Title: Joint structural-and-functional MRI analysis for predicting electroconvulsive therapy response in major depressive disorder Electroconvulsive therapy (ECT) is currently the most effective and fast-acting neuromodulation treatments for major depressive disorder (MDD). Understanding the mechanism of action of ECT-response may be a fruitful strategy to understand the pathophysiology of MDD. But ECT also frequently provokes cognitive adverse effects and is often considered as more invasive than pharmacotherapy. Consequently, there is a critical need to develop reliable prognostic tools for predicting individualized treatment response. The availability of such a predictor could provide guidelines for the clinician to optimize treatment strategy for individual patient and to support patient's decision-making process.
In Aim 1 of this project, we propose to develop a computational framework to integrate novel diffusion MRI (dMRI) and resting-state functional MRI (rsfMRI) measures in order to assess the properties of structural and functional connectivity of brain networks.
In Aim 2, we will apply the joint analysis of dMRI and rsfMRI to two large datasets with a total of 260 MDD patients and 170 controls to identify depression-related abnormal connections in brain networks. Our approach could provide a novel probabilistic MDD network that reflects heterogeneous brain abnormalities in different biotypes of MDD.
In Aim 3, we will focus our analysis on a subgroup of 120 patients treated by ECT. Specifically, we will use the joint structural-and-functional analysis to identify ECT-induced changes in brain connections for responders and non- responders, respectively. Moreover, we will analyze the correlation between changes in brain connections with changes in clinical MDD severities measures, and also with subject-specific E-field stimulated by ECT. Results from this project will support future prospective trials aiming to improve therapeutic response using individualized image-guided ECT in MDD. The training program provided by this K01 Mentored Research Scientist Award provides the critical knowledge and experience to the PI for him to complete the proposed project. Based his strong foundation in multimodal MRI analysis techniques, the PI seeks to obtain focused training on neuroanatomy, clinical aspects of depression and ECT, under the guidance of experienced experts from his mentoring team and advisory committee. In conclusion, this K01 Award will provide the PI with the training and research background to conduct independent research that integrates novel MRI techniques and E-field simulation approaches to develop optimal neuromodulation therapies for individual patients with MDD.
This project aims to develop a computational tool for integrating novel diffusion MRI and resting-state functional MRI measures to understand brain abnormalities related to major depressive disorder (MDD) by analyzing datasets from 260 patients and 170 controls. From a subgroup of 120 patients who were treated by the electroconvulsive therapy (ECT), we will identify ECT-related changes in brain connections and the relation between the brain changes with changes in clinical MDD severity measures, and with the E-field stimulated by ECT. Results will enhance our understanding into the mechanisms of action, brain target engagement, and predictors of ECT response.
