AIMC Topic: Brain

Clear Filters Showing 81 to 90 of 4186 articles

A comprehensive hybrid model: Combining bioinspired optimization and deep learning for Alzheimer's disease identification.

Computers in biology and medicine
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive ability and memory function. It is a progressive disease characterized by worsening dementia symptoms over time, starting with mild m...

Neurons throughout the brain embed robust signatures of their anatomical location into spike trains.

eLife
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical location wit...

Speech imagery brain-computer interfaces: a systematic literature review.

Journal of neural engineering
Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whos...

Disrupted functional topology of the white matter connectome in rhegmatogenous retinal detachment: insights from graph theory and machine learning.

Neuroreport
BACKGROUND: Rhegmatogenous retinal detachment (RRD) is known to induce functional alterations in the gray matter regions associated with vision. However, the impact of RRD on the white matter (WM) connectome remains largely unexplored.

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.

Brain topography
Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is ...

Unsupervised single-image super-resolution for infant brain MRI.

NeuroImage
Acquiring high-resolution (HR) MR images of infant brains is challenging due to lengthy scan times and limited subject compliance. Image super-resolution (SR) techniques can generate HR images from low-resolution (LR) inputs, reducing the need for ex...

Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys.

Computers in biology and medicine
BACKGROUND: Automated whole-brain delineation (WBD) techniques often struggle to generalize across pre-clinical studies due to variations in animal models, magnetic resonance imaging (MRI) scanners, and tissue contrasts. We developed a 3D U-Net neura...

BrainTract: segmentation of white matter fiber tractography and analysis of structural connectivity using hybrid convolutional neural network.

Neuroscience
Tractography uses diffusion Magnetic Resonance Imaging (dMRI) to noninvasively reconstruct brain white matter (WM) tracts, with Convolutional Neural Network (CNNs) like U-Net significantly advancing accuracy in medical image segmentation. This work p...

AI-driven discovery of brain-penetrant Galectin-3 inhibitors for Alzheimer's disease therapy.

Pharmacological research
Galectin-3 (Gal-3) has emerged as a critical regulator of neuroinflammation and a promising therapeutic target for Alzheimer's disease (AD). Nevertheless, the development of brain-penetrant small-molecule Gal-3 inhibitors poses a significant challeng...

Shared and distinct neural signatures of feature and spatial attention.

NeuroImage
The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent act...