Predicting Antidepressant Response Early in Treatment Using Neuroimaging To Assist Clinicians With Treatment Planning

ALI 1 项目总结 迫切需要在治疗早期预测抗抑郁药物的反应，以减少患者的痛苦。 和经济负担。传统的抗抑郁药物通常需要两个月的时间才能确定疗效，而且 三分之二的患者在一线治疗期间不会缓解(没有抑郁)。到目前为止还没有一项研究 确定了临床上有用的标记物，以预测治疗早期的抗抑郁反应。因此，长久以来- 术语 目标 这个项目的目的是在早期开发一种预测抗抑郁药物治疗效果的算法。 使用非侵入性脑成像进行治疗。这个 中心假说 这一提议的核心是大脑会发生变化， 经影像评估，可作为抗抑郁药物反应的早期预测指标。 磁共振成像(MRI)可以提供关于大脑结构和功能的有价值的信息 通过治疗早期的各种技术，可能与抗抑郁治疗的最终反应有关。 尽管这些成像技术已经被用来预测抗抑郁反应，但研究结果 不一致，很可能是因为研究设计可变和样本量小，而且没有任何成像 标记物已在临床上得到验证。为了填补这些空白，我将使用最近从一个大型 在治疗开始和第一周的患者样本，并量化他们的抑郁严重程度 定期由专业临床医生通过以下目标建立抗抑郁药物疗效的预测模型。 1)目标1：比较抗抑郁药物治疗前后的脑图像，以确定 需要改变才能使治疗有效的地区。我将使用来自中等规模数据的映像 设置严重抑郁障碍(MDD)患者在8周前和8周后进行成像的位置 抗抑郁治疗。我将测量大脑结构和他们的活动，在这些人中， 治疗并分析抑郁状态下的任何脑区是否与 赦免状态。然后，我将在一个大型成像数据集中探索这些区域，看看这些必要的大脑 在治疗的第一周就可以检测到变化。 2)目标2：根据脑成像检查治疗第一周以来的大脑变化 将它们纳入抗抑郁疗效的预测模型中。我将减少功能的数量 与大脑结构和活动相关，而不会丢失有关数据的信息。所选要素将为 输入一种名为XGBoost的机器学习算法，该算法既省时又省钱，并已被 用于检测抑郁症，效果一般。该模型将根据要素的贡献对要素进行排名 以预测抗抑郁药物的疗效。如果发现治疗反应与成像无关，这将通知 未来的替代成像(例如，EEG)或非成像(例如，睡眠、运动活动或位置)研究。 影响 ：如果成功，拟议的工作将对及早监测 抗抑郁药物的疗效及临床治疗计划决策工具的应用。 第1页，共1页

Ali 1 PROJECT SUMMARY There is a pressing need for predicting antidepressant response early in treatment to reduce patient suffering and economic burden. Conventional antidepressants typically require two months to determine efficacy, and two-thirds of patients will not remit (be free of depression) while on their first-line treatment. No study to date has identified clinically useful markers to predict antidepressant response early in treatment. Therefore, the long- term objective of this project is to develop a predictive algorithm for antidepressant treatment efficacy early in treatment by using noninvasive brain imaging. The central hypothesis of this proposal is that brain changes, assessed by imaging, can be used as early predictors of antidepressant response. Magnetic resonance imaging (MRI) can provide valuable information about brain structure and function through various techniques early in treatment that may relate to the final response to antidepressant treatment. Even though these imaging techniques have been used to predict antidepressant response, the findings have been inconsistent, most likely due to variable study design and small sample size, and none of the imaging markers have been clinically validated. To fill these gaps, I will use a recently acquired imaging data from a large sample of patients at their initiation and first week of treatment, and their depression severity was quantified regularly by expert clinicians, to build a prediction model for antidepressant efficacy through the following aims. 1) Aim 1: Compare brain images acquired before and after antidepressant treatment to identify regions that need to change for the treatment to be effective. I will use imaging from a moderately large data set where patients with major depressive disorder (MDD) were imaged before and after 8 weeks of antidepressant treatment. I will measure brain structures and their activity in individuals who got better with treatment and analyze if there is significant difference in any brain regions in their depressive state compared to remitted state. I will then explore those regions in a large imaging data set to see if these necessary brain changes can be detected early in the first week of treatment. 2) Aim 2: Examine brain changes from the first week of treatment based on brain imaging and incorporate them into a predictive model for antidepressant efficacy. I will reduce the number of features related to brain structure and activity without losing information about the data. The selected features will be entered in a machine learning algorithm called XGBoost, which is time-efficient and cost-effective and has been used for detecting depression with moderate success. The model will rank features based on their contribution to prediction of antidepressant efficacy. If treatment response is found to be unrelated to imaging, this will inform future alternative imaging (e.g., EEG) or non-imaging (e.g., sleep, motor activity or location) studies. Impact : If successful, the proposed work will have broad implications for early monitoring of antidepressant efficacy and application of an effective clinical decision-making tool for treatment planning. Page 1 of 1