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White Matter

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segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

Human brain mapping
White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and a...

Portable, low-field magnetic resonance imaging for evaluation of Alzheimer's disease.

Nature communications
Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer's disease (AD) in settings where conventional MRI cannot. However, image quality is limited by a lower signal-to-n...

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography.

Medical image analysis
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network desi...

Unmasking the Dark Triad: A Data Fusion Machine Learning Approach to Characterize the Neural Bases of Narcissistic, Machiavellian and Psychopathic Traits.

The European journal of neuroscience
The Dark Triad (DT), encompassing narcissism, Machiavellianism and psychopathy traits, poses significant societal challenges. Understanding the neural underpinnings of these traits is crucial for developing effective interventions and preventive stra...

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning.

European radiology
OBJECTIVES: To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance.

Deep learning-based free-water correction for single-shell diffusion MRI.

Magnetic resonance imaging
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Biomedical physics & engineering express
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...

Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.

BMC neurology
BACKGROUND AND PURPOSE: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a...

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.

Scientific reports
Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusi...