A multi-domain connectome convolutional neural network for identifying schizophrenia from EEG connectivity patterns
Objective: We exploit altered patterns in brain functional connectivity as features for
automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have …
automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have …
Statistical models for brain signals with properties that evolve across trials
Most neuroscience cognitive experiments involve repeated presentations of various stimuli
across several minutes or a few hours. It has been observed that brain responses, even to …
across several minutes or a few hours. It has been observed that brain responses, even to …
Statistical model for dynamically-changing correlation matrices with application to brain connectivity
Background Recent studies have indicated that functional connectivity is dynamic even
during rest. A common approach to modeling the dynamic functional connectivity in whole …
during rest. A common approach to modeling the dynamic functional connectivity in whole …
Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models
The study of functional brain networks has grown rapidly over the past decade. While most
functional connectivity (FC) analyses estimate one static network structure for the entire …
functional connectivity (FC) analyses estimate one static network structure for the entire …
A survey on brain effective connectivity network learning
Human brain effective connectivity characterizes the causal effects of neural activities
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …
Classification of EEG-based brain connectivity networks in schizophrenia using a multi-domain connectome convolutional neural network
We exploit altered patterns in brain functional connectivity as features for automatic
discriminative analysis of neuropsychiatric patients. Deep learning methods have been …
discriminative analysis of neuropsychiatric patients. Deep learning methods have been …
[HTML][HTML] Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance
MK Chung, CG Ramos, FB De Paiva, J Mathis… - NeuroImage, 2023 - Elsevier
Persistent homology offers a powerful tool for extracting hidden topological signals from
brain networks. It captures the evolution of topological structures across multiple scales …
brain networks. It captures the evolution of topological structures across multiple scales …
Learning brain dynamics of evolving manifold functional MRI data using geometric-attention neural network
Functional connectivities (FC) of brain network manifest remarkable geometric patterns,
which is the gateway to understanding brain dynamics. In this work, we present a novel …
which is the gateway to understanding brain dynamics. In this work, we present a novel …
Graph autoencoders for embedding learning in brain networks and major depressive disorder identification
Brain functional connectivity (FC) networks inferred from functional magnetic resonance
imaging (fMRI) have shown altered or aberrant brain functional connectome in various …
imaging (fMRI) have shown altered or aberrant brain functional connectome in various …