NeuroImage
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Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. ⋯ The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.
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In contrast to extended research interests in the maturation and aging of human brain, alterations of brain structure and function from early to middle adulthood have been much less studied. The aim of the present study was to investigate the extent and pattern of the alterations of functional interactions between brain regions from early to middle adulthood. We carried out the study by multivariate pattern analysis of resting-state fMRI (RS-fMRI) data of 63 adults aged 18 to 45 years. ⋯ In addition, the strengthening/weakening of the RSFCs associated with the left/right hemispheric ROIs, the weakening of cortico-cerebellar RSFCs and the strengthening of the RSFCs between the default mode network and other networks contributed much to both brain age estimation and age-group classification. All these alterations might reflect that aging of brain function is already in progress in middle adulthood. Overall, the present study indicated that the RSFCs undergo notable alterations from early to middle adulthood and highlighted the necessity of careful considerations of possible influences of these alterations in related studies.
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Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. ⋯ The utility of aE-REMCOR on the resting state fMRI connectivity of the default mode network is also examined. The motion-induced position-dependent error in the DMN connectivity analysis is shown to be reduced when aE-REMCOR is utilized. These results demonstrate that aE-REMCOR can be conveniently and efficiently used to improve fMRI motion correction in large clinical EEG-fMRI studies.