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White Matter

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Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

NeuroImage
Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a ...

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...

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...

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span.

Medical engineering & physics
In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI datase...

Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines.

Psychiatry research
Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from ...

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...

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

NeuroImage
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 month...

WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.

Human brain mapping
White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly use...

TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View.

Human brain mapping
Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions ar...

The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study.

Human brain mapping
The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if ...