Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates

@article{Demirbilek2021RecurrentMI,
  title={Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates},
  author={Oytun Demirbilek and Islem Rekik},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.03453}
}
Learning how to estimate a connectional brain template (CBT) from a population of brain multigraphs, where each graph (e.g., functional) quantifies a particular relationship between pairs of brain regions of interest (ROIs), allows to pin down the unique connectivity patterns shared across individuals. Specifically, a CBT is viewed as an integral representation of a set of highly heterogeneous graphs and ideally meeting the centeredness (i.e., minimum distance to all graphs in the population… 

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References

SHOWING 1-10 OF 24 REFERENCES
Estimation of connectional brain templates using selective multi-view network normalization
TLDR
NetNorm produces the most centered and representative connectional brain template (CBT) that consistently captures the unique and distinctive traits of a population of multi-view brain networks, and identifies disordered brain connections by comparing templates estimated using disordered and healthy brains, respectively, demonstrating the discriminative power of the estimated CBTs.
Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates
TLDR
Deep Graph Normalizer (DGN) is proposed, the first geometric deep learning (GDL) architecture for normalizing a population of MVBNs by integrating them into a single connectional brain template and significantly outperforms existing state-of-the-art methods on estimating CBTs on both small-scale and large-scale connectomic datasets.
Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation
TLDR
The SM-netFusion framework is proposed, which presents the first work for supervised network cross-diffusion based on graph topological measures, which can be further leveraged to design an efficient graph feature selection method for training predictive learners in network neuroscience.
Graph Neural Networks in Network Neuroscience
TLDR
Current GNN-based methods are reviewed, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification, and charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.
The connectomics of brain disorders
TLDR
This work considers how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes and the resources and processes that enable adaptation, and shows how knowledge of network topology allows for predictive models of the spread and functional consequences of brain disease.
A cross-disorder connectome landscape of brain dysconnectivity
TLDR
A cross-disorder ‘connectome landscape of dysconnectivity’ is outlined along principal dimensions of network organization that may place shared connectome alterations between brain disorders in a common framework.
Brain network similarity: methods and applications
TLDR
The potential use of brain network similarity to build a “network of networks” that may give new insights into the object categorization in the human brain is shown.
The Human Connectome Project: A data acquisition perspective
TLDR
The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults using multiple imaging modalities along with extensive behavioral and genetic data.
Graph convolutional networks: a comprehensive review
TLDR
A comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models, is conducted and several open challenges are presented and potential directions for future research are discussed.
Network neuroscience
TLDR
This work reviews emerging trends in network neuroscience and attempts to chart a path toward a better understanding of the brain as a multiscale networked system.
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