title: A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy.
abstract: We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task-related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task.
  
  
  
  To determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power.
  
  
  
  The identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio-temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio-temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.
authors.affiliation: Department of Computational and Data Sciences George Mason University Fairfax VA USA; National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA.
authors.forename: Nader
authors.initials: N
authors.lastname: Karamzadeh
authors.affiliation: Department of Neurology Center for Neuroscience and Regenerative Medicine Uniformed Services Bethesda MD USA.
authors.forename: Franck
authors.initials: F
authors.lastname: Amyot
authors.affiliation: Department of Neurology Center for Neuroscience and Regenerative Medicine Uniformed Services Bethesda MD USA.
authors.forename: Kimbra
authors.initials: K
authors.lastname: Kenney
authors.affiliation: National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA.
authors.forename: Afrouz
authors.initials: A
authors.lastname: Anderson
authors.affiliation: National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA.
authors.forename: Fatima
authors.initials: F
authors.lastname: Chowdhry
authors.affiliation: National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA.
authors.forename: Hadis
authors.initials: H
authors.lastname: Dashtestani
authors.affiliation: National Institute of Mental Health National Institutes of Healthy Bethesda MD USA.
authors.forename: Eric M
authors.initials: EM
authors.lastname: Wassermann
authors.affiliation: National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA.
authors.forename: Victor
authors.initials: V
authors.lastname: Chernomordik
authors.affiliation: Institut Langevin ESPCI-ParisTech Paris France.
authors.forename: Claude
authors.initials: C
authors.lastname: Boccara
authors.affiliation: Department of Computational and Data Sciences George Mason University Fairfax VA USA.
authors.forename: Edward
authors.initials: E
authors.lastname: Wegman
authors.affiliation: Department of Neurology Center for Neuroscience and Regenerative Medicine Uniformed Services Bethesda MD USA.
authors.forename: Ramon
authors.initials: R
authors.lastname: Diaz-Arrastia
authors.affiliation: National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA.
authors.forename: Amir H
authors.initials: AH
authors.lastname: Gandjbakhche
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D000070642:Brain Injuries, Traumatic / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000378:metabolism; D016022:Case-Control Studies; D005260:Female; D006439:Hemodynamics; D006801:Humans; D000069550:Machine Learning; D008297:Male; D017397:Prefrontal Cortex / Q000000981:diagnostic imaging / Q000378:metabolism*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D015415:Biomarkers
keywords: classification; feature selection; machine learning; near‐infrared spectroscopy; time series feature extraction; traumatic brain injury; wrapper method