ORIGINAL RESEARCH article
Front. Neurol.
Sec. Applied Neuroimaging
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1625888
Dynamic reconfiguration and transition of whole-brain networks in patients with MELAS revealed by hidden Markov model
Provisionally accepted
- 1Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
- 2Department of Radiology, HuaShan Hospital, Fudan University, Shanghai, China
- 3Department of Neurology, HuaShan Hospital, Fudan University, Shanghai, China
- 4Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, China
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Objectives: Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a rare maternally inherited disease. As a recurrent paroxysmal clinical event, the neuropathologic mechanistic basis and neural networks alterations of stroke-like episode (SLE) remain unclear. The hidden Markov model (HMM) can detect profound altered neural activities of whole-brain network. Materials and methods: We initially collected a prospective cohort from 2019 to 2024. The confirmed diagnosis of MELAS was genetic testing or muscle biopsy. Healthy control volunteers were recruited from the local community. By utilizing the HMM, we evaluated the temporal characteristics and transitions of HMM states and the specific community pattern of transitions and activation maps of whole brain for subjects. Results: Thirty-six MELAS patients at the acute stage (MELAS-acute group) and 30 healthy controls (HC group) were included in this study. Based on HMM, fractional occupancies of state 5 and 6 for MELAS were significantly decreased (p<0.001), but fractional occupancies of state 2, 3, 4, 7, 8, 9, 10 and 11 were significantly increased (p<0.05), compared with HCs. The lifetimes of HMM states had a similar decreased change with fractional occupancies. The switching frequency of HMM states was significantly increased for MELAS (p<0.001 ). Combined with special community patterns of transitions, MELAS displayed differential activities patterns in the crucial areas of default mode network (DMN) and visual network (VN). Conclusion: This study suggested the dynamic reconfiguration of HMM states, the special module of transitions and multiple transitions pathways for MELAS, providing novel insights for understanding the neural network mechanisms of MELAS. Keywords: MELAS, Stroke-like episode, Rs-fMRI, Hidden Markov model (HMM), Whole-brain network dynamics
Keywords: MELAS, Stroke-like episode, RS-fMRI, Hidden Markov Model (HMM), Whole-brain network dynamics
Received: 09 May 2025; Accepted: 26 Aug 2025.
Copyright: © 2025 Yu, Wang, Sun, Hu, Liu, Yang, Lin, Geng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yuxin Li, Department of Radiology, HuaShan Hospital, Fudan University, Shanghai, China
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