AIMC Topic: Brain Mapping
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Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.
Cerebral cortex (New York, N.Y. : 1991)
May 16, 2014
Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associate...
Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis.
NeuroImage
Jul 1, 2025
Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their ...
Representation of locomotive action affordances in human behavior, brains, and deep neural networks.
Proceedings of the National Academy of Sciences of the United States of America
Jun 17, 2025
To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immediate visual environment. The neural basis of our ability to recognize locomotive affordances is unknown...
Abnormal Static and Dynamic Functional Connectivity in Tension-Type Headache: A Support Vector Machine Analysis.
Journal of neuroscience research
Jun 1, 2025
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 ...
A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND AND OBJECTIVES: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challen...
Classifying athletes and non-athletes by differences in spontaneous brain activity: a machine learning and fMRI study.
Brain structure & function
May 24, 2025
Different types of sports training can induce distinct changes in brain activity and function; however, it remains unclear if there are commonalities across various sports disciplines. Moreover, the relationship between these brain activity alteratio...
Neural Networks and Chemical Messengers: Insights into Tobacco Addiction.
Brain topography
May 13, 2025
This study investigates changes in resting-state networks (RSNs) associated with tobacco addiction (TA) and whether these changes reflect alterations in neurotransmitter systems. A total of 90 patients with TA and 46 healthy controls (HCs) matched fo...
Application of improved graph convolutional network for cortical surface parcellation.
Scientific reports
May 12, 2025
Accurate cortical surface parcellation is essential for elucidating brain organizational principles, functional mechanisms, and the neural substrates underlying higher cognitive and emotional processes. However, the cortical surface is a highly folde...
Distributed Representations for Cognitive Control in Frontal Medial Cortex.
Journal of cognitive neuroscience
May 1, 2025
In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the...
Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.
Medicine
Feb 28, 2025
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...
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