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| A New Ensemble Clustering Dynamic Functional Connectivity Analysis Method for ExploringBrain Connectivity Variation in Schizophrenia |
| Fang Songke, Du Yuhui* |
| (School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China) |
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Abstract In recent years, dynamic functional connectivity based on functional magnetic resonance imaging data has shown great potentials in studying psychiatric disorders. Traditional clustering-based dynamic functional connectivity analysis methods, such as K-means, are sensitive to the influence of class numbers, initial values, and noise, which may result in unreliable functional connectivity states (FCSs). This paper proposed a new dynamic functional connectivity analysis method based on ensemble clustering. First, K-means was performed with multiple different class numbers (k values) to generate diverse clusters. Then, the similarity between different clusters was mined based on Jaccard coefficient and random walk to construct a weighted graph reflecting the relationship between clusters. Finally, reliable meta-clusters were obtained by community detection on the weighted graph, and each functional connectivity window was grouped into different meta-clusters by voting, and its centroid was calculated as the functional connectivity states. Based on fMRI data from 105 healthy controls (HC) and 70 schizophrenia (SZ) patients, we comprehensively compared the performance of the proposed method with the commonly used K-means based method for dynamic functional network analysis. Compared with K-means method, the average inter class similarity of the proposed method decreased from 83.2% to 81.1% on FCS 2, and decreased from 76.8% to 73.5% on FCS 3. The Davies Bouldin index decreased from 6.74 to 6.44, and the Silhouette coefficacy index increased from 0.018 to 0.031. In terms of group differences, results obtained from our proposed method showed that the differences between the HC and SZ groups were mainly concentrated in FCS 2. In contrast, group differences resulted from K-means method were dispersed across FCS 2, FCS 3, and FCS 4, primarily due to subpar clustering metrics and high average inter-cluster similarity. In summary, the proposed method in this study was able to automatically determine the number of FCSs and exhibit better clustering quality and more reliable FCSs. Additionally, the method revealed more meaningful inter-group differences than k-means, indicating the potentials of exploring biomarkers for mental disorders.
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Received: 28 July 2023
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Corresponding Authors:
* E-mail: duyuhui@sxu.edu.cn
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