AI Medical Compendium Journal:
Human brain mapping

Showing 111 to 120 of 142 articles

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...

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

Human brain mapping
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...

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...

Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI.

Human brain mapping
Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-...

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 ...

Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.

Human brain mapping
Compared to nonverbal cognition such as executive or memory functions, language-related cognition generally appears to remain more stable until later in life. Nevertheless, different language-related processes, for example, verbal fluency versus voca...

Machine Learning-Based Clustering of Layer-Resolved fMRI Activation and Functional Connectivity Within the Primary Somatosensory Cortex in Nonhuman Primates.

Human brain mapping
Delineating the functional organization of mesoscale cortical columnar structure is essential for understanding brain function. We have previously demonstrated a high spatial correspondence between BOLD fMRI and LFP responses to tactile stimuli in th...

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.

Human brain mapping
Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the...

Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.

Human brain mapping
While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain-behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning ...

Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.

Human brain mapping
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...