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Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability.
Cell ( IF 45.5 ) Pub Date : 2018-03-22 , DOI: 10.1016/j.cell.2018.02.012
Rainbo Hultman 1 , Kyle Ulrich 2 , Benjamin D Sachs 3 , Cameron Blount 1 , David E Carlson 4 , Nkemdilim Ndubuizu 1 , Rosemary C Bagot 5 , Eric M Parise 5 , Mai-Anh T Vu 6 , Neil M Gallagher 6 , Joyce Wang 1 , Alcino J Silva 7 , Karl Deisseroth 8 , Stephen D Mague 1 , Marc G Caron 9 , Eric J Nestler 5 , Lawrence Carin 2 , Kafui Dzirasa 10
Affiliation  

Brain-wide fluctuations in local field potential oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional pathology. Using machine learning, we discover a spatiotemporal dynamic network that predicts the emergence of major depressive disorder (MDD)-related behavioral dysfunction in mice subjected to chronic social defeat stress. Activity patterns in this network originate in prefrontal cortex and ventral striatum, relay through amygdala and ventral tegmental area, and converge in ventral hippocampus. This network is increased by acute threat, and it is also enhanced in three independent models of MDD vulnerability. Finally, we demonstrate that this vulnerability network is biologically distinct from the networks that encode dysfunction after stress. Thus, these findings reveal a convergent mechanism through which MDD vulnerability is mediated in the brain.

中文翻译:

全脑电时空动力学编码抑郁脆弱性。

局部场电位振荡的全脑波动反映了介导行为的紧急网络级信号。破解这些振荡在时间和空间中协调(时空动力学)以表示复杂行为的代码将为大脑如何发出情绪病理学信号提供基本的见解。使用机器学习,我们发现了一个时空动态网络,该网络可以预测遭受慢性社交失败压力的小鼠出现与重度抑郁症 (MDD) 相关的行为功能障碍。该网络中的活动模式起源于前额叶皮层和腹侧纹状体,通过杏仁核和腹侧被盖区传递,并汇聚到腹侧海马。该网络因严重威胁而增加,并且在 MDD 漏洞的三个独立模型中也得到增强。最后,我们证明了这种脆弱性网络在生物学上不同于编码压力后功能障碍的网络。因此,这些发现揭示了一种收敛机制,通过该机制,MDD 易感性在大脑中被调节。
更新日期:2018-03-02
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