AIMC Topic: Connectome
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DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.
Medical image analysis
Jan 29, 2025
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix a...
Graph Neural Network Learning on the Pediatric Structural Connectome.
Tomography (Ann Arbor, Mich.)
Jan 29, 2025
PURPOSE: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), s...
A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.
Scientific reports
Jan 22, 2025
Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Rece...
Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...
Deep learning-based free-water correction for single-shell diffusion MRI.
Magnetic resonance imaging
Jan 17, 2025
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white...
Identifying multilayer network hub by graph representation learning.
Medical image analysis
Jan 16, 2025
The recent advances in neuroimaging technology allow us to understand how the human brain is wired in vivo and how functional activity is synchronized across multiple regions. Growing evidence shows that the complexity of the functional connectivity ...
Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.
Scientific reports
Dec 30, 2024
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clu...
Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging.
Magnetic resonance imaging
Dec 13, 2024
PURPOSE: The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted...
Contrastive machine learning reveals species -shared and -specific brain functional architecture.
Medical image analysis
Dec 12, 2024
A deep comparative analysis of brain functional connectome across species in primates has the potential to yield valuable insights for both scientific and clinical applications. However, the interspecies commonality and differences are inherently ent...
The efficacy of topological properties of functional brain networks in identifying major depressive disorder.
Scientific reports
Nov 27, 2024
Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identi...
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