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Published on: June 26, 2013
Dynamic reconfiguration and transition of whole-brain networks in patients with MELAS revealed by a hidden Markov
Qingyun Yu1,2, Rong Wang2, Chong Sun3
1Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
Objectives:
Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a rare maternally inherited disease. The neuropathologic mechanisms and neural network alterations underlying stroke-like episodes (SLEs), a recurrent paroxysmal clinical event, remain unclear. The hidden Markov model (HMM) can detect profound alterations in neural activities across the whole-brain network.
Materials And Methods:
We initially collected data from a prospective cohort from 2019 to 2024. The confirmed diagnosis of MELAS was conducted through genetic testing or a 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 the 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 in states 5 and 6 for MELAS were significantly decreased (p < 0.001), but fractional occupancies in states 2, 3, 4, 7, 8, 9, 10, and 11 were significantly increased (p < 0.05), compared to HCs. The lifetimes of HMM states showed a similar decrease as fractional occupancies. The switching frequency of HMM states was significantly increased in MELAS (p < 0.001). Combined with the special community patterns of transitions, MELAS displayed differential activity patterns in crucial areas of the default mode network (DMN) and visual network (VN).
Conclusion:
This study suggests dynamic reconfiguration of HMM states, special transition modules, and multiple transition pathways in MELAS, providing novel insights into the neural network mechanisms underlying MELAS.
Insights
Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) patients show altered brain network dynamics. Hidden Markov modeling revealed significant changes in neural activity patterns, offering new insights into MELAS pathophysiology.
Area of Science:
- Neuroscience
- Genetics
- Medical Research
Background:
- Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a rare, maternally inherited disorder.
- The underlying neuropathologic mechanisms and neural network alterations of stroke-like episodes (SLEs) in MELAS are not fully understood.
- Hidden Markov models (HMM) can identify significant changes in neural activity across brain networks.
Purpose of the Study:
- To investigate the neural network alterations in MELAS patients during acute stroke-like episodes (SLEs).
- To utilize HMM to analyze the temporal dynamics and transition patterns of whole-brain neural activity in MELAS.
- To elucidate the neuropathologic mechanisms of SLEs in MELAS.
Main Methods:
- A prospective cohort study included 36 MELAS patients and 30 healthy controls (HCs).
- Diagnosis of MELAS was confirmed via genetic testing or muscle biopsy.
- HMM was employed to evaluate HMM state transitions, fractional occupancies, lifetimes, and community patterns of brain activity.
Main Results:
- MELAS patients exhibited significantly decreased fractional occupancies in HMM states 5 and 6, and increased occupancies in states 2, 3, 4, 7, 8, 9, 10, and 11 compared to HCs.
- The lifetimes of HMM states were reduced, and the switching frequency of HMM states was significantly increased in MELAS patients.
- Differential activity patterns were observed in the default mode network (DMN) and visual network (VN) of MELAS patients, linked to specific transition patterns.
Conclusions:
- MELAS is characterized by dynamic reconfiguration of HMM states and unique transition modules within neural networks.
- The findings provide novel insights into the neural network mechanisms underlying MELAS and its associated stroke-like episodes.
- This study highlights the utility of HMM in understanding complex neurological disorders like MELAS.
