Exploring heterogeneity in structural imaging patterns of aging brains using machine learning

Lan Lin; Lingyu Liu; Shuicai Wu

doi:10.1117/12.3011790

7 December 2023 Exploring heterogeneity in structural imaging patterns of aging brains using machine learning

Lan Lin, Lingyu Liu, Shuicai Wu

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129412C (2023) https://doi.org/10.1117/12.3011790
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The impact of age-related brain atrophy on the decline of cognitive function in older adults has been widely recognized. To investigate this issue, we conducted a study using machine learning algorithms to analyze Magnetic Resonance Imaging (MRI) data collected from a large UK Biobank sample of 6, 000 individuals, ranging in age from 48 to 84 years. Our methodology consisted of splitting the entire cohort into two groups based on brain age gap estimation from three image modalities: the Advanced Brain Age Group (ABAG) and the Normal and Healthy Brain Aging Group (NHBAG). Subsequently, using a mixture of experts to provide more clarity on the group heterogeneity of NHBAG, we identified four subtypes. These included the temporal lobe preservation subtype, the minimal atrophy subtype, the diffusion atrophy subtype, and the frontal lobe preservation subtype. These results emphasize the importance of exploring the complexity of age-related brain changes and their subtypes through advanced data-driven techniques.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lan Lin, Lingyu Liu, and Shuicai Wu "Exploring heterogeneity in structural imaging patterns of aging brains using machine learning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129412C (7 December 2023); https://doi.org/10.1117/12.3011790

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KEYWORDS

Brain

Neuroimaging

Machine learning

Magnetic resonance imaging

Data modeling

Structural imaging

Image processing

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