Dissecting and modifying temporal dynamics underlying major depressive disorder

标题：剖析和修正重度抑郁障碍的时间动力学 多项人类成像研究描述了特定的异常时空动力学 患有严重抑郁障碍的受试者的大脑网络。此外，对啮齿动物的研究 已经在遗传和压力中发现了大脑皮质边缘回路功能失调的同步性- 诱发的重度抑郁障碍模型。尽管如此，目前还不清楚是否 这些观察到的神经动力学变化起到因果作用或只是反映(即，相互关联 与)在严重抑郁障碍中观察到的行为状态。面临的几个主要挑战 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。 啮齿类动物的研究经典地监测毫秒级的动力学(反射电路)， 人类研究通常以秒为时间尺度(反射电路)监测大脑动态 和网络级活动)。2)啮齿动物的研究通常在它们的监测能力方面是有限的 在人类成像研究观察到的同时，来自多个大脑区域的大规模活动 整个大脑的活动。3)据我们所知，很少有方法/模型集成变化 在涉及抑郁症的细胞类型特异性基因表达中，电路和网络的变化- 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 啮齿动物研究社区。为了应对这些挑战，我们建议在 两种广泛使用的抑郁啮齿动物模型的活体神经记录。然后我们将利用 机器学习以确定共享的时空动态变化 这两个型号。接下来，我们将测试细胞分子操作是否与主要 抑郁障碍足以引起同样的时空动态变化。 最后，我们将通过直接归纳和归纳来验证这些时空动力学是因果的 抑制它们并测量它们对行为的影响。这一战略将产生一个 史无前例地了解特定大脑回路中的动态变化如何有助于 抑郁症。

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.