Search Results for author:
Found 7 papers, 3 papers with code
Benchmarking Graph Neural Networks for FMRI analysis
Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data.
fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models
Single-subject mapping of resting-state brain functional activity to non-imaging phenotypes is a major goal of neuroimaging.
Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models
We show that combining the framework and Graph-S4 can significantly improve the diagnostic performance of neuroimaging-based single subject prediction models of MDD and ASD on three open-source multi-center rs-fMRI clinical datasets.
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years.
A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain.
Fusing Structural and Functional MRIs using Graph Convolutional Networks for Autism Classification
We alleviate the use of such non-imaging metadata and propose a fully imaging-based approach where information from structural and functional Magnetic Resonance Imaging (MRI) data are fused to construct the edges and nodes of the graph.
Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism
Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders.
