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Diffusion Tensor Imaging

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Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning.

Human brain mapping
Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally...

Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease.

Journal of neuroscience methods
BACKGROUND: Brain MRI holds promise to gauge different aspects of Parkinson's disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data.

Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks.

NeuroImage
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...

Training shortest-path tractography: Automatic learning of spatial priors.

NeuroImage
Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the stu...

Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

Human brain mapping
Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been obser...

Studying depression using imaging and machine learning methods.

NeuroImage. Clinical
Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the ...

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
BACKGROUND AND PURPOSE: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.

NeuroImage
The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connect...

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

Human brain mapping
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying ...