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:剖析和修改重度抑郁症背后的时间动态
多项人类成像研究描述了特定区域的异常时空动态
患有重度抑郁症的受试者的大脑网络。此外,啮齿动物研究
已发现遗传和压力方面皮质边缘回路的同步功能失调
诱发重度抑郁症模型。尽管如此,是否仍需澄清
这些观察到的神经动力学变化起着因果作用或简单地反映了(即,相关
与)在重度抑郁症中观察到的行为状态。面临的几大挑战
解决这个问题是存在的。 1)大脑在多个时间尺度上同步动态。
啮齿动物研究通常以毫秒为单位监测动态(反射电路),
人类研究通常以秒为单位监测大脑动态(反射电路
和网络级活动)。 2) 啮齿动物研究的监测能力普遍有限
许多大脑区域同时进行大规模活动,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/模型能够整合变革
与抑郁症相关的细胞类型特异性基因表达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)尚未在整个系统中得到广泛实施
啮齿动物研究团体。为了应对这些挑战,我们建议在
两种广泛使用的抑郁症啮齿动物模型的体内神经记录。然后我们将利用
机器学习来确定之间共享的时空动态变化
两个模型。接下来,我们将测试细胞分子操作是否与主要
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验证这些时空动态是因果关系
抑制它们并衡量它们对行为的影响。该策略将产生
对特定大脑回路内的动态变化如何促成的前所未有的理解
沮丧。
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:
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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: