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Abstract #3769

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

Jiayi Li1, Chenyang Li2,3, Jiangyang Zhang2,3, and Yulin Ge2,3
1Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York City, NY, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York City, NY, United States, 3Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York City, NY, United States

Synopsis

Keywords: Aging, Aging, 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.

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Keywords