Resting-state fMRI using hidden Markov models reveals abnormal dynamic brain functional states in asthma

Area of Science:

• Neuroscience
• Medical Imaging
• Respiratory Medicine

Background:

• Asthma involves airway inflammation and abnormal central nervous system (CNS) regulation.
• Previous studies show static brain connectivity abnormalities in asthma.
• Dynamic brain functional states in asthma remain under-investigated.

Purpose of the Study:

• To investigate dynamic functional connectivity in asthma using Hidden Markov Models (HMM).
• To identify potential neurobiological markers related to asthma symptoms.

Main Methods:

• Resting-state fMRI data from 60 asthma patients and 60 healthy controls.
• Applied HMM to identify recurring brain functional states (9 identified).
• Compared group differences in fractional occupancy (FO), mean dwell time (MDT), and transition probabilities; correlated with Asthma Control Test (ACT) scores.

Main Results:

• Asthma patients showed significantly increased MDT and FO in State 2 (somatomotor/dorsal attention networks) versus controls (p < 0.05).
• Exploratory analysis indicated a positive correlation between State 2 MDT and ACT scores (r=0.30, p < 0.05, uncorrected).
• Identified altered brain state dynamics, specifically prolonged occupancy in a sensory-attention state.

Conclusions:

• Asthma is associated with altered brain state dynamics, characterized by prolonged engagement in a specific sensory-attention network state.
• These dynamic brain metrics offer novel insights into asthma's central mechanisms.
• Findings suggest potential preliminary neurobiological markers for asthma symptom monitoring and control.