AIMC Topic: Brain

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Abnormal Static and Dynamic Functional Connectivity in Tension-Type Headache: A Support Vector Machine Analysis.

Journal of neuroscience research
Tension-type headache (TTH) is a primary headache with the highest prevalence. Previous studies have revealed the local brain abnormalities of TTH patients. However, little is known about its brain connectivity disruption. Based on rs-fMRI data from ...

Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.

Brain and behavior
BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter v...

Do Transformers and CNNs Learn Different Concepts of Brain Age?

Human brain mapping
"Predicted brain age" refers to a biomarker of structural brain health derived from machine learning analysis of T1-weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convoluti...

Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.

Human brain mapping
The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the developm...

Multi-Objective Evolutionary Optimization Boosted Deep Neural Networks for Few-Shot Medical Segmentation With Noisy Labels.

IEEE journal of biomedical and health informatics
Fully-supervised deep neural networks have achieved remarkable progress in medical image segmentation, yet they heavily rely on extensive manually labeled data and exhibit inflexibility for unseen tasks. Few-shot segmentation (FSS) addresses these is...

Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction.

Human brain mapping
Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which bra...

Neural Metabolic Networks: Key Elements of Healthy Brain Function.

Journal of neurochemistry
Neural networks are responsible for processing sensory stimuli and driving the synaptic activity required for brain function and behavior. This computational capacity is expensive and requires a steady supply of energy and building blocks to operate....

Neuroimaging and machine learning in eating disorders: a systematic review.

Eating and weight disorders : EWD
PURPOSE: Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising ...

ML-Driven Alzheimer's disease prediction: A deep ensemble modeling approach.

SLAS technology
Alzheimer's disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. T...