title: Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity.
abstract: Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8-12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.
authors.affiliation: Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.|Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, British Columbia, Canada.
authors.forename: Vasily A
authors.initials: VA
authors.lastname: Vakorin
authors.affiliation: Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.|Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, British Columbia, Canada.|Department of Diagnostic Imaging, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.|Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.
authors.forename: Sam M
authors.initials: SM
authors.lastname: Doesburg
authors.affiliation: Department of Surgery, Division of Neurosurgery, Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada.|Department of Medical Imaging, Sunnybrook Hospital, Toronto, Ontario, Canada.
authors.forename: Leodante
authors.initials: L
authors.lastname: da Costa
authors.affiliation: Canadian Forces Health Services, Directorate of Mental Health, Ottawa, Ontario, Canada.
authors.forename: Rakesh
authors.initials: R
authors.lastname: Jetly
authors.affiliation: Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.|Department of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada.
authors.forename: Elizabeth W
authors.initials: EW
authors.lastname: Pang
authors.affiliation: Department of Diagnostic Imaging, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.|Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Ontario, Canada.|Department of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada.|Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.|Department of Psychology, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Margot J
authors.initials: MJ
authors.lastname: Taylor
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology; D000070642:Brain Injuries, Traumatic / Q000000981:diagnostic imaging* / Q000503:physiopathology; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult