AIMC Topic: Brain
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Flexible Patched Brain Transformer model for EEG decoding.
Scientific reports
Mar 29, 2025
Decoding the human brain using non-invasive methods is a significant challenge. This study aims to enhance electroencephalography (EEG) decoding by developing of machine learning methods. Specifically, we propose the novel, attention-based Patched Br...
Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients.
SLAS technology
Mar 28, 2025
BACKGROUND: Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal disease-specific brain structural and functional changes and their potential genetic basis by analyzing ...
Diffusion MRI GAN synthesizing fibre orientation distribution data using generative adversarial networks.
Communications biology
Mar 28, 2025
Machine learning may enhance clinical data analysis but requires large amounts of training data, which are scarce for rare pathologies. While generative neural network models can create realistic synthetic data such as 3D MRI volumes and, thus, augme...
Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.
Bulletin of experimental biology and medicine
Mar 28, 2025
The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesions in experimental animals with modeled brain infarct based on magnetic resonance imaging compared to an automated artificial intelligence approach. ...
Electroencephalography Decoding with Conditional Identification Generator.
International journal of neural systems
Mar 27, 2025
Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction systems. Recent advancements in deep neural networks (DNNs) have demonstrated significant promise in thi...
Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.
BMC medical imaging
Mar 27, 2025
BACKGROUND: To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting.
Modelling fourth-order hyperelasticity in soft solids using physics informed neural networks without labelled data.
Brain research bulletin
Mar 26, 2025
Mild traumatic brain injury can result from shear shock wave formation in the brain in the event of a head impact like in contact sports, road traffic accidents, etc. These highly nonlinear deformations are modelled by a fourth-order Landau hyperelas...
Ensemble network using oblique coronal MRI for Alzheimer's disease diagnosis.
NeuroImage
Mar 25, 2025
Alzheimer's disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer's disease. Therefore, distinguishing between normal...
An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.
Scientific reports
Mar 25, 2025
Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause severe impairment in everyday functioning. sMRI is a non-invasive neuroimaging technology that visualize...
Using machine learning to simultaneously quantify multiple cognitive components of episodic memory.
Nature communications
Mar 24, 2025
Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a u...
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