (2019): 157–166.
K. M. Arun , K. A. Smitha , P. N. Sylaja , and C. Kesavadas , “Identifying Resting‐State Functional Connectivity Changes in the Motor Cortex Using fNIRS During Recovery From Stroke,” Brain Topography 33, no. 6 (2020): 710–719.
Y. Song , Z. F. Sun , W. Z. Sun , et al., “Neuroplasticity Following Stroke From a Functional Laterality Perspective: A fNIRS Study,” Brain Topography 36, no. 3 (2023): 283–293.
D. Wang , J. Wang , H. Zhao , et al., “The Relationship Between the Prefrontal Cortex and Limb Motor Function in Stroke: A Study Based on Resting‐State Functional Near‐Infrared Spectroscopy,” Brain Research 1805 (2023): 148269.
C. Huo , X. Gongcheng , A. Sun , et al., “Cortical Response Induced by Task‐Oriented Training of the Upper Limb in Subacute Stroke Patients as Assessed by Functional Near‐Infrared Spectroscopy,” Journal of Biophotonics 16, no. 3 (2023): e202200228.
C. Huo , X. Gongcheng , H. Xie , et al., “Effect of High‐Frequency rTMS Combined With Bilateral Arm Training on Brain Functional Network in Patients With Chronic Stroke: An fNIRS Study,” Brain Research 1809 (2023): 148357.
D. R. Leff , F. Orihuela‐Espina , C. E. Elwell , et al., “Assessment of the Cerebral Cortex During Motor Task Behaviours in Adults: A Systematic Review of Functional Near Infrared Spectroscopy (fNIRS) Studies,” NeuroImage 54, no. 4 (2011): 2922–2936.
X. Gongcheng , C. Huo , J. Yin , et al., “Effective Brain Network Analysis in Unilateral and Bilateral Upper Limb Exercise Training in Subjects With Stroke,” Medical Physics 49, no. 5 (2022): 3333–3346.
L. Kuan , X. Gongcheng , W. Li , et al., “Frequency‐Specific Functional Connectivity Related to the Rehabilitation Task of Stroke Patients,” Medical Physics 46, no. 4 (2019): 1545–1560.
S. B. Lim and J. J. Eng , “Increased Sensorimotor Cortex Activation With Decreased Motor Performance During Functional Upper Extremity Tasks Poststroke,” Journal of Neurologic Physical Therapy 43, no. 3 (2019): 141–150.
S. B. Lim , C.‐l. Yang , S. Peters , T. Liu‐Ambrose , L. A. Boyd , and J. J. Eng , “Phase‐Dependent Brain Activation of the Frontal and Parietal Regions During Walking After Stroke‐An fNIRS Study,” Frontiers in Neurology 13 (2022): 904722.
A. K. Matarasso , J. D. Rieke , M. Keith White , M. Yusufali , and J. J. Daly , “Combined Real‐Time fMRI and Real Time fNIRS Brain Computer Interface (BCI): Training of Volitional Wrist Extension After Stroke, a Case Series Pilot Study,” PLoS One 16, no. 5 (2021): e0250431.
C. Coetsee and E. Terblanche , “Cerebral Oxygenation During Cortical Activation: The Differential Influence of Three Exercise Training Modalities. A Randomized Controlled Trial,” European Journal of Applied Physiology 117 (2017): 1617–1627.
H. Xie , X. Li , W. Huang , et al., “Effects of Robot‐Assisted Task‐Oriented Upper Limb Motor Training on Neuroplasticity in Stroke Patients With Different Degrees of Motor Dysfunction: A Neuroimaging Motor Evaluation Index,” Frontiers in Neuroscience 16 (2022): 957972.
J. Zhang , W. Huang , Z. Chen , H. Jiang , S. Minzhi , and C. Wang , “Effect of Auricular Acupuncture on Neuroplasticity of Stroke Patients With Motor Dysfunction: A fNIRS Study,” Neuroscience Letters 812 (2023): 137398.
D. Perpetuini , E. F. Russo , D. Cardone , et al., “Identification of Functional Cortical Plasticity in Children With Cerebral Palsy Associated to Robotic‐Assisted Gait Training: An fNIRS Study,” Journal of Clinical Medicine 11, no. 22 (2022): 6790.
V. Kaiser , G. Bauernfeind , A. Kreilinger , et al., “Cortical Effects of User Training in a Motor Imagery Based Brain–Computer Interface Measured by fNIRS and EEG,” NeuroImage 85 (2014): 432–444.
L. Ferreri , E. Bigand , P. Bard , and A. Bugaiska , “The Influence of Music on Prefrontal Cortex During Episodic Encoding and Retrieval of Verbal Information: A Multichannel fNIRS Study,” Behavioural Neurology 2015, no. 1 (2015): 707625.
S. B. Perlman , T. J. Huppert , and B. Luna , “Functional Near‐Infrared Spectroscopy Evidence for Development of Prefrontal Engagement in Working Memory in Early Through Middle Childhood,” Cerebral Cortex 26, no. 6 (2016): 2790–2799.
C. Artemenko , A. Coldea , M. Soltanlou , T. Dresler , H.‐C. Nuerk , and A.‐C. Ehlis , “The Neural Circuits of Number and Letter Copying: An fNIRS Study,” Experimental Brain Research 236 (2018): 1129–1138.
C. Artemenko , M. Soltanlou , T. Dresler , A.‐C. Ehlis , and H.‐C. Nuerk , “The Neural Correlates of Arithmetic Difficulty Depend on Mathematical Ability: Evidence From Combined fNIRS and ERP,” Brain Structure and Function 223 (2018): 2561–2574.
H. Zhao , X. Li , V. Karolis , Y. Feng , H. Niu , and B. Butterworth , “Arithmetic Learning Modifies the Functional Connectivity of the Fronto‐Parietal Network,” Cortex 111 (2019): 51–62.
H. Zhao , T. Zhang , T. Cheng , et al., “Neurocomputational Mechanisms of Young Children's Observational Learning of Delayed Gratification,” Cerebral Cortex 33, no. 10 (2023): 6063–6076.
A. Howell‐Munson , D. S. Unal , E. Walker , C. Arrington , and E. Solovey , “Preliminary Steps Towards Detection of Proactive and Reactive Control States During Learning With fNIRS Brain Signals,” in Proceedings of the First International Workshop on Multimodal Artificial Intelligence in Education (MAIED 2021) (2021).
Y. He , M.‐Y. Wang , D. Li , and Z. Yuan , “Optical Mapping of Brain Activation During the English to Chinese and Chinese to English Sight Translation,” Biomedical Optics Express 8, no. 12 (2017): 5399–5411.
N. Takeuchi , T. Mori , Y. Suzukamo , and S.‐I. Izumi , “Activity of Prefrontal Cortex in Teachers and Students During Teaching of an Insight Problem,” Mind, Brain, and Education 13, no. 3 (2019): 167–175.
L. Shi , L. Dong , W. Zhao , and D. Tan , “Improving Middle School Students’ Geometry Problem Solving Ability Through Hands‐On Experience: An fNIRS Study,” Frontiers in Psychology 14 (2023): 1126047.
A. J. Kersey and L. L. Emberson , “Tracing Trajectories of Audio‐Visual Learning in the Infant Brain,” Developmental Science 20, no. 6 (2017): e12480.
A. L. Ferry , A. Fló , P. Brusini , et al., “On the Edge of Language Acquisition: Inherent Constraints on Encoding Multisyllabic Sequences in the Neonate Brain,” Developmental Science 19, no. 3 (2016): 488–503.
F. Homae , H. Watanabe , T. Nakano , and G. Taga , “Large‐Scale Brain Networks Underlying Language Acquisition in Early Infancy,” Frontiers in Psychology 2 (2011): 93.
F. Homae , H. Watanabe , and G. Taga , “The Neural Substrates of Infant Speech Perception,” Language Learning 64, no. s2 (2014): 6–26.
T. Sakurada , M. Hirai , and E. Watanabe , “Individual Optimal Attentional Strategy During Implicit Motor Learning Boosts Frontoparietal Neural Processing Efficiency: A Functional Near‐Infrared Spectroscopy Study,” Brain and Behavior 9, no. 1 (2019): e01183.
O. Seidel , D. Carius , R. Kenville , and P. Ragert , “Motor Learning in a Complex Balance Task and Associated Neuroplasticity: A Comparison Between Endurance Athletes and Nonathletes,” Journal of Neurophysiology 118, no. 3 (2017): 1849–1860.
S. Sharmin , E. Bakhshipour , B. Kiafar , et al., “fNIRS Analysis of Interaction Techniques in Touchscreen‐Based Educational Gaming,” arXiv Preprint arXiv:2405.08906 (2024).
P. A. Shewokis , F. U. Shariff , Y. Liu , H. Ayaz , A. Castellanos , and D. S. Lind , “Acquisition, Retention and Transfer of Simulated Laparoscopic Tasks Using fNIR and a Contextual Interference Paradigm,” American Journal of Surgery 213, no. 2 (2017): 336–345.
M. Soltanlou , M. A. Sitnikova , H.‐C. Nuerk , and T. Dresler , “Applications of Functional Near‐Infrared Spectroscopy (fNIRS) in Studying Cognitive Development: The Case of Mathematics and Language,” Frontiers in Psychology 9 (2018): 277.
J. Kerr , C. Molloy , P. Reddy , P. A. Shewokis , and K. Izzetoglu , “Individual Differences in fNIRS Measures of Cognitive Workload During a UAS Mission,” in International Conference on Human‐Computer Interaction (Springer, 2021), 49–62.
M. P. Uysal , “Evaluation of Learning Environments for Object‐Oriented Programming: Measuring Cognitive Load With a Novel Measurement Technique,” Interactive Learning Environments 24, no. 7 (2016): 1590–1609.
P. Caçola , N. Getchell , D. Srinivasan , G. Alexandrakis , and H. Liu , “Cortical Activity in Fine‐Motor Tasks in Children With Developmental Coordination Disorder: A Preliminary fNIRS Study,” International Journal of Developmental Neuroscience 65 (2018): 83–90.
R. Prôa , J. Balardin , D. D. de Faria , et al., “Motor Cortex Activation During Writing in Focal Upper‐Limb Dystonia: An fNIRS Study,” Neurorehabilitation and Neural Repair 35, no. 8 (2021): 729–737.
X. Zhong , C. Wang , X. Mingchao , X. Yuan , and C. Jiang , “Physical Training Improves Inhibitory Control in Children Aged 7–12 Years: An fNIRS Study,” Behavioural Brain Research 463 (2024): 114902.
X. Zhong , Y. Dai , X. Mingchao , and C. Jiang , “Volleyball Training Improves Working Memory in Children Aged 7 to 12 Years Old: An fNIRS Study,” Cerebral Cortex 34, no. 7 (2024).
J. Zhang , Y. Wang , C. Leong , Y. Mao , and Z. Yuan , “Bridging Stories and Science: An fNIRS‐Based Hyperscanning Investigation Into Child Learning in STEM,” NeuroImage 285 (2024): 120486.
Z. Pi , H. Chai , L. Li , X. Zhang , and X. Li , “High Task Motivation Learners Co‐Viewing Video Lectures Facilitates Learning,” Journal of Computer Assisted Learning 40, no. 2 (2024): 588–599.
X. Lei and P.‐L. P. Rau , “Emotional Responses to Performance Feedback in an Educational Game During Cooperation and Competition With a Robot: Evidence From fNIRS,” Computers in Human Behavior 138 (2023): 107496.
E. D. Pinheiro , J. R. Sato , R. da Silva , S. Junior , C. Barreto , and A. Y. A. Oku , “Eye‐Tracker and fNIRS: Using Neuroscientific Tools to Assess the Learning Experience During Children's Educational Robotics Activities,” Trends in Neuroscience and Education (2024): 100234.
C. Barreto and M. Soltanlou , “Functional Near‐Infrared Spectroscopy as a Tool to Assess Brain Activity in Educational Settings: An Introduction for Educational Researchers,” South African Journal of Childhood Education 12, no. 1 (2022): 1138.
W. Trost , C. Trevor , N. Fernandez , F. Steiner , and S. Frühholz , “Live Music Stimulates the Affective Brain and Emotionally Entrains Listeners in Real Time,” Proceedings of the National Academy of Sciences 121, no. 10 (2024): e2316306121.
T. Nguyen , S. Ahn , H. Jang , S. C. Jun , and J. G. Kim , “Utilization of a Combined EEG/NIRS System to Predict Driver Drowsiness,” Scientific Reports 7, no. 1 (2017): 43933.
C.‐H. Chuang , Z. Cao , J.‐T. King , W. Bing‐Syun , Y.‐K. Wang , and C.‐T. Lin , “Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving,” Frontiers in Neuroscience 12 (2018): 181.
C.‐T. Lin , J.‐T. King , C.‐H. Chuang , et al., “Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements,” International Journal of Neural Systems 30, no. 01 (2020): 1950018.
A. J. Karran , T. Demazure , P.‐M. Leger , et al., “Toward a Hybrid Passive BCI for the Modulation of Sustained Attention Using EEG and fNIRS,” Frontiers in Human Neuroscience 13 (2019): 393.
C. Artemenko , M. Soltanlou , S. M. Bieck , A.‐C. Ehlis , T. Dresler , and H.‐C. Nuerk , “Individual Differences in Math Ability Determine Neurocognitive Processing of Arithmetic Complexity: A Combined fNIRS‐EEG Study,” Frontiers in Human Neuroscience 13 (2019): 227.
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 and Behavior 7, no. 5 (2017): e00686.
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),” Frontiers in Human Neuroscience 12 (2018): 395.
D. Rosenbaum , E. J. Leehr , A. Kroczek , et al., “Neuronal Correlates of Spider Phobia in a Combined fNIRS‐EEG Study,” Scientific Reports 10, no. 1 (2020): 12597.
Y. Sun , H. Ayaz , and A. N. Akansu , “Multimodal Affective State Assessment Using fNIRS+ EEG and Spontaneous Facial Expression,” Brain Sciences 10, no. 2 (2020): 85.
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 14, no. 6 (2017): 066003.
H. Hamid , N. Naseer , H. Nazeer , M. J. Khan , R. A. Khan , and U. S. Khan , “Analyzing Classification Performance of fNIRS‐BCI for Gait Rehabilitation Using Deep Neural Networks,” Sensors 22, no. 5 (2022): 1932.
Z. Wang , Y. Zhou , L. Chen , et al., “BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, no. 4 (2019): 780–787.
U. Asgher , M. J. Khan , M. H. A. Nizami , et al., “Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near‐Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI),” Frontiers in Neurorobotics 15 (2021): 605751.
A. Berger , F. Horst , S. Müller , F. Steinberg , and M. Doppelmayr , “Current State and Future Prospects of EEG and fNIRS in Robot‐Assisted Gait Rehabilitation: A Brief Review,” Frontiers in Human Neuroscience 13 (2019): 172.
E. Bourel‐Ponchel , M. Mahmoudzadeh , A. Delignières , P. Berquin , and F. Wallois , “Non‐Invasive, Multimodal Analysis of Cortical Activity, Blood Volume and Neurovascular Coupling in Infantile Spasms Using EEG‐fNIRS Monitoring,” NeuroImage: Clinical 15 (2017): 359–366.
A. Güven , M. Altınkaynak , N. Dolu , et al., “Combining Functional Near‐Infrared Spectroscopy and EEG Measurements for the Diagnosis of Attention‐Deficit Hyperactivity Disorder,” Neural Computing and Applications 32 (2020): 8367–8380.
E. E. Rizki , M. Uga , I. Dan , et al., “Determination of Epileptic Focus Side in Mesial Temporal Lobe Epilepsy Using Long‐Term Noninvasive fNIRS/EEG Monitoring for Presurgical Evaluation,” Neurophotonics 2, no. 2 (2015): 025003–025003.
D. Safi , R. Béland , D. K. Nguyen , et al., “Recruitment of the Left Precentral Gyrus in Reading Epilepsy: A Multimodal Neuroimaging Study,” Epilepsy & Behavior Case Reports 5 (2016): 19–22.
M. Abtahi , S. B. Borgheai , R. Jafari , et al., “Merging fNIRS‐EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinsons Disease,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 6 (2020): 1246–1253.
R. Li , S. Li , J. Roh , C. Wang , and Y. Zhang , “Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment: An Exploratory Study,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1099–1110.
J. Liang , Y. Song , A. N. Belkacem , et al., “Prediction of Balance Function for Stroke Based on EEG and fNIRS Features During Ankle Dorsiflexion,” Frontiers in Neuroscience 16 (2022): 968928.
S. Cho , W. K. Chang , J. Park , et al., “Feasibility Study of Immersive Virtual Prism Adaptation Therapy With Depth‐Sensing Camera Using Functional Near‐Infrared Spectroscopy in Healthy Adults,” Scientific Reports 12, no. 1 (2022): 767.
D. M. L. de Boer , F. Namdar , M. Lambers , and A. Cleeremans , “LIVE‐Streaming 3D Images: A Neuroscience Approach to Full‐Body Illusions,” Behavior Research Methods (2022): 1–12.
D. Zapała , P. Augustynowicz , and M. Tokovarov , “Recognition of Attentional States in VR Environment: An fNIRS Study,” Sensors 22, no. 9 (2022): 3133.
D. Dong , L. K. F. Wong , and Z. Luo , “Assess BA10 Activity in Slide‐Based and Immersive Virtual Reality Prospective Memory Task Using Functional Near‐Infrared Spectroscopy (fNIRS),” Applied Neuropsychology: Adult (2019).
F. Putze , C. Herff , C. Tremmel , T. Schultz , and D. J. Krusienski , “Decoding Mental Workload in Virtual Environments: A fNIRS Study Using an Immersive n‐Back Task,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2019 (IEEE), 3103–3106.
B. Seraglia , L. Gamberini , K. Priftis , P. Scatturin , M. Martinelli , and S. Cutini , “An Exploratory fNIRS Study With Immersive Virtual Reality: A New Method for Technical Implementation,” Frontiers in Human Neuroscience 5 (2011): 176.
R. Ge , Z. Wang , X. Yuan , et al., “The Effects of Two Game Interaction Modes on Cortical Activation in Subjects of Different Ages: A Functional Near‐Infrared Spectroscopy Study,” IEEE Access 9 (2021): 11405–11415.
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 11 (2017): 167.
J. Scheunemann , A. Unni , K. Ihme , M. Jipp , and J. W. Rieger , “Demonstrating Brain‐Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near‐Infrared Spectroscopy,” Frontiers in Human Neuroscience 12 (2019): 542.
R. Stojan , M. Mack , O. Bock , and C. Voelcker‐Rehage , “Inefficient Frontal and Parietal Brain Activation During Dual‐Task Walking in a Virtual Environment in Older Adults,” NeuroImage 273 (2023): 120070.
C. S. L. Cheah , S. P. Kaputsos , V. Mandalapu , et al., “Neurophysiological Variations in Food Decision‐Making Within Virtual and Real Environments,” in 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), vol. 2019 (IEEE), 1–4.
S. M. Max , P. A. Schroeder , J. Blechert , K. E. Giel , A.‐C. Ehlis , and C. Plewnia , “Mind the Food: Behavioural Characteristics and Imaging Signatures of the Specific Handling of Food Objects,” Brain Structure and Function 226 (2021): 1169–1183.
X. Wang , Y. He , L. Kelong , C. Deng , X. Qiao , and N. Hao , “How Does the Embodied Metaphor Affect Creative Thinking?,” NeuroImage 202 (2019): 116114.
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 (2018), 309–321.
S.‐G. Kuai , Q. Liang , Y.‐Y. He , and W. Hui‐Ning , “Higher Anxiety Rating Does Not Mean Poor Speech Performance: Dissociation of the Neural Mechanisms of Anticipation and Delivery of Public Speaking,” Brain Imaging and Behavior 15, no. 4 (2021): 1934–1943.
L. A. de With , N. Thammasan , and M. Poel , “Detecting Fear of Heights Response to a Virtual Reality Environment Using Functional Near‐Infrared Spectroscopy,” Frontiers in Computer Science 3 (2022): 652550.
G. Kaimal , K. Carroll‐Haskins , Y. Topoglu , A. Ramakrishnan , A. Arslanbek , and H. Ayaz , “Exploratory fNIRS Assessment of Differences in Activation in Virtual Reality Visual Self‐Expression Including With a Fragrance Stimulus,” Art Therapy 39, no. 3 (2022): 128–137.
M. Hinderaker , B. Sylcott , K. Williams , and C.‐C. Lin , “Aging Affects the Ability to Process the Optic Flow Stimulations: A Functional Near‐Infrared Spectrometry Study,” Journal of Motor Behavior 52, no. 4 (2020): 466–473.
J. Taguchi , A. Takami , and M. Makino , “Changes in Cerebral Blood Flow Before, During, and After Forward and Backward Walking in Stroke Patients Trained Using Virtual Reality Walking Videos With Deliberately Induced Inaccuracies in Walking Speed Estimations,” Journal of Physical Therapy Science 34, no. 10 (2022): 668–672.
K. M. T. Pöhlmann , H. A. Maior , J. Föcker , et al., “I Think I Don’t Feel Sick: Exploring the Relationship Between Cognitive Demand and Cybersickness in Virtual Reality Using fNIRS,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–16.
P. Kaimara , A. Oikonomou , and I. Deliyannis , “Could Virtual Reality Applications Pose Real Risks to Children and Adolescents? A Systematic Review of Ethical Issues and Concerns,” Virtual Reality 26, no. 2 (2022): 697–735.
L. Jones and P. Ekkekakis , “Affect and Prefrontal Hemodynamics During Exercise Under Immersive Audiovisual Stimulation: Improving the Experience of Exercise for Overweight Adults,” Journal of Sport and Health Science 8, no. 4 (2019): 325–338.
L. Jones and J. Wheat , “Green and Pleasant Lands: The Affective and Cerebral Hemodynamic Effects of Presence in Virtual Environments During Exercise,” Perceptual and Motor Skills 130, no. 2 (2023): 826–843.
M. Tian , Y. Cai , and J. Zhang , “The Impact of Virtual Reality‐Based Products on Mild Cognitive Impairment Senior Subjects: An Experimental Study Using Multiple Sources of Data,” Applied Sciences 13, no. 4 (2023): 2372.
S. Y. Lee , J. Y. Cha , J. W. Yoo , et al., “Effect of the Application of Virtual Reality on Pain Reduction and Cerebral Blood Flow in Robot‐Assisted Gait Training in Burn Patients,” Journal of Clinical Medicine 11, no. 13 (2022): 3762.
X.‐S. Hu , K. Beard , M. C. Sherbel , et al., “Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near‐Infrared Spectroscopy Study,” Journal of Medical Internet Research 23, no. 10 (2021): e27298.
T. Furubayashi , H. Mochizuki , Y. Terao , et al., “Cortical Hemoglobin Concentration Changes Underneath the Coil After Single‐Pulse Transcranial Magnetic Stimulation: A Near‐Infrared Spectroscopy Study,” Journal of Neurophysiology 109, no. 6 (2013): 1626–1637.
F. Vernieri , C. Altamura , P. Palazzo , et al., “1‐Hz Repetitive Transcranial Magnetic Stimulation Increases Cerebral Vasomotor Reactivity: A Possible Autonomic Nervous System Modulation,” Brain Stimulation 7, no. 2 (2014): 281–286.
E. Park , M. J. Kang , A. Lee , W. H. Chang , Y.‐I. Shin , and Y.‐H. Kim , “Real‐Time Measurement of Cerebral Blood Flow During and After Repetitive Transcranial Magnetic Stimulation: A Near‐Infrared Spectroscopy Study,” Neuroscience Letters 653 (2017): 78–83.
A. Curtin , J. Sun , H. Ayaz , et al., “Evaluation of Evoked Responses to Pulse‐Matched High Frequency and Intermittent Theta Burst Transcranial Magnetic Stimulation Using Simultaneous Functional Near‐Infrared Spectroscopy,” Neurophotonics 4, no. 4 (2017): 041405–041405.
R. Solianik , A. Satas , D. Mickeviciene , et al., “Task‐Relevant Cognitive and Motor Functions Are Prioritized During Prolonged Speed–Accuracy Motor Task Performance,” Experimental Brain Research 236 (2018): 1665–1678.
T. Shinba , N. Kariya , S. Matsuda , H. Matsuda , and Y. Obara , “Increase of Frontal Cerebral Blood Volume During Transcranial Magnetic Stimulation in Depression Is Related to Treatment Effectiveness: A Pilot Study With Near‐Infrared Spectroscopy,” Psychiatry and Clinical Neurosciences 72, no. 8 (2018): 602–610.
K. Yamanaka , H. Tomioka , S. Kawasaki , et al., “Effect of Parietal Transcranial Magnetic Stimulation on Spatial Working Memory in Healthy Elderly Persons‐Comparison of Near Infrared Spectroscopy for Young and Elderly,” PLoS One 9, no. 7 (2014): e102306.
A. Curtin , H. Ayaz , Y. Tang , J. Sun , J. Wang , and S. Tong , “Enhancing Neural Efficiency of Cognitive Processing Speed via Training and Neurostimulation: An fNIRS and TMS Study,” NeuroImage 198 (2019): 73–82.
T. Hara , M. Abo , K. Kakita , Y. Mori , M. Yoshida , and N. Sasaki , “The Effect of Selective Transcranial Magnetic Stimulation With Functional Near‐Infrared Spectroscopy and Intensive Speech Therapy on Individuals With Post‐Stroke Aphasia,” European Neurology 77, no. 3‐4 (2017): 186–194.
N. Urushidani , S. Kinoshita , T. Okamoto , H. Tamashiro , and M. Abo , “Low‐Frequency rTMS and Intensive Occupational Therapy Improve Upper Limb Motor Function and Cortical Reorganization Assessed by Functional Near‐Infrared Spectroscopy in a Subacute Stroke Patient,” Case Reports in Neurology 10, no. 2 (2018): 223–231.
H. Tamashiro , S. Kinoshita , T. Okamoto , N. Urushidani , and M. Abo , “Effect of Baseline Brain Activity on Response to Low‐Frequency rTMS/Intensive Occupational Therapy in Poststroke Patients With Upper Limb Hemiparesis: A Near‐Infrared Spectroscopy Study,” International Journal of Neuroscience 129, no. 4 (2019): 337–343.
S. V. Tupak , T. Dresler , M. Badewien , et al., “Inhibitory Transcranial Magnetic Theta Burst Stimulation Attenuates Prefrontal Cortex Oxygenation,” Human Brain Mapping 34, no. 1 (2013): 150–157.
A. Guhn , T. Dresler , M. Andreatta , et al., “Medial Prefrontal Cortex Stimulation Modulates the Processing of Conditioned Fear,” Frontiers in Behavioral Neuroscience 8 (2014): 44.
M. J. Maier , D. Rosenbaum , F. B. Haeussinger , et al., “Forgiveness and Cognitive Control–Provoking Revenge via Theta‐Burst‐Stimulation of the DLPFC,” NeuroImage 183 (2018): 769–775.
H. Guo , X. He , M. Liu , Z. Zhang , H. Zhenhua , and J. Tian , “Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography,” IEEE Transactions on Medical Imaging 36, no. 6 (2017): 1337–1346.
Y. Yamashita , A. Maki , and H. Koizumi , “Wavelength Dependence of the Precision of Noninvasive Optical Measurement of Oxy‐, Deoxy‐, and Total‐Hemoglobin Concentration,” Medical Physics 28, no. 6 (2001): 1108–1114.
P. Taroni , D. Comelli , A. Pifferi , A. Torricelli , and R. Cubeddu , “Absorption of Collagen: Effects on the Estimate of Breast Composition and Related Diagnostic Implications,” Journal of Biomedical Optics 12, no. 1 (2007): 014021–014021.
K. R. Ward , R. R. Ivatury , R. W. Barbee , et al., “Near Infrared Spectroscopy for Evaluation of the Trauma Patient: A Technology Review,” Resuscitation 68, no. 1 (2006): 27–44.
F. Scholkmann , S. Kleiser , A. J. Metz , et al., “A Review on Continuous Wave Functional Near‐Infrared Spectroscopy and Imaging Instrumentation and Methodology,” NeuroImage 85 (2014): 6–27.
X. Song , X. Chen , Z. Wang , X. An , and D. Ming , “MBLL With Weighted Partial Path Length for Multi‐Distance Probe Configuration of fNIRS,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2019 (IEEE), 4766–4769.
X. Chen , X. Song , L. Chen , X. An , and D. Ming , “Performance Improvement for Detecting Brain Function Using fNIRS: A Multi‐Distance Probe Configuration With PPL Method,” Frontiers in Human Neuroscience 14 (2020): 569508.
A. M. Chiarelli , D. Perpetuini , C. Filippini , D. Cardone , and A. Merla , “Differential Pathlength Factor in Continuous Wave Functional Near‐Infrared Spectroscopy: Reducing Hemoglobin's Cross Talk in High‐Density Recordings,” Neurophotonics 6, no. 3 (2019): 035005–035005.
R. Huang and K.‐S. Hong , “Multi‐Channel‐Based Differential Pathlength Factor Estimation for Continuous‐Wave fNIRS,” IEEE Access 9 (2021): 37386–37396.
O. Seidel , D. Carius , J. Roediger , S. Rumpf , and P. Ragert , “Changes in Neurovascular Coupling During Cycling Exercise Measured by Multi‐Distance fNIRS: A Comparison Between Endurance Athletes and Physically Active Controls,” Experimental Brain Research 237 (2019): 2957–2972.
P. Bouguer , “Essai D'optique Sur la Gradation de la lumière,” in Essai D’optique Sur la Gradation de la lumière, ed. C. Jombert (1729).
A. Beer and P. Beer , “Determination of the Absorption of Red Light in Colored Liquids,” Annalen der Physik Und Chemie 86, no. 5 (1852): 78–88.
S. Perrey , “Non‐Invasive NIR Spectroscopy of Human Brain Function During Exercise,” Methods 45, no. 4 (2008): 289–299.
Y. Hoshi , “Functional Near‐Infrared Optical Imaging: Utility and Limitations in Human Brain Mapping,” Psychophysiology 40, no. 4 (2003): 511–520.
Z. Einalou , K. Maghooli , S. K. Setarehdan , and A. Akin , “Graph Theoretical Approach to Functional Connectivity in Prefrontal Cortex via fNIRS,” Neurophotonics 4, no. 4 (2017): 041407–041407.
M. A. Yücel , A. V. Lühmann , F. Scholkmann , et al., “Best Practices for fNIRS Publications,” Neurophotonics 8, no. 1 (2021): 012101.
D. T. Delpy , M. Cope , P. van der Zee , S. Arridge , S. Wray , and J. S. Wyatt , “Estimation of Optical Pathlength Through Tissue From Direct Time of Flight Measurement,” Physics in Medicine & Biology 33, no. 12 (1988): 1433.
H. Obrig and A. Villringer , “Beyond the Visible—Imaging the Human Brain With Light,” Journal of Cerebral Blood Flow & Metabolism 23, no. 1 (2003): 1–18.
S. Fantini and A. Sassaroli , “Frequency‐Domain Techniques for Cerebral and Functional Near‐Infrared Spectroscopy,” Frontiers in Neuroscience 14 (2020): 300.
M. Izzetoglu and R. Holtzer , “Effects of Processing Methods on fNIRS Signals Assessed During Active Walking Tasks in Older Adults,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 3 (2020): 699–709.
E. Gratton , W. W. Mantulin , M. J. v. Ven , J. B. Fishkin , M. B. Maris , and B. Chance , “A Novel Approach to Laser Tomography,” Bioimaging 1, no. 1 (1993): 40–46.
A. Duncan , J. H. Meek , M. Clemence , et al., “Optical Pathlength Measurements on Adult Head, Calf and Forearm and the Head of the Newborn Infant Using Phase Resolved Optical Spectroscopy,” Physics in Medicine & Biology 40, no. 2 (1995): 295.
R. A. De Blasi , S. Fantini , M. A. Franceschini , M. Ferrari , and E. Gratton , “Cerebral and Muscle Oxygen Saturation Measurement by Frequency‐Domain Near‐Infra‐Red Spectrometer,” Medical and Biological Engineering and Computing 33 (1995): 228–230.
L. Wj , “Near‐Infrared Measurement of Cerebral Oxygenation: Collelation With Electroencephalographic Ischemia During Ventricular Fibrillation,” Anesthesiology 83 (1995): 738–746.
C. W. Davies , “Quantification of Optical Parameters Using Frequency Domain Functional Near‐Infrared Spectroscopy (FD‐fNIRS),” (Master's Thesis, Wright State University), 2019.
I. J. Bigio and S. Fantini , Quantitative Biomedical Optics: Theory, Methods, and Applications (Cambridge University Press, 2016).
J. B. Fishkin and E. Gratton , “Propagation of Photon‐Density Waves in Strongly Scattering Media Containing an Absorbing Semi‐Infinite Plane Bounded by a Straight Edge,” Journal of the Optical Society of America A 10, no. 1 (1993): 127–140.
B. J. Tromberg , L. O. Svaasand , T.‐T. Tsay , and R. C. Haskell , “Properties of Photon Density Waves in Multiple‐Scattering Media,” Applied Optics 32, no. 4 (1993): 607–616.
G. Blaney , A. Sassaroli , T. Pham , C. Fernandez , and S. Fantini , “Phase Dual‐Slopes in Frequency‐Domain Near‐Infrared Spectroscopy for Enhanced Sensitivity to Brain Tissue: First Applications to Human Subjects,” Journal of Biophotonics 13, no. 1 (2020): e201960018.
A. Sassaroli and S. Fantini , “Comment on the Modified Beer–Lambert Law for Scattering Media,” Physics in Medicine & Biology 49, no. 14 (2004): N255.
A. Sassaroli , G. Blaney , and S. Fantini , “Dual‐Slope Method for Enhanced Depth Sensitivity in Diffuse Optical Spectroscopy,” Journal of the Optical Society of America A 36, no. 10 (2019): 1743–1761.
E. M. Sevick , B. Chance , J. Leigh , S. Nioka , and M. Maris , “Quantitation of Time‐and Frequency‐Resolved Optical Spectra for the Determination of Tissue Oxygenation,” Analytical Biochemistry 195, no. 2 (1991): 330–351.
G. Blaney , C. Fernandez , A. Sassaroli , and S. Fantini , “Dual‐Slope Imaging of Cerebral Hemodynamics With Frequency‐Domain Near‐Infrared Spectroscopy,” Neurophotonics 10, no. 1 (2023): 013508–013508.
R. C. Haskell , L. O. Svaasand , T.‐T. Tsay , T.‐C. Feng , M. S. McAdams , and B. J. Tromberg , “Boundary Conditions for the Diffusion Equation in Radiative Transfer,” Journal of the Optical Society of America A 11, no. 10 (1994): 2727–2741.
T. Kerz , C. Beyer , A. Huthmann , et al., “Continuous‐Wave Near‐Infrared Spectroscopy Is Not Related to Brain Tissue Oxygen Tension,” Journal of Clinical Monitoring and Computing 30 (2016): 641–647.
D. J. Davies , M. Clancy , D. Lighter , et al., “Frequency‐Domain vs Continuous‐Wave Near‐Infrared Spectroscopy Devices: A Comparison of Clinically Viable Monitors in Controlled Hypoxia,” Journal of Clinical Monitoring and Computing 31 (2017): 967–974.
A. Torricelli , D. Contini , A. Pifferi , et al., “Time Domain Functional NIRS Imaging for Human Brain Mapping,” NeuroImage 85 (2014): 28–50.
R. Re , I. Pirovano , D. Contini , et al., “Reliable Fast (20 Hz) Acquisition Rate by a TD fNIRS Device: Brain Resting‐State Oscillation Studies,” Sensors 23, no. 1 (2022): 196.
A. Ortega‐Martinez , D.’. J. Rogers , J. Anderson , et al., “How Much Do Time‐Domain Functional Near‐Infrared Spectroscopy (fNIRS) Moments Improve Estimation of Brain Activity Over Traditional fNIRS?,” Neurophotonics 10, no. 1 (2023): 013504–013504.
M. S. Patterson , B. Chance , and B. C. Wilson , “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical Properties,” Applied Optics 28, no. 12 (1989): 2331–2336.
Y. Yamada , H. Suzuki , and Y. Yamashita , “Time‐Domain Near‐Infrared Spectroscopy and Imaging: A Review,” Applied Sciences 9, no. 6 (2019): 1127.
H. Wabnitz , D. Contini , L. Spinelli , A. Torricelli , and A. Liebert , “Depth‐Selective Data Analysis for Time‐Domain fNIRS: Moments vs. Time Windows,” Biomedical Optics Express 11, no. 8 (2020): 4224–4243.
L. Zucchelli , D. Contini , R. Re , A. Torricelli , and L. Spinelli , “Method for the Discrimination of Superficial and Deep Absorption Variations by Time Domain fNIRS,” Biomedical Optics Express 4, no. 12 (2013): 2893–2910.
E. Kirilina , Y. Na , A. Jelzow , H. Wabnitz , A. M. Jacobs , and I. Tachtsidis , “Identifying and Quantifying Main Components of Physiological Noise in Functional Near Infrared Spectroscopy on the Prefrontal Cortex,” Frontiers in Human Neuroscience 7 (2013): 864.
A. Jelzow , H. Wabnitz , I. Tachtsidis , E. Kirilina , R. Brühl , and R. Macdonald , “Separation of Superficial and Cerebral Hemodynamics Using a Single Distance Time‐Domain NIRS Measurement,” Biomedical Optics Express 5, no. 5 (2014): 1465–1482.
M. Muthalib , R. Re , L. Zucchelli , et al., “Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study,” PLoS One 10, no. 7 (2015): e0131951.
Y. Xia , J. Chen , J. Li , et al., “A Deep‐Learning Empowered, Real‐Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (2025).
J. Uchitel , B. Blanco , E. Vidal‐Rosas , L. Collins‐Jones , and R. J. Cooper , “Reliability and Similarity of Resting State Functional Connectivity Networks Imaged Using Wearable, High‐Density Diffuse Optical Tomography in the Home Setting,” NeuroImage 263 (2022): 119663.
H. Zhao , E. M. Frijia , E. V. Rosas , et al., “Design and Validation of a Mechanically Flexible and Ultra‐Lightweight High‐Density Diffuse Optical Tomography System for Functional Neuroimaging of Newborns,” Neurophotonics 8, no. 1 (2021): 015011–015011.
Z. E. Markow , J. W. Trobaugh , E. J. Richter , et al., “Ultra High Density Imaging Arrays in Diffuse Optical Tomography for Human Brain Mapping Improve Image Quality and Decoding Performance,” Scientific Reports 15, no. 1 (2025): 3175.
J. Chen , H. Yang , Y. Xia , et al., “Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High‐Density Diffuse Optical Tomography,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (2025).
Z. E. Markow , J. W. Trobaugh , E. J. Richter , et al., “Ultra‐High Density Imaging Arrays for Diffuse Optical Tomography of Human Brain Improve Resolution, Signal‐To‐Noise, and Information Decoding,” bioRxiv (2023) 2023–07.
E. E. Vidal‐Rosas , A. Von Lühmann , P. Pinti , and R. J. Cooper , “Wearable, High‐Density fNIRS and Diffuse Optical Tomography Technologies: A Perspective,” Neurophotonics 10, no. 2 (2023): 023513–023513.
A. Sherafati , A. Z. Snyder , A. T. Eggebrecht , et al., “Global Motion Detection and Censoring in High‐Density Diffuse Optical Tomography,” Human Brain Mapping 41, no. 14 (2020): 4093–4112.
E. E. Vidal‐Rosas , H. Zhao , R. W. Nixon‐Hill , et al., “Evaluating a New Generation of Wearable High‐Density Diffuse Optical Tomography Technology via Retinotopic Mapping of the Adult Visual Cortex,” Neurophotonics 8, no. 2 (2021): 025002–025002.
K. Tripathy , Z. E. Markow , A. K. Fishell , et al., “Decoding Visual Information From High‐Density Diffuse Optical Tomography Neuroimaging Data,” NeuroImage 226 (2021): 117516.
G. A. Perkins , A. T. Eggebrecht , and H. Dehghani , “Quantitative Evaluation of Frequency Domain Measurements in High Density Diffuse Optical Tomography,” Journal of Biomedical Optics 26, no. 5 (2021): 056001–056001.
B. R. White and J. P. Culver , “Quantitative Evaluation of High‐Density Diffuse Optical Tomography: In Vivo Resolution and Mapping Performance,” Journal of Biomedical Optics 15, no. 2 (2010): 026006–026006.
W. Fan , H. Dehghani , and A. T. Eggebrecht , “Investigation of Effect of Modulation Frequency on High‐Density Diffuse Optical Tomography Image Quality,” Neurophotonics 8, no. 4 (2021): 045002–045002.
A. A. Phillips , F. H. N. Chan , M. M. Z. Zheng , A. V. Krassioukov , and P. N. Ainslie , “Neurovascular Coupling in Humans: Physiology, Methodological Advances and Clinical Implications,” Journal of Cerebral Blood Flow & Metabolism 36, no. 4 (2016): 647–664.
V. Quaresima and M. Ferrari , “Functional Near‐Infrared Spectroscopy (Fnirs) for Assessing Cerebral Cortex Function During Human Behavior in Natural/Social Situations: A Concise Review,” Organizational Research Methods 22, no. 1 (2019): 46–68.
A. Mishra , C. N. Hall , C. Howarth , and R. D. Freeman , “Key Relationships Between Non‐Invasive Functional Neuroimaging and the Underlying Neuronal Activity,” Philosophical Transactions of the Royal Society B 2021, no. 376 (1815): 20190622.
T. Liu , L. Duan , R. Dai , M. Pelowski , and C. Zhu , “Team‐Work, Team‐Brain: Exploring Synchrony and Team Interdependence in a Nine‐Person Drumming Task via Multiparticipant Hyperscanning and Inter‐Brain Network Topology With Fnirs,” NeuroImage 237 (2021): 118147.
K. A. Johnson , N. C. Fox , R. A. Sperling , and W. E. Klunk , “Brain Imaging in Alzheimer Disease,” Cold Spring Harbor Perspectives in Medicine 2, no. 4 (2012): a006213.
N. Tournier , C. Comtat , V. Lebon , and J.‐L. Gennisson , “Challenges and Perspectives of the Hybridization of Pet With Functional Mri or Ultrasound for Neuroimaging,” Neuroscience 474 (2021): 80–93.
A. Shmuel and A. Maier , “Locally Measured Neuronal Correlates of Functional Mri Signals,” in EEG‐fMRI: Physiological Basis, Technique, and Applications (Springer, 2023), 79–104.
E. Gibson , N. J. Lobaugh , S. Joordens , and A. R. McIntosh , “Eeg Variability: Task‐Driven or Subject‐Driven Signal of Interest?,” NeuroImage 252 (2022): 119034.
X. Hou , Z. Zhang , C. Zhao , et al., “Nirs‐Kit: A Matlab Toolbox for Both Resting‐State and Task Fnirs Data Analysis,” Neurophotonics 8, no. 1 (2021): 010802–010802.
P. Pinti , I. Tachtsidis , A. Hamilton , et al., “The Present and Future Use of Functional Near‐Infrared Spectroscopy (fNIRS) for Cognitive Neuroscience,” Annals of the new York Academy of Sciences 1464, no. 1 (2020): 5–29.
M. K. Yeung and J. Lin , “Probing Depression, Schizophrenia, and Other Psychiatric Disorders Using fNIRS and the Verbal Fluency Test: A Systematic Review and Meta‐Analysis,” Journal of Psychiatric Research 140 (2021): 416–435.
E. Poliakova , A. L. Conrad , K. M. Schieltz , and M. J. O’Brien , “Using fNIRS to Evaluate ADHD Medication Effects on Neuronal Activity: A Systematic Literature Review,” Frontiers in Neuroimaging 2 (2023): 1083036.
M. K. Yeung and A. S. Chan , “A Systematic Review of the Application of Functional Near‐Infrared Spectroscopy to the Study of Cerebral Hemodynamics in Healthy Aging,” Neuropsychology Review 31, no. 1 (2021): 139–166.
M. Sousani , R. F. Rojas , E. Preston , and M. Ghahramani , “Towards a Multi‐Modal Brain‐Body Assessment in Parkinson's Disease: A Systematic Review in fNIRS (February 2023),” IEEE Journal of Biomedical and Health Informatics (2023).
C. Huo , X. Gongcheng , W. Li , et al., “A Review on Functional Near‐Infrared Spectroscopy and Application in Stroke Rehabilitation,” Medicine in Novel Technology and Devices 11 (2021): 100064.
Z. Zhan , Q. Yang , L. Luo , and X. Zhang , “Applying Functional Near‐Infrared Spectroscopy (fNIRS) in Educational Research: A Systematic Review,” Current Psychology 43, no. 11 (2024): 9676–9691.
R. Li , D. Yang , F. Fang , K.‐S. Hong , A. L. Reiss , and Y. Zhang , “Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology‐Focused Review,” Sensors 22, no. 15 (2022): 5865.
J. Chen , Y. Xia , X. Zhou , et al., “fNIRS‐EEG BCIs for Motor Rehabilitation: A Review,” Bioengineering 10, no. 12 (2023): 1393.
K. Peng , Z. Moussavi , K. D. Karunakaran , D. Borsook , F. Lesage , and D. K. Nguyen , “iVR‐fNIRS: Studying Brain Functions in a Fully Immersive Virtual Environment,” Neurophotonics 11, no. 2 (2024): 020601–020601.
A. Curtin , S. Tong , J. Sun , J. Wang , B. Onaral , and H. Ayaz , “A Systematic Review of Integrated Functional Near‐Infrared Spectroscopy (fNIRS) and Transcranial Magnetic Stimulation (TMS) Studies,” Frontiers in Neuroscience 13 (2019): 84.
V. Tuchin , “Tissue Optics and Photonics: Light‐Tissue Interaction II,” Journal of Biomedical Photonics & Engineering 2, no. 3 (2016): 030201.
N. E. Karishma Shaik , B. Widdicombe , D. Sun , et al., “Longwave Infrared Multispectral Image Sensor System Using Aluminum‐Germanium Plasmonic Filter Arrays,” Nano Research 16, no. 7 (2023): 10018–10025.
E. B. Wang , R. Kaur , M. Fierro , E. Austin , L. R. Jones , and J. Jagdeo , “Safety and Penetration of Light Into the Brain,” in Photobiomodulation in the Brain (Elsevier, 2019), 49–66.
L. Zhu , L. Lu , S. Wang , et al., “Oral Absorption Basics: Pathways and Physicochemical and Biological Factors Affecting Absorption,” in Developing Solid Oral Dosage Forms (Elsevier, 2017), 297–329.
V. O. Korhonen , T. S. Myllylä , M. Y. Kirillin , et al., “Light Propagation in NIR Spectroscopy of the Human Brain,” IEEE Journal of Selected Topics in Quantum Electronics 20, no. 2 (2013): 289–298.
Y. Zhu , T. Jiang , Y. Zhou , and L. Zhao , “Discriminative Analysis of Functional Near‐Infrared Spectroscopy Signals for Development of Neuroimaging Biomarkers of Elderly Depression,” Journal of Innovative Optical Health Sciences 3, no. 01 (2010): 69–74.
D. Rosenbaum , K. Hagen , S. Deppermann , et al., “State‐Dependent Altered Connectivity in Late‐Life Depression: A Functional Near‐Infrared Spectroscopy Study,” Neurobiology of Aging 39 (2016): 57–68.
Y. Tong , Q. Wang , X. Wang , et al., “A Scoping Review of Functional Near‐Infrared Spectroscopy Biomarkers in Late‐Life Depression: Depressive Symptoms, Cognitive Functioning, and Social Functioning,” Psychiatry Research: Neuroimaging (2024): 111810.
H. Lyu , M. Zhao , X. Pengfeng , et al., “Gender Differences in Brain Region Activation During Verbal Fluency Task as Detected by fNIRS in Patients With Depression,” World Journal of Biological Psychiatry 25, no. 2 (2024): 141–150.
R. Briggs , D. Carey , P. Claffey , et al., “The Association Between Frontal Lobe Perfusion and Depressive Symptoms in Later Life,” British Journal of Psychiatry 214, no. 4 (2019): 230–236.
P.‐H. Chou , W.‐C. Liu , W.‐H. Lin , C.‐W. Hsu , S.‐C. Wang , and S. Kuan‐Pin , “NIRS‐Aided Differential Diagnosis Among Patients With Major Depressive Disorder, Bipolar Disorder, and Schizophrenia,” Journal of Affective Disorders 341 (2023): 366–373.
W. Huifen , L. Baoquan , Y. Zhang , and T. Li , “Differences in Prefrontal Cortex Activation in Chinese College Students With Different Severities of Depressive Symptoms: A Large Sample of Functional Near‐Infrared Spectroscopy (fNIRS) Findings,” Journal of Affective Disorders 350 (2024): 521–530.
J. E. Shin , Y. S. Lee , S. Y. Park , et al., “The Relationship Between Depression Severity and Prefrontal Hemodynamic Changes in Adolescents With Major Depression Disorder: A Functional Near‐Infrared Spectroscopy Study,” Clinical Psychopharmacology and Neuroscience 22, no. 1 (2024): 118.
G. Li , K. Ma , K. Rossbach , et al., “Cortical Activation for Adolescent‐Onset Minor Depression and Major Depressive Disorder: An fNIRS Study,” Annals of General Psychiatry 23, no. 1 (2024): 17.
X. Liang , D. Qiao , T. Ren , et al., “Neural Association Between Cognitive Function and Anhedonia in Adolescents With Melancholic Major Depressive Disorder: A fNIRS Study,” Journal of Affective Disorders 338 (2023): 305–311.
X. Liu , Q. Chen , F. Cheng , et al., “The Abnormal Brain Activation Pattern of Adolescents With Major Depressive Disorder Based on Working Memory Tasks: A fNIRS Study,” Journal of Psychiatric Research 169 (2024): 31–37.
Y. Zhang , X. Li , Y. Guo , et al., “Dorsolateral Prefrontal Activation in Emotional Autobiographical Task in Depressed and Anxious College Students: An fNIRS Study,” International Journal of Environmental Research and Public Health 19, no. 21 (2022): 14335.
M. Zheng , H. Da , X. Pan , et al., “Dorsolateral Prefrontal Activation in Depressed Young Adults With and Without Suicidal Ideation During an Emotional Autobiographical Memory Task: A fNIRS Study,” Journal of Affective Disorders 326 (2023): 216–224.
X. Liu , F. Cheng , H. Shasha , et al., “Cortical Activation and Functional Connectivity During the Verbal Fluency Task for Adolescent‐Onset Depression: A Multi‐Channel NIRS Study,” Journal of Psychiatric Research 147 (2022): 254–261.
Z. Song , Y. Zhou , Y. Zheng , et al., “Factors Influencing Functional Near‐Infrared Spectroscopy in Postpartum Depression: A Cross‐Sectional Study,” International Journal of Gynecology & Obstetrics 161, no. 3 (2023): 1046–1052.
S. Wang , C. Ding , C. Dou , et al., “Associations Between Maternal Prenatal Depression and Neonatal Behavior and Brain Function—Evidence From the Functional Near‐Infrared Spectroscopy,” Psychoneuroendocrinology 146 (2022): 105896.
J. A. Porto , J. Bick , K. L. Perdue , J. E. Richards , M. L. Nunes , and C. A. Nelson , “The Influence of Maternal Anxiety and Depression Symptoms on fNIRS Brain Responses to Emotional Faces in 5‐and 7‐Month‐Old Infants,” Infant Behavior and Development 59 (2020): 101447.
W. Huifen , T. Li , C. Peng , et al., “The Right Prefrontal Cortex (PFC) can Distinguish Anxious Depression From Non‐Anxious Depression: A Promising Functional Near Infrared Spectroscopy Study (fNIRS),” Journal of Affective Disorders 317 (2022): 319–328.
J. Chao , S. Zheng , W. Hongtong , et al., “fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 2211–2221.
Y. Zhu , W. Quan , H. Wang , et al., “Prefrontal Activation During a Working Memory Task Differs Between Patients With Unipolar and Bipolar Depression: A Preliminary Exploratory Study,” Journal of Affective Disorders 225 (2018): 64–70.
M. K. Yeung , T. L. Lee , and A. S. Chan , “Depressive and Anxiety Symptoms Are Related to Decreased Lateral Prefrontal Cortex Functioning During Cognitive Control in Older People,” Biological Psychology 166 (2021): 108224.
K. Uemura , H. Shimada , T. Doi , H. Makizako , H. Park , and T. Suzuki , “Depressive Symptoms in Older Adults Are Associated With Decreased Cerebral Oxygenation of the Prefrontal Cortex During a Trail‐Making Test,” Archives of Gerontology and Geriatrics 59, no. 2 (2014): 422–428.
D. Rosenbaum , I. Int‐Veen , H. Laicher , et al., “Insights From a Laboratory and Naturalistic Investigation on Stress, Rumination and Frontal Brain Functioning in MDD: An fNIRS Study,” Neurobiology of Stress 15 (2021): 100344.
X. You , Y. Wang , H. Nannan , et al., “Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting‐State fNIRS Study,” Brain Sciences 12, no. 11 (2022): 1562.
Y. Zhu , J. K. Jayagopal , R. K. Mehta , et al., “Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 4 (2020): 961–969.
T. Ma , H. Lyu , J. Liu , et al., “Distinguishing Bipolar Depression From Major Depressive Disorder Using Fnirs and Deep Neural Network,” Progress in Electromagnetics Research 169 (2020): 73–86.
Y. Kawabata , S.‐i. Imazu , K. Matsumoto , et al., “rTMS Therapy Reduces Hypofrontality in Patients With Depression as Measured by fNIRS,” Frontiers in Psychiatry 13 (2022): 814611.
K. Feng , C.‐Y. Shen , X.‐Y. Ma , et al., “Effects of Music Therapy on Major Depressive Disorder: A Study of Prefrontal Hemodynamic Functions Using fNIRS,” Psychiatry Research 275 (2019): 86–93.
T. Yang , H. Wang , H. Dai , et al., “The fNIRS Evaluation of Frontal and Temporal Lobe Cortical Activation in Chinese First‐Episode Medication‐Naïve and Recurrent Depression During a Verbal Fluency Task,” Frontiers in Psychiatry 14 (2023): 1132666.
A. S. Huhn , M. M. Sweeney , R. K. Brooner , et al., “Prefrontal Cortex Response to Drug Cues, Craving, and Current Depressive Symptoms Are Associated With Treatment Outcomes in Methadone‐Maintained Patients,” Neuropsychopharmacology 44, no. 4 (2019): 826–833.
C. Gao , H. Zhou , J. Liu , et al., “Characteristics of Frontal Activity Relevant to Cognitive Function in Bipolar Depression: An fNIRS Study,” Biomedical Optics Express 13, no. 3 (2022): 1551–1563.
S. F. Husain , T.‐B. Tang , W. W. Tam , B. X. Tran , C. S. Ho , and R. C. Ho , “Cortical Haemodynamic Response During the Verbal Fluency Task in Patients With Bipolar Disorder and Borderline Personality Disorder: A Preliminary Functional Near‐Infrared Spectroscopy Study,” BMC Psychiatry 21 (2021): 1–10.
M. Mao , G. Chen , K. Feng , et al., “Characteristics of Prefrontal Activity During Emotional and Cognitive Processing in Patients With Bipolar Disorder: A Multi‐Channel Functional Near‐Infrared Spectroscopy Study,” Frontiers in Neuroscience 16 (2022): 946543.
A. Manelis , T. J. Huppert , E. Rodgers , H. A. Swartz , and M. L. Phillips , “The Role of the Right Prefrontal Cortex in Recognition of Facial Emotional Expressions in Depressed Individuals: fNIRS Study,” Journal of Affective Disorders 258 (2019): 151–158.
R. Segar , H. Chhabra , V. S. Sreeraj , et al., “fNIRS Study of Prefrontal Activation During Emotion Recognition–a Potential Endophenotype for Bipolar I Disorder?,” Journal of Affective Disorders 282 (2021): 869–875.
T. Matsubara , K. Matsuo , M. Nakashima , et al., “Prefrontal Activation in Response to Emotional Words in Patients With Bipolar Disorder and Major Depressive Disorder,” NeuroImage 85 (2014): 489–497.
T. Matsubara , C. Chen , M. Hirotsu , et al., “Prefrontal Cortex Activities During Verbal Fluency and Emotional Words Tasks in Major Depressive, Adjustment, and Bipolar Disorders With Depressive States,” Journal of Affective Disorders 316 (2022): 109–117.
Y. H. Alici , H. D. Özgüven , E. Kale , I. Yenihayat , and B. Baskak , “Prefrontal Activity Measured by Functional Near Infrared Spectroscopy During Divergent and Convergent Thinking in Bipolar Disorder,” Archives of Neuropsychiatry 56, no. 2 (2019): 86.
A. Aleksandrowicz , F. Hagenmuller , H. Haker , et al., “Frontal Brain Activity in Individuals at Risk for Schizophrenic Psychosis and Bipolar Disorder During the Emotional Stroop Task–An fNIRS Study,” NeuroImage: Clinical 26 (2020): 102232.
S. Hassel , J. R. C. Almeida , N. Kerr , et al., “Elevated Striatal and Decreased Dorsolateral Prefrontal Cortical Activity in Response to Emotional Stimuli in Euthymic Bipolar Disorder: No Associations With Psychotropic Medication Load,” Bipolar Disorders 10, no. 8 (2008): 916–927.
Y. Ren , G. Cui , X. Zhang , K. Feng , Y. Chenchao , and P. Liu , “The Promising fNIRS: Uncovering the Function of Prefrontal Working Memory Networks Based on Multi‐Cognitive Tasks,” Frontiers in Psychiatry 13 (2022): 985076.
H. Zhu , X. Jie , J. Li , et al., “Decreased Functional Connectivity and Disrupted Neural Network in the Prefrontal Cortex of Affective Disorders: A Resting‐State fNIRS Study,” Journal of Affective Disorders 221 (2017): 132–144.
Y. Monden , H. Dan , M. Nagashima , et al., “Clinically‐Oriented Monitoring of Acute Effects of Methylphenidate on Cerebral Hemodynamics in ADHD Children Using fNIRS,” Clinical Neurophysiology 123, no. 6 (2012): 1147–1157.
S. Jang , J. K. Choi , O. Jooyoung , et al., “Use of Virtual Reality Working Memory Task and Functional Near‐Infrared Spectroscopy to Assess Brain Hemodynamic Responses to Methylphenidate in ADHD Children,” Frontiers in Psychiatry 11 (2021): 564618.
S. Grazioli , E. Rosi , M. Mauri , et al., “Patterns of Response to Methylphenidate Administration in Children With ADHD: A Personalized Medicine Approach Through Clustering Analysis,” Children 8, no. 11 (2021): 1008.
S. B. Wigal , C. M. Polzonetti , A. Stehli , and E. Gratton , “Phase Synchronization of Oxygenation Waves in the Frontal Areas of Children With Attention‐Deficit Hyperactivity Disorder Detected by Optical Diffusion Spectroscopy Correlates With Medication,” Journal of Biomedical Optics 17, no. 12 (2012): 127002–127002.
J. Li , W.‐J. Yan , W. Yan , X.‐X. Tian , and Y.‐W. Zhang , “Synaptosomal‐Associated Protein 25 Gene Polymorphisms Affect Treatment Efficiency of Methylphenidate in Children With Attention‐Deficit Hyperactivity Disorder: An fNIRS Study,” Frontiers in Behavioral Neuroscience 15 (2022): 793643.
A. Ishii‐Takahashi , R. Takizawa , Y. Nishimura , et al., “Neuroimaging‐Aided Prediction of the Effect of Methylphenidate in Children With Attention‐Deficit Hyperactivity Disorder: A Randomized Controlled Trial,” Neuropsychopharmacology 40, no. 12 (2015): 2676–2685.
Y. Nakanishi , T. Ota , J. Iida , et al., “Differential Therapeutic Effects of Atomoxetine and Methylphenidate in Childhood Attention Deficit/Hyperactivity Disorder as Measured by Near‐Infrared Spectroscopy,” Child and Adolescent Psychiatry and Mental Health 11 (2017): 1–11.
N. Dolu , M. Altınkaynak , A. Güven , et al., “Effects of Methylphenidate Treatment in Children With ADHD: A Multimodal EEG/fNIRS Approach,” Psychiatry and Clinical Psychopharmacology 29, no. 3 (2019): 285–292.
A. Araki , M. Ikegami , A. Okayama , et al., “Improved Prefrontal Activity in AD/HD Children Treated With Atomoxetine: A NIRS Study,” Brain and Development 37, no. 1 (2015): 76–87.
T. Ota , J. Iida , Y. Nakanishi , et al., “Increased Prefrontal Hemodynamic Change After Atomoxetine Administration in Pediatric Attention‐Deficit/Hyperactivity Disorder as Measured by Near‐Infrared Spectroscopy,” Psychiatry and Clinical Neurosciences 69, no. 3 (2015): 161–170.
M. Nagashima , Y. Monden , I. Dan , et al., “Neuropharmacological Effect of Atomoxetine on Attention Network in Children With Attention Deficit Hyperactivity Disorder During Oddball Paradigms as Assessed Using Functional Near‐Infrared Spectroscopy,” Neurophotonics 1, no. 2 (2014): 025007–025007.
M. Nagashima , Y. Monden , I. Dan , et al., “Neuropharmacological Effect of Methylphenidate on Attention Network in Children With Attention Deficit Hyperactivity Disorder During Oddball Paradigms as Assessed Using Functional Near‐Infrared Spectroscopy,” Neurophotonics 1, no. 1 (2014): 015001–015001.
D. B. Clemow and C. J. Bushe , “Atomoxetine in Patients With ADHD: A Clinical and Pharmacological Review of the Onset, Trajectory, Duration of Response and Implications for Patients,” Journal of Psychopharmacology 29, no. 12 (2015): 1221–1230.
T. Ikeda , A. Inoue , D. Tanaka , et al., “Visualizing Neuropharmacological Effects of Guanfacine Extended Release in Attention Deficit Hyperactivity Disorder Using Functional Near‐Infrared Spectroscopy,” Frontiers in Neuroergonomics 2 (2021): 657657.
C. Kawai , K. Mori , T. Tanioka , et al., “Usefulness of Near‐Infrared Spectroscopy (NIRS) for Evaluating Drug Effects and Improvements in Medication Adherence in Children With Attention Deficit Hyperactivity Disorder (ADHD),” Journal of Medical Investigation 68, no. 1.2 (2021): 53–58.
F. Makedon , R. Zhang , G. Alexandrakis , C. B. Owen , H. Huang , and A. J. Saykin , “An Interactive User Interface System for Alzheimer's Intervention,” in Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments (2010), 1–5.
B. P. Acevedo , N. Dattatri , J. Le , C. Lappinga , and N. L. Collins , “Cognitive Training With Neurofeedback Using fNIRS Improves Cognitive Function in Older Adults,” International Journal of Environmental Research and Public Health 19, no. 9 (2022): 5531.
K. Sato , A. Ochi , K. Watanabe , and K. Yamada , “Effects of Dance Video Game Training on Cognitive Functions of Community‐Dwelling Older Adults With Mild Cognitive Impairment,” Aging Clinical and Experimental Research 35, no. 5 (2023): 987–994.
P. Eggenberger , M. Wolf , M. Schumann , and E. D. De Bruin , “Exergame and Balance Training Modulate Prefrontal Brain Activity During Walking and Enhance Executive Function in Older Adults,” Frontiers in Aging Neuroscience 8 (2016): 66.
Y.‐Y. Liao , I.‐H. Chen , W.‐C. Hsu , H.‐Y. Tseng , and R.‐Y. Wang , “Effect of Exergaming Versus Combined Exercise on Cognitive Function and Brain Activation in Frail Older Adults: A Randomised Controlled Trial,” Annals of Physical and Rehabilitation Medicine 64, no. 5 (2021): 101492.
Y. Jiulong , X. Zhang , J. Yang , et al., “A Functional Near‐Infrared Spectroscopy Study of the Effects of Video Game‐Based Bilateral Upper Limb Training on Brain Cortical Activation and Functional Connectivity,” Experimental Gerontology 169 (2022): 111962.
P. C. Thumm , I. Maidan , M. Brozgol , et al., “Treadmill Walking Reduces Pre‐Frontal Activation in Patients With Parkinson's Disease,” Gait & Posture 62 (2018): 384–387.
N. R. Conceição , L. T. B. Gobbi , P. Nóbrega‐Sousa , et al., “Aerobic Exercise Combined With Transcranial Direct Current Stimulation Over the Prefrontal Cortex in Parkinson Disease: Effects on Cortical Activity, Gait, and Cognition,” Neurorehabilitation and Neural Repair 35, no. 8 (2021): 717–728.
I. Maidan , R. Hacham , I. Galperin , et al., “Neural Variability in the Prefrontal Cortex as a Reflection of Neural Flexibility and Stability in Patients With Parkinson Disease,” Neurology 98, no. 8 (2022): e839–e847.
D. Orcioli‐Silva , R. Vitório , P. NóbregaSousa , et al., “Cortical Activity Underlying Gait Improvements Achieved With Dopaminergic Medication During Usual Walking and Obstacle Avoidance in Parkinson Disease,” Neurorehabilitation and Neural Repair 35, no. 5 (2021): 406–418.
M. Dagan , T. Herman , H. Bernad‐Elazari , et al., “Dopaminergic Therapy and Prefrontal Activation During Walking in Individuals With Parkinson's Disease: Does the Levodopa Overdose Hypothesis Extend to Gait?,” Journal of Neurology 268 (2021): 658–668.
T. Sharon , I. Kurz , H. Bernad‐Elazari , et al., “Which Obstacle Attributes Place Additional Demands on Higher‐Level Cognitive Function in Patients With Parkinson's Disease?,” Parkinsonism & Related Disorders 78 (2020): 178–183.
F. Nieuwhof , M. F. Reelick , I. Maidan , et al., “Measuring Prefrontal Cortical Activity During Dual Task Walking in Patients With Parkinson's Disease: Feasibility of Using a New Portable fNIRS Device,” Pilot and Feasibility Studies 2 (2016): 1–11.
D. Orcioli‐Silva , R. Vitório , P. NóbregaSousa , et al., “Levodopa Facilitates Prefrontal Cortex Activation During Dual Task Walking in Parkinson Disease,” Neurorehabilitation and Neural Repair 34, no. 7 (2020): 589–599.
I. Maidan , F. Nieuwhof , H. Bernad‐Elazari , et al., “The Role of the Frontal Lobe in Complex Walking Among Patients With Parkinson's Disease and Healthy Older Adults: An fNIRS Study,” Neurorehabilitation and Neural Repair 30, no. 10 (2016): 963–971.
I. Maidan , F. Nieuwhof , H. Bernad‐Elazari , et al., “Evidence for Differential Effects of 2 Forms of Exercise on Prefrontal Plasticity During Walking in Parkinson's Disease,” Neurorehabilitation and Neural Repair 32, no. 3 (2018): 200–208.
S. Stuart and M. Mancini , “Prefrontal Cortical Activation With Open and Closed‐Loop Tactile Cueing When Walking and Turning in Parkinson Disease: A Pilot Study,” Journal of Neurologic Physical Therapy 44, no. 2 (2020): 121–131.
V. Belluscio , S. Stuart , E. Bergamini , G. Vannozzi , and M. Mancini , “The Association Between Prefrontal Cortex Activity and Turning Behavior in People With and Without Freezing of Gait,” Neuroscience 416 (2019): 168–176.
I. Maidan , H. Bernad‐Elazari , N. Giladi , J. M. Hausdorff , and A. Mirelman , “When Is Higher Level Cognitive Control Needed for Locomotor Tasks Among Patients With Parkinson's Disease?,” Brain Topography 30 (2017): 531–538.
I. Maidan , H. Bernad‐Elazari , E. Gazit , N. Giladi , J. M. Hausdorff , and A. Mirelman , “Changes in Oxygenated Hemoglobin Link Freezing of Gait to Frontal Activation in Patients With Parkinson Disease: An fNIRS Study of Transient Motor‐Cognitive Failures,” Journal of Neurology 262 (2015): 899–908.
R. Vitorio , S. Stuart , and M. Mancini , “Executive Control of Walking in People With Parkinson's Disease With Freezing of Gait,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1138–1149.
A. Shumway‐Cook , Motor Control: Translating Research Into Clinical Practice ( Lippincoot Williams & Wilkins , 2007).
P. H. S. Pelicioni , S. R. Lord , Y. Okubo , D. L. Sturnieks , and J. C. Menant , “People With Parkinson's Disease Exhibit Reduced Cognitive and Motor Cortical Activity When Undertaking Complex Stepping Tasks Requiring Inhibitory Control,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1088–1098.
S. S. Kantak , J. W. Stinear , E. R. Buch , and L. G. Cohen , “Rewiring the Brain: Potential Role of the Premotor Cortex in Motor Control, Learning, and Recovery of Function Following Brain Injury,” Neurorehabilitation and Neural Repair 26, no. 3 (2012): 282–292.
C. H. Kasess , C. Windischberger , R. Cunnington , R. Lanzenberger , L. Pezawas , and E. Moser , “The Suppressive Influence of SMA on M1 in Motor Imagery Revealed by fMRI and Dynamic Causal Modeling,” NeuroImage 40, no. 2 (2008): 828–837.
J.‐H. Jang , S. Park , J. An , et al., “Gait Disturbance Improvement and Cerebral Cortex Rearrangement by Acupuncture in Parkinson's Disease: A Pilot Assessor‐Blinded, Randomized, Controlled, Parallel‐Group Trial,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1111–1123.
O. Klempir , R. Krupicka , J. Mehnert , et al., “Reshaping Cortical Activity With Subthalamic Stimulation in Parkinson's Disease During Finger Tapping and Gait Mapped by Near Infrared Spectroscopy,” Journal of Applied Biomedicine 17 (2019): 157–166.
K. M. Arun , K. A. Smitha , P. N. Sylaja , and C. Kesavadas , “Identifying Resting‐State Functional Connectivity Changes in the Motor Cortex Using fNIRS During Recovery From Stroke,” Brain Topography 33, no. 6 (2020): 710–719.
Y. Song , Z. F. Sun , W. Z. Sun , et al., “Neuroplasticity Following Stroke From a Functional Laterality Perspective: A fNIRS Study,” Brain Topography 36, no. 3 (2023): 283–293.
D. Wang , J. Wang , H. Zhao , et al., “The Relationship Between the Prefrontal Cortex and Limb Motor Function in Stroke: A Study Based on Resting‐State Functional Near‐Infrared Spectroscopy,” Brain Research 1805 (2023): 148269.
C. Huo , X. Gongcheng , A. Sun , et al., “Cortical Response Induced by Task‐Oriented Training of the Upper Limb in Subacute Stroke Patients as Assessed by Functional Near‐Infrared Spectroscopy,” Journal of Biophotonics 16, no. 3 (2023): e202200228.
C. Huo , X. Gongcheng , H. Xie , et al., “Effect of High‐Frequency rTMS Combined With Bilateral Arm Training on Brain Functional Network in Patients With Chronic Stroke: An fNIRS Study,” Brain Research 1809 (2023): 148357.
D. R. Leff , F. Orihuela‐Espina , C. E. Elwell , et al., “Assessment of the Cerebral Cortex During Motor Task Behaviours in Adults: A Systematic Review of Functional Near Infrared Spectroscopy (fNIRS) Studies,” NeuroImage 54, no. 4 (2011): 2922–2936.
X. Gongcheng , C. Huo , J. Yin , et al., “Effective Brain Network Analysis in Unilateral and Bilateral Upper Limb Exercise Training in Subjects With Stroke,” Medical Physics 49, no. 5 (2022): 3333–3346.
L. Kuan , X. Gongcheng , W. Li , et al., “Frequency‐Specific Functional Connectivity Related to the Rehabilitation Task of Stroke Patients,” Medical Physics 46, no. 4 (2019): 1545–1560.
S. B. Lim and J. J. Eng , “Increased Sensorimotor Cortex Activation With Decreased Motor Performance During Functional Upper Extremity Tasks Poststroke,” Journal of Neurologic Physical Therapy 43, no. 3 (2019): 141–150.
S. B. Lim , C.‐l. Yang , S. Peters , T. Liu‐Ambrose , L. A. Boyd , and J. J. Eng , “Phase‐Dependent Brain Activation of the Frontal and Parietal Regions During Walking After Stroke‐An fNIRS Study,” Frontiers in Neurology 13 (2022): 904722.
A. K. Matarasso , J. D. Rieke , M. Keith White , M. Yusufali , and J. J. Daly , “Combined Real‐Time fMRI and Real Time fNIRS Brain Computer Interface (BCI): Training of Volitional Wrist Extension After Stroke, a Case Series Pilot Study,” PLoS One 16, no. 5 (2021): e0250431.
C. Coetsee and E. Terblanche , “Cerebral Oxygenation During Cortical Activation: The Differential Influence of Three Exercise Training Modalities. A Randomized Controlled Trial,” European Journal of Applied Physiology 117 (2017): 1617–1627.
H. Xie , X. Li , W. Huang , et al., “Effects of Robot‐Assisted Task‐Oriented Upper Limb Motor Training on Neuroplasticity in Stroke Patients With Different Degrees of Motor Dysfunction: A Neuroimaging Motor Evaluation Index,” Frontiers in Neuroscience 16 (2022): 957972.
J. Zhang , W. Huang , Z. Chen , H. Jiang , S. Minzhi , and C. Wang , “Effect of Auricular Acupuncture on Neuroplasticity of Stroke Patients With Motor Dysfunction: A fNIRS Study,” Neuroscience Letters 812 (2023): 137398.
D. Perpetuini , E. F. Russo , D. Cardone , et al., “Identification of Functional Cortical Plasticity in Children With Cerebral Palsy Associated to Robotic‐Assisted Gait Training: An fNIRS Study,” Journal of Clinical Medicine 11, no. 22 (2022): 6790.
V. Kaiser , G. Bauernfeind , A. Kreilinger , et al., “Cortical Effects of User Training in a Motor Imagery Based Brain–Computer Interface Measured by fNIRS and EEG,” NeuroImage 85 (2014): 432–444.
L. Ferreri , E. Bigand , P. Bard , and A. Bugaiska , “The Influence of Music on Prefrontal Cortex During Episodic Encoding and Retrieval of Verbal Information: A Multichannel fNIRS Study,” Behavioural Neurology 2015, no. 1 (2015): 707625.
S. B. Perlman , T. J. Huppert , and B. Luna , “Functional Near‐Infrared Spectroscopy Evidence for Development of Prefrontal Engagement in Working Memory in Early Through Middle Childhood,” Cerebral Cortex 26, no. 6 (2016): 2790–2799.
C. Artemenko , A. Coldea , M. Soltanlou , T. Dresler , H.‐C. Nuerk , and A.‐C. Ehlis , “The Neural Circuits of Number and Letter Copying: An fNIRS Study,” Experimental Brain Research 236 (2018): 1129–1138.
C. Artemenko , M. Soltanlou , T. Dresler , A.‐C. Ehlis , and H.‐C. Nuerk , “The Neural Correlates of Arithmetic Difficulty Depend on Mathematical Ability: Evidence From Combined fNIRS and ERP,” Brain Structure and Function 223 (2018): 2561–2574.
H. Zhao , X. Li , V. Karolis , Y. Feng , H. Niu , and B. Butterworth , “Arithmetic Learning Modifies the Functional Connectivity of the Fronto‐Parietal Network,” Cortex 111 (2019): 51–62.
H. Zhao , T. Zhang , T. Cheng , et al., “Neurocomputational Mechanisms of Young Children's Observational Learning of Delayed Gratification,” Cerebral Cortex 33, no. 10 (2023): 6063–6076.
A. Howell‐Munson , D. S. Unal , E. Walker , C. Arrington , and E. Solovey , “Preliminary Steps Towards Detection of Proactive and Reactive Control States During Learning With fNIRS Brain Signals,” in Proceedings of the First International Workshop on Multimodal Artificial Intelligence in Education (MAIED 2021) (2021).
Y. He , M.‐Y. Wang , D. Li , and Z. Yuan , “Optical Mapping of Brain Activation During the English to Chinese and Chinese to English Sight Translation,” Biomedical Optics Express 8, no. 12 (2017): 5399–5411.
N. Takeuchi , T. Mori , Y. Suzukamo , and S.‐I. Izumi , “Activity of Prefrontal Cortex in Teachers and Students During Teaching of an Insight Problem,” Mind, Brain, and Education 13, no. 3 (2019): 167–175.
L. Shi , L. Dong , W. Zhao , and D. Tan , “Improving Middle School Students’ Geometry Problem Solving Ability Through Hands‐On Experience: An fNIRS Study,” Frontiers in Psychology 14 (2023): 1126047.
A. J. Kersey and L. L. Emberson , “Tracing Trajectories of Audio‐Visual Learning in the Infant Brain,” Developmental Science 20, no. 6 (2017): e12480.
A. L. Ferry , A. Fló , P. Brusini , et al., “On the Edge of Language Acquisition: Inherent Constraints on Encoding Multisyllabic Sequences in the Neonate Brain,” Developmental Science 19, no. 3 (2016): 488–503.
F. Homae , H. Watanabe , T. Nakano , and G. Taga , “Large‐Scale Brain Networks Underlying Language Acquisition in Early Infancy,” Frontiers in Psychology 2 (2011): 93.
F. Homae , H. Watanabe , and G. Taga , “The Neural Substrates of Infant Speech Perception,” Language Learning 64, no. s2 (2014): 6–26.
T. Sakurada , M. Hirai , and E. Watanabe , “Individual Optimal Attentional Strategy During Implicit Motor Learning Boosts Frontoparietal Neural Processing Efficiency: A Functional Near‐Infrared Spectroscopy Study,” Brain and Behavior 9, no. 1 (2019): e01183.
O. Seidel , D. Carius , R. Kenville , and P. Ragert , “Motor Learning in a Complex Balance Task and Associated Neuroplasticity: A Comparison Between Endurance Athletes and Nonathletes,” Journal of Neurophysiology 118, no. 3 (2017): 1849–1860.
S. Sharmin , E. Bakhshipour , B. Kiafar , et al., “fNIRS Analysis of Interaction Techniques in Touchscreen‐Based Educational Gaming,” arXiv Preprint arXiv:2405.08906 (2024).
P. A. Shewokis , F. U. Shariff , Y. Liu , H. Ayaz , A. Castellanos , and D. S. Lind , “Acquisition, Retention and Transfer of Simulated Laparoscopic Tasks Using fNIR and a Contextual Interference Paradigm,” American Journal of Surgery 213, no. 2 (2017): 336–345.
M. Soltanlou , M. A. Sitnikova , H.‐C. Nuerk , and T. Dresler , “Applications of Functional Near‐Infrared Spectroscopy (fNIRS) in Studying Cognitive Development: The Case of Mathematics and Language,” Frontiers in Psychology 9 (2018): 277.
J. Kerr , C. Molloy , P. Reddy , P. A. Shewokis , and K. Izzetoglu , “Individual Differences in fNIRS Measures of Cognitive Workload During a UAS Mission,” in International Conference on Human‐Computer Interaction (Springer, 2021), 49–62.
M. P. Uysal , “Evaluation of Learning Environments for Object‐Oriented Programming: Measuring Cognitive Load With a Novel Measurement Technique,” Interactive Learning Environments 24, no. 7 (2016): 1590–1609.
P. Caçola , N. Getchell , D. Srinivasan , G. Alexandrakis , and H. Liu , “Cortical Activity in Fine‐Motor Tasks in Children With Developmental Coordination Disorder: A Preliminary fNIRS Study,” International Journal of Developmental Neuroscience 65 (2018): 83–90.
R. Prôa , J. Balardin , D. D. de Faria , et al., “Motor Cortex Activation During Writing in Focal Upper‐Limb Dystonia: An fNIRS Study,” Neurorehabilitation and Neural Repair 35, no. 8 (2021): 729–737.
X. Zhong , C. Wang , X. Mingchao , X. Yuan , and C. Jiang , “Physical Training Improves Inhibitory Control in Children Aged 7–12 Years: An fNIRS Study,” Behavioural Brain Research 463 (2024): 114902.
X. Zhong , Y. Dai , X. Mingchao , and C. Jiang , “Volleyball Training Improves Working Memory in Children Aged 7 to 12 Years Old: An fNIRS Study,” Cerebral Cortex 34, no. 7 (2024).
J. Zhang , Y. Wang , C. Leong , Y. Mao , and Z. Yuan , “Bridging Stories and Science: An fNIRS‐Based Hyperscanning Investigation Into Child Learning in STEM,” NeuroImage 285 (2024): 120486.
Z. Pi , H. Chai , L. Li , X. Zhang , and X. Li , “High Task Motivation Learners Co‐Viewing Video Lectures Facilitates Learning,” Journal of Computer Assisted Learning 40, no. 2 (2024): 588–599.
X. Lei and P.‐L. P. Rau , “Emotional Responses to Performance Feedback in an Educational Game During Cooperation and Competition With a Robot: Evidence From fNIRS,” Computers in Human Behavior 138 (2023): 107496.
E. D. Pinheiro , J. R. Sato , R. da Silva , S. Junior , C. Barreto , and A. Y. A. Oku , “Eye‐Tracker and fNIRS: Using Neuroscientific Tools to Assess the Learning Experience During Children's Educational Robotics Activities,” Trends in Neuroscience and Education (2024): 100234.
C. Barreto and M. Soltanlou , “Functional Near‐Infrared Spectroscopy as a Tool to Assess Brain Activity in Educational Settings: An Introduction for Educational Researchers,” South African Journal of Childhood Education 12, no. 1 (2022): 1138.
W. Trost , C. Trevor , N. Fernandez , F. Steiner , and S. Frühholz , “Live Music Stimulates the Affective Brain and Emotionally Entrains Listeners in Real Time,” Proceedings of the National Academy of Sciences 121, no. 10 (2024): e2316306121.
T. Nguyen , S. Ahn , H. Jang , S. C. Jun , and J. G. Kim , “Utilization of a Combined EEG/NIRS System to Predict Driver Drowsiness,” Scientific Reports 7, no. 1 (2017): 43933.
C.‐H. Chuang , Z. Cao , J.‐T. King , W. Bing‐Syun , Y.‐K. Wang , and C.‐T. Lin , “Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving,” Frontiers in Neuroscience 12 (2018): 181.
C.‐T. Lin , J.‐T. King , C.‐H. Chuang , et al., “Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements,” International Journal of Neural Systems 30, no. 01 (2020): 1950018.
A. J. Karran , T. Demazure , P.‐M. Leger , et al., “Toward a Hybrid Passive BCI for the Modulation of Sustained Attention Using EEG and fNIRS,” Frontiers in Human Neuroscience 13 (2019): 393.
C. Artemenko , M. Soltanlou , S. M. Bieck , A.‐C. Ehlis , T. Dresler , and H.‐C. Nuerk , “Individual Differences in Math Ability Determine Neurocognitive Processing of Arithmetic Complexity: A Combined fNIRS‐EEG Study,” Frontiers in Human Neuroscience 13 (2019): 227.
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 and Behavior 7, no. 5 (2017): e00686.
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),” Frontiers in Human Neuroscience 12 (2018): 395.
D. Rosenbaum , E. J. Leehr , A. Kroczek , et al., “Neuronal Correlates of Spider Phobia in a Combined fNIRS‐EEG Study,” Scientific Reports 10, no. 1 (2020): 12597.
Y. Sun , H. Ayaz , and A. N. Akansu , “Multimodal Affective State Assessment Using fNIRS+ EEG and Spontaneous Facial Expression,” Brain Sciences 10, no. 2 (2020): 85.
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 14, no. 6 (2017): 066003.
H. Hamid , N. Naseer , H. Nazeer , M. J. Khan , R. A. Khan , and U. S. Khan , “Analyzing Classification Performance of fNIRS‐BCI for Gait Rehabilitation Using Deep Neural Networks,” Sensors 22, no. 5 (2022): 1932.
Z. Wang , Y. Zhou , L. Chen , et al., “BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, no. 4 (2019): 780–787.
U. Asgher , M. J. Khan , M. H. A. Nizami , et al., “Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near‐Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI),” Frontiers in Neurorobotics 15 (2021): 605751.
A. Berger , F. Horst , S. Müller , F. Steinberg , and M. Doppelmayr , “Current State and Future Prospects of EEG and fNIRS in Robot‐Assisted Gait Rehabilitation: A Brief Review,” Frontiers in Human Neuroscience 13 (2019): 172.
E. Bourel‐Ponchel , M. Mahmoudzadeh , A. Delignières , P. Berquin , and F. Wallois , “Non‐Invasive, Multimodal Analysis of Cortical Activity, Blood Volume and Neurovascular Coupling in Infantile Spasms Using EEG‐fNIRS Monitoring,” NeuroImage: Clinical 15 (2017): 359–366.
A. Güven , M. Altınkaynak , N. Dolu , et al., “Combining Functional Near‐Infrared Spectroscopy and EEG Measurements for the Diagnosis of Attention‐Deficit Hyperactivity Disorder,” Neural Computing and Applications 32 (2020): 8367–8380.
E. E. Rizki , M. Uga , I. Dan , et al., “Determination of Epileptic Focus Side in Mesial Temporal Lobe Epilepsy Using Long‐Term Noninvasive fNIRS/EEG Monitoring for Presurgical Evaluation,” Neurophotonics 2, no. 2 (2015): 025003–025003.
D. Safi , R. Béland , D. K. Nguyen , et al., “Recruitment of the Left Precentral Gyrus in Reading Epilepsy: A Multimodal Neuroimaging Study,” Epilepsy & Behavior Case Reports 5 (2016): 19–22.
M. Abtahi , S. B. Borgheai , R. Jafari , et al., “Merging fNIRS‐EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinsons Disease,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 6 (2020): 1246–1253.
R. Li , S. Li , J. Roh , C. Wang , and Y. Zhang , “Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment: An Exploratory Study,” Neurorehabilitation and Neural Repair 34, no. 12 (2020): 1099–1110.
J. Liang , Y. Song , A. N. Belkacem , et al., “Prediction of Balance Function for Stroke Based on EEG and fNIRS Features During Ankle Dorsiflexion,” Frontiers in Neuroscience 16 (2022): 968928.
S. Cho , W. K. Chang , J. Park , et al., “Feasibility Study of Immersive Virtual Prism Adaptation Therapy With Depth‐Sensing Camera Using Functional Near‐Infrared Spectroscopy in Healthy Adults,” Scientific Reports 12, no. 1 (2022): 767.
D. M. L. de Boer , F. Namdar , M. Lambers , and A. Cleeremans , “LIVE‐Streaming 3D Images: A Neuroscience Approach to Full‐Body Illusions,” Behavior Research Methods (2022): 1–12.
D. Zapała , P. Augustynowicz , and M. Tokovarov , “Recognition of Attentional States in VR Environment: An fNIRS Study,” Sensors 22, no. 9 (2022): 3133.
D. Dong , L. K. F. Wong , and Z. Luo , “Assess BA10 Activity in Slide‐Based and Immersive Virtual Reality Prospective Memory Task Using Functional Near‐Infrared Spectroscopy (fNIRS),” Applied Neuropsychology: Adult (2019).
F. Putze , C. Herff , C. Tremmel , T. Schultz , and D. J. Krusienski , “Decoding Mental Workload in Virtual Environments: A fNIRS Study Using an Immersive n‐Back Task,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2019 (IEEE), 3103–3106.
B. Seraglia , L. Gamberini , K. Priftis , P. Scatturin , M. Martinelli , and S. Cutini , “An Exploratory fNIRS Study With Immersive Virtual Reality: A New Method for Technical Implementation,” Frontiers in Human Neuroscience 5 (2011): 176.
R. Ge , Z. Wang , X. Yuan , et al., “The Effects of Two Game Interaction Modes on Cortical Activation in Subjects of Different Ages: A Functional Near‐Infrared Spectroscopy Study,” IEEE Access 9 (2021): 11405–11415.
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 11 (2017): 167.
J. Scheunemann , A. Unni , K. Ihme , M. Jipp , and J. W. Rieger , “Demonstrating Brain‐Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near‐Infrared Spectroscopy,” Frontiers in Human Neuroscience 12 (2019): 542.
R. Stojan , M. Mack , O. Bock , and C. Voelcker‐Rehage , “Inefficient Frontal and Parietal Brain Activation During Dual‐Task Walking in a Virtual Environment in Older Adults,” NeuroImage 273 (2023): 120070.
C. S. L. Cheah , S. P. Kaputsos , V. Mandalapu , et al., “Neurophysiological Variations in Food Decision‐Making Within Virtual and Real Environments,” in 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), vol. 2019 (IEEE), 1–4.
S. M. Max , P. A. Schroeder , J. Blechert , K. E. Giel , A.‐C. Ehlis , and C. Plewnia , “Mind the Food: Behavioural Characteristics and Imaging Signatures of the Specific Handling of Food Objects,” Brain Structure and Function 226 (2021): 1169–1183.
X. Wang , Y. He , L. Kelong , C. Deng , X. Qiao , and N. Hao , “How Does the Embodied Metaphor Affect Creative Thinking?,” NeuroImage 202 (2019): 116114.
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 (2018), 309–321.
S.‐G. Kuai , Q. Liang , Y.‐Y. He , and W. Hui‐Ning , “Higher Anxiety Rating Does Not Mean Poor Speech Performance: Dissociation of the Neural Mechanisms of Anticipation and Delivery of Public Speaking,” Brain Imaging and Behavior 15, no. 4 (2021): 1934–1943.
L. A. de With , N. Thammasan , and M. Poel , “Detecting Fear of Heights Response to a Virtual Reality Environment Using Functional Near‐Infrared Spectroscopy,” Frontiers in Computer Science 3 (2022): 652550.
G. Kaimal , K. Carroll‐Haskins , Y. Topoglu , A. Ramakrishnan , A. Arslanbek , and H. Ayaz , “Exploratory fNIRS Assessment of Differences in Activation in Virtual Reality Visual Self‐Expression Including With a Fragrance Stimulus,” Art Therapy 39, no. 3 (2022): 128–137.
M. Hinderaker , B. Sylcott , K. Williams , and C.‐C. Lin , “Aging Affects the Ability to Process the Optic Flow Stimulations: A Functional Near‐Infrared Spectrometry Study,” Journal of Motor Behavior 52, no. 4 (2020): 466–473.
J. Taguchi , A. Takami , and M. Makino , “Changes in Cerebral Blood Flow Before, During, and After Forward and Backward Walking in Stroke Patients Trained Using Virtual Reality Walking Videos With Deliberately Induced Inaccuracies in Walking Speed Estimations,” Journal of Physical Therapy Science 34, no. 10 (2022): 668–672.
K. M. T. Pöhlmann , H. A. Maior , J. Föcker , et al., “I Think I Don’t Feel Sick: Exploring the Relationship Between Cognitive Demand and Cybersickness in Virtual Reality Using fNIRS,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–16.
P. Kaimara , A. Oikonomou , and I. Deliyannis , “Could Virtual Reality Applications Pose Real Risks to Children and Adolescents? A Systematic Review of Ethical Issues and Concerns,” Virtual Reality 26, no. 2 (2022): 697–735.
L. Jones and P. Ekkekakis , “Affect and Prefrontal Hemodynamics During Exercise Under Immersive Audiovisual Stimulation: Improving the Experience of Exercise for Overweight Adults,” Journal of Sport and Health Science 8, no. 4 (2019): 325–338.
L. Jones and J. Wheat , “Green and Pleasant Lands: The Affective and Cerebral Hemodynamic Effects of Presence in Virtual Environments During Exercise,” Perceptual and Motor Skills 130, no. 2 (2023): 826–843.
M. Tian , Y. Cai , and J. Zhang , “The Impact of Virtual Reality‐Based Products on Mild Cognitive Impairment Senior Subjects: An Experimental Study Using Multiple Sources of Data,” Applied Sciences 13, no. 4 (2023): 2372.
S. Y. Lee , J. Y. Cha , J. W. Yoo , et al., “Effect of the Application of Virtual Reality on Pain Reduction and Cerebral Blood Flow in Robot‐Assisted Gait Training in Burn Patients,” Journal of Clinical Medicine 11, no. 13 (2022): 3762.
X.‐S. Hu , K. Beard , M. C. Sherbel , et al., “Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near‐Infrared Spectroscopy Study,” Journal of Medical Internet Research 23, no. 10 (2021): e27298.
T. Furubayashi , H. Mochizuki , Y. Terao , et al., “Cortical Hemoglobin Concentration Changes Underneath the Coil After Single‐Pulse Transcranial Magnetic Stimulation: A Near‐Infrared Spectroscopy Study,” Journal of Neurophysiology 109, no. 6 (2013): 1626–1637.
F. Vernieri , C. Altamura , P. Palazzo , et al., “1‐Hz Repetitive Transcranial Magnetic Stimulation Increases Cerebral Vasomotor Reactivity: A Possible Autonomic Nervous System Modulation,” Brain Stimulation 7, no. 2 (2014): 281–286.
E. Park , M. J. Kang , A. Lee , W. H. Chang , Y.‐I. Shin , and Y.‐H. Kim , “Real‐Time Measurement of Cerebral Blood Flow During and After Repetitive Transcranial Magnetic Stimulation: A Near‐Infrared Spectroscopy Study,” Neuroscience Letters 653 (2017): 78–83.
A. Curtin , J. Sun , H. Ayaz , et al., “Evaluation of Evoked Responses to Pulse‐Matched High Frequency and Intermittent Theta Burst Transcranial Magnetic Stimulation Using Simultaneous Functional Near‐Infrared Spectroscopy,” Neurophotonics 4, no. 4 (2017): 041405–041405.
R. Solianik , A. Satas , D. Mickeviciene , et al., “Task‐Relevant Cognitive and Motor Functions Are Prioritized During Prolonged Speed–Accuracy Motor Task Performance,” Experimental Brain Research 236 (2018): 1665–1678.
T. Shinba , N. Kariya , S. Matsuda , H. Matsuda , and Y. Obara , “Increase of Frontal Cerebral Blood Volume During Transcranial Magnetic Stimulation in Depression Is Related to Treatment Effectiveness: A Pilot Study With Near‐Infrared Spectroscopy,” Psychiatry and Clinical Neurosciences 72, no. 8 (2018): 602–610.
K. Yamanaka , H. Tomioka , S. Kawasaki , et al., “Effect of Parietal Transcranial Magnetic Stimulation on Spatial Working Memory in Healthy Elderly Persons‐Comparison of Near Infrared Spectroscopy for Young and Elderly,” PLoS One 9, no. 7 (2014): e102306.
A. Curtin , H. Ayaz , Y. Tang , J. Sun , J. Wang , and S. Tong , “Enhancing Neural Efficiency of Cognitive Processing Speed via Training and Neurostimulation: An fNIRS and TMS Study,” NeuroImage 198 (2019): 73–82.
T. Hara , M. Abo , K. Kakita , Y. Mori , M. Yoshida , and N. Sasaki , “The Effect of Selective Transcranial Magnetic Stimulation With Functional Near‐Infrared Spectroscopy and Intensive Speech Therapy on Individuals With Post‐Stroke Aphasia,” European Neurology 77, no. 3‐4 (2017): 186–194.
N. Urushidani , S. Kinoshita , T. Okamoto , H. Tamashiro , and M. Abo , “Low‐Frequency rTMS and Intensive Occupational Therapy Improve Upper Limb Motor Function and Cortical Reorganization Assessed by Functional Near‐Infrared Spectroscopy in a Subacute Stroke Patient,” Case Reports in Neurology 10, no. 2 (2018): 223–231.
H. Tamashiro , S. Kinoshita , T. Okamoto , N. Urushidani , and M. Abo , “Effect of Baseline Brain Activity on Response to Low‐Frequency rTMS/Intensive Occupational Therapy in Poststroke Patients With Upper Limb Hemiparesis: A Near‐Infrared Spectroscopy Study,” International Journal of Neuroscience 129, no. 4 (2019): 337–343.
S. V. Tupak , T. Dresler , M. Badewien , et al., “Inhibitory Transcranial Magnetic Theta Burst Stimulation Attenuates Prefrontal Cortex Oxygenation,” Human Brain Mapping 34, no. 1 (2013): 150–157.
A. Guhn , T. Dresler , M. Andreatta , et al., “Medial Prefrontal Cortex Stimulation Modulates the Processing of Conditioned Fear,” Frontiers in Behavioral Neuroscience 8 (2014): 44.
M. J. Maier , D. Rosenbaum , F. B. Haeussinger , et al., “Forgiveness and Cognitive Control–Provoking Revenge via Theta‐Burst‐Stimulation of the DLPFC,” NeuroImage 183 (2018): 769–775.
H. Guo , X. He , M. Liu , Z. Zhang , H. Zhenhua , and J. Tian , “Weight Multispectral Reconstruction Strategy for Enhanced Reconstruction Accuracy and Stability With Cerenkov Luminescence Tomography,” IEEE Transactions on Medical Imaging 36, no. 6 (2017): 1337–1346.
Y. Yamashita , A. Maki , and H. Koizumi , “Wavelength Dependence of the Precision of Noninvasive Optical Measurement of Oxy‐, Deoxy‐, and Total‐Hemoglobin Concentration,” Medical Physics 28, no. 6 (2001): 1108–1114.
P. Taroni , D. Comelli , A. Pifferi , A. Torricelli , and R. Cubeddu , “Absorption of Collagen: Effects on the Estimate of Breast Composition and Related Diagnostic Implications,” Journal of Biomedical Optics 12, no. 1 (2007): 014021–014021.
K. R. Ward , R. R. Ivatury , R. W. Barbee , et al., “Near Infrared Spectroscopy for Evaluation of the Trauma Patient: A Technology Review,” Resuscitation 68, no. 1 (2006): 27–44.
F. Scholkmann , S. Kleiser , A. J. Metz , et al., “A Review on Continuous Wave Functional Near‐Infrared Spectroscopy and Imaging Instrumentation and Methodology,” NeuroImage 85 (2014): 6–27.
X. Song , X. Chen , Z. Wang , X. An , and D. Ming , “MBLL With Weighted Partial Path Length for Multi‐Distance Probe Configuration of fNIRS,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2019 (IEEE), 4766–4769.
X. Chen , X. Song , L. Chen , X. An , and D. Ming , “Performance Improvement for Detecting Brain Function Using fNIRS: A Multi‐Distance Probe Configuration With PPL Method,” Frontiers in Human Neuroscience 14 (2020): 569508.
A. M. Chiarelli , D. Perpetuini , C. Filippini , D. Cardone , and A. Merla , “Differential Pathlength Factor in Continuous Wave Functional Near‐Infrared Spectroscopy: Reducing Hemoglobin's Cross Talk in High‐Density Recordings,” Neurophotonics 6, no. 3 (2019): 035005–035005.
R. Huang and K.‐S. Hong , “Multi‐Channel‐Based Differential Pathlength Factor Estimation for Continuous‐Wave fNIRS,” IEEE Access 9 (2021): 37386–37396.
O. Seidel , D. Carius , J. Roediger , S. Rumpf , and P. Ragert , “Changes in Neurovascular Coupling During Cycling Exercise Measured by Multi‐Distance fNIRS: A Comparison Between Endurance Athletes and Physically Active Controls,” Experimental Brain Research 237 (2019): 2957–2972.
P. Bouguer , “Essai D'optique Sur la Gradation de la lumière,” in Essai D’optique Sur la Gradation de la lumière, ed. C. Jombert (1729).
A. Beer and P. Beer , “Determination of the Absorption of Red Light in Colored Liquids,” Annalen der Physik Und Chemie 86, no. 5 (1852): 78–88.
S. Perrey , “Non‐Invasive NIR Spectroscopy of Human Brain Function During Exercise,” Methods 45, no. 4 (2008): 289–299.
Y. Hoshi , “Functional Near‐Infrared Optical Imaging: Utility and Limitations in Human Brain Mapping,” Psychophysiology 40, no. 4 (2003): 511–520.
Z. Einalou , K. Maghooli , S. K. Setarehdan , and A. Akin , “Graph Theoretical Approach to Functional Connectivity in Prefrontal Cortex via fNIRS,” Neurophotonics 4, no. 4 (2017): 041407–041407.
M. A. Yücel , A. V. Lühmann , F. Scholkmann , et al., “Best Practices for fNIRS Publications,” Neurophotonics 8, no. 1 (2021): 012101.
D. T. Delpy , M. Cope , P. van der Zee , S. Arridge , S. Wray , and J. S. Wyatt , “Estimation of Optical Pathlength Through Tissue From Direct Time of Flight Measurement,” Physics in Medicine & Biology 33, no. 12 (1988): 1433.
H. Obrig and A. Villringer , “Beyond the Visible—Imaging the Human Brain With Light,” Journal of Cerebral Blood Flow & Metabolism 23, no. 1 (2003): 1–18.
S. Fantini and A. Sassaroli , “Frequency‐Domain Techniques for Cerebral and Functional Near‐Infrared Spectroscopy,” Frontiers in Neuroscience 14 (2020): 300.
M. Izzetoglu and R. Holtzer , “Effects of Processing Methods on fNIRS Signals Assessed During Active Walking Tasks in Older Adults,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 3 (2020): 699–709.
E. Gratton , W. W. Mantulin , M. J. v. Ven , J. B. Fishkin , M. B. Maris , and B. Chance , “A Novel Approach to Laser Tomography,” Bioimaging 1, no. 1 (1993): 40–46.
A. Duncan , J. H. Meek , M. Clemence , et al., “Optical Pathlength Measurements on Adult Head, Calf and Forearm and the Head of the Newborn Infant Using Phase Resolved Optical Spectroscopy,” Physics in Medicine & Biology 40, no. 2 (1995): 295.
R. A. De Blasi , S. Fantini , M. A. Franceschini , M. Ferrari , and E. Gratton , “Cerebral and Muscle Oxygen Saturation Measurement by Frequency‐Domain Near‐Infra‐Red Spectrometer,” Medical and Biological Engineering and Computing 33 (1995): 228–230.
L. Wj , “Near‐Infrared Measurement of Cerebral Oxygenation: Collelation With Electroencephalographic Ischemia During Ventricular Fibrillation,” Anesthesiology 83 (1995): 738–746.
C. W. Davies , “Quantification of Optical Parameters Using Frequency Domain Functional Near‐Infrared Spectroscopy (FD‐fNIRS),” (Master's Thesis, Wright State University), 2019.
I. J. Bigio and S. Fantini , Quantitative Biomedical Optics: Theory, Methods, and Applications (Cambridge University Press, 2016).
J. B. Fishkin and E. Gratton , “Propagation of Photon‐Density Waves in Strongly Scattering Media Containing an Absorbing Semi‐Infinite Plane Bounded by a Straight Edge,” Journal of the Optical Society of America A 10, no. 1 (1993): 127–140.
B. J. Tromberg , L. O. Svaasand , T.‐T. Tsay , and R. C. Haskell , “Properties of Photon Density Waves in Multiple‐Scattering Media,” Applied Optics 32, no. 4 (1993): 607–616.
G. Blaney , A. Sassaroli , T. Pham , C. Fernandez , and S. Fantini , “Phase Dual‐Slopes in Frequency‐Domain Near‐Infrared Spectroscopy for Enhanced Sensitivity to Brain Tissue: First Applications to Human Subjects,” Journal of Biophotonics 13, no. 1 (2020): e201960018.
A. Sassaroli and S. Fantini , “Comment on the Modified Beer–Lambert Law for Scattering Media,” Physics in Medicine & Biology 49, no. 14 (2004): N255.
A. Sassaroli , G. Blaney , and S. Fantini , “Dual‐Slope Method for Enhanced Depth Sensitivity in Diffuse Optical Spectroscopy,” Journal of the Optical Society of America A 36, no. 10 (2019): 1743–1761.
E. M. Sevick , B. Chance , J. Leigh , S. Nioka , and M. Maris , “Quantitation of Time‐and Frequency‐Resolved Optical Spectra for the Determination of Tissue Oxygenation,” Analytical Biochemistry 195, no. 2 (1991): 330–351.
G. Blaney , C. Fernandez , A. Sassaroli , and S. Fantini , “Dual‐Slope Imaging of Cerebral Hemodynamics With Frequency‐Domain Near‐Infrared Spectroscopy,” Neurophotonics 10, no. 1 (2023): 013508–013508.
R. C. Haskell , L. O. Svaasand , T.‐T. Tsay , T.‐C. Feng , M. S. McAdams , and B. J. Tromberg , “Boundary Conditions for the Diffusion Equation in Radiative Transfer,” Journal of the Optical Society of America A 11, no. 10 (1994): 2727–2741.
T. Kerz , C. Beyer , A. Huthmann , et al., “Continuous‐Wave Near‐Infrared Spectroscopy Is Not Related to Brain Tissue Oxygen Tension,” Journal of Clinical Monitoring and Computing 30 (2016): 641–647.
D. J. Davies , M. Clancy , D. Lighter , et al., “Frequency‐Domain vs Continuous‐Wave Near‐Infrared Spectroscopy Devices: A Comparison of Clinically Viable Monitors in Controlled Hypoxia,” Journal of Clinical Monitoring and Computing 31 (2017): 967–974.
A. Torricelli , D. Contini , A. Pifferi , et al., “Time Domain Functional NIRS Imaging for Human Brain Mapping,” NeuroImage 85 (2014): 28–50.
R. Re , I. Pirovano , D. Contini , et al., “Reliable Fast (20 Hz) Acquisition Rate by a TD fNIRS Device: Brain Resting‐State Oscillation Studies,” Sensors 23, no. 1 (2022): 196.
A. Ortega‐Martinez , D.’. J. Rogers , J. Anderson , et al., “How Much Do Time‐Domain Functional Near‐Infrared Spectroscopy (fNIRS) Moments Improve Estimation of Brain Activity Over Traditional fNIRS?,” Neurophotonics 10, no. 1 (2023): 013504–013504.
M. S. Patterson , B. Chance , and B. C. Wilson , “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical Properties,” Applied Optics 28, no. 12 (1989): 2331–2336.
Y. Yamada , H. Suzuki , and Y. Yamashita , “Time‐Domain Near‐Infrared Spectroscopy and Imaging: A Review,” Applied Sciences 9, no. 6 (2019): 1127.
H. Wabnitz , D. Contini , L. Spinelli , A. Torricelli , and A. Liebert , “Depth‐Selective Data Analysis for Time‐Domain fNIRS: Moments vs. Time Windows,” Biomedical Optics Express 11, no. 8 (2020): 4224–4243.
L. Zucchelli , D. Contini , R. Re , A. Torricelli , and L. Spinelli , “Method for the Discrimination of Superficial and Deep Absorption Variations by Time Domain fNIRS,” Biomedical Optics Express 4, no. 12 (2013): 2893–2910.
E. Kirilina , Y. Na , A. Jelzow , H. Wabnitz , A. M. Jacobs , and I. Tachtsidis , “Identifying and Quantifying Main Components of Physiological Noise in Functional Near Infrared Spectroscopy on the Prefrontal Cortex,” Frontiers in Human Neuroscience 7 (2013): 864.
A. Jelzow , H. Wabnitz , I. Tachtsidis , E. Kirilina , R. Brühl , and R. Macdonald , “Separation of Superficial and Cerebral Hemodynamics Using a Single Distance Time‐Domain NIRS Measurement,” Biomedical Optics Express 5, no. 5 (2014): 1465–1482.
M. Muthalib , R. Re , L. Zucchelli , et al., “Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study,” PLoS One 10, no. 7 (2015): e0131951.
Y. Xia , J. Chen , J. Li , et al., “A Deep‐Learning Empowered, Real‐Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (2025).
J. Uchitel , B. Blanco , E. Vidal‐Rosas , L. Collins‐Jones , and R. J. Cooper , “Reliability and Similarity of Resting State Functional Connectivity Networks Imaged Using Wearable, High‐Density Diffuse Optical Tomography in the Home Setting,” NeuroImage 263 (2022): 119663.
H. Zhao , E. M. Frijia , E. V. Rosas , et al., “Design and Validation of a Mechanically Flexible and Ultra‐Lightweight High‐Density Diffuse Optical Tomography System for Functional Neuroimaging of Newborns,” Neurophotonics 8, no. 1 (2021): 015011–015011.
Z. E. Markow , J. W. Trobaugh , E. J. Richter , et al., “Ultra High Density Imaging Arrays in Diffuse Optical Tomography for Human Brain Mapping Improve Image Quality and Decoding Performance,” Scientific Reports 15, no. 1 (2025): 3175.
J. Chen , H. Yang , Y. Xia , et al., “Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High‐Density Diffuse Optical Tomography,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (2025).
Z. E. Markow , J. W. Trobaugh , E. J. Richter , et al., “Ultra‐High Density Imaging Arrays for Diffuse Optical Tomography of Human Brain Improve Resolution, Signal‐To‐Noise, and Information Decoding,” bioRxiv (2023) 2023–07.
E. E. Vidal‐Rosas , A. Von Lühmann , P. Pinti , and R. J. Cooper , “Wearable, High‐Density fNIRS and Diffuse Optical Tomography Technologies: A Perspective,” Neurophotonics 10, no. 2 (2023): 023513–023513.
A. Sherafati , A. Z. Snyder , A. T. Eggebrecht , et al., “Global Motion Detection and Censoring in High‐Density Diffuse Optical Tomography,” Human Brain Mapping 41, no. 14 (2020): 4093–4112.
E. E. Vidal‐Rosas , H. Zhao , R. W. Nixon‐Hill , et al., “Evaluating a New Generation of Wearable High‐Density Diffuse Optical Tomography Technology via Retinotopic Mapping of the Adult Visual Cortex,” Neurophotonics 8, no. 2 (2021): 025002–025002.
K. Tripathy , Z. E. Markow , A. K. Fishell , et al., “Decoding Visual Information From High‐Density Diffuse Optical Tomography Neuroimaging Data,” NeuroImage 226 (2021): 117516.
G. A. Perkins , A. T. Eggebrecht , and H. Dehghani , “Quantitative Evaluation of Frequency Domain Measurements in High Density Diffuse Optical Tomography,” Journal of Biomedical Optics 26, no. 5 (2021): 056001–056001.
B. R. White and J. P. Culver , “Quantitative Evaluation of High‐Density Diffuse Optical Tomography: In Vivo Resolution and Mapping Performance,” Journal of Biomedical Optics 15, no. 2 (2010): 026006–026006.
W. Fan , H. Dehghani , and A. T. Eggebrecht , “Investigation of Effect of Modulation Frequency on High‐Density Diffuse Optical Tomography Image Quality,” Neurophotonics 8, no. 4 (2021): 045002–045002.
Grant Information:
62201459 National Natural Science Foundation of China; 12271434 National Natural Science Foundation of China; 62271394 National Natural Science Foundation of China; 2025SF-YBXM-380 General Project of the Key Research and Development Plan of Shaanxi Province; 2025SF-YBXM-240 General Project of the Key Research and Development Plan of Shaanxi Province; 2023-JC-JQ-57 Natural Science Basic Research Program of Shaanxi Province; 2023KJXX-035 Scientific and Technology New Star in Shaanxi Province of China
Contributed Indexing:
Keywords: applications; continuous wave fNIRS; diffuse optical tomography fNIRS; frequency domain fNIRS; physical principles; time domain fNIRS
Entry Date(s):
Date Created: 20250812 Date Completed: 20251116 Latest Revision: 20251116
Update Code:
20260130
DOI:
10.1002/jbio.202500147
PMID:
40796273
Database:
MEDLINE
Weitere Informationen
Functional near-infrared spectroscopy (fNIRS) is an imaging technique that uses near-infrared light to monitor blood oxygen level changes in the cerebral cortex and noninvasively study brain function. This review provides an overview of the expanding applications and physical principles of fNIRS to enhance understanding of its imaging process and promote awareness of its broad applicability and potential. We systematically searched PubMed, Web of Science, and Google Scholar, analyzing studies on fNIRS applications in psychiatry, neurology, education, and multimodal imaging. We also introduce the imaging principles of continuous wave fNIRS, frequency domain fNIRS, time domain fNIRS, and diffuse optical tomography fNIRS (including high-density diffuse optical tomography). These studies have demonstrated the potential of fNIRS technology in measuring cerebral hemodynamics with high temporal and spatial resolution. The results of this review indicate that fNIRS is a versatile neuroimaging tool with great potential in research and clinical applications.
(© 2025 Wiley‐VCH GmbH.)