AIMC Topic: 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...

Photon-Counting Detector CT of the Brain Reduces Variability of Hounsfield Units and Has a Mean Offset Compared with Energy-Integrating Detector CT.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Distinguishing GM from WM is essential for CT of the brain. The recently established photon-counting detector (PCD)-CT technology uses a novel detection technique that might allow more precise measurement of tissue attenuation...

Towards reliable WMH segmentation under domain shift: An application study using maximum entropy regularization to improve uncertainty estimation.

Computers in biology and medicine
BACKGROUND: Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, such as variations in MRI machine types or acquisition parame...