Abstract
Adolescence is a period of substantial experience-dependent brain development. A major goal of the Adolescent Brain Cognitive Development (ABCD) study is to understand how brain development is associated with various environmental factors such as socioeconomic characteristics. While ABCD study offers a large sample size, it still requires a sensitive method to detect subtle associations when studying typically developing children. Therefore, we propose a novel transform, i.e. covariance-based multi-scale transform (COVLET), which derives a multi-scale representation from a structured data (i.e., P features from N samples) that increases performance of downstream analyses. The theory driving our work stems from wavelet transform in signal processing and orthonormality of the principal components of a covariance matrix. Given the microstructural properties of brain regions from children enrolled in the ABCD study, we demonstrate a multi-variate statistical group analysis on family income using the multi-scale feature derived from brain structure and validate improvement in the statistical outcomes. Furthermore, our multi-scale descriptor reliably identifies specific regions of the brain that are susceptible to socioeconomic disparity.
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Acknowledgement
This research was supported by GAANN Doctoral Fellowships in Computer Science and Engineering at UTA sponsored by the U.S. Department of Education, NIH R01 AG059312 and IITP-2020-2015-0-00742. Numerous funding agencies have continued to support the ABCD study. A full list is provided at https://abcdstudy.org. We also would like to thank Dr. Rui Meng for insightful discussions and comments that greatly improved the manuscript.
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Yang, F., Isaiah, A., Kim, W.H. (2020). COVLET: Covariance-Based Wavelet-Like Transform for Statistical Analysis of Brain Characteristics in Children. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_9
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