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White Matter

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Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks.

Nature neuroscience
Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timesc...

Retinal image analytics detects white matter hyperintensities in healthy adults.

Annals of clinical and translational neurology
OBJECTIVE: We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard.

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 ...

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...

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...

In vivo neuropil density from anatomical MRI and machine learning.

Cerebral cortex (New York, N.Y. : 1991)
Brain energy budgets specify metabolic costs emerging from underlying mechanisms of cellular and synaptic activities. While current bottom-up energy budgets use prototypical values of cellular density and synaptic density, predicting metabolism from ...