Structural connectivity guided sparse effective connectivity for MCI identification

Yang Li, Jingyu Liu, Meilin Luo, Ke Li, Pew Thian Yap, Minjeong Kim, Chong Yaw Wee, Dinggang Shen

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

Abstract

Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages299-306
Number of pages8
Volume10541 LNCS
ISBN (Print)9783319673882
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 102017 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1017/9/10

Fingerprint

Brain
Connectivity
Functional Magnetic Resonance Imaging
Network Structure
Diffusion tensor imaging
Neuroimaging
Magnetic resonance
Bagging
Network Connectivity
Network Modeling
Magnetic Resonance Imaging
Classifiers
Interaction
Aging of materials
Anomaly
Guidance
Disorder
Regression Model
Imaging techniques
Tensor

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Li, Y., Liu, J., Luo, M., Li, K., Yap, P. T., Kim, M., ... Shen, D. (2017). Structural connectivity guided sparse effective connectivity for MCI identification. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10541 LNCS, pp. 299-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_35

Structural connectivity guided sparse effective connectivity for MCI identification. / Li, Yang; Liu, Jingyu; Luo, Meilin; Li, Ke; Yap, Pew Thian; Kim, Minjeong; Wee, Chong Yaw; Shen, Dinggang.

Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS Springer Verlag, 2017. p. 299-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS).

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

Li, Y, Liu, J, Luo, M, Li, K, Yap, PT, Kim, M, Wee, CY & Shen, D 2017, Structural connectivity guided sparse effective connectivity for MCI identification. in Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. vol. 10541 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10541 LNCS, Springer Verlag, pp. 299-306, 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/10. https://doi.org/10.1007/978-3-319-67389-9_35
Li Y, Liu J, Luo M, Li K, Yap PT, Kim M et al. Structural connectivity guided sparse effective connectivity for MCI identification. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS. Springer Verlag. 2017. p. 299-306. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67389-9_35
Li, Yang ; Liu, Jingyu ; Luo, Meilin ; Li, Ke ; Yap, Pew Thian ; Kim, Minjeong ; Wee, Chong Yaw ; Shen, Dinggang. / Structural connectivity guided sparse effective connectivity for MCI identification. Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS Springer Verlag, 2017. pp. 299-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{f7eb704fba4f435a8c7962101986439e,
title = "Structural connectivity guided sparse effective connectivity for MCI identification",
abstract = "Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.",
author = "Yang Li and Jingyu Liu and Meilin Luo and Ke Li and Yap, {Pew Thian} and Minjeong Kim and Wee, {Chong Yaw} and Dinggang Shen",
year = "2017",
doi = "10.1007/978-3-319-67389-9_35",
language = "English",
isbn = "9783319673882",
volume = "10541 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "299--306",
booktitle = "Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",

}

TY - GEN

T1 - Structural connectivity guided sparse effective connectivity for MCI identification

AU - Li, Yang

AU - Liu, Jingyu

AU - Luo, Meilin

AU - Li, Ke

AU - Yap, Pew Thian

AU - Kim, Minjeong

AU - Wee, Chong Yaw

AU - Shen, Dinggang

PY - 2017

Y1 - 2017

N2 - Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

AB - Recent advances in network modelling techniques have enabled the study of neurological disorders at a whole-brain level based on functional connectivity inferred from resting-state magnetic resonance imaging (rs-fMRI) scan possible. However, constructing a directed effective connectivity, which provides a more comprehensive characterization of functional interactions among the brain regions, is still a challenging task particularly when the ultimate goal is to identify disease associated brain functional interaction anomalies. In this paper, we propose a novel method for inferring effective connectivity from multimodal neuroimaging data for brain disease classification. Specifically, we apply a newly devised weighted sparse regression model on rs-fMRI data to determine the network structure of effective connectivity with the guidance from diffusion tensor imaging (DTI) data. We further employ a regression algorithm to estimate the effective connectivity strengths based on the previously identified network structure. We finally utilize a bagging classifier to evaluate the performance of the proposed sparse effective connectivity network through identifying mild cognitive impairment from healthy aging.

UR - http://www.scopus.com/inward/record.url?scp=85029727054&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029727054&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-67389-9_35

DO - 10.1007/978-3-319-67389-9_35

M3 - Conference contribution

AN - SCOPUS:85029727054

SN - 9783319673882

VL - 10541 LNCS

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

SP - 299

EP - 306

BT - Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings

PB - Springer Verlag

ER -