Abstract
AbstractThe clinical presentation and neurobiology of mild traumatic brain injury (mTBI) - also referred to as concussion - are complex and multifaceted, and interrelationships between neurobiological measures derived from neuroimaging are poorly understood. This study applied machine learning (ML) to multimodal whole-brain functional connectomes from magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and structural connectomes from diffusion tensor imaging (DTI) in a test of discriminative accuracy in cases of mTBI. Resting state MEG (amplitude envelope correlations), fMRI (BOLD correlations), and DTI (fractional anisotropy, FA; streamline count, SC) connectome data was acquired in 26 controls without mTBI (all male; 27.6 ± 4.7 years) and 24 participants with mTBI (all male; 29.7 ± 6.7 years) in the acute-subacute phase of injury. ML with data fusion was used to optimally identify modalities and brain features for discriminating individuals with mTBI from those without. Univariate group differences were only found for MEG functional connectivity, while no differences were found for fMRI or DTI. Functional connectivity (fMRI and MEG) showed robust unimodal classification accuracy for mTBI, followed by structural connectivity (DTI), where FA showed marginally better classification performance than SC, but SC outperformed FA in data interpretation and fusion. Perfect, unsupervised separation of participants with and without mTBI was achieved through participant fusion maps featuring all three data modalities. Finally, the MEG-only full feature fusion map showed group differences, and this effect was eliminated upon integrating DTI and fMRI datasets. The markers identified here align well with prior multimodal findings in concussion and highlight modality-specific considerations for their use in understanding network abnormalities of mTBI.
Publisher
Cold Spring Harbor Laboratory