A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near‐infrared spectroscopy

Autor: Amir H. Gandjbakhche, Victor Chernomordik, Afrouz Anderson, Ramon Diaz-Arrastia, Nader Shahni Karamzadeh, Franck Amyot, Eric M. Wassermann, Claude Boccara, Edward J. Wegman, Hadis Dashtestani, Fatima Chowdhry, Kimbra Kenney
Rok vydání: 2016
Předmět:
Adult
Male
wrapper method
Multivariate statistics
Multivariate analysis
Traumatic brain injury
Prefrontal Cortex
Feature selection
Machine learning
computer.software_genre
050105 experimental psychology
Machine Learning
03 medical and health sciences
Behavioral Neuroscience
feature selection
0302 clinical medicine
Brain Injuries
Traumatic
medicine
Humans
0501 psychology and cognitive sciences
Prefrontal cortex
Original Research
Spectroscopy
Near-Infrared
business.industry
traumatic brain injury
05 social sciences
Hemodynamics
near‐infrared spectroscopy
medicine.disease
Statistical classification
classification
Feature (computer vision)
Case-Control Studies
time series feature extraction
Functional near-infrared spectroscopy
Female
Artificial intelligence
Psychology
business
computer
Biomarkers
030217 neurology & neurosurgery
Zdroj: Brain and Behavior
ISSN: 2162-3279
DOI: 10.1002/brb3.541
Popis: Background 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. Methods 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. Results and Conclusions 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.
Databáze: OpenAIRE
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