# Integrating Machine Learning and Genomic Approaches to Understand Cerebral Small Vessel Disease Pathogenesis from White Matter Hyperintensity Patterns > **NIH NIH K23** · WASHINGTON UNIVERSITY · 2020 · $178,809 ## Abstract PROJECT SUMMARY As a neurointensivist and neurologist at Washington University School of Medicine in St. Louis (WUSM), my career goal is to develop an independent research program as a computational biologist capable of using advanced bioinformatics and statistical methods to integrate analysis of large-scale neuroimaging and genetic data, with the aim of deepening understanding of the biological mechanisms influencing cerebral small vessel disease (SVD) and identifying new targets for therapeutic development. As a first step towards this goal, I have designed an innovative proposal that combine machine-learning (ML) methods and integrated imaging genetic analyses of large-scale neuroimaging and genetic data to improve characterization of SVD disease mechanisms. The clinical, imaging, and etiologic heterogeneity of SVD have impeded efforts to uncover the pathophysiology of this common and debilitating neurological disease. White matter hyperintensities (WMH), a major imaging endpoint of SVD, are comprised of multiple SVD pathologic processes. Growing evidence suggests location-specific vulnerability of brain parenchyma to different underlying SVD pathologic processes, in which spatially localized WMH patterns may reflect distinct SVD etiologies. Characterizing WMH spatial pattern variations in SVD will not only provide insights into underlying pathogenesis, such as vascular amyloid deposition, arteriolosclerosis, and other less well defined or as-yet unknown disease mechanisms, but also lead to creation of novel imaging biomarkers of these SVD pathologic processes. This proposal addresses a key inadequacy, as existing WMH pattern definitions are determined empirically and cannot distinguish overlapping SVD etiologies and risk factors. In this proposal, I aim to capture WMH spatial pattern variations that reflect distinct SVD etiologies in an unbiased manner, by applying clustering analysis/ML methods to structural MRI data to create novel etiology-specific SVD imaging phenotypes. Moreover, given that genetics influence the variance of WMH, I will integrate genetic analyses of these WMH patterns to uncover novel mechanisms that influence SVD pathogenesis. My preliminary data demonstrate the feasibility of identifying data-driven WMH spatial pattern variations, which are specific to distinct SVD etiologies, and allow detection of genetic risk variants that may help inform SVD pathologic processes. My career plan leverages the extensive resources and exceptional environments at WUSM, under the guidance of a multidisciplinary mentorship team with expertise across diverse fields including cerebrovascular physiology, neuroimaging, informatics, genetics, and machine learning (Drs. Jin-Moo Lee, Daniel Marcus, Carlos Cruchaga and Yasheng Chen). In this Career Development Award, I propose to: 1) determine distinct WMH spatial patterns that can discriminate underlying SVD pathology and/or risk factors by applying pattern analysis ML methods to structural... ## Key facts - **NIH application ID:** 10022173 - **Project number:** 5K23NS110927-02 - **Recipient organization:** WASHINGTON UNIVERSITY - **Principal Investigator:** Chia-Ling Phuah - **Activity code:** K23 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $178,809 - **Award type:** 5 - **Project period:** 2019-09-20 → 2024-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10022173 ## Citation > US National Institutes of Health, RePORTER application 10022173, Integrating Machine Learning and Genomic Approaches to Understand Cerebral Small Vessel Disease Pathogenesis from White Matter Hyperintensity Patterns (5K23NS110927-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10022173. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*