A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference
Joint Authors
Li, Junning
McKeown, Martin J.
Wang, Z. Jane
Liu, Aiping
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-11-04
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Graphical models appear well suited for inferring brain connectivity from fMRI data, as they can distinguish between direct and indirect brain connectivity.
Nevertheless, biological interpretation requires not only that the multivariate time series are adequately modeled, but also that there is accurate error-control of the inferred edges.
The PCfdr algorithm, which was developed by Li and Wang, was to provide a computationally efficient means to control the false discovery rate (FDR) of computed edges asymptotically.
The original PCfdr algorithm was unable to accommodate a priori information about connectivity and was designed to infer connectivity from a single subject rather than a group of subjects.
Here we extend the original PCfdr algorithm and propose a multisubject, error-rate-controlled brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity.
In simulations, we show that the two proposed extensions can still control the FDR around or below a specified threshold.
When the proposed approach is applied to fMRI data in a Parkinson’s disease study, we find robust group evidence of the disease-related changes, the compensatory changes, and the normalizing effect of L-dopa medication.
The proposed method provides a robust, accurate, and practical method for the assessment of brain connectivity patterns from functional neuroimaging data.
American Psychological Association (APA)
Liu, Aiping& Li, Junning& Wang, Z. Jane& McKeown, Martin J.. 2012. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-512126
Modern Language Association (MLA)
Liu, Aiping…[et al.]. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-14.
https://search.emarefa.net/detail/BIM-512126
American Medical Association (AMA)
Liu, Aiping& Li, Junning& Wang, Z. Jane& McKeown, Martin J.. A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-512126
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-512126