Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex

Abstract

Cortical responses to sensory stimuli are modulated by behavioral state. In the primary visual cortex (V1), visual responses of pyramidal neurons increase during locomotion. This response gain was suggested to be mediated through inhibitory neurons, resulting in the disinhibition of pyramidal neurons. Using in vivo two-photon calcium imaging in layers 2/3 and 4 in mouse V1, we reveal that locomotion increases the activity of vasoactive intestinal peptide (VIP), somatostatin (SST) and parvalbumin (PV)-positive interneurons during visual stimulation, challenging the disinhibition model. In darkness, while most VIP and PV neurons remained locomotion responsive, SST and excitatory neurons were largely non-responsive. Context-dependent locomotion responses were found in each cell type, with the highest proportion among SST neurons. These findings establish that modulation of neuronal activity by locomotion is context-dependent and contest the generality of a disinhibitory circuit for gain control of sensory responses by behavioral state.

Article and author information

Author details

  1. Janelle MP Pakan

    Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9384-8067
  2. Scott C Lowe

    Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Evelyn Dylda

    Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1883-4498
  4. Sander W Keemink

    Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Stephen P Currie

    Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Christopher A Coutts

    Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Nathalie LI Rochefort

    Centre for Integrative Physiology, University of Edinburgh, Edinburgh, United Kingdom
    For correspondence
    n.rochefort@ed.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3498-6221

Funding

Wellcome (102857/Z/13/Z)

  • Nathalie LI Rochefort

EuroSpin Erasmus Mundus Program

  • Sander W Keemink

Royal Society (102857/Z/13/Z)

  • Nathalie LI Rochefort

European Commission (Marie Curie Actions (FP7), MC-CIG 631770)

  • Nathalie LI Rochefort

Patrick Wild Centre

  • Nathalie LI Rochefort

The Shirley Foundation

  • Nathalie LI Rochefort

RS MacDonald Charitable Trust (Seedcorn Grant 21)

  • Nathalie LI Rochefort

University Of Edinburgh (Graduate School of Life Sciences)

  • Evelyn Dylda

European Commission (Marie Curie Actions (FP7), IEF 624461)

  • Janelle MP Pakan

EPSRC Doctoral Training Centre in Neuroinformatics (EP/F500385/1 and BB/F529254/1)

  • Sander W Keemink

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All procedures were approved by the University of Edinburgh animal welfare committee, and were performed under a UK Home Office Project License (PPL No. 60/4570).

Copyright

© 2016, Pakan et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Janelle MP Pakan
  2. Scott C Lowe
  3. Evelyn Dylda
  4. Sander W Keemink
  5. Stephen P Currie
  6. Christopher A Coutts
  7. Nathalie LI Rochefort
(2016)
Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex
eLife 5:e14985.
https://doi.org/10.7554/eLife.14985

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https://doi.org/10.7554/eLife.14985

Further reading

    1. Neuroscience
    Moritz F Wurm, Doruk Yiğit Erigüç
    Research Article

    Recognizing goal-directed actions is a computationally challenging task, requiring not only the visual analysis of body movements, but also analysis of how these movements causally impact, and thereby induce a change in, those objects targeted by an action. We tested the hypothesis that the analysis of body movements and the effects they induce relies on distinct neural representations in superior and anterior inferior parietal lobe (SPL and aIPL). In four fMRI sessions, participants observed videos of actions (e.g. breaking stick, squashing plastic bottle) along with corresponding point-light-display (PLD) stick figures, pantomimes, and abstract animations of agent–object interactions (e.g. dividing or compressing a circle). Cross-decoding between actions and animations revealed that aIPL encodes abstract representations of action effect structures independent of motion and object identity. By contrast, cross-decoding between actions and PLDs revealed that SPL is disproportionally tuned to body movements independent of visible interactions with objects. Lateral occipitotemporal cortex (LOTC) was sensitive to both action effects and body movements. These results demonstrate that parietal cortex and LOTC are tuned to physical action features, such as how body parts move in space relative to each other and how body parts interact with objects to induce a change (e.g. in position or shape/configuration). The high level of abstraction revealed by cross-decoding suggests a general neural code supporting mechanical reasoning about how entities interact with, and have effects on, each other.