# Statistical and Machine Learning Methods for Integrating Clinical and Multimodal Imaging Data to Select Optimal Antidepressant Treatment > **NIH NIH K01** · GEORGE WASHINGTON UNIVERSITY · 2021 · $160,963 ## Abstract Summary: The public health burden of major depressive disorder (MDD) is immense and current approaches for selecting antidepressant treatment have had limited success. By some estimates, fewer than one in three MDD patients will respond to their prescribed antidepressant and the quest for a treatment that will work is typically characterized by a lengthy course of trial-and-error. The need to identify patient characteristics (biomarkers) that can be used to objectively select personalized antidepressant treatment is clear. Accordingly, large clinical studies like the NIMH-funded Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) study have collected massive amounts of baseline measures including those from various neuroimaging sources in the hope that some can be used to guide antidepressant treatment selection. These data bring with them many statistical challenges that have yet to be effectively addressed. These challenges include (1) dealing with high-dimensionality, (2) handling data missingness, and (3) determining how best to simultaneously model relationships between measures from multiple imaging modalities and the response of interest. The goal of this project is to acquire the essential training and experience to make significant progress in this area by addressing each of these challenges. Aim 1 of this project will employ state-of-the-art ensemble machine learning algorithms and targeted estimation to identify moderators of antidepressant treatment effect using scalar clinical, demographic, and summary neuroimaging data from clinical trials of antidepressant treatments, including EMBARC. Strategies for handling missing data in this context will also be investigated and guidelines on best practices will be proposed. Aim 2 will extend the methods used in Aim 1 and develop user-friendly software to directly incorporate high- dimensional multimodal neuroimaging data into treatment decision rules. Included in this aim will be an investigation into best practices for handling missing high-dimensional imaging data in the context of estimating treatment decision rules. Aim 3 will employ the novel methods developed in Aim 2 and the estimated treatment decision rules will be evaluated and compared with those developed in Aim 1. I have put together a training program that directly supports the completion of these research aims. It includes instruction, mentoring, and hands-on-experience (1) in psychopathology and the neural basis for psychiatric disorders and treatment for those disorders; (2) in the use of neuroimaging data to understand depression and response to antidepressant treatment; (3) in the use of modern algorithms to store, process, manipulate, and analyze big biomedical data like those arising in multimodal neuroimaging studies. This K01 Mentored Research Scientist Development Award will provide the training, time, and resources to be able to make substantial progress in addressing this i... ## Key facts - **NIH application ID:** 10241351 - **Project number:** 5K01MH113850-04 - **Recipient organization:** GEORGE WASHINGTON UNIVERSITY - **Principal Investigator:** Adam Ciarleglio - **Activity code:** K01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $160,963 - **Award type:** 5 - **Project period:** 2018-09-15 → 2023-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10241351 ## Citation > US National Institutes of Health, RePORTER application 10241351, Statistical and Machine Learning Methods for Integrating Clinical and Multimodal Imaging Data to Select Optimal Antidepressant Treatment (5K01MH113850-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241351. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*