Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability

Hultman, Rainbo; Ulrich, Kyle; Sachs, Benjamin D.; Blount, Cameron; Carlson, David; Ndubuizu, Nkemdilim; Bagot, Rosemary C.; Parise, Eric M.; Vu, Mai-Anh; Gallagher, Neil M.; Wang, Joyce; Silva, Alcino J.; Deisseroth, Karl; Mague, Stephen D.; Caron, Marc G.; Nestler, Eric J.; Carin, Lawrence; Dzirasa, Kafui

doi:10.1016/j.cell.2018.02.012

Cited by 143 publications

(148 citation statements)

References 43 publications

(90 reference statements)

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“…At T4 however recovery of SI behavior seemed to occur when looking at group dynamics. Several brain regions have been assigned to be involved in social avoidance and depressive behavior after CSD exposure including the prefrontal cortex, amygdala, the ventral tegmental area, the ventral hippocampus and the nucleus accumbens (14)(15)(16)(17)(18).…”

Section: Discussionmentioning

confidence: 99%

Dynamic longitudinal behavior in animals exposed to chronic social defeat stress

Wendelmuth

Willam

Todorov

et al. 2020

Preprint

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Chronic social defeat (CSD) can lead to impairments in social interaction and other behaviors that are supposed to model features of major depressive disorder (MDD).Not all animals subjected to CSD, however, develop these impairments, and maintained social interaction in some animals is widely used as a model for resilience to stress-induced mental dysfunctions. So far, animals have mainly been studied shortly (24 hours and 7 days) after CSD exposure and longitudinal development of behavioral phenotypes in individual animals has been mostly neglected. We have analyzed social interaction and novel object recognition behavior of stressed mice at different time points after CSD and have found very dynamic courses of behavior of individual animals. Instead of the two groups, resilient or susceptible, that are found at early time points our data suggest four groups with (i, ii) animals behaving resilient or susceptible at early and late time points, respectively (iii) animals that start susceptible and recover with time or (iv) animals that are resilient at early time points but develop vulnerability later on.

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Section: Discussionmentioning

confidence: 99%

Dynamic longitudinal behavior in animals exposed to chronic social defeat stress

Wendelmuth

Willam

Todorov

et al. 2020

Preprint

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“…Experimental models of stress vulnerability usually count on natural individual predispositions to either susceptible or resilient phenotypes (Feder et al, 2009;e.g. Bagot et al, 2016;Christensen et al, 2011;Hultman et al, 2018;Krishnan et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Mounting evidence indicates that theta oscillations have a role in the processing of aversive information (Bocchio et al, 2017;Çalışkan and Stork, 2019;Gray and McNaughton, 2000;Likhtik and Gordon, 2014). Many reports describe increased theta power and synchrony during different stressors, including exposure to distant predators, aggressive conspecifics, anxiogenic environments, and conditioned fear (Adhikari et al, 2010;Hultman et al, 2018;Lesting et al, 2011;Mikulovic et al, 2018;Sainsbury et al, 1987;Seidenbecher et al, 2003). These observations suggest that theta oscillations may signal aversiveness and represent a correlate of fear and anxiety.…”

Section: Theta Functions and Stressor Controllabilitymentioning

confidence: 99%

A Distinctive Pattern of Hippocampal-Prefrontal Cortical Network Activity during Stress Predicts Learned Resistance

Marques

Ruggiero

Bueno-Júnior

et al. 2019

Preprint

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The perception of control over a stressful experience may determine its impacts and generate resistance against future stressors. Although the medial prefrontal cortex (mPFC) and the hippocampus are implicated in the encoding of stressor controllability, the neural dynamics underlying this process are unknown. Here, we recorded CA1 and mPFC neural activities in rats during the exposure to controllable, uncontrollable, or no shocks, and investigated electrophysiological predictors of escape performance upon exposure to subsequent uncontrollable shocks. We were able to accurately discriminate stressed from non-stressed animals and predict resistant or helpless individuals based on neural oscillatory dynamics. We identified a pattern of enhanced CA1-mPFC theta power, synchrony, cross-frequency interaction, and neuronal coupling that strongly predicted learned resistance, and that was lacking in helpless individuals. Our findings suggest that hippocampal-prefrontal network theta activity supports cognitive mechanisms of stress coping, and its impairment may underlie vulnerability to stress-related disorders.

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“…First, the vStr LFP shows a full spectrum of rhythmic activity, including prominent gamma-band oscillations but also delta, theta, and beta-band activity, which are modulated in association with behavior and task variables and relate to local spiking activity (Leung and Yim, 1993; van der Meer and Redish, 2009; Berke, 2009; Kalenscher et al, 2010; Howe et al, 2011; Donnelly et al, 2014; Dejean et al, 2017; Dwiel et al, 2019). Manipulations of the dopaminergic and endocannabinoid system modify vStr LFP oscillations (Berke, 2009; Lemaire et al, 2012; Morra et al, 2012) and drug and disease states are associated with altered striatal LFPs (Dejean et al, 2017; Naze et al, 2018; Wu et al, 2018; Hultman et al, 2018). Striatal neurons show intrinsic rhythmic activity and frequency-specific resonance (Bracci et al, 2003; Taverna et al, 2007; Beatty et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive characterization of rhythmic spiking activity in the rat ventral striatum

Dennis

Gmaz

Carmichael³

2019

Preprint

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Word Count: 219 Introduction Word Count: approx. 750 Discussion Word Count:: approx. 1600 Main Text Word Count: approx. 7800 (including figure captions) Acknowledgments: These data were recorded in the lab of A. David Redish at the University of Minnesota. Author contributions: MvdM performed experiments and pre-processed the data. JMG, JEC and MvdM wrote analysis code and performed data analysis. MvdM wrote the paper with comments from JMG and JEC. Abstract 1The ventral striatum (vStr) is anatomically interconnected with brain structures that exhibit prominent rhyth-2 mic activity, suggesting that oscillations in ventral striatal activity are potentially informative about systems-3 level interactions between these structures. However, rhythmic activity in ventral striatal neurons during 4 behavior has only been characterized piecemeal, with individual studies focusing on a single cell type or 5 frequency band. We performed a comprehensive analysis of (1) rhythmic activity in vStr neurons without 6 reference to the local field potential, and (2) average as well as time-resolved spike-field relationships. Spike 7 train rhythmicity tended to be limited to low frequencies such as delta and theta, whereas spike-field rela-8 tionships were seen across a broad spectrum of frequencies, with about 90% of neurons showing spike-field 9 locking to at least one rhythm. Using a novel time-resolved generalized linear model approach, we further 10 show that the contribution of local field potential (LFP) phase to spike timing is dynamic over time, and 11 enhanced by the inclusion of the LFP from the hippocampus -a new measure of inter-area coupling. These 12 results provide a foundation for a more accurate interpretation of the ventral striatal LFP, suggest the possi-13 bility of an oscillatory taxonomy of ventral striatal neurons, and provide a starting point for understanding 14 how rhythmic activity links cell-, circuit-, and systems-level phenomena in the ventral striatum. 15 Significance Statement 16Oscillations in neural activity are ubiquitous in the brain, readily accessible in the clinic and the lab, and 17 shared by humans and animals to facilitate translational work. The ventral striatum (vStr) is a promising 18 target structure for such a rhythmic activity perspective, not in the least because its local field potential (LFP) 19 shows prominent task-related oscillations across a range of frequencies. However, recent work has shown 20 that major components of the vStr LFP are in fact generated elsewhere in the brain, raising the question of 21 how the LFP relates to local spiking activity. Unlike previous studies that focused on a specific cell type or 22 frequency band of interest, we characterize rhythmic activity across a full range range of frequencies and cell 23 types, and include novel analyses appropriate for a non-local LFP. Our results provide a foundation for more 24 accurate interpretation of the vStr LFP and a starting point for an oscillatory taxonomy of vStr neurons. 25Rhythmic fluctuations in neural...

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Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability

Cited by 143 publications

References 43 publications

Dynamic longitudinal behavior in animals exposed to chronic social defeat stress

Dynamic longitudinal behavior in animals exposed to chronic social defeat stress

A Distinctive Pattern of Hippocampal-Prefrontal Cortical Network Activity during Stress Predicts Learned Resistance

A comprehensive characterization of rhythmic spiking activity in the rat ventral striatum

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