Highly unstable heterogeneous representations in VIP interneurons of the anterior cingulate cortex

Johnson, C. Starck; Kretsge, Lisa N.; Yen, William; Sriram, Balaji; O’Connor, Alexandra; Liu, Ruichen Sky; Jimenez, Jessica C.; Phadke, Rhushikesh A.; Wingfield, Kelly K.; Yeung, Chun Yu; Jinadasa, Tushare; Nguyen, Thanh; Cho, Eun Seon; Fuchs, Erelle; Spevack, Eli D.; Velasco, Berta Escude; Hausmann, Frances S.; Fournier, Luke A; Brack, Alison; Melzer, Sarah; Cruz-Martín, Alberto

doi:10.1038/s41380-022-01485-y

Cited by 11 publications

(12 citation statements)

References 105 publications

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“…A study in cingulate cortex also found a high degree of heterogeneity in Vip representations across behavioral contexts and time (Johnson et al, 2022), suggesting that functional flexibility may be a general property of Vip cells across the brain.…”

Section: Functional Diversity Is Revealed By Noveltymentioning

confidence: 97%

Stimulus novelty uncovers coding diversity in visual cortical circuits

Garrett

Groblewski

Piet

et al. 2023

Preprint

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The detection of novel stimuli is critical to learn and survive in a dynamic environment. Though novel stimuli powerfully affect brain activity, their impact on specific cell types and circuits is not well understood. Disinhibition is one candidate mechanism for novelty-induced enhancements in activity. Here we characterize the impact of stimulus novelty on disinhibitory circuit components using longitudinal 2-photon calcium imaging of Vip, Sst, and excitatory populations in the mouse visual cortex. Mice learn a behavioral task with stimuli that become highly familiar, then are tested on both familiar and novel stimuli. Mice consistently perform the task with novel stimuli, yet responses to stimulus presentations and stimulus omissions are dramatically altered. Further, we find that novelty modifies coding of visual as well as behavioral and task information. At the population level, the direction of these changes is consistent with engagement of the Vip-Sst disinhibitory circuit. At the single cell level, we identify separate clusters of Vip, Sst, and excitatory cells with unique patterns of novelty-induced coding changes. This study and the accompanying open-access dataset reveals the impact of novelty on sensory and behavioral representations in visual cortical circuits and establishes novelty as a key driver of cellular functional diversity.

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Section: Functional Diversity Is Revealed By Noveltymentioning

confidence: 97%

Stimulus novelty uncovers coding diversity in visual cortical circuits

Garrett

Groblewski

Piet

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…For research focused primarily on tracking kinematics or an animal’s location (e.g., presence within some ROI), this may be sufficient for the desired analyses. For example, DLC has been used in EPM, EZM, and open field tests to measure time spent in different areas, distance traveled, location, and velocity (Cui et al, 2021 ; Lu et al, 2021 ; Sun et al, 2021 ; Johnson et al, 2022 ; Sánchez-Bellot et al, 2022 ). However, pose estimation also offers the possibility of extracting more general information about an animal’s actions and behavioral state from moment to moment.…”

Section: Tracking and Analyzing Pose Estimation Datamentioning

confidence: 99%

“…DLC-derived data in a rat exposure assay was used for automated classification of defensive behaviors and correlated with dorsal periaqueductal gray activity (Reis et al, 2021 ). DLC-derived data from mice in the EZM and open field tests was used to extract kinematics and identify correlations between the activity of interneurons in the ACC and anxiety and social behavior (Johnson et al, 2022 ). In a study on prosocial affiliative touch, a deep CNN and an LSTM-based RNN identified that affiliative allogrooming was controlled by the medial amygdala (Wu et al, 2021 ).…”

Section: Correlating Neural Activity With Emotional Behaviormentioning

confidence: 99%

“…The use of animal models in combination with advanced recording techniques has furthered our understanding of the biological basis of emotional behavior (Kirlic et al, 2017;Xia and Kheirbek, 2020), but precise identification of the specific neural substrates and mechanisms for emotions and emotional behavior have proved elusive (Celeghin et al, 2017). 10.3389/fnbeh.2022.1044492 Various brain regions have been implicated in emotional behavior, including the anterior cingulate cortex (ACC; Johnson et al, 2022), insular cortex (Dolensek et al, 2020), and subcortical deep brain structures such as the amygdala (Joëls et al, 2018;Wu et al, 2021), nucleus accumbens (NAc; Aragona and Wang, 2009), and periaqueductal gray (Buhle et al, 2013;Reis et al, 2021). Because animal models rely solely on observations of behavior to quantify emotional states, accurate and nuanced behavior quantification is required to understand the neural basis of emotional states (Xia and Kheirbek, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Various brain regions have been implicated in emotional behavior, including the anterior cingulate cortex (ACC; Johnson et al, 2022 ), insular cortex (Dolensek et al, 2020 ), and subcortical deep brain structures such as the amygdala (Joëls et al, 2018 ; Wu et al, 2021 ), nucleus accumbens (NAc; Aragona and Wang, 2009 ), and periaqueductal gray (Buhle et al, 2013 ; Reis et al, 2021 ). Because animal models rely solely on observations of behavior to quantify emotional states, accurate and nuanced behavior quantification is required to understand the neural basis of emotional states (Xia and Kheirbek, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Using deep learning to study emotional behavior in rodent models

Kuo

Denman

Beacher

et al. 2022

Front. Behav. Neurosci.

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Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.

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Levels of Plasticity

Lux

2024

SpringerBriefs in Psychology

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Highly unstable heterogeneous representations in VIP interneurons of the anterior cingulate cortex

Cited by 11 publications

References 105 publications

Stimulus novelty uncovers coding diversity in visual cortical circuits

Stimulus novelty uncovers coding diversity in visual cortical circuits

Using deep learning to study emotional behavior in rodent models

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