TY - JOUR
T1 - Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model
AU - Zhang, Aiying
AU - Fang, Jian
AU - Liang, Faming
AU - Calhoun, Vince D.
AU - Wang, Yu Ping
N1 - Funding Information:
Manuscript received January 14, 2018; revised May 25, 2018; accepted June 28, 2018. Date of publication September 10, 2018; date of current version July 1, 2019. The work was supported in part by the National Institutes of Health under Grants R01GM109068, R01MH104680, R01MH107354, P20GM103472, 2R01EB005846, and 1R01EB006841, and in part by the National Science Foundation #1539067. (Corresponding author: Yu-Ping Wang.) A. Zhang, J. Fang, and Y.-P. Wang are with the Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118 USA (e-mail:, azhang4@tulane.edu; jfang3@tulane.edu; wyp@tulane.edu).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/7
Y1 - 2019/7
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. With the development of functional magnetic resonance imaging (fMRI), further exploration of brain connectivity was made possible. Region-based networks are commonly used for mapping brain connectivity. However, they fail to illustrate the connectivity within regions of interest (ROIs) and lose precise location information. Voxel-based networks provide higher precision, but are difficult to construct and interpret due to the high dimensionality of the data. In this paper, we adopt a novel high-dimensional Gaussian graphical model - ψ-learning method, which can help ease computational burden and provide more accurate inference for the underlying networks. This method has been proven to be an equivalent measure of the partial correlation coefficient and, thus, is flexible for network comparison through statistical tests. The fMRI data we used were collected by the mind clinical imaging consortium using an auditory task in which there are 92 SZ patients and 116 healthy controls. We compared the networks at three different scales by using global measurements, community structure, and edge-wise comparisons within the networks. Our results reveal, at the highest voxel resolution, sets of distinct aberrant patterns for the SZ patients, and more precise local structures are provided within ROIs for further investigation.
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. With the development of functional magnetic resonance imaging (fMRI), further exploration of brain connectivity was made possible. Region-based networks are commonly used for mapping brain connectivity. However, they fail to illustrate the connectivity within regions of interest (ROIs) and lose precise location information. Voxel-based networks provide higher precision, but are difficult to construct and interpret due to the high dimensionality of the data. In this paper, we adopt a novel high-dimensional Gaussian graphical model - ψ-learning method, which can help ease computational burden and provide more accurate inference for the underlying networks. This method has been proven to be an equivalent measure of the partial correlation coefficient and, thus, is flexible for network comparison through statistical tests. The fMRI data we used were collected by the mind clinical imaging consortium using an auditory task in which there are 92 SZ patients and 116 healthy controls. We compared the networks at three different scales by using global measurements, community structure, and edge-wise comparisons within the networks. Our results reveal, at the highest voxel resolution, sets of distinct aberrant patterns for the SZ patients, and more precise local structures are provided within ROIs for further investigation.
KW - Gaussian graphical models (GGMs)
KW - Schizophrenia (SZ)
KW - brain connectivity
KW - fMRI
KW - network comparison
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U2 - 10.1109/JBHI.2018.2854659
DO - 10.1109/JBHI.2018.2854659
M3 - Article
C2 - 29994624
AN - SCOPUS:85049688281
SN - 2168-2194
VL - 23
SP - 1479
EP - 1489
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 4
M1 - 8408706
ER -