title: Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.
abstract: Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.
authors.affiliation: Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, 54124, Greece ; NeuroInformatics Group, Aristotle University of Thessaloniki, Greece.
authors.forename: Stavros I
authors.initials: SI
authors.lastname: Dimitriadis
authors.affiliation: Basque Center on Cognition, Brain and Language (BCBL), Paseo Mikeletegi 69, 20009 Donostia-San Sebastián, Spain ; Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
authors.forename: George
authors.initials: G
authors.lastname: Zouridakis
authors.affiliation: Department of Pediatrics, Division of Clinical Neurosciences, University of Tennessee Health Science Center, Memphis, TN, USA ; Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.
authors.forename: Roozbeh
authors.initials: R
authors.lastname: Rezaie
authors.affiliation: Department of Pediatrics, Division of Clinical Neurosciences, University of Tennessee Health Science Center, Memphis, TN, USA ; Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.
authors.forename: Abbas
authors.initials: A
authors.lastname: Babajani-Feremi
authors.affiliation: Department of Pediatrics, Division of Clinical Neurosciences, University of Tennessee Health Science Center, Memphis, TN, USA ; Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA ; Department of Neurobiology and Anatomy, University of Tennessee Health Science Center, Memphis, TN, USA.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Papanicolaou
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D001930:Brain Injuries / Q000473:pathology*; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D015225:Magnetoencephalography; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000503:physiopathology*; D009483:Neuropsychological Tests; D009716:Numerical Analysis, Computer-Assisted; D012146:Rest; D055815:Young Adult
keywords: Biomarkers; Computational connectomics; Diagnosis; MEG; Mild traumatic brain injury; Tensors