Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia

Aiying Zhang, Vince Daniel Calhoun, Yu Ping Wang

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

Abstract

Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies, but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including both medical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a new perspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brain regions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copula model, where we used the Gaussian copula model to address the data integration issue and the joint network estimation for different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposed method not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractions all at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC), which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183 subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hub DNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Our analysis results are shown to be both statistically and biologically significant.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsPeter R. Bak, Po-Hao Chen
PublisherSPIE
ISBN (Electronic)9781510625556
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications - San Diego, United States
Duration: Feb 17 2019Feb 18 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10954
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CitySan Diego
Period2/17/192/18/19

Fingerprint

schizophrenia
methylation
Epigenomics
Brain
Schizophrenia
Joints
brain
Methylation
Imaging techniques
DNA Methylation
hubs
deoxyribonucleic acid
Magnetic Resonance Imaging
data integration
disorders
Data integration
Independent component analysis
Medical imaging
interactions
Mental Disorders

Keywords

  • Data integration
  • DNA methylation
  • Fmri
  • Gaussian copula model
  • Imaging epigenetics
  • Joint estimation
  • Schizophrenia

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Zhang, A., Calhoun, V. D., & Wang, Y. P. (2019). Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia. In P. R. Bak, & P-H. Chen (Eds.), Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications [109540R] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10954). SPIE. https://doi.org/10.1117/12.2513050

Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia. / Zhang, Aiying; Calhoun, Vince Daniel; Wang, Yu Ping.

Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications. ed. / Peter R. Bak; Po-Hao Chen. SPIE, 2019. 109540R (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10954).

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

Zhang, A, Calhoun, VD & Wang, YP 2019, Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia. in PR Bak & P-H Chen (eds), Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications., 109540R, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10954, SPIE, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2513050
Zhang A, Calhoun VD, Wang YP. Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia. In Bak PR, Chen P-H, editors, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications. SPIE. 2019. 109540R. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2513050
Zhang, Aiying ; Calhoun, Vince Daniel ; Wang, Yu Ping. / Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia. Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications. editor / Peter R. Bak ; Po-Hao Chen. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{723f07b96ae9470fb6ba822305de74c2,
title = "Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia",
abstract = "Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies, but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including both medical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a new perspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brain regions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copula model, where we used the Gaussian copula model to address the data integration issue and the joint network estimation for different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposed method not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractions all at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC), which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183 subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hub DNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Our analysis results are shown to be both statistically and biologically significant.",
keywords = "Data integration, DNA methylation, Fmri, Gaussian copula model, Imaging epigenetics, Joint estimation, Schizophrenia",
author = "Aiying Zhang and Calhoun, {Vince Daniel} and Wang, {Yu Ping}",
year = "2019",
month = "1",
day = "1",
doi = "10.1117/12.2513050",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Bak, {Peter R.} and Po-Hao Chen",
booktitle = "Medical Imaging 2019",

}

TY - GEN

T1 - Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia

AU - Zhang, Aiying

AU - Calhoun, Vince Daniel

AU - Wang, Yu Ping

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies, but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including both medical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a new perspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brain regions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copula model, where we used the Gaussian copula model to address the data integration issue and the joint network estimation for different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposed method not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractions all at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC), which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183 subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hub DNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Our analysis results are shown to be both statistically and biologically significant.

AB - Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies, but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including both medical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a new perspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brain regions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copula model, where we used the Gaussian copula model to address the data integration issue and the joint network estimation for different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposed method not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractions all at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC), which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183 subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hub DNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Our analysis results are shown to be both statistically and biologically significant.

KW - Data integration

KW - DNA methylation

KW - Fmri

KW - Gaussian copula model

KW - Imaging epigenetics

KW - Joint estimation

KW - Schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=85068576049&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068576049&partnerID=8YFLogxK

U2 - 10.1117/12.2513050

DO - 10.1117/12.2513050

M3 - Conference contribution

AN - SCOPUS:85068576049

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2019

A2 - Bak, Peter R.

A2 - Chen, Po-Hao

PB - SPIE

ER -