Construction of Multi-Scale Consistent Brain Networks: Methods and Applications

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common cor...

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Bibliographic Details
Published in:PloS one Vol. 10; no. 4; p. e0118175
Main Authors: Ge, Bao, Tian, Yin, Hu, Xintao, Chen, Hanbo, Zhu, Dajiang, Zhang, Tuo, Han, Junwei, Guo, Lei, Liu, Tianming
Format: Journal Article
Language:English
Published: United States Public Library of Science 13.04.2015
Public Library of Science (PLoS)
Subjects:
Adult
Algorithms
Automation
Brain
Brain - anatomy & histology
Brain - pathology
Brain mapping
Brain research
Cluster Analysis
Clustering
Computer science
Construction
Cortex
Data processing
Diffusion Tensor Imaging
Humans
Mapping
Mental disorders
Methods
Modelling
Models, Anatomic
Morphology
Multiscale analysis
Nerve Net - anatomy & histology
Nerve Net - pathology
Networks
Neural networks
Neurosciences
NMR
Nuclear magnetic resonance
Reproducibility
Reproducibility of Results
Schizophrenia
Schizophrenia - pathology
Studies
Young Adult
United States > US
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.
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Conceived and designed the experiments: BG TL. Performed the experiments: BG TZ HC YT. Analyzed the data: BG TL. Contributed reagents/materials/analysis tools: XH DZ JH LG. Wrote the paper: BG TL YT.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0118175