Multiple-network classification of childhood autism using functional connectivity dynamics.

True Price, Chong Yaw Wee, Wei Gao, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined. In this study, we combine dynamic connectivity features in a multi-network, multi-scale approach to evaluate the method's potential in better classifying childhood autism. Specifically, from a set of group-level intrinsic connectivity networks (ICNs), we use sliding window correlations to compute intra-network connectivity on the subject level. We derive dynamic FC features for all ICNs over a large range of window sizes and then use a multiple kernel support vector machine (MK-SVM) model to combine a subset of these features for classification. We compare the performance our multi-network, dynamic approach to the best results obtained from single-network dynamic FC features and those obtained from both single- and multi-network static FC features. Our experiments show that integrating multiple networks on different dynamic scales has a clear superiority over these existing methods.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages177-184
Number of pages8
Volume17
EditionPt 3
Publication statusPublished - 2014 Jan 1

Fingerprint

Autistic Disorder
Magnetic Resonance Imaging
Brain
Support Vector Machine

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Price, T., Wee, C. Y., Gao, W., & Shen, D. (2014). Multiple-network classification of childhood autism using functional connectivity dynamics. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 17, pp. 177-184)

Multiple-network classification of childhood autism using functional connectivity dynamics. / Price, True; Wee, Chong Yaw; Gao, Wei; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 17 Pt 3. ed. 2014. p. 177-184.

Research output: Chapter in Book/Report/Conference proceedingChapter

Price, T, Wee, CY, Gao, W & Shen, D 2014, Multiple-network classification of childhood autism using functional connectivity dynamics. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 17, pp. 177-184.
Price T, Wee CY, Gao W, Shen D. Multiple-network classification of childhood autism using functional connectivity dynamics. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 17. 2014. p. 177-184
Price, True ; Wee, Chong Yaw ; Gao, Wei ; Shen, Dinggang. / Multiple-network classification of childhood autism using functional connectivity dynamics. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 17 Pt 3. ed. 2014. pp. 177-184
@inbook{b0ef9a9b164c406591b419694df53f2e,
title = "Multiple-network classification of childhood autism using functional connectivity dynamics.",
abstract = "Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined. In this study, we combine dynamic connectivity features in a multi-network, multi-scale approach to evaluate the method's potential in better classifying childhood autism. Specifically, from a set of group-level intrinsic connectivity networks (ICNs), we use sliding window correlations to compute intra-network connectivity on the subject level. We derive dynamic FC features for all ICNs over a large range of window sizes and then use a multiple kernel support vector machine (MK-SVM) model to combine a subset of these features for classification. We compare the performance our multi-network, dynamic approach to the best results obtained from single-network dynamic FC features and those obtained from both single- and multi-network static FC features. Our experiments show that integrating multiple networks on different dynamic scales has a clear superiority over these existing methods.",
author = "True Price and Wee, {Chong Yaw} and Wei Gao and Dinggang Shen",
year = "2014",
month = "1",
day = "1",
language = "English",
volume = "17",
pages = "177--184",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 3",

}

TY - CHAP

T1 - Multiple-network classification of childhood autism using functional connectivity dynamics.

AU - Price, True

AU - Wee, Chong Yaw

AU - Gao, Wei

AU - Shen, Dinggang

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined. In this study, we combine dynamic connectivity features in a multi-network, multi-scale approach to evaluate the method's potential in better classifying childhood autism. Specifically, from a set of group-level intrinsic connectivity networks (ICNs), we use sliding window correlations to compute intra-network connectivity on the subject level. We derive dynamic FC features for all ICNs over a large range of window sizes and then use a multiple kernel support vector machine (MK-SVM) model to combine a subset of these features for classification. We compare the performance our multi-network, dynamic approach to the best results obtained from single-network dynamic FC features and those obtained from both single- and multi-network static FC features. Our experiments show that integrating multiple networks on different dynamic scales has a clear superiority over these existing methods.

AB - Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined. In this study, we combine dynamic connectivity features in a multi-network, multi-scale approach to evaluate the method's potential in better classifying childhood autism. Specifically, from a set of group-level intrinsic connectivity networks (ICNs), we use sliding window correlations to compute intra-network connectivity on the subject level. We derive dynamic FC features for all ICNs over a large range of window sizes and then use a multiple kernel support vector machine (MK-SVM) model to combine a subset of these features for classification. We compare the performance our multi-network, dynamic approach to the best results obtained from single-network dynamic FC features and those obtained from both single- and multi-network static FC features. Our experiments show that integrating multiple networks on different dynamic scales has a clear superiority over these existing methods.

UR - http://www.scopus.com/inward/record.url?scp=84909632491&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84909632491&partnerID=8YFLogxK

M3 - Chapter

C2 - 25320797

VL - 17

SP - 177

EP - 184

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -