Breathlessness in COPD: linking symptom clusters with brain activity.

Area of Science:

• Neuroscience
• Pulmonology
• Psychology

Background:

• Current models of breathlessness do not fully explain the discrepancy between patient experience and objective lung function measures.
• Factors like mood, attention, and expectation are known modulators of breathlessness, but their interplay remains unclear.
• A mechanistic understanding of breathlessness discordance is needed to improve patient care.

Purpose of the Study:

• To develop a novel model integrating brain activity, behavior, and expectation to understand breathlessness in chronic obstructive pulmonary disease (COPD).
• To identify distinct patient clusters based on breathlessness experience and associated factors using machine learning.
• To investigate differences in brain activity, specifically in expectation-related regions, between these identified patient groups.

Main Methods:

• A cohort of 91 individuals with mild-to-moderate COPD participated.
• Functional brain imaging, self-report questionnaires, and clinical respiratory function measures were collected.
• Unsupervised machine learning techniques, including exploratory factor analysis and hierarchical cluster modeling, were employed.

Main Results:

• Participants were stratified into four factors: mood, symptom burden, and two capability measures.
• Two primary groups emerged: high and low symptom burden.
• The low symptom burden group exhibited significantly greater activity in the anterior insula compared to the high symptom burden group.

Conclusions:

• This is the largest functional neuroimaging study in COPD to date, establishing a model linking brain, behavior, and breathlessness expectation.
• Stratification into groups revealed distinct brain activity patterns, highlighting the heterogeneity of breathlessness experience.
• Multimodal models are valuable for identifying behavioral phenotypes and understanding variations in breathlessness burden in COPD patients.