Depression as a disease of network disruption: learning from multiple sclerosis

抑郁症是一种网络中断疾病：从多发性硬化症中学习

• 批准号: 10643057
• 负责人: Erica Berlin Baller
• 金额: $ 19.49万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643057
• 关键词: Acceleration; Adult; Affect; Age; Atrophic; Attention; Biological Markers; Brain; Brain region; Chronic Disease; Clinical; Clinical Trials; Clinical assessments; Cognition; Computational Technique; Computerized Medical Record; Computing Methodologies; Data; Data Set; Diagnosis; Dimensions; Disease; Dorsal; Exclusion; Fascicle; Female; Fiber; Foundations; Functional disorder; Heterogeneity; Image; Immune; Impairment; Knowledge; Learning; Lesion; Location; Magnetic Resonance Imaging; Maps; Measures; Mediating; Mediation; Medical; Mental Depression; Mentored Patient-Oriented Research Career Development Award; Mentorship; Mimosa; Modeling; Multiple Sclerosis; Neurocognitive; Participant; Patients; Pattern; Pennsylvania; Performance; Persons; Phenotype; Prevalence; Process; Psychiatry; Public Health; Recording of previous events; Reproducibility; Research; Sampling; Severities; Sex Differences; Societies; Stroke; Structure; Symptoms; System; Techniques; Testing; Training; United States; University resources; Visit; White Matter Disease; algorithmic methodologies; automated segmentation; biotypes; clinical care; cognitive function; cognitive testing; comorbid depression; comorbidity; cost; depressive symptoms; experience; gray matter; individual variation; instrument; machine learning algorithm; machine learning method; male; network dysfunction; neuropsychiatric symptom; predictive modeling; programs; prospective; psychiatric symptom; recruit; supervised learning; symptomatology; tool; treatment stratification; white matter

PROJECT ABSTRACT/SUMMARY Multiplesclerosis (MS) is an immune-mediatedneurological disorder that affects one million people in the United States. Up to 50% of patients with MS experience depression, yet the mechanisms of depression in MS remain under-investigated. MS is characterized by white matter lesions, suggesting that brain network disruption may underly depression symptoms. Studies of medically healthy participants with depression have described associations between white matter variability and depressive symptoms, but frequently exclude participants with medical comorbidities and thus cannot be extrapolated to people with intracranial diseases. Previous research using lesion network mapping, a technique for mapping heterogeneous gray matter lesions to neuropsychiatric symptoms, has demonstrated that strokes in gray matter associated with depression disrupt a reproducible depression network. However, such techniques have never been applied to white matter disease or MS. Studying white matter lesions associated with depression in MS may provide a way to understand both the pathophysiology of depression in MS and general network mechanisms of depression more broadly. The purpose of this current study is to investigate how brain network disruption underlies depression by learning from the example of multiple sclerosis. In Aim 1, I will delineate how depression in adults with MS is associated with white matter lesion location and burden in a retrospective sample of 1,554 MS patients with research-grade 3T MRIs acquired as part of clinical care. Depression and MS diagnoses will be obtained from the electronic medical record. While this sample provides an ideal dataset for developing a model, the electronic medical record does not contain granular depression measures. In Aim 2, I will obtain structured clinical and cognitive assessments for MS patients and prospectively evaluate white matter integrity as a predictor of dimensional depressive symptoms. However, it is possible that symptoms of depression may reflect heterogenous brain network disruption patterns. Therefore, in Aim 3, I will use advanced semi-supervised machine learning methods to parse heterogeneity in MS white matter lesion burden in the retrospective sample and test whether this model predicts phenotypic heterogeneity in our deeply-phenotyped prospective sample. The support of the K23 award will provide the applicant with the training necessary to achieve these aims. The training objectives will be accomplished with the support of an outstanding mentorship team, Drs. Satterthwaite, Shinohara, Bassett, Bar- Or, Fox, McCoy, and the world class resources of the University of Pennsylvania. Together, the proposed scientific aims and training objectives will form the foundation for an independent research program that will use techniques from computational psychiatry to understand depression in patients with medical comorbidities.

项目摘要/总结 多发性硬化症（MS）是一种免疫介导的神经系统疾病， 美国的高达50%的MS患者患有抑郁症，但抑郁症的机制， MS仍在调查中。MS的特征是白色病变，表明脑网络 精神分裂可能是抑郁症的潜在症状。对健康的抑郁症参与者的研究表明， 描述了白色物质变异性和抑郁症状之间的联系，但经常排除 参与者患有医学共病，因此不能外推到患有颅内疾病的人。 以前的研究使用病变网络映射，映射异质性灰质病变的技术 神经精神症状，已经证明，中风的灰质与抑郁症中断， 一个可复制的抑郁网络然而，此类技术从未应用于白色物质疾病 研究多发性硬化症中与抑郁症相关的白色病变可能提供一种理解两者的方法 MS中抑郁症的病理生理学和更广泛的抑郁症的一般网络机制。的 目前这项研究的目的是研究如何通过学习大脑网络中断导致抑郁症， 多发性硬化症的例子。在目标1中，我将描述成年多发性硬化症患者的抑郁症与以下因素的关系： 1，554例研究级3 T MS患者的回顾性样本中的白色病变位置和负荷 MRI作为临床护理的一部分。抑郁症和MS诊断将从电子医疗设备中获得。 记录虽然此示例为开发模型提供了理想的数据集， 不含颗粒状抑制措施。在目标2中，我将获得结构化的临床和认知评估 并前瞻性评价白色物质完整性作为维度抑郁的预测因子 症状然而，抑郁症的症状可能反映了异质性的脑网络 破坏模式因此，在目标3中，我将使用先进的半监督机器学习方法来解析 回顾性样本中MS白色病变负荷的异质性，并检验该模型是否预测 表型异质性在我们的深表型前瞻性样本。K23奖的支持将 向申请人提供实现这些目标所需的培训。培训目标将是 在一个杰出的导师团队的支持下完成，Satterthwaite博士，Shinohara，Bassett，Bar- 或者，福克斯，麦考伊，和宾夕法尼亚大学的世界级资源。在一起，拟议的 科学目标和训练目标将构成一个独立研究计划的基础， 从计算精神病学的技术来了解抑郁症患者的医疗合并症。