The measurements of the similarity of dynamic brain functional network.
10.7507/1001-5515.202103079
- Author:
Yongquan HE
1
;
Li ZHANG
2
;
Shan FANG
2
;
Yaqin ZENG
2
;
Wei YANG
2
;
Weidong CHEN
3
;
Yuling SHAO
2
;
Ruidong CHENG
2
;
Xiangming YE
2
;
Dongrong XU
1
Author Information
1. Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China.
2. Department of Rehabilitation Medicine, Zhejiang Province People's Hospital, Hangzhou Medical College, Hangzhou 310014, P. R. China.
3. Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain functional network;
Dynamic evolution;
Functional magnetic resonance imaging;
Graph theory;
Similarity
- MeSH:
Aging/physiology*;
Brain/physiology*;
Brain Mapping;
Humans;
Magnetic Resonance Imaging/methods*;
Nerve Net/physiology*;
Transcranial Direct Current Stimulation/methods*
- From:
Journal of Biomedical Engineering
2022;39(2):237-247
- CountryChina
- Language:Chinese
-
Abstract:
Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.
