A neural circuit for flexible control of persistent behavioral states

ABSTRACTTo adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely-moving animals, we identify stable, circuit-wide activity patterns corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the opposing network states. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.

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A neural circuit for flexible control of persistent behavioral states

eLife ◽

10.7554/elife.62889 ◽

2021 ◽

Vol 10 ◽

Author(s):

Ni Ji ◽

Gurrein K Madan ◽

Guadalupe I Fabre ◽

Alyssa Dayan ◽

Casey M Baker ◽

...

Keyword(s):

Neural Activity ◽

Neural Circuit ◽

Activity Patterns ◽

State Transitions ◽

Flexible Control ◽

C Elegans ◽

Food Odors ◽

Long Time ◽

Behavioral States ◽

Learning Analysis

To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely-moving animals, we identify stereotyped changes in circuit activity corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the neural activity patterns observed in each state. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.

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Cell Assembly Signatures Defined by Short-Term Synaptic Plasticity in Cortical Networks

International Journal of Neural Systems ◽

10.1142/s0129065715500264 ◽

2015 ◽

Vol 25 (07) ◽

pp. 1550026 ◽

Cited By ~ 18

Author(s):

Luis Carrillo-Reid ◽

Violeta G. Lopez-Huerta ◽

Marianela Garcia-Munoz ◽

Stephan Theiss ◽

Gordon W. Arbuthnott

Keyword(s):

Population Analysis ◽

Activity Patterns ◽

Original Description ◽

State Transitions ◽

Cell Assembly ◽

Short Term ◽

Cortical Networks ◽

Cell Pair ◽

Neuronal Interactions ◽

Network States

The cell assembly (CA) hypothesis has been used as a conceptual framework to explain how groups of neurons form memories. CAs are defined as neuronal pools with synchronous, recurrent and sequential activity patterns. However, neuronal interactions and synaptic properties that define CAs signatures have been difficult to examine because identities and locations of assembly members are usually unknown. In order to study synaptic properties that define CAs, we used optical and electrophysiological approaches to record activity of identified neurons in mouse cortical cultures. Population analysis and graph theory techniques allowed us to find sequential patterns that represent repetitive transitions between network states. Whole cell pair recordings of neurons participating in repeated sequences demonstrated that synchrony is exhibited by groups of neurons with strong synaptic connectivity (concomitant firing) showing short-term synaptic depression (STD), whereas alternation (sequential firing) is seen in groups of neurons with weaker synaptic connections showing short-term synaptic facilitation (STF). Decreasing synaptic weights of a network promoted the generation of sequential activity patterns, whereas increasing synaptic weights restricted state transitions. Thus in simple cortical networks of real neurons, basic signatures of CAs, the properties that underlie perception and memory in Hebb's original description, are already present.

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PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans

Nature Neuroscience ◽

10.1038/nn.3253 ◽

2012 ◽

Vol 15 (12) ◽

pp. 1675-1682 ◽

Cited By ~ 68

Author(s):

Arantza Barrios ◽

Rajarshi Ghosh ◽

Chunhui Fang ◽

Scott W Emmons ◽

Maureen M Barr

Keyword(s):

Neural Circuit ◽

Searching Behavior ◽

Mate Searching ◽

C Elegans

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Correlation between fetal brain activity patterns and behavioral states: An exploratory fetal magnetoencephalography study

Experimental Neurology ◽

10.1016/j.expneurol.2011.01.003 ◽

2011 ◽

Vol 228 (2) ◽

pp. 200-205 ◽

Cited By ~ 18

Author(s):

Naim Haddad ◽

Rathinaswamy B. Govindan ◽

Srinivasan Vairavan ◽

Eric Siegel ◽

Jessica Temple ◽

...

Keyword(s):

Brain Activity ◽

Activity Patterns ◽

Fetal Brain ◽

Behavioral States

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Allopregnanolone mediates affective switching through modulation of oscillatory states in the basolateral amygdala

10.1101/2021.03.08.434156 ◽

2021 ◽

Cited By ~ 1

Author(s):

Pantelis Antonoudiou ◽

Phillip LW Colmers ◽

Najah L Walton ◽

Grant L Weiss ◽

Anne C Smith ◽

...

Keyword(s):

Basolateral Amygdala ◽

Post Partum ◽

Critical Role ◽

Behavioral Outcomes ◽

Theta Oscillations ◽

Allosteric Modulators ◽

Post Partum Depression ◽

Antidepressant Effects ◽

Behavioral States ◽

Network States

AbstractBrexanolone (allopregnanolone), was recently approved by the FDA for the treatment of post-partum depression, demonstrating long-lasting antidepressant effects. Despite our understanding of the mechanism of action of neurosteroids as positive allosteric modulators (PAMs) of GABAa receptors, we still do not fully understand how allopregnanolone exerts these persistent antidepressant effects. Here, we demonstrate that allopregnanolone and similar synthetic neuroactive steroid analogs, SGE-516 (tool-compound) and zuranolone (SAGE-217, investigational-compound), are capable of modulating oscillatory states across species, which we propose may contribute to long-lasting changes in behavioral states. We identified a critical role for interneurons in generating oscillations in the basolateral amygdala (BLA) and a role for delta-containing GABAaRs in mediating the ability of neurosteroids to modulate network and behavioral states. Actions of allopregnanolone in the BLA is sufficient to alter behavioral states and enhance BLA high-theta oscillations (6-12Hz) through delta-containing GABAa receptors, a mechanism distinct from other GABAa PAMs, such as benzodiazepines. Moreover, treatment with the allopregnanolone analog SGE-516 induces long-lasting protection from chronic stress-induced disruption of network states, which correlates with improved behavioral outcomes. Our findings demonstrate a novel molecular and cellular mechanism mediating the well-established anxiolytic and antidepressant effects of neuroactive steroids.

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Schizophrenia-associated variation at ZNF804A correlates with altered experience-dependent dynamics of sleep slow waves and spindles in healthy young adults

SLEEP ◽

10.1093/sleep/zsab191 ◽

2021 ◽

Author(s):

Ullrich Bartsch ◽

Laura J Corbin ◽

Charlotte Hellmich ◽

Michelle Taylor ◽

Kayleigh E Easey ◽

...

Keyword(s):

Memory Consolidation ◽

Neural Circuit ◽

Activity Patterns ◽

Nrem Sleep ◽

Network Activity ◽

Healthy Population ◽

Adult Males ◽

Motor Sequence ◽

Performance Improvements ◽

Common Genetic Variants

Abstract The rs1344706 polymorphism in ZNF804A is robustly associated with schizophrenia and schizophrenia is, in turn, associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. We recruited healthy adult males with no history of psychiatric disorder from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the schizophrenia-associated ‘A’ allele (N=22) or the alternative ‘C’ allele (N=18) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequence task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. Average MST learning and sleep-dependent performance improvements were similar across genotype groups, albeit more variable in the AA group. During sleep after learning, CC participants showed increased slow-wave (SW) and spindle amplitudes, plus augmented coupling of SW activity across recording electrodes. SW and spindles in those with the AA genotype were insensitive to learning, whilst SW coherence decreased following MST training. Accordingly, NREM neurophysiology robustly predicted the degree of overnight motor memory consolidation in CC carriers, but not in AA carriers. We describe evidence that rs1344706 polymorphism in ZNF804A is associated with changes in the coordinated neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of schizophrenia-associated common genetic variants on neural circuit oscillations and function.

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Behavioral States

Genetics ◽

10.1534/genetics.120.303539 ◽

2020 ◽

Vol 216 (2) ◽

pp. 315-332 ◽

Cited By ~ 2

Author(s):

Steven W. Flavell ◽

David M. Raizen ◽

Young-Jai You

Keyword(s):

Caenorhabditis Elegans ◽

Global Search ◽

Mate Finding ◽

Internal Factors ◽

C Elegans ◽

Metabolic States ◽

Behavioral States ◽

Past Experiences ◽

And Behavior

Caenorhabditis elegans’ behavioral states, like those of other animals, are shaped by its immediate environment, its past experiences, and by internal factors. We here review the literature on C. elegans behavioral states and their regulation. We discuss dwelling and roaming, local and global search, mate finding, sleep, and the interaction between internal metabolic states and behavior.

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Novel Technological Advances in Functional Connectomics in C. elegans

Journal of Developmental Biology ◽

10.3390/jdb7020008 ◽

2019 ◽

Vol 7 (2) ◽

pp. 8 ◽

Cited By ~ 6

Author(s):

DiLoreto ◽

Chute ◽

Bryce ◽

Srinivasan

Keyword(s):

Nervous System ◽

Computational Modeling ◽

Activity Patterns ◽

Sensory Information ◽

Neuronal Cell ◽

Network Models ◽

Connectivity Matrix ◽

C Elegans ◽

Technological Advances

The complete structure and connectivity of the Caenorhabditis elegans nervous system (“mind of a worm”) was first published in 1986, representing a critical milestone in the field of connectomics. The reconstruction of the nervous system (connectome) at the level of synapses provided a unique perspective of understanding how behavior can be coded within the nervous system. The following decades have seen the development of technologies that help understand how neural activity patterns are connected to behavior and modulated by sensory input. Investigations on the developmental origins of the connectome highlight the importance of role of neuronal cell lineages in the final connectivity matrix of the nervous system. Computational modeling of neuronal dynamics not only helps reconstruct the biophysical properties of individual neurons but also allows for subsequent reconstruction of whole-organism neuronal network models. Hence, combining experimental datasets with theoretical modeling of neurons generates a better understanding of organismal behavior. This review discusses some recent technological advances used to analyze and perturb whole-organism neuronal function along with developments in computational modeling, which allows for interrogation of both local and global neural circuits, leading to different behaviors. Combining these approaches will shed light into how neural networks process sensory information to generate the appropriate behavioral output, providing a complete understanding of the worm nervous system.

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