Filters








11,907 Hits in 5.0 sec

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

Xi Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, Fei Wang
2018 AMIA Annual Symposium Proceedings  
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

Kristoffer Jon Albers, Matthew G. Liptrot, Karen Sandø Ambrosen, Rasmus Røge, Tue Herlau, Kasper Winther Andersen, Hartwig R. Siebner, Lars Kai Hansen, Tim B. Dyrby, Kristoffer H. Madsen, Mikkel N. Schmidt, Morten Mørup
2022 Frontiers in Neuroscience  
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]

Islem Rekik, Mustafa Burak Gurbuz
2021 arXiv   pre-print
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]

Xi Sheryl Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, Fei Wang
2019 arXiv   pre-print
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]

James D. Wilson, Melanie Baybay, Rishi Sankar, Paul Stillman, Abbie M. Popa
2020 arXiv   pre-print
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

Michał Bola, Bernhard A. Sabel
2015 NeuroImage  
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]

Chee-Ming Ting, S. Balqis Samdin, Meini Tang, Hernando Ombao
2020 arXiv   pre-print
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

Chee-Ming Ting, S. Balqis Samdin, Meini Tang, Hernando Ombao
2020 IEEE Transactions on Medical Imaging  
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]

Michał Bola, Bernhard Sabel
2014 bioRxiv   pre-print
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]

Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
2022 arXiv   pre-print
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

Ying Chu, Guangyu Wang, Liang Cao, Lishan Qiao, Mingxia Liu
2022 Frontiers in Neuroinformatics  
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

Avraam D. Marimpis, Stavros I. Dimitriadis, Rainer Goebel
2021 Human Brain Mapping  
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]

Xiaoxuan Jia, Joshua H. Siegle, Séverine Durand, Greggory Heller, Tamina Ramirez, Shawn R. Olsen
2020 bioRxiv   pre-print
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]

Jalal Mirakhorli
2022 arXiv   pre-print
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]

Alin Banka, Inis Buzi, Islem Rekik
2020 arXiv   pre-print
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
« Previous Showing results 1 — 15 out of 11,907 results