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 proceedingConference contribution

32 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages177-184
Number of pages8
Volume8675 LNCS
EditionPART 3
ISBN (Print)9783319104423
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: 2014 Sep 142014 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8675 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period14/9/1414/9/18

Fingerprint

Connectivity
Network Connectivity
Network Dynamics
Multiple Time Scales
Functional Magnetic Resonance Imaging
Sliding Window
Stationary States
Support vector machines
Brain
Chemical activation
Activation
Support Vector Machine
kernel
Subset
Evaluate
Range of data
Experiment
Experiments

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Price, T., Wee, C. Y., Gao, W., & Shen, D. (2014). Multiple-network classification of childhood autism using functional connectivity dynamics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8675 LNCS, pp. 177-184). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_23

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8675 LNCS PART 3. ed. Springer Verlag, 2014. p. 177-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Price, T, Wee, CY, Gao, W & Shen, D 2014, Multiple-network classification of childhood autism using functional connectivity dynamics. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 8675 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8675 LNCS, Springer Verlag, pp. 177-184, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, Boston, MA, United States, 14/9/14. https://doi.org/10.1007/978-3-319-10443-0_23
Price T, Wee CY, Gao W, Shen D. Multiple-network classification of childhood autism using functional connectivity dynamics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 8675 LNCS. Springer Verlag. 2014. p. 177-184. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-319-10443-0_23
Price, True ; Wee, Chong Yaw ; Gao, Wei ; Shen, Dinggang. / Multiple-network classification of childhood autism using functional connectivity dynamics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8675 LNCS PART 3. ed. Springer Verlag, 2014. pp. 177-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{61c11233d54641748f466895097e6f02,
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",
doi = "10.1007/978-3-319-10443-0_23",
language = "English",
isbn = "9783319104423",
volume = "8675 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 3",
pages = "177--184",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 3",

}

TY - GEN

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=84906977910&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906977910&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-10443-0_23

DO - 10.1007/978-3-319-10443-0_23

M3 - Conference contribution

C2 - 25320797

AN - SCOPUS:84906977910

SN - 9783319104423

VL - 8675 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 177

EP - 184

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -