Cerebral Neurovascular Networks May Serve as Potential Targets for Identifying Disorders of Consciousness: A Synchronous Electroencephalography and Functional Near-Infrared Spectroscopy Study

Nan Wang , Juanning Si , Yifang He , Jiuxiang Song , Xiaoke Chai , Dongsheng Liu , Jingqi Li , Tan Zhang , Tianqing Cao , Qiheng He , Sipeng Zhu , Yitong Jia , Wenbin Ma , Yi Yang , Jizong Zhao

MedComm ›› 2025, Vol. 6 ›› Issue (12) : e70530

PDF
MedComm ›› 2025, Vol. 6 ›› Issue (12) :e70530 DOI: 10.1002/mco2.70530
ORIGINAL ARTICLE
Cerebral Neurovascular Networks May Serve as Potential Targets for Identifying Disorders of Consciousness: A Synchronous Electroencephalography and Functional Near-Infrared Spectroscopy Study
Author information +
History +
PDF

Abstract

The diagnosis and management of disorders of consciousness (DoC) remain a critical challenge in clinical medicine and neuroscience. The key bottleneck is the lack of reliable biomarkers and an incomplete understanding of the pathophysiological mechanisms that underlie DoC. In view of this, a bedside-compatible, multimodal technique based on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was utilized to simultaneously capture neuronal oscillations and accompanying hemodynamics, so as to explore neurovascular biomarkers that can effectively discriminate different states of DoC. Resting-state EEG-fNIRS data from 13 regions of interest (ROIs) were acquired and compared across healthy controls (HC), minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS) groups. Hemodynamics-based functional connectivity and the spectral power of neuronal activity were quantified and subsequently employed to interrogate neurovascular coupling. The results demonstrated significantly stronger neurovascular coupling and beta-band power in premotor and Broca's areas of the MCS group. A multimodal classifier achieved an accuracy of 87.9% in distinguishing between MCS and UWS. The noninvasive, bedside-suitable nature of this tool underscores its potential for routine monitoring and prognostic assessment in DoC, addressing a critical need for accessible and reliable biomarkers in both neurology and intensive-care practice.

Keywords

disorders of consciousness / electroencephalography / functional near-infrared spectroscopy / neurovascular coupling / noninvasive brain–computer interfaces / resting state

Cite this article

Download citation ▾
Nan Wang, Juanning Si, Yifang He, Jiuxiang Song, Xiaoke Chai, Dongsheng Liu, Jingqi Li, Tan Zhang, Tianqing Cao, Qiheng He, Sipeng Zhu, Yitong Jia, Wenbin Ma, Yi Yang, Jizong Zhao. Cerebral Neurovascular Networks May Serve as Potential Targets for Identifying Disorders of Consciousness: A Synchronous Electroencephalography and Functional Near-Infrared Spectroscopy Study. MedComm, 2025, 6(12): e70530 DOI:10.1002/mco2.70530

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

B. L. Edlow, J. Claassen, N. D. Schiff, and D. M. Greer, “Recovery From Disorders of Consciousness: Mechanisms, Prognosis and Emerging Therapies,” Nature Reviews Neurology 17, no. 3 (2021): 135–156.

[2]

D. Kondziella, A. Bender, K. Diserens, et al., “European Academy of Neurology Guideline on the Diagnosis of Coma and Other Disorders of Consciousness,” European Journal of Neurology 27, no. 5 (2020): 741–756.

[3]

M. M. Monti, A. Vanhaudenhuyse, M. R. Coleman, et al., “Willful Modulation of Brain Activity in Disorders of Consciousness,” New England Journal of Medicine 362, no. 7 (2010): 579–589.

[4]

A. M. Owen, M. R. Coleman, M. Boly, et al., “Detecting Awareness in the Vegetative State,” Science 313, no. 5792 (2006): 1402.

[5]

H. Yuan, V. Zotev, R. Phillips, W. C. Drevets, and J. Bodurka, “Spatiotemporal Dynamics of the Brain at Rest–Exploring EEG Microstates as Electrophysiological Signatures of BOLD Resting State Networks,” NeuroImage 60, no. 4 (2012): 2062–2072.

[6]

D. Rossi Sebastiano, F. Panzica, and E. Visani, “Significance of Multiple Neurophysiological Measures in Patients With Chronic Disorders of Consciousness,” Clinical Neurophysiology 126, no. 3 (2015): 558–564.

[7]

A. Naro, P. Bramanti, A. Leo, et al., “Towards a Method to Differentiate Chronic Disorder of Consciousness Patients' Awareness: The Low-Resolution Brain Electromagnetic Tomography Analysis,” Journal of the Neurological Sciences 368 (2016): 178–183.

[8]

J. Lechinger, K. Bothe, G. Pichler, et al., “CRS-R Score in Disorders of Consciousness Is Strongly Related to Spectral EEG at Rest,” Journal of Neurology 260, no. 9 (2013): 2348–2356.

[9]

J. D. Sitt, J. R. King, I. El Karoui, et al., “Large Scale Screening of Neural Signatures of Consciousness in Patients in a Vegetative or Minimally Conscious State,” Brain 137 (2014): 2258–2270.

[10]

P. Gui, Y. Jiang, and D. Zang, “Assessing the Depth of Language Processing in Patients With Disorders of Consciousness,” Nature Neuroscience 23, no. 6 (2020): 761–770.

[11]

E. Fló, D. Fraiman, and J. D. Sitt, “Assessing Brain-Muscle Networks During Motor Imagery to Detect Covert Command-Following,” BMC Medicine [Electronic Resource] 23, no. 1 (2025): 68.

[12]

J. Hu, C. Chen, M. Wu, et al., “Assessing Consciousness in Acute Coma Using Name-Evoked Responses,” Brain Research Bulletin 218 (2024): 111091.

[13]

S. Chennu, J. Annen, S. Wannez, et al., “Brain Networks Predict Metabolism, Diagnosis and Prognosis at the Bedside in Disorders of Consciousness,” Brain 140, no. 8 (2017): 2120–2132.

[14]

V. Tarantino, M. L. Fontana, A. Buttà, et al., “Increase in EEG Alpha-to-Theta Ratio After Transcranial Direct Current Stimulation (tDCS) in Patients With Disorders of Consciousness: A Pilot Study,” Neurorehabilitation 55, no. 4 (2024): 440–447.

[15]

Z. Liu, S. Wu, S. Wang, et al., “Can Repetitive Transcranial Magnetic Stimulation Promote Recovery of Consciousness in Patients With Disorders of Consciousness? A Randomized Controlled Trial,” NeuroImage: Clinical 46 (2025): 103802.

[16]

J. Sun, J. Yan, L. Zhao, et al., “Spinal Cord Stimulation for Prolonged Disorders of Consciousness: A Study on Scalp Electroencephalography,” CNS Neuroscience & Therapeutics 30, no. 12 (2024): e70180.

[17]

T. Hosseinian, F. Yavari, M. C. Biagi, et al., “External Induction and Stabilization of Brain Oscillations in the Human,” Brain Stimulation 14, no. 3 (2021): 579–587.

[18]

K. Kazazian, M. M. Monti, and A. M. Owen, “Functional Neuroimaging in Disorders of Consciousness: Towards Clinical Implementation,” Brain 148, no. 7 (2025): 2283–2298.

[19]

J. Stender, O. Gosseries, M. A. Bruno, et al., “Diagnostic Precision of PET Imaging and Functional MRI in Disorders of Consciousness: A Clinical Validation Study,” Lancet 384, no. 9942 (2014): 514–522.

[20]

J. T. Giacino, D. I. Katz, N. D. Schiff, et al., “Practice Guideline Update Recommendations Summary: Disorders of Consciousness: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research,” Neurology 91, no. 10 (2018): 450–460.

[21]

A. Thibaut, N. Schiff, J. Giacino, S. Laureys, and O. Gosseries, “Therapeutic Interventions in Patients With Prolonged Disorders of Consciousness,” Lancet Neurology 18, no. 6 (2019): 600–614.

[22]

A. M. Owen, “The Search for Consciousness,” Neuron 102, no. 3 (2019): 526–528.

[23]

Y. G. Bodien, J. Allanson, P. Cardone, et al., “Cognitive Motor Dissociation in Disorders of Consciousness,” New England Journal of Medicine 391, no. 7 (2024): 598–608.

[24]

R. Jain and A. G. Ramakrishnan, “Electrophysiological and Neuroimaging Studies—During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review,” Frontiers in Neuroscience 14 (2020): 555093.

[25]

Y. He, N. Wang, D. Liu, et al., “Assessment of Residual Awareness in Patients With Disorders of Consciousness Using Functional Near-Infrared Spectroscopy-Based Connectivity: A Pilot Study,” Neurophotonics 11, no. 4 (2024): 045013.

[26]

A. M. Kempny, L. James, K. Yelden, et al., “Functional Near Infrared Spectroscopy as a Probe of Brain Function in People With Prolonged Disorders of Consciousness,” NeuroImage: Clinical 12 (2016): 312–319.

[27]

Y. Luo, L. Wang, Y. Yang, et al., “Exploration of Resting-State Brain Functional Connectivity as Preclinical Markers for Arousal Prediction in Prolonged Disorders of Consciousness: A Pilot Study Based on Functional Near-Infrared Spectroscopy,” Brain and Behavior 14, no. 8 (2024): e70002.

[28]

Y. Wang, W. Zeng, L. Zou, et al., “Detecting Cognitive Motor Dissociation by Functional Near-Infrared Spectroscopy,” Frontiers in Neurology 16 (2025): 1532804.

[29]

J. Lu, J. Wu, Z. Shu, et al., “Brain Temporal-Spectral Functional Variability Reveals Neural Improvements of DBS Treatment for Disorders of Consciousness,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 32 (2024): 923–933.

[30]

Y. Zhang, Y. Yang, J. Si, et al., “Influence of Inter-Stimulus Interval of Spinal Cord Stimulation in Patients With Disorders of Consciousness: A Preliminary Functional Near-Infrared Spectroscopy Study,” NeuroImage: Clinical 17 (2018): 1–9.

[31]

H. Peng, Q. Ge, T. Xu, et al., “Repetitive Transcranial Magnetic Stimulation Frequency Influences the Hemodynamic Responses in Patients With Disorders of Consciousness,” Neuroscience Research 213 (2025): 72–85.

[32]

W. Li, C. Gao, Z. Li, et al., “BrainFusion: A Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research,” Advanced Science 12, no. 32 (2025): e17408.

[33]

W. Yi, J. Chen, D. Wang, et al., “A Multi-Modal Dataset of Electroencephalography and Functional Near-Infrared Spectroscopy Recordings for Motor Imagery of Multi-Types of Joints From Unilateral Upper Limb,” Scientific Data 12, no. 1 (2025): 953.

[34]

R. Li, D. Yang, F. Fang, et al., “Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review,” Sensors 22, no. 15 (2022): 5865.

[35]

R. Li, T. Potter, W. Huang, and Y. Zhang, “Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features,” Frontiers in Human Neuroscience 11 (2017): 462.

[36]

X. Yin, B. Xu, C. Jiang, et al., “A Hybrid BCI Based on EEG and fNIRS Signals Improves the Performance of Decoding Motor Imagery of Both Force and Speed of Hand Clenching,” Journal of Neural Engineering 12, no. 3 (2015): 036004.

[37]

M. H. Othman, M. Bhattacharya, K. Møller, et al., “Resting-State NIRS-EEG in Unresponsive Patients With Acute Brain Injury: A Proof-of-Concept Study,” Neurocritical Care 34, no. 1 (2021): 31–44.

[38]

R. Blanco, M. G. Preti, C. Koba, D. V. Ville, and A. Crimi, “Comparing Structure-Function Relationships in Brain Networks Using EEG and fNIRS,” Scientific Reports 14, no. 1 (2024): 28976.

[39]

N. Scolding, A. M. Owen, and J. Keown, “Prolonged Disorders of Consciousness: A Critical Evaluation of the New UK Guidelines,” Brain 144, no. 6 (2021): 1655–1660.

[40]

X. Liu, K. K. Lauer, and B. Douglas Ward, “Propofol Attenuates Low-Frequency Fluctuations of Resting-State fMRI BOLD Signal in the Anterior Frontal Cortex Upon Loss of Consciousness,” Neuroimage 147 (2017): 295–301.

[41]

A. Demertzi, G. Antonopoulos, L. Heine, et al., “Intrinsic Functional Connectivity Differentiates Minimally Conscious From Unresponsive Patients,” Brain 138 (2015): 2619–2631.

[42]

M. Amiri, P. M. Fisher, F. Raimondo, et al., “Multimodal Prediction of Residual Consciousness in the Intensive Care Unit: The CONNECT-ME Study,” Brain 146, no. 1 (2023): 50–64.

[43]

M. Kolisnyk, K. Kazazian, K. Rego, et al., “Predicting Neurologic Recovery After Severe Acute Brain Injury Using Resting-State Networks,” Journal of Neurology 270, no. 12 (2023): 6071–6080.

[44]

S. Chennu, P. Finoia, E. Kamau, et al., “Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness,” Plos Computational Biology 10, no. 10 (2014): e1003887.

[45]

S. Chennu, S. O'Connor, R. Adapa, D. K. Menon, and T. A. Bekinschtein, “Brain Connectivity Dissociates Responsiveness From Drug Exposure During Propofol-Induced Transitions of Consciousness,” Plos Computational Biology 12, no. 1 (2016): e1004669.

[46]

A. Piarulli, M. Bergamasco, A. Thibaut, et al., “EEG Ultradian Rhythmicity Differences in Disorders of Consciousness During Wakefulness,” Journal of Neurology 263, no. 9 (2016): 1746–1760.

[47]

A. Naro, A. Bramanti, A. Leo, et al., “Shedding New Light on Disorders of Consciousness Diagnosis: The Dynamic Functional Connectivity,” Cortex 103 (2018): 316–328.

[48]

S. Laureys, A. M. Owen, and N. D. Schiff, “Brain Function in Coma, Vegetative State, and Related Disorders,” Lancet Neurology 3, no. 9 (2004): 537–546.

[49]

S. Laureys and N. D. Schiff, “Coma and Consciousness: Paradigms (re)Framed by Neuroimaging,” NeuroImage 61, no. 2 (2012): 478–491.

[50]

J. Si, Y. Yang, L. Xu, et al., “Evaluation of Residual Cognition in Patients With Disorders of Consciousness Based on Functional Near-Infrared Spectroscopy,” Neurophotonics 10, no. 2 (2023): 025003.

[51]

J. M. Shine, P. G. Bissett, P. T. Bell, et al., “The Dynamics of Functional Brain Networks: Integrated Network States During Cognitive Task Performance,” Neuron 92, no. 2 (2016): 544–554.

[52]

J. Li, W. H. Curley, B. Guerin, et al., “Mapping the Subcortical Connectivity of the Human Default Mode Network,” NeuroImage 245 (2021): 118758.

[53]

E. Juan, U. Górska, and C. Kozma, “Distinct Signatures of Loss of Consciousness in Focal Impaired Awareness Versus Tonic-Clonic Seizures,” Brain 146, no. 1 (2023): 109–123.

[54]

S. Laureys, “The Neural Correlate of (un)Awareness: Lessons From the Vegetative State,” Trends in Cognitive Sciences 9, no. 12 (2005): 556–559.

[55]

J. Xiong, L. M. Parsons, J. H. Gao, and P. T. Fox, “Interregional Connectivity to Primary Motor Cortex Revealed Using MRI Resting State Images,” Human Brain Mapping 8, no. 2–3 (1999): 151–156.

[56]

S. Laureys, M. E. Faymonville, C. Degueldre, et al., “Auditory Processing in the Vegetative State,” Brain 123 (2000): 1589–1601.

[57]

J. J. Foxe and A. C. Snyder, “The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism During Selective Attention,” Frontiers in Psychology 2 (2011): 154.

[58]

G. Northoff and F. Zilio, “Temporo-Spatial Theory of Consciousness (TTC)—Bridging the Gap of Neuronal Activity and Phenomenal States,” Behavioural Brain Research 424 (2022): 113788.

[59]

M. E. Raichle and M. A. Mintun, “Brain Work and Brain Imaging,” Annual Review of Neuroscience 29 (2006): 449–476.

[60]

C. Koch, M. Massimini, M. Boly, and G. Tononi, “Neural Correlates of Consciousness: Progress and Problems,” Nature Reviews Neuroscience 17, no. 5 (2016): 307–321.

[61]

M. Lundqvist, E. K. Miller, J. Nordmark, J. Liljefors, and P. Herman, “Beta: Bursts of Cognition,” Trends in Cognitive Sciences 28, no. 7 (2024): 662–676.

[62]

M. Lundqvist, J. Rose, P. Herman, et al., “Gamma and Beta Bursts Underlie Working Memory,” Neuron 90, no. 1 (2016): 152–164.

[63]

C. Li, P. Chen, Y. Deng, et al., “Abnormalities of Cortical and Subcortical Spontaneous Brain Activity Unveil Mechanisms of Disorders of Consciousness and Prognosis in Patients With Severe Traumatic Brain Injury,” International Journal of Clinical and Health Psychology 24, no. 4 (2024): 100528.

[64]

H. Mao, H. Meng, Q. Tan, et al., “Evaluation of Neurovascular Coupling Behaviors for FES-Induced Wrist Movements Based on Synchronized EEG-fNIRS Signals,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 33 (2025): 2622–2630.

[65]

J. Chen, Q. Liu, G. Chen, et al., “iTBS on RDLPFC Improves Performance of Motor Imagery: A Brain-Computer Interface Study Combining EEG and fNIRS,” Journal of NeuroEngineering and Rehabilitation 22, no. 1 (2025): 172.

[66]

J. Chen, K. Yu, Y. Bi, X. Ji, and D. Zhang, “Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications,” Brain Sciences 14, no. 10 (2024): 1022.

[67]

X. Sun, C. Dai, X. Wu, et al., “Current Implications of EEG and fNIRS as Functional Neuroimaging Techniques for Motor Recovery After Stroke,” Medical Review 4, no. 6 (2024): 492–509.

[68]

A. K. Engel and P. Fries, “Beta-Band Oscillations–Signalling the Status Quo?,” Current Opinion in Neurobiology 20, no. 2 (2010): 156–165.

[69]

H. Li, X. Zhang, X. Sun, et al., “Corrigendum: Functional Networks in Prolonged Disorders of Consciousness,” Frontiers in Neuroscience 17 (2023): 1208095.

[70]

A. Mitra, A. Z. Snyder, E. Tagliazucchi, H. Laufs, and M. E. Raichle, “Propagated Infra-Slow Intrinsic Brain Activity Reorganizes Across Wake and Slow Wave Sleep,” eLife 4 (2015): e10781.

[71]

P. Qin, X. Wu, C. Wu, et al., “Higher-Order Sensorimotor Circuit of the Brain's Global Network Supports Human Consciousness,” NeuroImage 231 (2021): 117850.

[72]

N. D. Schiff, “Cognitive Motor Dissociation Following Severe Brain Injuries,” JAMA Neurology 72, no. 12 (2015): 1413–1415.

[73]

D. Kondziella, C. K. Friberg, V. G. Frokjaer, M. Fabricius, and K. Møller, “Preserved Consciousness in Vegetative and Minimal Conscious States: Systematic Review and Meta-Analysis,” Journal of Neurology, Neurosurgery, and Psychiatry 87, no. 5 (2016): 485–492.

[74]

G. Laforge, M. Kolisnyk, S. Novi, et al., “Parallel EEG-fNIRS Assessments of Covert Cognition in Behaviorally Non-Responsive ICU Patients: A Multi-Task Feasibility Study in a Case of Acute Motor Sensory Axonal Neuropathy,” Journal of Neurology 272, no. 2 (2025): 148.

[75]

H. Morioka, A. Kanemura, S. Morimoto, et al., “Decoding Spatial Attention by Using Cortical Currents Estimated From Electroencephalography With Near-Infrared Spectroscopy Prior Information,” NeuroImage 90 (2014): 128–139.

[76]

S. Fazli, J. Mehnert, J. Steinbrink, et al., “Enhanced Performance by a Hybrid NIRS-EEG Brain Computer Interface,” NeuroImage 59, no. 1 (2012): 519–529.

[77]

H. Khan, N. Naseer, A. Yazidi, et al., “Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review,” Frontiers in Human Neuroscience 14 (2020): 613254.

[78]

M. Naddaf, “Science in 2025: The Events to Watch for in the Coming Year,” Nature 637, no. 8044 (2025): 9–11.

[79]

C. C. Serdar, M. Cihan, D. Yücel, and M. A. Serdar, “Sample Size, Power and Effect Size Revisited: Simplified and Practical Approaches in Pre-Clinical, Clinical and Laboratory Studies,” Biochemia Medica 31, no. 1 (2021): 010502.

[80]

S. Getzmann, P. D. Gajewski, D. Schneider, and E. Wascher, “Resting-State EEG Data Before and After Cognitive Activity Across the Adult Lifespan and a 5-Year Follow-Up,” Scientific Data 11, no. 1 (2024): 988.

[81]

M. Hill, M. Moreda, J. Navarro, and M. Mulkey, “Assessing Patients With Altered Level of Consciousness,” Critical Care Nurse 43, no. 4 (2023): 58–65.

[82]

P. Liuzzi, A. Grippo, S. Campagnini, et al., “Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022): 1504–1513.

[83]

N. Wang, Y. He, S. Zhu, et al., “Functional Near-Infrared Spectroscopy for the Assessment and Treatment of Patients With Disorders of Consciousness,” Frontiers in Neurology 16 (2025): 1524806.

[84]

J. L. Bernat, “Chronic Disorders of Consciousness,” Lancet 367, no. 9517 (2006): 1181–1192.

[85]

J. T. Giacino, K. Kalmar, and J. Whyte, “The JFK Coma Recovery Scale-Revised: Measurement Characteristics and Diagnostic Utility,” Archives of Physical Medicine and Rehabilitation 85, no. 12 (2004): 2020–2029.

[86]

J. T. Giacino, S. Ashwal, N. Childs, et al., “The Minimally Conscious State: Definition and Diagnostic Criteria,” Neurology 58, no. 3 (2002): 349–353.

[87]

G. Barbati, C. Porcaro, F. Zappasodi, P. M. Rossini, and F. Tecchio, “Optimization of an Independent Component Analysis Approach for Artifact Identification and Removal in Magnetoencephalographic Signals,” Clinical Neurophysiology 115, no. 5 (2004): 1220–1232.

[88]

P. Croce, F. Zappasodi, L. Marzetti, et al., “Deep Convolutional Neural Networks for Feature-Less Automatic Classification of Independent Components in Multi-Channel Electrophysiological Brain Recordings,” IEEE Transactions on Bio-Medical Engineering 66, no. 8 (2019): 2372–2380.

[89]

M. Akin and M. K. Kiymik, “Application of Periodogram and AR Spectral Analysis to EEG Signals,” Journal of Medical Systems 24, no. 4 (2000): 247–256.

[90]

J. Xu, X. Liu, J. Zhang, et al., “FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data,” BioMed Research International 2015 (2015): 248724.

[91]

L. Kocsis, P. Herman, and A. Eke, “The Modified Beer–Lambert Law Revisited,” Physics in Medicine & Biology 51, no. 5 (2006): N91.

[92]

M. Wang, Z. Hu, L. Liu, et al., “Disrupted Functional Brain Connectivity Networks in Children With Attention-Deficit/Hyperactivity Disorder: Evidence From Resting-State Functional Near-Infrared Spectroscopy,” Neurophotonics 7, no. 1 (2020): 015012.

[93]

M. Xia, J. Wang, and Y. He, “BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics,” PLoS ONE 8, no. 7 (2013): e68910.

[94]

X. Robin, N. Turck, and A. Hainard, “pROC: An Open-Source Package for R and S+ to Analyze and Compare ROC Curves,” BMC Bioinformatics [Electronic Resource] 12 (2011): 77.

RIGHTS & PERMISSIONS

2025 The Author(s). MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

PDF

3

Accesses

0

Citation

Detail

Sections
Recommended

/