Share Email Print
cover

Proceedings Paper

Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis (MCA): convergent cross-mapping and non-metric clustering
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We explore a computational framework for functional connectivity analysis in resting-state functional MRI (fMRI) data acquired from the human brain for recovering the underlying network structure and understanding causality between network components. Termed mutual connectivity analysis (MCA), this framework involves two steps, the first of which is to evaluate the pair-wise cross-prediction performance between fMRI pixel time series within the brain. In a second step, the underlying network structure is subsequently recovered from the affinity matrix using non-metric network clustering approaches, such as the so-called Louvain method. Finally, we use convergent cross-mapping (CCM) to study causality between different network components. We demonstrate our MCA framework in the problem of recovering the motor cortex network associated with hand movement from resting state fMRI data. Results are compared with a ground truth of active motor cortex regions as identified by a task-based fMRI sequence involving a finger-tapping stimulation experiment. Our results regarding causation between regions of the motor cortex revealed a significant directional variability and were not readily interpretable in a consistent manner across subjects. However, our results on whole-slice fMRI analysis demonstrate that MCA-based model-free recovery of regions associated with the primary motor cortex and supplementary motor area are in close agreement with localization of similar regions achieved with a task-based fMRI acquisition. Thus, we conclude that our MCA methodology can extract and visualize valuable information concerning the underlying network structure between different regions of the brain in resting state fMRI.

Paper Details

Date Published: 17 March 2015
PDF: 9 pages
Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 94170M (17 March 2015); doi: 10.1117/12.2082124
Show Author Affiliations
Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)
Univ. of Rochester (United States)
Ludwig Maximilian Univ. (Germany)
Anas Z. Abidin, Univ. of Rochester Medical Ctr. (United States)
Univ. of Rochester (United States)
Adora M. D'Souza, Univ. of Rochester (United States)
Xixi Wang, Univ. of Rochester Medical Ctr. (United States)
Univ. of Rochester (United States)
Susan K. Hobbs, Univ. of Rochester Medical Ctr. (United States)
Lutz Leistritz, Friedrich-Schiller-Univ. Jena (Germany)
Mahesh B. Nagarajan, Univ. of Rochester Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 9417:
Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Robert C. Molthen, Editor(s)

© SPIE. Terms of Use
Back to Top