AIMC Topic: Brain

Clear Filters Showing 581 to 590 of 4413 articles

Identification and External Validation of a Problem Cannabis Risk Network.

Biological psychiatry
BACKGROUND: Cannabis use is common, particularly during emerging adulthood when brain development is ongoing, and its use is associated with harmful outcomes for a subset of people. An improved understanding of the neural mechanisms underlying risk f...

EEG-based fatigue state evaluation by combining complex network and frequency-spatial features.

Journal of neuroscience methods
BACKGROUND: The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers. In order to rapidly and accurately detect drivers' fatigue, this paper proposed an electroencephalo...

AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.

Journal of computational biology : a journal of computational molecular cell biology
The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the di...

Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.

Medical image analysis
Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across different sites and devices, which adversely affects the generalization of deep neural networks. To alleviat...

Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Brain research
Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as non...

Deep learning to decode sites of RNA translation in normal and cancerous tissues.

Nature communications
The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Accurate delineation of RNA translation variation represents a significant challenge due to the complexity of the process a...

Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.

Neural networks : the official journal of the International Neural Network Society
The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the pro...

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

NeuroImage
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...

Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.

Magma (New York, N.Y.)
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...

Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.

Neuroinformatics
The bidirectional interactions between brain and heart through autonomic nervous system is the prime focus of neuro-cardiology community. The computer models designed to analyze brain and heart signals are either complex in terms of molecular and cel...