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

使用神经影像学在治疗早期预测抗抑郁反应，以协助临床医生制定治疗计划

• 批准号: 10577901
• 负责人: Farzana Zulfiqur Ali
• 金额: $ 3.48万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10577901
• 关键词: Aftercare; Algorithms; Antidepressive Agents; Area; Brain; Brain imaging; Brain region; Cerebrovascular Circulation; Characteristics; Clinical; Clinical Decision Support Systems; Consensus; Data; Data Set; Depressed mood; Detection; Dimensions; Disease remission; Double-Blind Method; Early Diagnosis; Early treatment; Economic Burden; Electroencephalography; Engineering; Failure; Functional disorder; Future; Goals; Hamilton Rating Scale for Depression; Hospitalization; Image; Imaging Techniques; Individual; Learning; Location; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Measures; Medical; Mental Depression; Modeling; Monitor; Motor Activity; National Institute of Mental Health; Neurons; Participant; Pathologic; Pathology; Patients; Performance; Physicians; Placebos; Positioning Attribute; Prediction of Response to Therapy; Predictive Analytics; Principal Component Analysis; Psychiatry; Research; Research Design; Sample Size; Sampling; Scientist; Severities; Site; Sleep; Statistical Data Interpretation; Structure; Suicide; Techniques; Testing; Time; Training; Treatment Efficacy; Trees; Work; arterial spin labeling; clinical decision-making; cost effective; depressive symptoms; efficacy evaluation; feature selection; gradient boosting; high dimensionality; imaging biomarker; imaging modality; imaging study; improved; insight; large datasets; longitudinal analysis; machine learning algorithm; machine learning model; machine learning prediction algorithm; neural; neuroimaging; novel; novel therapeutics; prediction algorithm; predictive marker; predictive modeling; predictive tools; preservation; prevent; response; success; tool; treatment planning; treatment response

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

阿里1 项目摘要 迫切需要在治疗早期预测抗抑郁反应以减少患者的痛苦 和经济负担。常规抗抑郁药通常需要两个月才能确定功效，并且 在一线治疗时，三分之二的患者将不累积（没有抑郁症）。迄今为止没有学习 确定了临床上有用的标记，可以预测治疗早期抗抑郁反应。因此，长期 学期 客观的 该项目的是开发一种预测算法，以提高抗抑郁治疗功效 通过使用无创脑成像进行处理。这 中央假设 该建议是大脑变化， 通过成像评估，可以用作抗抑郁反应的早期预测指标。 磁共振成像（MRI）可以提供有关大脑结构和功能的宝贵信息 通过在治疗早期的各种技术中，可能与抗抑郁药治疗的最终反应有关。 即使这些成像技术已被用来预测抗抑郁反应，但这些发现还是 不一致，很可能是由于可变的研究设计和较小的样本量，而没有成像 标记已通过临床验证。为了填补这些空白，我将使用来自大型的最近获得的成像数据 患者在开始时和治疗的第一周的样本，并量化了抑郁症的严重程度 专家临床医生经常通过以下目的建立抗抑郁功效的预测模型。 1）目标1：比较抗抑郁治疗前后获得的脑图像以识别 需要改变以使治疗有效的区域。我将使用中等大数据中的成像 在8周之前和之后对患有重度抑郁症（MDD）的患者（MDD）的设置 抗抑郁治疗。我将衡量大脑结构及其在变得更好的个人中的活动 治疗和分析与其抑郁状态的任何大脑区域是否存在显着差异 汇出状态。然后，我将在大型成像数据集中探索这些区域，以查看这些大脑是否必要 可以在治疗的第一周初检测到变化。 2）目标2：检查基于脑成像的治疗第一周的大脑变化 将它们纳入抗抑郁疗效的预测模型中。我将减少功能数量 与大脑结构和活动有关，而不会丢失有关数据的信息。选定的功能将是 输入一种称为XGBoost的机器学习算法，该算法是时间效率且具有成本效益的，已经是 用于检测抑郁症的中等成功。该模型将根据其贡献对特征进行排名 预测抗抑郁药的功效。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，脑电图）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控有广泛的影响 抗抑郁药的功效和有效的临床决策工具用于治疗计划。 第1页，共1页