Neuroimaging insights into lung disease-related brain changes: from structure to function

Area of Science:

• Neuroimaging
• Pulmonology
• Neurology

Background:

• Lung diseases are increasingly recognized to affect brain structure and function, leading to neurological and cognitive deficits.
• The lung-brain axis, a complex interplay between pulmonary and central nervous systems, is explored through various pathways, including neuroimaging.
• Existing research highlights the need for a comprehensive overview of neuroimaging findings in diverse lung diseases.

Purpose of the Study:

• To survey and categorize neuroimaging studies examining the relationship between lung diseases and brain structure/function.
• To discuss distinct patterns of brain changes observed in chronic obstructive pulmonary disease (COPD), COVID-19, and lung cancer.
• To analyze pathological mechanisms, clinical significance, limitations, and future research directions in this field.

Main Methods:

• Systematic collation and categorization of existing lung disease studies focusing on neuroimaging, particularly magnetic resonance imaging (MRI).
• Categorization into four types: COPD, COVID-19, lung cancer, and other lung diseases.
• Discussion of observed structural, functional, and cognitive changes, alongside pathological mechanisms and clinical relevance.

Main Results:

• Neuroimaging in COPD shows frontal lobe changes (gray matter atrophy, white matter damage, reduced perfusion).
• COVID-19 patients exhibit microhemorrhages and neuroinflammation, with significant changes in olfactory-connected brain regions.
• Lung cancer-related brain changes are linked to treatment toxicity, affecting subcortical structures; cancer pain correlates with motor/visual network hyperconnectivity.

Conclusions:

• Lung diseases induce diverse and specific neuroimaging alterations, underscoring the critical lung-brain axis.
• Current research faces limitations including small sample sizes, non-standardized criteria, and lack of longitudinal data.
• Future research should focus on AI-driven biomarker development, longitudinal multicenter studies, and understanding systemic effects for neuromodulation strategies.