AIMC Topic: Connectome
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Functional connectome-based predictive modeling of suicidal ideation.
Journal of affective disorders
May 27, 2025
Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanisms of suicidal behavior. We apply a connectome predictive modeling machine learning approach to ident...
From resting-state functional hippocampal centrality to functional outcome: An extended neurocognitive model of psychosis.
Psychiatry research
May 16, 2025
BACKGROUND: We previously proposed a neurocognitive model of psychosis in which reduced morphometric hippocampal-cortical connectivity precedes impaired episodic memory, social cognition, negative symptoms, and functional outcome. We provided support...
Light-microscopy-based connectomic reconstruction of mammalian brain tissue.
Nature
May 7, 2025
The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can b...
How Can Anomalous-Diffusion Neural Networks Under Connectomics Generate Optimized Spatiotemporal Dynamics.
IEEE transactions on neural networks and learning systems
May 2, 2025
Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions and hallucinations in Alzheimer's disease. However, two practical considerations are rarely mentione...
ConnectomeAE: Multimodal brain connectome-based dual-branch autoencoder and its application in the diagnosis of brain diseases.
Computer methods and programs in biomedicine
Apr 23, 2025
BACKGROUND AND OBJECTIVE: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patient...
Accelerated diffusion tensor imaging with self-supervision and fine-tuning.
Scientific reports
Apr 14, 2025
Diffusion tensor imaging (DTI) is essential for assessing brain microstructure but requires long acquisition times, limiting clinical use. Recent deep learning (DL) approaches, such as SuperDTI or deepDTI, improve DTI metrics but demand large, high-q...
Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.
Medical image analysis
Apr 9, 2025
Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitate...
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...
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...
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