{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T13:32:04Z","timestamp":1765632724007,"version":"3.48.0"},"posted":{"date-parts":[[2025,12,11]]},"group-title":"Neurology","reference-count":92,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,12,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Assessing consciousness at the bedside in the neurocritical care unit is complicated by sedation and other treatment effects. While EEG is commonly used, it offers a limited view of the neurovascular unit. We evaluated whether combining cerebral blood flow (CBF) features with EEG improves binary classification of consciousness in patients with severe brain injury.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We retrospectively analyzed 35 adults who underwent multimodal neuromonitoring. Signals were segmented into 30-min windows after each probe recalibration. We used parameters including CBF low-frequency bands (Band IV 0.027-0.073 Hz, Band V 0.01-0.027 Hz, and Band All 0-0.5 Hz) and EEG band powers (delta-beta), alpha-delta ratio (ADR), alpha\/(delta+theta) (ADTR), total power. A random forest (RF) model trained using K-fold cross-validation achieved optimal classification. Highly correlated features (r &gt; 0.8) were excluded from simultaneous use. Performance was summarized with ROC-AUC and accuracy, with confusion matrices shown for the best combinations.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Multimodal feature combinations significantly improved classification compared to EEG features alone. The best-performing combination (EEG ADR, total EEG power, and CBF Band V) achieved a ROC-AUC of 0.86 and an accuracy of 82%, representing up to 69% improvement over EEG-only models. This model also performed well on noninvasive optical blood flow data.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Combining EEG and CBF metrics, particularly low-frequency oscillations in perfusion fluctuations, enhances classification of consciousness in critically ill patients and may support future bedside tools for real-time neurovascular monitoring and in decision making about treatment and rehabilitation.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2025.12.09.25341792","type":"posted-content","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T23:25:14Z","timestamp":1765495514000},"source":"Crossref","is-referenced-by-count":0,"title":["Binary Classification of Consciousness Using Cerebral Blood Flow and EEG Features"],"prefix":"10.64898","author":[{"given":"Farzad","family":"Azizi Zade","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irfaan","family":"Dar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brandon","family":"Foreman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ulas","family":"Sunar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"issue":"12","key":"2025121305301754000_2025.12.09.25341792v1.1","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.tins.2019.08.011","article-title":"Covert Consciousness in the Intensive Care Unit","volume":"42","year":"2019","journal-title":"Trends Neurosci"},{"key":"2025121305301754000_2025.12.09.25341792v1.2","doi-asserted-by":"crossref","unstructured":"J. T. Giacino , D. I. Katz , N. D. Schiff , J. Whyte , E. J. Ashman , S. Ashwal , R. Barbano , F. M. Hammond , S. Laureys , G. S. F. Ling , R. Nakase-Richardson , R. T. Seel , S. Yablon , T. S. D. Getchius , G. S. Gronseth , and M. J. Armstrong, \u201cComprehensive systematic review update 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 th\u2026,\u201d Neurology 91(10), 461 (2018).","DOI":"10.1212\/WNL.0000000000005928"},{"key":"2025121305301754000_2025.12.09.25341792v1.3","doi-asserted-by":"crossref","unstructured":"C. Schnakers , A. Vanhaudenhuyse , J. Giacino , M. Ventura , M. Boly , S. Majerus , G. Moonen , and S. Laureys , \u201cDiagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment,\u201d BMC Neurol 9, (2009).","DOI":"10.1186\/1471-2377-9-35"},{"key":"2025121305301754000_2025.12.09.25341792v1.4","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1812757"},{"key":"2025121305301754000_2025.12.09.25341792v1.5","doi-asserted-by":"crossref","unstructured":"E. S. Rosenthal , S. Biswal , S. F. Zafar , K. L. O\u2019Connor , S. Bechek , A. V Shenoy , E. J. Boyle , M. M. Shafi , E. J. Gilmore , B. P. Foreman , N. Gaspard , T. M. Leslie-Mazwi , J. Rosand , D. B. Hoch , C. Ayata , S. S. Cash , A. J. Cole , A. B. Patel , and M. B. Westover , \u201cContinuous Electroencephalography Predicts Delayed Cerebral Ischemia after Subarachnoid Hemorrhage: A Prospective Study of Diagnostic Accuracy,\u201d Ann Neurol 83(5), 958 (2018).","DOI":"10.1002\/ana.25232"},{"key":"2025121305301754000_2025.12.09.25341792v1.6","doi-asserted-by":"crossref","unstructured":"B. Hermann , J. Stender , M. O. Habert , A. Kas , M. Denis-Valente , F. Raimondo , P. P\u00e9rez , B. Rohaut , J. D. Sitt , and L. Naccache , \u201cMultimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness,\u201d Neuroimage Clin 30, 102601 (2021).","DOI":"10.1016\/j.nicl.2021.102601"},{"key":"2025121305301754000_2025.12.09.25341792v1.7","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-020-01051-w"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.8","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.bbe.2021.11.003","article-title":"A novel approach for detection of consciousness level in comatose patients from EEG signals with 1-D convolutional neural network","volume":"42","year":"2022","journal-title":"Biocybern Biomed Eng"},{"key":"2025121305301754000_2025.12.09.25341792v1.9","doi-asserted-by":"crossref","unstructured":"S. Qu , X. Wu , Y. Tang , Q. Zhang , L. Huang , B. Cui , S. Jiao , Q. Sun , and F. Zeng , \u201cAnalyzing brain-activation responses to auditory stimuli improves the diagnosis of a disorder of consciousness by non-linear dynamic analysis of the EEG,\u201d Sci Rep 14(1), 17446 (2024).","DOI":"10.1038\/s41598-024-67825-w"},{"issue":"2","key":"2025121305301754000_2025.12.09.25341792v1.10","first-page":"1","article-title":"Consensus Summary Statement of the International Multidisciplinary Consensus Conference on Multimodality Monitoring in Neurocritical Care: A statement for healthcare professionals from the Neurocritical Care Society and the European Society of Intensive Care Medicine","volume":"21","year":"2014","journal-title":"Neurocrit Care"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.11","first-page":"50","article-title":"Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study","volume":"146","year":"2022","journal-title":"Brain"},{"key":"2025121305301754000_2025.12.09.25341792v1.12","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-023-01816-z"},{"key":"2025121305301754000_2025.12.09.25341792v1.13","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awx163"},{"key":"2025121305301754000_2025.12.09.25341792v1.14","doi-asserted-by":"crossref","unstructured":"H. M. Keijzer , P. A. M. Lange , F. J. A. Meijer , B. A. R. Tonino , M. J. Blans , C. J. M. Klijn , C. W. E. Hoedemaekers , J. Hofmeijer , and R. C. Helmich , \u201cMRI markers of brain network integrity relate to neurological outcome in postanoxic coma,\u201d Neuroimage Clin 36, 103171 (2022).","DOI":"10.1016\/j.nicl.2022.103171"},{"key":"2025121305301754000_2025.12.09.25341792v1.15","doi-asserted-by":"crossref","unstructured":"T. Kustermann , N. Ata Nguepnjo Nguissi , C. Pfeiffer , M. Haenggi , R. Kurmann , F. Zubler , M. Oddo , A. O. Rossetti , and M. De Lucia , \u201cBrain functional connectivity during the first day of coma reflects long-term outcome,\u201d Neuroimage Clin 27, 102295 (2020).","DOI":"10.1016\/j.nicl.2020.102295"},{"key":"2025121305301754000_2025.12.09.25341792v1.16","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awv169"},{"issue":"4","key":"2025121305301754000_2025.12.09.25341792v1.17","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1007\/s12264-018-0243-5","article-title":"Brain Network Studies in Chronic Disorders of Consciousness: Advances and Perspectives","volume":"34","year":"2018","journal-title":"Neuroscience Bulletin"},{"key":"2025121305301754000_2025.12.09.25341792v1.18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-68683-y"},{"key":"2025121305301754000_2025.12.09.25341792v1.19","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awv169"},{"key":"2025121305301754000_2025.12.09.25341792v1.20","doi-asserted-by":"crossref","unstructured":"L. Huang , Y. Zheng , Z. Zeng , M. Li , L. Zhang , and Y. Gao , \u201cFractional Amplitude of Low-Frequency Fluctuations and Functional Connectivity in Comatose Patients Subjected to Resting-State Functional Magnetic Resonance Imaging,\u201d Ann Indian Acad Neurol 22(2), 203 (2019).","DOI":"10.4103\/aian.AIAN_420_17"},{"key":"2025121305301754000_2025.12.09.25341792v1.21","doi-asserted-by":"publisher","DOI":"10.1073\/PNAS.2402723121"},{"key":"2025121305301754000_2025.12.09.25341792v1.22","doi-asserted-by":"crossref","unstructured":"M. H. Othman , M. Bhattacharya , K. M\u00f8ller , S. Kjeldsen , J. Grand , J. Kjaergaard , A. Dutta , and D. Kondziella , \u201cResting-State NIRS\u2013EEG in Unresponsive Patients with Acute Brain Injury: A Proof-of-Concept Study,\u201d Neurocrit Care 34(1), (2021).","DOI":"10.1007\/s12028-020-00971-x"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.23","first-page":"1","article-title":"Low frequency oscillations reflect neurovascular coupling and disappear after cerebral death","volume":"14","year":"2024","journal-title":"Scientific Reports"},{"key":"2025121305301754000_2025.12.09.25341792v1.24","doi-asserted-by":"crossref","unstructured":"G. Bicciato , E. Keller , M. Wolf , G. Brandi , S. Schulthess , S. G. Friedl , J. F. Willms , and G. Narula , \u201cIncrease in Low-Frequency Oscillations in fNIRS as Cerebral Response to Auditory Stimulation with Familiar Music,\u201d Brain Sci 12(1), 42 (2021).","DOI":"10.3390\/brainsci12010042"},{"key":"2025121305301754000_2025.12.09.25341792v1.25","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2022.10.002"},{"key":"2025121305301754000_2025.12.09.25341792v1.26","doi-asserted-by":"crossref","unstructured":"L. Bastian , T. N\u00e4her , A. Vorreuther , M. L\u00fchrs , A. Benitez Andonegui , P. Fries , L. Riecke , and B. Sorger , \u201cSensitive and specific fNIRS-based approach for awareness detection in disorders of consciousness: proof of principle in healthy adults,\u201d Neurophotonics 12(4), (2025).","DOI":"10.1117\/1.NPh.12.4.045001"},{"key":"2025121305301754000_2025.12.09.25341792v1.27","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2011.0250"},{"key":"2025121305301754000_2025.12.09.25341792v1.28","doi-asserted-by":"crossref","unstructured":"T. Durduran , R. Choe , W. B. Baker , and A. G. Yodh , \u201cDiffuse optics for tissue monitoring and tomography,\u201d Reports on Progress in Physics 73(7), (2010).","DOI":"10.1088\/0034-4885\/73\/7\/076701"},{"key":"2025121305301754000_2025.12.09.25341792v1.29","doi-asserted-by":"publisher","DOI":"10.1016\/j.earlhumdev.2006.09.003"},{"key":"2025121305301754000_2025.12.09.25341792v1.30","doi-asserted-by":"publisher","DOI":"10.1159\/000151642"},{"key":"2025121305301754000_2025.12.09.25341792v1.31","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.14.000192"},{"key":"2025121305301754000_2025.12.09.25341792v1.32","doi-asserted-by":"crossref","unstructured":"D. A. Boas , L. E. Campbell , and A. G. Yodh , \u201cScattering and imaging with diffusing temporal field correlations,\u201d Phys Rev Lett (1995).","DOI":"10.1103\/PhysRevLett.75.1855"},{"key":"2025121305301754000_2025.12.09.25341792v1.33","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2011.0232"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.34","doi-asserted-by":"crossref","first-page":"11009","DOI":"10.1117\/1.NPh.1.1.011009","article-title":"Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects","volume":"1","year":"2014","journal-title":"Neurophotonics"},{"key":"2025121305301754000_2025.12.09.25341792v1.35","doi-asserted-by":"publisher","DOI":"10.1117\/1.nph.1.1.015005"},{"key":"2025121305301754000_2025.12.09.25341792v1.36","doi-asserted-by":"crossref","unstructured":"C. S. Poon , J. Li , J. Kress , D. J. Rohrbach , and U. Sunar , \u201cResting-State Functional Connectivity measured by Diffuse Correlation Spectroscopy,\u201d in Proceedings of the Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy\/Translational\/Brain\/OTS (n.d.).","DOI":"10.1364\/TRANSLATIONAL.2018.JTh3A.63"},{"key":"2025121305301754000_2025.12.09.25341792v1.37","doi-asserted-by":"crossref","unstructured":"J. Li , C.-S. Poon , J. Kress , D. J. Rohrbach , and U. Sunar , \u201cResting-state functional connectivity measured by diffuse correlation spectroscopy,\u201d J. Biophotonics 11, 201700165 (n.d.).","DOI":"10.1002\/jbio.201700165"},{"key":"2025121305301754000_2025.12.09.25341792v1.38","doi-asserted-by":"crossref","unstructured":"C. S. Poon , J. Li , J. Kress , D. J. Rohrbach , and U. Sunar , \u201cResting-State Functional Connectivity measured by Diffuse Correlation Spectroscopy,\u201d in Optics InfoBase Conference Papers (2018).","DOI":"10.1364\/TRANSLATIONAL.2018.JTh3A.63"},{"key":"2025121305301754000_2025.12.09.25341792v1.39","unstructured":"C. Poon , B. Rinehart , J. Li , and U. Sunar , \u201cCerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy,\u201d J. Vis. Exp 60765 (n.d.)."},{"key":"2025121305301754000_2025.12.09.25341792v1.40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.06.017"},{"key":"2025121305301754000_2025.12.09.25341792v1.41","unstructured":"G. Yu , T. Durduran , C. Zhou , R. Cheng , and A. G. Yodh , \u201cNear-Infrared Diffuse Correlation Spectroscopy for Assessment of Tissue Blood Flow,\u201d in Handbook of Biomedical Optics (2011), pp. 195\u2013216."},{"key":"2025121305301754000_2025.12.09.25341792v1.42","doi-asserted-by":"publisher","DOI":"10.1038\/jcbfm.2015.161"},{"key":"2025121305301754000_2025.12.09.25341792v1.43","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.1.000553"},{"key":"2025121305301754000_2025.12.09.25341792v1.44","doi-asserted-by":"crossref","unstructured":"J. Selb , K.-C. Wu , J. Sutin , P.-Y. (Ivy) Lin , P. Farzam , S. Bechek , and A. Shenoy , \u201cProlonged monitoring of cerebral blood flow and autoregulation with diffuse correlation spectroscopy in neurocritical care patients,\u201d Neurophotonics 5(04), (2018).","DOI":"10.1117\/1.NPh.5.4.045005"},{"key":"2025121305301754000_2025.12.09.25341792v1.45","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-018-0573-1"},{"key":"2025121305301754000_2025.12.09.25341792v1.46","doi-asserted-by":"publisher","DOI":"10.1177\/0271678X19846657"},{"key":"2025121305301754000_2025.12.09.25341792v1.47","doi-asserted-by":"crossref","unstructured":"I. A. Dar , I. R. Khan , R. K. Maddox , O. Selioutski , K. L. Donohue , M. A. Marinescu , S. M. Prasad , N. H. Quazi , J. S. Donlon , E. A. Loose , G. A. Ramirez , J. Ren , J. B. Majeski , K. Abramson , T. Durduran , D. R. Busch , and R. Choe , \u201cTowards detection of brain injury using multimodal non-invasive neuromonitoring in adults undergoing extracorporeal membrane oxygenation,\u201d Biomed Opt Express 11(11), (2020).","DOI":"10.1364\/BOE.401641"},{"key":"2025121305301754000_2025.12.09.25341792v1.48","doi-asserted-by":"crossref","unstructured":"C. S. Poon , B. Rinehart , D. S. Langri , T. M. Rambo , A. J. Miller , B. Foreman , and U. Sunar , \u201cNoninvasive optical monitoring of cerebral blood flow and eeg spectral responses after severe traumatic brain injury: A case report,\u201d Brain Sci 11(8), (2021).","DOI":"10.3390\/brainsci11081093"},{"issue":"3","key":"2025121305301754000_2025.12.09.25341792v1.49","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1364\/BOE.448135","article-title":"First-in-clinical application of a time-gated diffuse correlation spectroscopy system at 1064 nm using superconducting nanowire single photon detectors in a neuro intensive care unit","volume":"13","year":"2022","journal-title":"Biomed Opt Express"},{"key":"2025121305301754000_2025.12.09.25341792v1.50","doi-asserted-by":"crossref","unstructured":"S. Sabaghian , C.-S. Poon , C. Kim , C. H. Moore , I. Dar , T. M. Rambo , A. J. Miller , S. Swarna , N. Lubin , S. Mofakkam , C. Mikell , J. Wang , B. Foreman , and U. Sunar , \u201cDepth-Sensitive Cerebral Blood Flow and Low-Frequency Oscillations for Consciousness Assessment Using Time-Domain Diffuse Correlation Spectroscopy,\u201d medRxiv 2025.08.31.25334647 (2025).","DOI":"10.1101\/2025.08.31.25334647"},{"key":"2025121305301754000_2025.12.09.25341792v1.51","doi-asserted-by":"crossref","unstructured":"C. S. Poon , B. Rinehart , D. S. Langri , T. M. Rambo , A. J. Miller , B. Foreman , and U. Sunar , \u201cNoninvasive optical monitoring of cerebral blood flow and eeg spectral responses after severe traumatic brain injury: A case report,\u201d Brain Sci 11(8), (2021).","DOI":"10.3390\/brainsci11081093"},{"key":"2025121305301754000_2025.12.09.25341792v1.52","doi-asserted-by":"crossref","unstructured":"A. Stefanovska , \u201cCoupled oscillators: complex but not complicated cardiovascular and brain interactions,\u201d Conf Proc IEEE Eng Med Biol Soc 2006, 437\u2013440 (2006).","DOI":"10.1109\/IEMBS.2006.259557"},{"key":"2025121305301754000_2025.12.09.25341792v1.53","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2016.02.018"},{"issue":"10","key":"2025121305301754000_2025.12.09.25341792v1.54","doi-asserted-by":"crossref","first-page":"e0299752","DOI":"10.1371\/journal.pone.0299752","article-title":"Wavelet and time-based cerebral autoregulation analysis using diffuse correlation spectroscopy on adults undergoing extracorporeal membrane oxygenation therapy","volume":"19","year":"2024","journal-title":"PLoS One"},{"key":"2025121305301754000_2025.12.09.25341792v1.55","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"2025121305301754000_2025.12.09.25341792v1.56","doi-asserted-by":"crossref","unstructured":"J. Herman and W. Usher , \u201cSALib: An open-source Python library for Sensitivity Analysis,\u201d The Journal of Open Source Software 2(9), (2017).","DOI":"10.21105\/joss.00097"},{"key":"2025121305301754000_2025.12.09.25341792v1.57","doi-asserted-by":"publisher","DOI":"10.18174\/sesmo.18155"},{"issue":"10","key":"2025121305301754000_2025.12.09.25341792v1.58","doi-asserted-by":"crossref","first-page":"8083","DOI":"10.1007\/s10973-025-14250-4","article-title":"Experimental and machine learning optimization of ultrasonic-enhanced heat convection in a spiral heater","volume":"150","year":"2025","journal-title":"J Therm Anal Calorim"},{"key":"2025121305301754000_2025.12.09.25341792v1.59","doi-asserted-by":"crossref","first-page":"110086","DOI":"10.1016\/j.ijthermalsci.2025.110086","article-title":"Machine learning regression modeling of liquid jet impingement cooling: Based on computational fluid dynamics (CFD)","volume":"217","year":"2025","journal-title":"International Journal of Thermal Sciences"},{"key":"2025121305301754000_2025.12.09.25341792v1.60","doi-asserted-by":"crossref","first-page":"104304","DOI":"10.1016\/j.jwpe.2023.104304","article-title":"Predictive machine learning models for optimization of direct solar steam generation","volume":"56","year":"2023","journal-title":"Journal of Water Process Engineering"},{"key":"2025121305301754000_2025.12.09.25341792v1.61","doi-asserted-by":"crossref","first-page":"554633","DOI":"10.3389\/fneur.2020.554633","article-title":"\u201dMachine learning applications in the neuro ICU: a solution to big data mayhem?","volume":"11","year":"2020","journal-title":"Front Neurol"},{"issue":"6","key":"2025121305301754000_2025.12.09.25341792v1.62","doi-asserted-by":"crossref","first-page":"817","DOI":"10.2214\/AJR.23.29579","article-title":"Machine learning using presentation CT perfusion imaging for predicting clinical outcomes in patients with aneurysmal subarachnoid Hemorrhage","volume":"221","year":"2023","journal-title":"American Journal of Roentgenology"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.63","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s13017-022-00449-5","article-title":"Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury","volume":"17","year":"2022","journal-title":"World Journal of Emergency Surgery"},{"issue":"2","key":"2025121305301754000_2025.12.09.25341792v1.64","doi-asserted-by":"crossref","first-page":"528","DOI":"10.3171\/2022.12.JNS221860","article-title":"Prediction of intracranial pressure crises after severe traumatic brain injury using machine learning algorithms","volume":"139","year":"2023","journal-title":"J Neurosurg"},{"key":"2025121305301754000_2025.12.09.25341792v1.65","doi-asserted-by":"crossref","first-page":"e1348","DOI":"10.1016\/j.wneu.2024.03.085","article-title":"Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms","volume":"185","year":"2024","journal-title":"World Neurosurg"},{"key":"2025121305301754000_2025.12.09.25341792v1.66","doi-asserted-by":"crossref","unstructured":"J. Han , N. W. Duncan , L. Huang , C. Lee , C.-M. Yang , Y.-C. Wu , D. Y.-T. Chen , and T. J. Lane , \u201cArterial spin labeling as a promising alternative to FDG-PET for clinical diagnosis of patients with disorders of consciousness,\u201d medRxiv 2024.08.07.24311648 (2024).","DOI":"10.1101\/2024.08.07.24311648"},{"key":"2025121305301754000_2025.12.09.25341792v1.67","doi-asserted-by":"crossref","unstructured":"M. Zabihi , D. B. Rubin , S. E. Ack , E. J. Gilmore , V. M. Junior , S. F. Zafar , Q. Li , M. J. Young , B. L. Edlow , Y. G. Bodien , and E. S. Rosenthal, \u201cResting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury,\u201d bioRxiv 2022.09.30.510334 (2022).","DOI":"10.1101\/2022.09.30.510334"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.68","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/s12911-023-02128-0","article-title":"Machine learning and network analysis for diagnosis and prediction in disorders of consciousness","volume":"23","year":"2023","journal-title":"BMC Med Inform Decis Mak"},{"key":"2025121305301754000_2025.12.09.25341792v1.69","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awac340"},{"key":"2025121305301754000_2025.12.09.25341792v1.70","doi-asserted-by":"crossref","unstructured":"Z. Xu , J. Wang , R. Wang , Z. Zhang , and S. Yang , \u201cDeep Convolutional Neural Network for Detection of Disorders of Consciousness,\u201d 2020 39th Chinese Control Conference (CCC) 7084\u20137089 (2020).","DOI":"10.23919\/CCC50068.2020.9189502"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.71","doi-asserted-by":"crossref","first-page":"13471","DOI":"10.1038\/s41598-022-17561-w","article-title":"Predicting outcome of patients with prolonged disorders of consciousness using machine learning models based on medical complexity","volume":"12","year":"2022","journal-title":"Sci Rep"},{"key":"2025121305301754000_2025.12.09.25341792v1.72","doi-asserted-by":"publisher","DOI":"10.1515\/REVNEURO-2023-0159"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.73","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1148\/radiol.2021211239","article-title":"Two for the Price of One? Analyzing Task-based fMRI Data with Resting-State fMRI Methods","volume":"301","year":"2021","journal-title":"Radiology"},{"key":"2025121305301754000_2025.12.09.25341792v1.74","first-page":"758","article-title":"A Review on Random Forest: An Ensemble Classifier","volume":"26","year":"2019","journal-title":"International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI"},{"key":"2025121305301754000_2025.12.09.25341792v1.75","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrc.2013.08.020"},{"key":"2025121305301754000_2025.12.09.25341792v1.76","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(99)00131-5"},{"key":"2025121305301754000_2025.12.09.25341792v1.77","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2003.04.001"},{"issue":"1","key":"2025121305301754000_2025.12.09.25341792v1.78","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1152\/jn.00658.2020","article-title":"Clinical significance of mismatch negativity in predicting the awakening of comatose patients after severe brain injury","volume":"126","year":"2021","journal-title":"J Neurophysiol"},{"issue":"10","key":"2025121305301754000_2025.12.09.25341792v1.79","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1016\/j.cortex.2011.04.014","article-title":"fMRI in disorders of consciousness: future diagnostic opportunities, methodological and legal challenges","volume":"47","year":"2011","journal-title":"Cortex"},{"key":"2025121305301754000_2025.12.09.25341792v1.80","doi-asserted-by":"crossref","unstructured":"A. Abdalmalak , D. Milej , L. Norton , D. B. Debicki , A. M. Owen , and K. St. Lawrence , \u201cThe Potential Role of fNIRS in Evaluating Levels of Consciousness,\u201d Front Hum Neurosci 15, (2021).","DOI":"10.3389\/fnhum.2021.703405"},{"key":"2025121305301754000_2025.12.09.25341792v1.81","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3389\/fneur.2018.00350","article-title":"Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness","volume":"9","year":"2018","journal-title":"Front Neurol"},{"issue":"Suppl 2","key":"2025121305301754000_2025.12.09.25341792v1.82","first-page":"S24001","article-title":"Optical imaging and spectroscopy for the study of the human brain: status report","volume":"9","year":"2022","journal-title":"Neurophotonics"},{"key":"2025121305301754000_2025.12.09.25341792v1.83","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.004"},{"issue":"4","key":"2025121305301754000_2025.12.09.25341792v1.84","first-page":"45002","article-title":"Direct assessment of extracerebral signal contamination on optical measurements of cerebral blood flow, oxygenation, and metabolism","volume":"7","year":"2020","journal-title":"Neurophotonics"},{"issue":"3","key":"2025121305301754000_2025.12.09.25341792v1.85","doi-asserted-by":"crossref","first-page":"31412","DOI":"10.1117\/1.NPh.3.3.031412","article-title":"Establishing the diffuse correlation spectroscopy signal relationship with blood flow","volume":"3","year":"2016","journal-title":"Neurophotonics"},{"issue":"7","key":"2025121305301754000_2025.12.09.25341792v1.86","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1097\/WNP.0000000000001035","article-title":"Correlations Between Quantitative EEG Parameters and Cortical Blood Flow in Patients Undergoing Extracorporeal Membrane Oxygenation With and Without Encephalopathy","volume":"41","year":"2024","journal-title":"J Clin Neurophysiol"},{"key":"2025121305301754000_2025.12.09.25341792v1.87","doi-asserted-by":"publisher","DOI":"10.1177\/0271678X19846657"},{"key":"2025121305301754000_2025.12.09.25341792v1.88","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-018-0573-1"},{"key":"2025121305301754000_2025.12.09.25341792v1.89","doi-asserted-by":"crossref","unstructured":"J. Selb , K.-C. Wu , J. Sutin , P.-Y. (Ivy) Lin , P. Farzam , S. Bechek , and A. Shenoy , \u201cProlonged monitoring of cerebral blood flow and autoregulation with diffuse correlation spectroscopy in neurocritical care patients,\u201d Neurophotonics 5(04), 1 (2018).","DOI":"10.1117\/1.NPh.5.4.045005"},{"key":"2025121305301754000_2025.12.09.25341792v1.90","doi-asserted-by":"crossref","unstructured":"K. C. Wu , J. Sunwoo , F. Sheriff , P. Farzam , P. Y. Farzam , F. Orihuela-Espina , S. L. LaRose , A. D. Monk , M. A. Aziz-Sultan , N. Patel , H. Vaitkevicius , and M. A. Franceschini , \u201cValidation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients,\u201d J Biomed Opt 26(3), (2021).","DOI":"10.1117\/1.JBO.26.3.036008"},{"key":"2025121305301754000_2025.12.09.25341792v1.91","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.1.000553"},{"key":"2025121305301754000_2025.12.09.25341792v1.92","doi-asserted-by":"publisher","DOI":"10.1177\/0271678X19883751"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2025.12.09.25341792","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T13:30:30Z","timestamp":1765632630000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2025.12.09.25341792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,11]]},"references-count":92,"URL":"https:\/\/doi.org\/10.64898\/2025.12.09.25341792","relation":{},"subject":[],"published":{"date-parts":[[2025,12,11]]},"subtype":"preprint"}}