Complex Dynamic Systems in Mood Disorders

情绪障碍中的复杂动态系统

• 批准号: 8033503
• 负责人: ANDREW A NIERENBERG
• 金额: $ 16.82万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-28 至 2012-09-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8033503
• 关键词: Accounting; Adult; Age; Age of Onset; Am 80; Arousal; Award; Biological; Biological Markers; Biology; Bipolar Disorder; Brain; Breathing; Caring; Characteristics; Chronic; Chronic Disease; Circadian Rhythms; Clinical; Cognition; Cognitive; Cognitive Therapy; Collaborations; Complex; Data; Data Analyses; Data Collection; Development; Devices; Diagnosis; Disease; Disease remission; Economic Burden; Educational process of instructing; Engineering; Facilities and Administrative Costs; Functional disorder; Funding; Future; Galvanic Skin Response; Goals; Heart Rate; Heritability; Hospitalization; Hour; Human; Individual; Institutes; Israel; K-18 conjugate; Lead; Learning; Major Depressive Disorder; Manic; Measurement; Measures; Mediator of activation protein; Medical center; Medicine; Mental Depression; Mentors; Mentorship; Methods; Modeling; Mood Disorders; Movement; National Institute of Mental Health; Neurosecretory Systems; Nonlinear Dynamics; Outcome; Pathologic; Patients; Pattern; Phase; Phase Transition; Phenotype; Physiological; Pilot Projects; Population; Prevalence; Procedures; Process; Reading; Recovery; Recurrence; Reporting; Research Personnel; Research Training; Respiration; Sampling; Signal Transduction; Sleep Stages; Sleep Wake Cycle; Societies; Statistical Methods; Stereotyping; Stress; Suicide; System; Systems Analysis; Time; Training Programs; Translating; Variant; Voice; Waxes; abstracting; alternative treatment; base; clinically relevant; cognitive rigidity; cost; depressive symptoms; disability; heart rate variability; hypomania; innovation; new technology; novel; programs; response; sensor; sex; single episode major depressive disorder

DESCRIPTION (provided by applicant): This K18 application is to provide advanced mentorship in complex dynamic system analyses of clinical and biomarker data for major depressive disorder (MDD) and bipolar disorder (BD). MDD is a serious, debilitating illness that is projected to become the second global leading cause of disability by 2020. Bipolar disorder (BD) is a lifelong, chronic and highly recurrent, mood disorder characterized by episodes of mania or hypomania and episodes of major depression and is one of the top 10 causes of disability worldwide. The gap is that clinically relevant phenotypes and pathophysiology of MDD and BD are poorly delineated. One obstacle has been the prevalent use of static linear statistical measures to assess complex dynamic relationships between waxing and waning variables. Conventional analyses assume that signals are linear and stationary, and operate on a single (characteristic time) scale criteria that are routinely violated by real-world signals, i.e, they are highly nonlinear, operate on multiple time scales and are also nonstationary due to physiologic and pathologic phase transitions and other abrupt changes (bifurcations). The developmental aim of this K18 application is to learn methods to analyze complex dynamic nonlinear data to apply to the study of mood disorders with 12 months of intensive mentorship to the applicant through didactics and supervised analyses mentored by Ary L. Goldberger, Director of the Rey Institute for Nonlinear Dynamics in Medicine at Beth Israel Deaconess Medical Center and Wyss Institute for Biological Inspired Engineering at Harvard. The pilot project will analyze measures of arousal, heart rate, respiration, movement, and sleep stages using a 24 hour wearable devices from 5 patients with MDD, 5 age and sex matched patients with BP, and 5 matched healthy controls. These analyses will a) explore differences between MDD and healthy controls; b) BD and healthy controls; and c) MDD and BD. Methods for analyzing complex dynamic systems can open innovative new windows into understanding mood disorders. The applicant's long-term goal is to be able to apply nonlinear dynamics to the daunting problem of finding biologically relevant phenotypes and clinically useful biomarkers. PUBLIC HEALTH RELEVANCE: This research training program will teach the applicant how to use advanced statistical methods to analyze complicated data that vary over time from patients with mood disorders. Voice patterns, heart rate, breathing patterns, and movements over 24 hours are a few examples. These methods can lead to better diagnoses, better ways to understand the biology of mood disorders, and ways to measure response to treatment.

描述（由申请人提供）：该K18申请旨在为重度抑郁症（MDD）和双相情感障碍（BD）的临床和生物标志物数据的复杂动态系统分析提供高级指导。重度抑郁症是一种严重的使人衰弱的疾病，预计到2020年将成为全球第二大致残原因。双相情感障碍（BD）是一种终身、慢性和高度复发的情绪障碍，其特征是躁狂或轻躁狂发作和重度抑郁症发作，是全球十大致残原因之一。差距是临床相关的表型和病理生理的MDD和双相障碍是很差的描述。一个障碍是普遍使用静态线性统计方法来评估盈亏变量之间复杂的动态关系。传统的分析假设信号是线性和平稳的，并且在单一（特征时间）尺度上运行，而现实世界的信号通常会违反这些标准，即它们是高度非线性的，在多个时间尺度上运行，并且由于生理和病理相变以及其他突变（分岔）而是非平稳的。这个K18应用程序的发展目标是学习分析复杂动态非线性数据的方法，以应用于情绪障碍的研究。申请人将接受为期12个月的密集指导，通过教学和监督分析，由哈佛大学贝斯以色列女Deaconess医疗中心雷伊医学非线性动力学研究所所长和哈佛大学怀斯生物启发工程研究所所长Ary L. Goldberger指导。该试点项目将使用24小时可穿戴设备分析5名重度抑郁症患者、5名年龄和性别匹配的血压患者和5名匹配的健康对照者的觉醒、心率、呼吸、运动和睡眠阶段。这些分析将a)探索重度抑郁症与健康对照之间的差异；b) BD和健康对照；c) MDD和BD。分析复杂动态系统的方法可以为理解情绪障碍打开创新的新窗口。申请人的长期目标是能够将非线性动力学应用于寻找生物学相关表型和临床有用的生物标志物的艰巨问题。