Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

Hanbo Chen, Kaiming Li, Dajiang Zhu, Xi Jiang, Yixuan Yuan, Peili Lv, Tuo Zhang, Lei Guo, Dinggang Shen, Tianming Liu

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.

Original languageEnglish
Article number6512602
Pages (from-to)1576-1586
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number9
DOIs
Publication statusPublished - 2013 Sep 9

Fingerprint

Cluster Analysis
Diffusion tensor imaging
Brain
Diffusion Tensor Imaging
Magnetic Resonance Imaging
Neuroimaging

Keywords

  • Diffusion tensor imaging (DTI)
  • functional magnetic resonance imaging (fMRI)
  • multi-view clustering
  • multimodal brain connectome

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

Cite this

Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. / Chen, Hanbo; Li, Kaiming; Zhu, Dajiang; Jiang, Xi; Yuan, Yixuan; Lv, Peili; Zhang, Tuo; Guo, Lei; Shen, Dinggang; Liu, Tianming.

In: IEEE Transactions on Medical Imaging, Vol. 32, No. 9, 6512602, 09.09.2013, p. 1576-1586.

Research output: Contribution to journalArticle

Chen, H, Li, K, Zhu, D, Jiang, X, Yuan, Y, Lv, P, Zhang, T, Guo, L, Shen, D & Liu, T 2013, 'Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering', IEEE Transactions on Medical Imaging, vol. 32, no. 9, 6512602, pp. 1576-1586. https://doi.org/10.1109/TMI.2013.2259248
Chen, Hanbo ; Li, Kaiming ; Zhu, Dajiang ; Jiang, Xi ; Yuan, Yixuan ; Lv, Peili ; Zhang, Tuo ; Guo, Lei ; Shen, Dinggang ; Liu, Tianming. / Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. In: IEEE Transactions on Medical Imaging. 2013 ; Vol. 32, No. 9. pp. 1576-1586.
@article{c39883b45929471692717b88dcb8296d,
title = "Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering",
abstract = "Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.",
keywords = "Diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), multi-view clustering, multimodal brain connectome",
author = "Hanbo Chen and Kaiming Li and Dajiang Zhu and Xi Jiang and Yixuan Yuan and Peili Lv and Tuo Zhang and Lei Guo and Dinggang Shen and Tianming Liu",
year = "2013",
month = "9",
day = "9",
doi = "10.1109/TMI.2013.2259248",
language = "English",
volume = "32",
pages = "1576--1586",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "9",

}

TY - JOUR

T1 - Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering

AU - Chen, Hanbo

AU - Li, Kaiming

AU - Zhu, Dajiang

AU - Jiang, Xi

AU - Yuan, Yixuan

AU - Lv, Peili

AU - Zhang, Tuo

AU - Guo, Lei

AU - Shen, Dinggang

AU - Liu, Tianming

PY - 2013/9/9

Y1 - 2013/9/9

N2 - Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.

AB - Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.

KW - Diffusion tensor imaging (DTI)

KW - functional magnetic resonance imaging (fMRI)

KW - multi-view clustering

KW - multimodal brain connectome

UR - http://www.scopus.com/inward/record.url?scp=84883443204&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883443204&partnerID=8YFLogxK

U2 - 10.1109/TMI.2013.2259248

DO - 10.1109/TMI.2013.2259248

M3 - Article

C2 - 23661312

AN - SCOPUS:84883443204

VL - 32

SP - 1576

EP - 1586

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 9

M1 - 6512602

ER -