Improved dynamic functional connectivity estimation with an alternating hidden Markov model

Zhiying Long; Xuanping Liu; Yantong Niu; Huajie Shang; Hui Lu; Junying Zhang; Li Yao

doi:10.1007/s11571-022-09874-3

Improved dynamic functional connectivity estimation with an alternating hidden Markov model

Cogn Neurodyn. 2023 Oct;17(5):1381-1398. doi: 10.1007/s11571-022-09874-3. Epub 2022 Nov 3.

Authors

Zhiying Long¹, Xuanping Liu², Yantong Niu², Huajie Shang^{2

3}, Hui Lu^{2

3}, Junying Zhang^{4

3}, Li Yao^{1

2}

Affiliations

¹ School of Artificial Intelligence, Beijing Normal University, Beijing, 100875 China.
² The State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China.
³ BABRI Centre, Beijing Normal University, Beijing, 100875 China.
⁴ Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700 China.

PMID: 37786659
PMCID: PMC10542089 (available on 2024-10-01)
DOI: 10.1007/s11571-022-09874-3

Abstract

Dynamic functional connectivity (DFC) analysis has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal the time-varying functional interactions between brain regions. Although the sliding window (SW) method is popular for DFC analysis, the selection of window length is hard, and the temporal resolution is limited by the window length. The hidden Markov model (HMM) without the limitation of window length has been proven to be able to estimate time-varying brain states from fMRI data. However, HMM tends to be overfitted in DFC analysis of fMRI data because of the high spatial dimension and the limited sample size of fMRI data. In this study, we proposed an alternating HMM (aHMM) method that used the functional connectivity estimation of SW to initialize the covariance matrix of HMM and adopted an alternating HMM procedure to reduce the number of parameters during each optimization. The simulated and real fMRI resting data from the Human Connectome Projects showed that aHMM produced better robustness to noise, parameter number and sample size in DFC estimation than SW and HMM. For the real fMRI resting data of cerebral small vessel disease (CSVD), results of aHMM revealed that amnesia and mild cognitive impairment (aMCI) caused the CSVD with aMCI (CSVD-aMCI) group tended to spend more time on the brain state with overall weak connections and less time on the state with overall strong connections than the CSVD-controls. Moreover, CSVD-aMCI showed significantly lower connectivity amplitude and higher connectivity fluctuation than CSVD-control. In contrast, HMM did not detect intergroup differences of the connectivity amplitude and fluctuations and SW did not detect intergroup differences of connectivity fluctuations and fraction of time. The results further indicated that aHMM outperformed HMM and SW in detecting inter-group differences of temporal properties of DFC and connectivity fluctuations.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-022-09874-3.

Keywords: Brain state; Dynamic; Functional connectivity; HMM; fMRI.

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