AIMC Topic: Brain

Clear Filters Showing 3821 to 3830 of 4413 articles

Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography.

International journal of neural systems
Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages or youth, and ASD follows a repetitive pattern of behavior in most cases. Accurate diagnosis of ASD...

A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
In this analytical cross-sectional method comparison study, we evaluated brain MR images in 30 dogs and cats with and without using a DICOM-based deep-learning (DL) denoising algorithm developed specifically for veterinary patients. Quantitative comp...

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Radiology. Artificial intelligence
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computation...

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.

Medicine
The accurate assessment of the brain's functional network is seen as crucial for the understanding of complex relationships between different brain regions. Hidden information within different frequency bands, which is often overlooked by traditional...

[Classification of Alzheimer's disease based on multi-example learning and multi-scale feature fusion].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary...

Replay as a Basis for Backpropagation Through Time in the Brain.

Neural computation
How episodic memories are formed in the brain is a continuing puzzle for the neuroscience community. The brain areas that are critical for episodic learning (e.g., the hippocampus) are characterized by recurrent connectivity and generate frequent off...

The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.

Cerebral cortex (New York, N.Y. : 1991)
This study explores the diagnostic value of dopamine system imaging characteristics in children with autism spectrum disorder. Functional magnetic resonance data from 551 children in the Autism Brain Imaging Data Exchange database were analyzed, focu...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Cerebral cortex (New York, N.Y. : 1991)
Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated t...

Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Cerebral cortex (New York, N.Y. : 1991)
Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alzheimer's disease is beneficial for its prevention and early intervention treatment. In this study, we propose a novel framework, FusionNet-ISBOA-MK-SV...

Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.