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:10004169
- 负责人:
- 金额:$ 71.33万
- 依托单位:
- 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:
- 关键词:AddressAmygdaloid structureAntidepressive AgentsAnxietyAnxiety DisordersBehaviorBehavior ControlBehavioralBrainBrain DiseasesBrain regionCellsChronicCodeCognitionCognitiveCollectionCommunitiesCouplingDataDevelopmentDiseaseDisease modelEmotionalEmotionsEtiologyExhibitsFrequenciesFunctional disorderGene ExpressionGeneticHealthHippocampus (Brain)HumanImpairmentIndividualLinkLocationMachine LearningMajor Depressive DisorderMeasuresMediatingMemoryMental DepressionMental disordersMethodsModelingMolecularMonitorMood DisordersMusNeuronsNucleus AccumbensOutcomePlayPre-Clinical ModelPrefrontal CortexResearchRodentRodent ModelRoleSchizophreniaSignal TransductionSiteSocial BehaviorSocial FunctioningStressTechniquesTestingThalamic structureTherapeutic InterventionTimeValidationVentral Tegmental AreaViralWorkautism spectrum disorderbasecell typecognitive functiondepression modeldepressive symptomsemotional functioninghuman imagingimaging studyin vivomillisecondmouse modelmultidisciplinarynetwork modelsneuromechanismnovelnovel therapeuticsoptogeneticspreclinical studyrelating to nervous systemreward processingsocial defeatspatiotemporaltool
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:剖析和修改重性抑郁症的时间动力学基础
多项人类成像研究已经描述了特定的异常时空动态
重度抑郁症受试者的大脑网络。此外,啮齿动物研究
已经发现了在遗传和压力下大脑皮层边缘回路的同步功能障碍-
重度抑郁症的诱导模型。然而,仍有待澄清的是,
这些观察到的神经动力学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。若干重大挑战,
解决这个问题的方法是存在的。1)大脑在多个时间尺度上保持动态。
啮齿动物研究通常在毫秒时间尺度上监测动态(反射电路),
并且人类研究通常以秒时间尺度(反射电路)监测大脑动力学
以及网络级活动)。2)啮齿类动物的研究通常在监测能力方面受到限制,
大规模的活动,从许多大脑区域同时进行,而人类成像研究观察到,
整个大脑的活动。3)据我们所知,很少有办法/模式纳入变革,
在细胞类型特异性基因表达中,抑郁症与回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
同步和交叉频率耦合)还没有在整个
啮齿动物研究社区。为了应对这些挑战,我们建议在
在两种广泛使用的抑郁症啮齿动物模型中的体内神经记录。我们将利用
机器学习,以确定共享的时空动态变化,
两个模型。下一步,我们将测试是否涉及细胞分子操纵的主要
抑郁症足以引起相同的时空动态变化。
最后,我们将通过直接归纳和
抑制它们并测量它们对行为的影响。这一战略将产生
对特定脑回路内改变的动力学如何有助于
萧条
项目成果
期刊论文数量(0)
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Kafui Dzirasa其他文献
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{{ truncateString('Kafui Dzirasa', 18)}}的其他基金
Precision editing of neural circuits using engineered electrical synapses
使用工程电突触精确编辑神经回路
- 批准号:
10487711 - 财政年份:2022
- 资助金额:
$ 71.33万 - 项目类别:
Precision editing of neural circuits using engineered electrical synapses
使用工程电突触精确编辑神经回路
- 批准号:
10700919 - 财政年份:2022
- 资助金额:
$ 71.33万 - 项目类别:
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
- 批准号:
10085101 - 财政年份:2020
- 资助金额:
$ 71.33万 - 项目类别:
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
- 批准号:
10226122 - 财政年份:2019
- 资助金额:
$ 71.33万 - 项目类别:
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
- 批准号:
10670070 - 财政年份:2019
- 资助金额:
$ 71.33万 - 项目类别:
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
- 批准号:
10441495 - 财政年份:2019
- 资助金额:
$ 71.33万 - 项目类别:
A fully biological platform for monitoring mesoscale neural activity
用于监测中尺度神经活动的全生物平台
- 批准号:
9764377 - 财政年份:2018
- 资助金额:
$ 71.33万 - 项目类别:
Characterizing sensorimotor gaiting dysfunction in mouse models of schizophrenia
精神分裂症小鼠模型感觉运动步态功能障碍的特征
- 批准号:
8582022 - 财政年份:2013
- 资助金额:
$ 71.33万 - 项目类别:
Characterizing sensorimotor gaiting dysfunction in mouse models of schizophrenia
精神分裂症小鼠模型感觉运动步态功能障碍的特征
- 批准号:
8701406 - 财政年份:2013
- 资助金额:
$ 71.33万 - 项目类别:














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