Principle of relevant information for graph sparsification
Graph sparsification aims to reduce the number of edges of a graph while maintaining its
structural properties. In this paper, we propose the first general and effective information …
structural properties. In this paper, we propose the first general and effective information …
Deep manifold harmonic network with dual attention for brain disorder classification
Numerous studies have shown that accurate analysis of neurological disorders contributes
to the early diagnosis of brain disorders and provides a window to diagnose psychiatric …
to the early diagnosis of brain disorders and provides a window to diagnose psychiatric …
Brain network classification based on dynamic graph attention information bottleneck
C Dong, D Sun - Computer Methods and Programs in Biomedicine, 2024 - Elsevier
Abstract Background and Objectives Graph neural networks (GNN) have demonstrated
remarkable encoding capabilities in the context of brain network classification tasks. They …
remarkable encoding capabilities in the context of brain network classification tasks. They …
General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network
The general psychopathology factor (p-factor) represents shared variance across mental
disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive …
disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive …
RH-BrainFS: regional heterogeneous multimodal brain networks fusion strategy
H Ye, Y Zheng, Y Li, K Zhang… - Advances in Neural …, 2024 - proceedings.neurips.cc
Multimodal fusion has become an important research technique in neuroscience that
completes downstream tasks by extracting complementary information from multiple …
completes downstream tasks by extracting complementary information from multiple …
Brainib: Interpretable brain network-based psychiatric diagnosis with graph information bottleneck
Developing a new diagnostic models based on the underlying biological mechanisms rather
than subjective symptoms for psychiatric disorders is an emerging consensus. Recently …
than subjective symptoms for psychiatric disorders is an emerging consensus. Recently …
Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex
We present analysis of neuronal activity recordings from a subset of neurons in the medial
prefrontal cortex of rats before and after the administration of cocaine. Using an underlying …
prefrontal cortex of rats before and after the administration of cocaine. Using an underlying …
[HTML][HTML] BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping
Converging evidence increasingly suggests that psychiatric disorders, such as major
depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases …
depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases …
Inference-based statistical network analysis uncovers star-like brain functional architectures for internalizing psychopathology in children
To improve the statistical power for imaging biomarker detection, we propose a latent
variable-based statistical network analysis (LatentSNA) that combines brain functional …
variable-based statistical network analysis (LatentSNA) that combines brain functional …
Genetic underpinnings of brain structural connectome for young adults
With distinct advantages in power over behavioral phenotypes, brain imaging traits have
become emerging endophenotypes to dissect molecular contributions to behaviors and …
become emerging endophenotypes to dissect molecular contributions to behaviors and …