Abstract #1248

Longitudinal Modeling of Iron Accumulation in the Thalamus and Putamen Using Change-Point Mixed Effects Analysis

Fahad Salman^1,2, Joseph Boccardo³, Niels Bergsland¹, Michael G. Dwyer^1,4, Bianca Weinstock-Guttman⁵, Robert Zivadinov^1,4, Gregory Wilding³, and Ferdinand Schweser^1,4

¹Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ²Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY, United States, ⁴Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States, ⁵Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States

Synopsis

Keywords: Aging, biomarkers, Thalamus, putamen, healthy controls, iron, susceptibility, normal aging, mixed-effects model, iron trajectory

Motivation: Iron accumulation in deep gray matter (DGM) regions, essential for brain function, varies across individuals, complicating detection of disease-related changes. Cross-sectional studies struggle to capture individual iron accumulation trajectories due to inter-subject variability.

Goal(s): This study aimed to define normative, intra-subject changes in DGM iron over time using longitudinal quantitative susceptibility mapping (QSM), focusing on thalamus and putamen regions.

Approach: Novel change-point mixed effects model was appplied to healthy adults’ QSM data across multiple time points. Analyses examined intra-subject trajectories and sex-specific patterns.

Results: Our model revealed consistent intra-subject iron trajectories, with sex-specific change points. Minimal inter-subject slope variability confirmed stable individual trajectories.

Impact: This study’s normative DGM iron trajectories provide a benchmark, offering a sensitive longitudinal biomarker for detecting early pathological changes in neurodegenerative diseases.

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