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Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
2018
AMIA Annual Symposium Proceedings
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In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing ...
Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. ...
Conclusion We propose a multi-view graph convolutional network method called MVGCN in this paper, which can directly take brain graphs from multiple views as inputs and do prediction on that. ...
pmid:30815157
pmcid:PMC6371363
fatcat:zmpvxtlwvvetlneze7tgvaaou4
Uncovering Cortical Units of Processing From Multi-Layered Connectomes
2022
Frontiers in Neuroscience
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We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering ...
Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity ...
FIGURE 1 | 1 FIGURE 1 | Concept of data-driven multi-layer network modeling. ...
doi:10.3389/fnins.2022.836259
pmid:35360166
pmcid:PMC8960198
fatcat:plsebvrmbfgqrd7efddggav4u4
MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
[article]
2021
arXiv
pre-print
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We present the multi-view graph normalizer network (MGN-Net; https://github.com/basiralab/MGN-Net), a graph neural network based method to normalize and integrate a set of multi-view biological networks ...
We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer's disease and Autism spectrum disorder ...
Multi-view Graph Normalizer Network Graphs (i.e. networks) are used extensively in various fields ranging from drug discovery to computational linguistics. ...
arXiv:2104.03895v1
fatcat:bj3dpen2ibfhthjpdps7rtj7dy
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
[article]
2019
arXiv
pre-print
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In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing ...
Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. ...
Conclusion We propose a multi-view graph convolutional network method called MVGCN in this paper, which can directly take brain graphs from multiple views as inputs and do prediction on that. ...
arXiv:1805.08801v4
fatcat:6jcp4zvtirccjgp63ysukdjctm
Analysis of Population Functional Connectivity Data via Multilayer Network Embeddings
[article]
2020
arXiv
pre-print
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We demonstrate how multilayer network embeddings can be used to visualize, cluster, and classify functional regions of the brain for these individuals. ...
Population analyses of functional connectivity have provided a rich understanding of how brain function differs across time, individual, and cognitive task. ...
A multilayer network of length m is a collection of networks or graphs {G 1 , . . . , G m }, where the graph G models the relational structure of the th layer of the network. ...
arXiv:1809.06437v2
fatcat:drl25ovggjflvdw573svgqvjgy
Dynamic reorganization of brain functional networks during cognition
2015
NeuroImage
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We therefore set out to study topological properties of brain functional networks during visual perception and cognition. ...
We hypothesized that cognitive processing in the oddball task is related to rapid, transient, and frequency-specific topological reorganization of brain functional networks. ...
Acknowledgements: The study was supported by Otto-von-Guericke University of Magdeburg, and by the BMBF network ERA-net Neuron "Restoration of Vision after Stroke (REVIS)" (Grant nr 01EW1210). ...
doi:10.1016/j.neuroimage.2015.03.057
pmid:25828884
fatcat:q3dtak3ienbtjg66nhpkrqlqwu
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
[article]
2020
arXiv
pre-print
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We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. ...
We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. ...
MODELING MULTI-SUBJECT DYNAMIC COMMUNITY STRUCTURE IN BRAIN NETWORKS We first describe a novel multilayer SBM for modeling community structure in multi-subject, time-varying brain functional networks. ...
arXiv:2004.04362v3
fatcat:34ooiejxxvgwla76v62efiq46a
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
2020
IEEE Transactions on Medical Imaging
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We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. ...
We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. ...
MODELING MULTI-SUBJECT DYNAMIC COMMUNITY STRUCTURE IN BRAIN NETWORKS We first describe a novel multilayer SBM for modeling community structure in multi-subject, time-varying brain functional networks. ...
doi:10.1109/tmi.2020.3030047
pmid:33044929
fatcat:fawa67nmj5ee7atmhycnarezie
Dynamic reorganization of brain functional networks during cognition
[article]
2014
bioRxiv
pre-print
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networks were characterized with graph measures. ...
Thus, it is not known whether cognitive processing merely changes strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. ...
Acknowledgements: The study was supported by Otto-von-Guericke University of Magdeburg, and by the BMBF network ERA-net Neuron "Restoration of Vision after Stroke (REVIS)" (Grant nr 01EW1210). ...
doi:10.1101/012922
fatcat:kscbd5vghrh43nmem66n2iukau
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
[article]
2022
arXiv
pre-print
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The development of graph convolutional networks (GCNs) has created the opportunity to address this information complexity via graph-driven architectures, since GCNs can perform feature aggregation, interaction ...
We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice. ...
The use of GCNs here can be helpful to augment the architecture of human brain networks and has achieved remarkable progress in explaining the functional abnormality from the network mechanism (Sporns ...
arXiv:2202.08916v3
fatcat:zskcqvgjpnb6vdklmyy5rozswq
Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI
2022
Frontiers in Neuroinformatics
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Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. ...
The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale ...
Feature Extraction of Functional Connectivity Networks (FCNs) Functional MRI has been widely used to establish brain FCNs (Yu et al., 2019; Xue et al., 2020) by focusing on measuring the FC between two ...
doi:10.3389/fninf.2021.802305
pmid:35095453
pmcid:PMC8792610
fatcat:rfhgnmrblrg63o323xmvqspage
Dyconnmap: Dynamic connectome mapping—A neuroimaging python module
2021
Human Brain Mapping
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Functional brain networks could also increase the multi-faced nature of the dynamic networks revealing complementary information. ...
For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via ...
| Multi-layer networks In recent years, the network neuroscience community introduced in the analysis of functional brain connectivity the notion of multi-layer networks. ...
doi:10.1002/hbm.25589
pmid:34250674
pmcid:PMC8449119
fatcat:5i5zajsi3vhkrpb3hpd4qno7tq
Multi-area functional modules mediate feedforward and recurrent processing in visual cortical hierarchy
[article]
2020
bioRxiv
pre-print
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We generated multi-area, directed graphs of neuronal communication and uncovered two spatially-distributed functional modules. ...
The modules differ in layer and area distributions, convergence and divergence, and population-level temporal dynamics. ...
Supporting the robustness of these clusters, we observed similar network modules using spectral clustering and bi-clustering algorithms (20) (Fig. S4 ). ...
doi:10.1101/2020.08.30.272948
fatcat:aal7fi6ding7dmwzrtd2shsx7e
Inferring Brain Dynamics via Multimodal Joint Graph Representation EEG-fMRI
[article]
2022
arXiv
pre-print
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Graph-based analyzes, which have many similarities to brain structure, can overcome the complexities of brain mapping analysis. ...
Recent studies have shown that multi-modeling methods can provide new insights into the analysis of brain components that are not possible when each modality is acquired separately. ...
In this study use a graph topology [26] such as the brain topology and analyze it in terms of time and patterning of brain functions to model and configure a multi-modalities system. ...
arXiv:2201.08747v1
fatcat:ia72xmqjrraj3h6pjt2iyf372e
Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification
[article]
2020
arXiv
pre-print
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Second, existing graph embedding techniques cannot be easily adapted to multi-view graph data with heterogeneous distributions. ...
Graph embedding is a powerful method to represent graph neurological data (e.g., brain connectomes) in a low dimensional space for brain connectivity mapping, prediction and classification. ...
brains or unsupervised clustering of brain states. ...
arXiv:2009.11553v1
fatcat:pkdsaihiqrarvfjs4wep3wdw3m
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