Research Paper Volume 12, Issue 17 pp 17328—17342
Functional connectivity network estimation with an inter-similarity prior for mild cognitive impairment classification
- 1 College of Computer Science Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
- 2 Universal Medical Imaging Diagnostic Center, Shanghai 20030, China
- 3 Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai 20065, China
- 4 College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 40074, China
Received: May 10, 2020 Accepted: July 6, 2020 Published: September 13, 2020https://doi.org/10.18632/aging.103719
How to Cite
Copyright: © 2020 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Functional connectivity network (FCN) analysis is an effective technique for modeling human brain patterns and diagnosing neurological disorders such as Alzheimer’s disease (AD) and its early stage, Mild Cognitive Impairment. However, accurately estimating biologically meaningful and discriminative FCNs remains challenging due to the poor quality of functional magnetic resonance imaging (fMRI) data and our limited understanding of the human brain. Inspired by the inter-similarity nature of FCNs, similar regions of interest tend to share similar connection patterns. Here, we propose a functional brain network modeling scheme by encoding Inter-similarity prior into a graph-regularization term, which can be easily solved with an efficient optimization algorithm. To illustrate its effectiveness, we conducted experiments to distinguish Mild Cognitive Impairment from normal controls based on their respective FCNs. Our method outperformed the baseline and state-of-the-art methods by achieving an 88.19% classification accuracy. Furthermore, post hoc inspection of the informative features showed that our method yielded more biologically meaningful functional brain connectivity.