Connectivity-Driven Brain Parcellation via Consensus Clustering

@article{Kurmukov2018ConnectivityDrivenBP,
  title={Connectivity-Driven Brain Parcellation via Consensus Clustering},
  author={Anvar Kurmukov and Ayagoz Mussabayeva and Yu. L. Denisova and Daniel Moyer and Boris A. Gutman},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.04262}
}
We present two related methods for deriving connectivity-based brain atlases from individual connectomes. [...] Key Method The proposed methods exploit a previously proposed dense connectivity representation, termed continuous connectivity, by first performing graph-based hierarchical clustering of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation.Expand
1 Citations
Mapping the community structure of the rat cerebral cortex with weighted stochastic block modeling
TLDR
The findings demonstrate the potential benefits of adopting the WSBM, which can be applied to a single weighted and directed matrix such as the rat cerebral cortex connectome, to identify community structure with a broad definition that transcends the common modular approach.

References

SHOWING 1-10 OF 20 REFERENCES
GraMPa: Graph-Based Multi-modal Parcellation of the Cortex Using Fusion Moves
TLDR
A graph-based multi-modal parcellation method that iteratively computes a set of modality specific parcellations and merges them using the concept of fusion moves to yield parcels that are more reproducible and more representative of the underlying connectivity.
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex
TLDR
An extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand.
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
TLDR
35 structural connectome-building pipelines are compared by comparing 35 diffusion reconstruction models, tractography algorithms and parcellations to find out how variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque.
Continuous representations of brain connectivity using spatial point processes
TLDR
A continuous model for structural brain connectivity based on the Poisson point process is presented, and an analysis of sex effects on the proposed continuous representation is provided, demonstrating the utility of this approach.
Community detection in weighted brain connectivity networks beyond the resolution limit
TLDR
This work proposes Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground‐truth modular structure, and demonstrates a heterogeneous modular organization.
Within brain area tractography suggests local modularity using high resolution connectomics
TLDR
It is shown that the global network has a scale invariant topological organisation, which means there is a hierarchical organisation of the modular architecture, and it is suggested that spatial locations of white matter modules overlap with cytoarchitecturally distinct grey matter areas and may serve as the structural basis for function specialisation within brain areas.
Classifying Phenotypes Based on the Community Structure of Human Brain Networks
TLDR
This work considers network partitionings into both non-overlapping and overlapping communities and introduces a distance between connectomes based on whether or not they cluster into modules similarly, and constructs a classifier that uses partitioning-based kernels to predict a phenotype from brain networks.
The WU-Minn Human Connectome Project: An overview
TLDR
Progress made during the first half of the Human Connectome Project project in refining the methods for data acquisition and analysis provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
Modular and Hierarchically Modular Organization of Brain Networks
TLDR
Some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks are reviewed and some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data is summarized.
Fast unfolding of communities in large networks
TLDR
This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
...
1
2
...