Characterizing Age-related Morphological Changes in Frontal and Temporal Lobes of the Human Brain Using Multi-feature Clustering Analysis

Motivation: Age-related changes in frontal and temporal lobes are critical for understanding cognitive decline but their coordinated patterns of morphological alterations remain unclear. Goal(s): To characterize the synergistic morphological patterns (e.g., volume, thickness, and curvature) of frontal and temporal structures associated with aging using structural MRI measures. Approach: Using HCP-Aging dataset (n=675, age: 36-100 years), we applied GLM to identify age effects and K-Medoids clustering to explore natural grouping of two lobes' structures based on cortical volume, thickness, and surface curvature. Results: Three distinct morphological patterns with significant age differences were identified, suggesting heterogeneous ageing pattern in the two lobes. Impact: This multi-feature analysis reveals three distinct aging patterns in frontal and temporal regions, providing new insights into synergistic morphological pattern between frontal and temporal lobes and potential framework for multi-feature explicable analysis for age-related neurodegenerative diseases.