Dissecting and modifying temporal dynamics underlying major depressive disorder

剖析和修改重度抑郁症背后的时间动态

基本信息

批准号：
10670070
负责人：
Kafui Dzirasa
金额：
$ 50.83万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10670070
关键词：
Address Amygdaloid structure Antidepressive Agents Anxiety Anxiety Disorders Behavior Behavior Control Behavioral Brain Brain Diseases Brain region Cells Chronic Code Cognition Cognitive Collection Communities Coupling Data Development Disease Disease model Emotional Emotions Etiology Exhibits Frequencies Functional disorder Gene Expression Genetic Health Hippocampus Human Impairment Individual Link Location Machine Learning Major Depressive Disorder Measures Mediating Memory Mental Depression Mental disorders Methods Modeling Molecular Monitor Mood Disorders Mus Neurons Nucleus Accumbens Outcome Play Pre-Clinical Model Prefrontal Cortex Research Rodent Rodent Model Role Schizophrenia Signal Transduction Site Social Behavior Social Functioning Stress Techniques Testing Thalamic structure Therapeutic Intervention Time Validation Ventral Tegmental Area Viral Work autism spectrum disorder cell type cognitive function depression model depressive symptoms emotional functioning human imaging imaging study in vivo millisecond mouse model multidisciplinary network models neural neuromechanism novel novel therapeutics optogenetics preclinical study reward processing social defeat spatiotemporal tool

项目摘要

Title: Dissecting and modifying temporal dynamics underlying major depressive disorder Multiple human imaging studies have described aberrant spatiotemporal dynamics in specific brain networks across subjects with major depressive disorder. Furthermore, rodent studies have identified dysfunctional synchrony across cortical limbic circuits in genetic and stress- induced models of major depressive disorder. Nevertheless, it remains to be clarified whether these observed changes in neural dynamics play a causal role or simply reflect (i.e., correlate with) the behavioral-state observed in major depressive disorder. Several major challenges to addressing this question exist. 1) The brain synchronizes dynamics across multiple timescales. Rodent studies classically monitor dynamics at the millisecond time scale (reflecting circuits), and human studies typically monitor brain dynamics at the seconds time scale (reflect circuit and network level activity). 2) Rodent studies are generally limited in their ability to monitor large-scale activity from many brain regions concurrently, while human imaging studies observe activity across the whole brain. 3) To our knowledge, few approaches/models integrate changes in cell-type specific gene expression implicated in depression to changes in circuit and network- specific brain dynamics. 4) Techniques which directly manipulate brain dynamics (neural synchrony and cross-frequency coupling) have yet to be largely implemented throughout the rodent research community. To address these challenges, we propose to perform multi-circuit in vivo neural recordings in the two widely used rodent models of depression. We will then utilize machine learning to determine the spatiotemporal dynamic alterations that are shared between the two models. Next, we will test whether cellular molecular manipulations implicated in major depressive disorder are sufficient to induce the same spatiotemporal dynamic alterations. Finally, we will verify that these spatiotemporal dynamics are causal by directly inducing and suppressing them and measuring their impact on behavior. This strategy will yield an unprecedented understanding of how altered dynamics within specific brain circuits contribute to depression.

标题：剖析和修改重大抑郁症的基础的时间动力多个人类成像研究描述了特定的异常时空动力学患有严重抑郁症的受试者的大脑网络。此外，啮齿动物研究在遗传和应激中确定了跨皮质边缘电路之间的功能失调引起的主要抑郁症模型。然而，是否尚待澄清是否是否这些观察到的神经动力学的变化起因果作用或简单地反映（即相关与）在重度抑郁症中观察到的行为状态。几个主要挑战解决这个问题。 1）大脑在多个时间尺度上同步动力学。啮齿动物研究以毫秒的时间尺度（反映电路），经典监测动力学，人类研究通常以秒时间尺度监测脑动力学（反映电路和网络级活动）。 2）啮齿动物研究通常受到监测的能力限制来自许多大脑区域的大规模活动同时进行，而人类成像研究观察到整个大脑的活动。 3）据我们所知，很少有方法/模型整合变化在细胞类型的特异性基因表达中，与抑郁症有关，与电路和网络的变化有关特定的大脑动力学。 4）直接操纵大脑动力学的技术（神经同步和跨频耦合）尚未在整个过程中实施啮齿动物研究社区。为了应对这些挑战，我们建议在两种广泛使用的啮齿动物抑郁模型中的体内神经记录。然后我们将使用机器学习以确定在之间共享的时空动态变化这两个模型。接下来，我们将测试是否与主要的分子操作有关抑郁症足以诱导相同的时空动态改变。最后，我们将通过直接诱导和抑制它们并衡量它们对行为的影响。该策略将产生对特定大脑电路中动态的改变如何有助于沮丧。