Dynamical prefrontal population coding during defensive behaviours

Jercog, Daniel; Winke, Nânci; Sung, Kibong; Martin-Fernandez, Mario; Francioni, Claire; Rajot, Domitille; Courtin, Julien; Chaudun, Fabrice; Jercog, Pablo E.; Valério, Stéphane; Herry, Cyril

doi:10.1038/s41586-021-03726-6

Cited by 51 publications

(82 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We first used standard methods to detect freezing and sleep. Freezing is typically defined as all immobility periods longer than a particular threshold (0.5–2 s; e.g., Bagur et al, 2018 ; Moberly et al, 2018 ; Gründemann et al, 2019 ; Jercog et al, 2021 ; Ressler et al, 2021 ). Nonetheless, standard sleep-scoring algorithms assume that all such immobility corresponds to rest states.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Discriminating Sleep From Freezing With Cortical Spindle Oscillations

Pompili

Todorova

2022

Front. Neural Circuits

View full text Add to dashboard Cite

In-vivo longitudinal recordings require reliable means to automatically discriminate between distinct behavioral states, in particular between awake and sleep epochs. The typical approach is to use some measure of motor activity together with extracellular electrophysiological signals, namely the relative contribution of theta and delta frequency bands to the Local Field Potential (LFP). However, these bands can partially overlap with oscillations characterizing other behaviors such as the 4 Hz accompanying rodent freezing. Here, we first demonstrate how standard methods fail to discriminate between sleep and freezing in protocols where both behaviors are observed. Then, as an alternative, we propose to use the smoothed cortical spindle power to detect sleep epochs. Finally, we show the effectiveness of this method in discriminating between sleep and freezing in our recordings.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Typically, the minimum duration of immobility to be classified as freezing ranges between 0.5 and 2 s, and brief movements of 0.1–0.2 s are ignored (e.g., Courtin et al, 2014 ; Jercog et al, 2021 ). We therefore defined freezing as moments where the animals remained immobile for more than 1.5 s, ignoring brief movements of <0.2 s.…”

Section: Methodsmentioning

confidence: 99%

Discriminating Sleep From Freezing With Cortical Spindle Oscillations

Pompili

Todorova

2022

Front. Neural Circuits

View full text Add to dashboard Cite

show abstract

“…To evaluate the impact of memory strengthening on CS representation in BA networks, we used a population decoding approach based on the recorded BA activity across mice 16 . This strategy is illustrated in Figure 2B in which the firing activity from the ensemble of recorded units from different mice at a given time point t (so-called pseudo-population vector) is compared on different conditions such as CS + and CS - trials versus baseline ( Figure 2C ; Methods ).…”

Section: Resultsmentioning

confidence: 99%

Basal amygdala inputs to the medial prefrontal cortex mediate fear memory strengthening

Einarsson

Flores

Jercog

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Survival critically depends on the memorization of stimuli recurrently predicting pleasant or aversive experiences. Memory strengthening by additional learning is a critical process allowing the long-term stabilization of learned aversive experience. However, the underlying neuronal circuits and mechanisms are still largely unknown. Using a combination of single unit recordings and optogenetic manipulations, we observed that strengthened fear responses following successive fear learning episodes led to a remapping in the population of basal amygdala (BA) neurons encoding the conditioned stimulus. Moreover, strengthened fear responses were preferentially carried by BA neurons projecting to the medial prefrontal cortex (mPFC). Selective optogenetic inhibition of BA terminals in the mPFC during a second learning episode prevented fear memory strengthening. Together, our findings suggest that fear memory strengthening with additional training critically depends on the selective remapping of a subpopulation of BA neurons projecting to the mPFC.

show abstract

“…With these tools in hand, we are now able to discover how the encoding of complex cognitive and emotional behaviors maps onto specific cell types across the brain. By recording the activity of hundreds of neurons simultaneously, we can also begin to understand the population codes that produce complex, freely moving behaviors 25,26 . To do so, however, we need improved methods that allow us to rapidly and accurately quantify freely moving behaviors with reference to salient environmental stimuli and to align these detailed behavioral measurements with neurophysiological recordings.…”

Section: Use Case 2: Ca 2+ Imaging With Miniscopes During Signaled Avoidancementioning

confidence: 99%

BehaviorDEPOT: a tool for automated behavior classification and analysis in rodents

Zeidler

et al. 2021

Preprint

View full text Add to dashboard Cite

Quantitative descriptions of animal behavior are essential to understand the underlying neural substrates. Fear conditioning in rodents is a widely used assay that allows neuroscientists to probe the neural mechanisms of memory. To date, quantification of freezing behavior, a proxy for fear memory strength, is usually performed by hand or with expensive and inflexible commercial software. To overcome these barriers, we developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a MATLAB-based application containing six independent modules. The Experiment Module runs fear conditioning experiments using an Arduino-based design that interfaces with commercial hardware. The Analysis Module classifies freezing and analyzes spatiotemporal behavioral statistics in user-defined ways. The remaining modules can develop custom classifiers. Of note, the Inter-Rater Module establishes reliable ground-truth human labels, making it broadly useful for scientists developing classifiers with any application. BehaviorDEPOT provides a simple, flexible, automated pipeline to move from pose tracking to reliably quantifying task-relevant behaviors.

show abstract

Dynamical prefrontal population coding during defensive behaviours

Cited by 51 publications

References 36 publications

Discriminating Sleep From Freezing With Cortical Spindle Oscillations

Discriminating Sleep From Freezing With Cortical Spindle Oscillations

Basal amygdala inputs to the medial prefrontal cortex mediate fear memory strengthening

BehaviorDEPOT: a tool for automated behavior classification and analysis in rodents

Contact Info

Product

Resources

About