AIMC Topic: Connectome
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Heterogeneous Graph Representation Learning Framework for Resting-State Functional Connectivity Analysis.
IEEE transactions on medical imaging
Mar 17, 2025
Brain functional connectivity analysis is important for understanding brain development and brain disorders. Recent studies have suggested that the variations of functional connectivity among multiple subnetworks are closely related to the developmen...
A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.
Medical image analysis
Mar 14, 2025
Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as va...
Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.
Medical image analysis
Mar 13, 2025
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatomy, existing...
MCDGLN: Masked connection-based dynamic graph learning network for autism spectrum disorder.
Brain research bulletin
Mar 7, 2025
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's dynamic nature and the challen...
Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder.
Journal of affective disorders
Mar 3, 2025
BACKGROUND: Major depressive disorder (MDD) involves molecular alterations and pathway dysregulation. Metabolic interconnections are crucial for normal functioning, yet current analysis focuses on individual pathways or biomarkers, overlooking intric...
Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to "silent" fMRI.
Computers in biology and medicine
Mar 3, 2025
Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly resul...
Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.
Medical image analysis
Feb 25, 2025
Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In addition, interactions between SC and FC within distributed association regions are related to alte...
Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.
Scientific reports
Feb 24, 2025
Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusi...
Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study.
Journal of affective disorders
Feb 21, 2025
BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.
Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.
Journal of psychiatric research
Feb 21, 2025
BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. ...
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