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 …
brain regions are represented by correlation values across time series has been very …
Brainsteam: A practical pipeline for connectome-based fmri analysis towards subject classification
Functional brain networks represent dynamic and complex interactions among anatomical
regions of interest (ROIs), providing crucial clinical insights for neural pattern discovery and …
regions of interest (ROIs), providing crucial clinical insights for neural pattern discovery and …
A deep spatiotemporal graph learning architecture for brain connectivity analysis
In recent years, the conceptualisation of the brain as a" connectome" as summary measures
derived from graph theory analyses, has become increasingly popular. Still, such …
derived from graph theory analyses, has become increasingly popular. Still, such …
A plug-in graph neural network to boost temporal sensitivity in fmri analysis
Learning-based methods offer performance leaps over traditional methods in classification
analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning …
analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning …
[HTML][HTML] A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional MRI data
Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully
employed to understand the organisation of the human brain. Typically, the brain is …
employed to understand the organisation of the human brain. Typically, the brain is …
Benchmarking graph neural networks for fMRI analysis
Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-
structured data. A paramount example of such data is the brain, which operates as a …
structured data. A paramount example of such data is the brain, which operates as a …
Towards a predictive spatio-temporal representation of brain data
The characterisation of the brain as a" connectome", in which the connections are
represented by correlational values across timeseries and as summary measures derived …
represented by correlational values across timeseries and as summary measures derived …
GATE: Graph CCA for temporal self-supervised learning for label-efficient fMRI analysis
In this work, we focus on the challenging task, neuro-disease classification, using functional
magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph …
magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph …
Fbnetgen: Task-aware gnn-based fmri analysis via functional brain network generation
Functional magnetic resonance imaging (fMRI) is one of the most common imaging
modalities to investigate brain functions. Recent studies in neuroscience stress the great …
modalities to investigate brain functions. Recent studies in neuroscience stress the great …
Integration of network topological features and graph Fourier transform for fMRI data analysis
Motivated by the recent progress in both graph signal processing and brain imaging, we
integrate both techniques for complex brain network analysis. In particular, we address the …
integrate both techniques for complex brain network analysis. In particular, we address the …