title: Construction of multi-scale consistent brain networks: methods and applications.
abstract: 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.
authors: Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an, China; School of Physics & Information Technology, Shaanxi Normal University, Xi'an, China.
authors: Bao
authors: B
authors: Ge
authors: Department of Communication, Xi'an Communications Institute, Xi'an, China.
authors: Yin
authors: Y
authors: Tian
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Xintao
authors: X
authors: Hu
authors: Cortical Architecture Imaging and Discovery lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America.
authors: Hanbo
authors: H
authors: Chen
authors: Cortical Architecture Imaging and Discovery lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America.
authors: Dajiang
authors: D
authors: Zhu
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Tuo
authors: T
authors: Zhang
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Junwei
authors: J
authors: Han
authors: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors: Lei
authors: L
authors: Guo
authors: Cortical Architecture Imaging and Discovery lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, United States of America.
authors: Tianming
authors: T
authors: Liu
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D016000:Cluster Analysis; D056324:Diffusion Tensor Imaging; D006801:Humans; D008953:Models, Anatomic; D009415:Nerve Net / Q000033:anatomy & histology* / Q000473:pathology; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000473:pathology; D055815:Young Adult