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Improved dynamic functional connectivity estimation with an alternating hidden Markov model.

Zhiying Long1, Xuanping Liu2, Yantong Niu2

  • 1School of Artificial Intelligence, Beijing Normal University, Beijing, 100875 China.

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Summary

A new alternating hidden Markov model (aHMM) method improves dynamic functional connectivity (DFC) analysis in fMRI data. aHMM offers better robustness and detects subtle brain state differences in patients with cerebral small vessel disease and cognitive impairment.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Dynamic functional connectivity (DFC) analysis reveals time-varying brain interactions using fMRI.
  • Sliding window (SW) and hidden Markov model (HMM) are common DFC methods, but SW has limited temporal resolution and HMM can overfit fMRI data.

Purpose of the Study:

  • To introduce an alternating HMM (aHMM) for robust DFC estimation.
  • To compare aHMM with SW and HMM using simulated and real fMRI data.
  • To investigate DFC alterations in cerebral small vessel disease with amnesia and mild cognitive impairment (CSVD-aMCI).

Main Methods:

  • Proposed an alternating HMM (aHMM) initializing HMM with SW connectivity and using an alternating procedure to reduce parameters.
  • Validated aHMM on simulated and Human Connectome Project fMRI data.
  • Applied aHMM to fMRI data from CSVD-aMCI patients and controls.

Main Results:

  • aHMM demonstrated superior robustness to noise, parameter count, and sample size compared to SW and HMM.
  • CSVD-aMCI patients exhibited altered brain states, spending more time in weak connectivity states and less in strong ones.
  • CSVD-aMCI patients showed lower connectivity amplitude and higher fluctuation than controls, differences not detected by HMM or SW.

Conclusions:

  • aHMM is a more robust and sensitive method for DFC analysis than SW and HMM.
  • aHMM effectively identifies intergroup differences in DFC temporal properties and connectivity fluctuations.
  • The findings highlight aHMM's potential for clinical applications in neurological disorders like CSVD-aMCI.