Unit Size Determination for Exploratory Brain Imaging Analysis: A Quest for a Resolution-Invariant Metric

Jihnhee Yu (), HyunAh Lee and Zohi Sternberg
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Jihnhee Yu: Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA
HyunAh Lee: Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA
Zohi Sternberg: Department of Neurology, University at Buffalo, Buffalo, NY 14203, USA

Mathematics, 2025, vol. 13, issue 7, 1-18

Abstract: Defining an adequate unit size is often crucial in brain imaging analysis, where datasets are complex, high-dimensional, and computationally demanding. Unit size refers to the spatial resolution at which brain data is aggregated for analysis. Optimizing unit size in data aggregation requires balancing computational efficiency in handling large-scale data sets with the preservation of brain activity patterns, minimizing signal dilution. We propose using the Calinski–Harabasz index, demonstrating its invariance to sample size changes due to varying image resolutions when no distributional differences are present, while the index effectively identifies an appropriate unit size for detecting suspected regions in image comparisons. The resolution-independent metric can be used for unit size evaluation, ensuring adaptability across different imaging protocols and modalities. This study enhances the scalability and efficiency of brain imaging research by providing a robust framework for unit size optimization, ultimately strengthening analytical tools for investigating brain function and structure.

Keywords: data aggregation; data disintegration; DTI data; fMRI data; resolution free metric; sample size invariant metric (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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