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

阿里1号 项目摘要 目前迫切需要在治疗早期预测抗抑郁药的反应，以减少患者的痛苦 经济负担。传统的抗抑郁药通常需要两个月的时间来确定疗效， 三分之二的患者在一线治疗期间不会缓解（没有抑郁）。迄今为止， 确定了临床上有用的标志物，以预测抗抑郁药的反应早期治疗。因此，长期以来- term 目的 该项目的目的是开发一种早期抗抑郁治疗疗效的预测算法， 使用非侵入性脑成像进行治疗。的 中心假设 是大脑的变化， 通过成像评估，可用作抗抑郁反应的早期预测因子。 磁共振成像（MRI）可以提供有关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，这些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被用于预测抗抑郁药的反应，但这些发现仍然存在。 不一致，最可能是由于可变研究设计和样本量小，并且没有成像 标记物已被临床验证。为了填补这些空白，我将使用一个最近获得的成像数据从一个大的 在他们开始和治疗的第一周，他们的抑郁症严重程度进行了量化 定期由专家临床医生，通过以下目的建立抗抑郁药疗效的预测模型。 1)目的1：比较抗抑郁药治疗前后获得的脑图像， 这些区域需要改变才能使治疗有效。我将从一个中等规模的数据中 在8周的治疗前后对重度抑郁症（MDD）患者进行成像。 抗抑郁治疗。我将测量那些在治疗过程中变得更好的人的大脑结构及其活动， 治疗和分析是否有显着差异，在任何大脑区域在他们的抑郁状态相比， 被赦免的国家然后，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 可以在治疗的第一周早期检测到变化。 2)目的2：根据脑成像检查治疗第一周的脑变化， 将它们整合到抗抑郁药功效的预测模型中。我会减少特征的数量 与大脑结构和活动相关，而不会丢失数据信息。所选功能将是 输入一种名为XGBoost的机器学习算法，该算法具有时间效率和成本效益， 用于检测抑郁症，并取得了中等成功。该模型将根据要素的贡献对要素进行排名 预测抗抑郁药的疗效。如果发现治疗反应与成像无关，这将告知 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，拟议的工作将对早期监测 抗抑郁疗效和治疗计划的有效临床决策工具的应用。 页1的1

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