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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：
10441495
负责人：
Kafui Dzirasa
金额：
$ 68.46万
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/10441495
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 (Brain)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 base cell type cognitive function depression model depressive symptoms emotional functioning human imaging imaging study in vivo millisecond mouse model multidisciplinary network models neuromechanism novel novel therapeutics optogenetics preclinical study relating to nervous system 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：重性抑郁障碍的时间动力学分析与修正