AIMC Topic: Brain

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GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Magnetic resonance imaging
PURPOSE: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural de...

Recognition of autism in subcortical brain volumetric images using autoencoding-based region selection method and Siamese Convolutional Neural Network.

International journal of medical informatics
BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interactions and behavior. Accurate and early diagnosis of ASD is still challenging even with the improvements in neuroimaging technology and machine lea...

Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients.

NeuroImage
INTRODUCTION: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this ex...

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Medical & biological engineering & computing
Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into...

Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.

Scientific reports
Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intel...

Generalizable self-supervised learning for brain CTA in acute stroke.

Computers in biology and medicine
Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks able to learn from unlabeled data do not exist. We propose a linear probed self-supervised contrasti...

Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Accident; analysis and prevention
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account...

Analysis of behavioral flow resolves latent phenotypes.

Nature methods
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple te...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Scientific reports
Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task-re...