Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques.
Publication/Presentation Date
1-1-2022
Abstract
UNLABELLED: Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample
CLINICAL TRIAL REGISTRATION: [clinicaltrials.gov], identifier [NCT03241732].
Volume
16
First Page
1099560
Last Page
1099560
ISSN
1662-4548
Published In/Presented At
Vedaei, F., Mashhadi, N., Zabrecky, G., Monti, D., Navarreto, E., Hriso, C., Wintering, N., Newberg, A. B., & Mohamed, F. B. (2023). Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Frontiers in neuroscience, 16, 1099560. https://doi.org/10.3389/fnins.2022.1099560
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
36699521
Department(s)
Administration and Leadership
Document Type
Article