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A Midbrain Mechanism for Computing Escape Decisions

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A Midbrain Mechanism for Computing Escape Decisions AMIDBRAIN MECHANISM FOR COMPUTING ESCAPE DECISIONS IN THE MOUSE DISSERTATION submitted for the degree of Doctor of Philosophy in Biological Science by Dominic Evans Darwin College University of Cambridge Neurobiology Division MRC Laboratory of Molecular Biology Francis Crick Avenue, Cambridge Submitted in September, 2017 Preface All the work described in this thesis was carried out in the Neurobiology Division of the Medical Research Council Laboratory of Molecular Biology, and the Sainsbury Wellcome Centre for Neural Circuits and Behaviour, under the supervision of Dr. Tiago Branco. This dissertation is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed 60,000 words, excluding figures, tables, appendices and bibliography, as specified by the Degree Committee. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others except as specified in the text and summarised in the Statement of Contributions. ii Statement of contributions Due to the collaborative and interdisciplinary nature of this project, some of the data presented was acquired with the help of other people. These people are: Dr. Tiago Branco, and his lab members Dr. Vanessa Stempel, Dr. Sabine Rühle and Dr. Ruben do Vale. All experiments in this thesis were conceived and designed by my PhD supervisor, Dr. Tiago Branco and me, with additional input from the other investigators for the particular experiments they contributed to. In the following I state the contribution of other people to the data presented by chapter: Chapter 3 Dr. Tiago Branco contributed the behavioural modelling. Dr. Sabine Rühle assisted with preliminary behavioural experiments, the plasticity experiments, and final video annotation analysis. Chapter 4 Some muscimol experiments were performed with the assistance Dr. Ruben do Vale and Dr. Sabine Rühle, and analysed with the help of Dr. Sabine Rühle. I performed all other inactivation experiments. Chapter 5 I performed all calcium imaging experiments, and Dr. Tiago Branco contributed to the analysis. Chapter 6 Dr. Sabine Rühle and Dr. Ruben do Vale assisted me with the preliminary optogenetic experiments. Dr. Vanessa Stempel performed the rabies tracing experiments, and the majority of the analysis, which I contributed to. Dr. Vanessa Stempel and Dr. Tiago Branco contributed the electrophysiological recordings and analysis, while I assisted with the injections. iii Summary Animals face frequent threats from predators and must generate appropriate behavioural responses to ensure their survival. To achieve this, they process sensory cues to correctly identify the presence and imminence of a predatory threat, and transform this information into defensive actions. However, despite much research in identifying the circuits that may be responsible for such transformations, little is known about how this occurs mechanistically. We focus on how escape behaviour in the mouse is generated from visual predatory threats, and use a combination of behavioural, neurophysiological and anatomical methods to identify the relevant neurons and understand how they perform this computation. In this work, we developed an innate decision making paradigm in which a mouse detects and assesses sensory stimuli of varying threat evidence during exploration, choosing whether to escape to a shelter, or not. The performance data in this task were best formalised with a drift- diffusion model of decision making, providing a framework to understand innate behavioural tasks in terms of evidence accumulation and boundaries. Next, we performed calcium imaging in freely-moving mice to probe for neural correlates of decision elements and flight behaviour in brain areas that we show to be necessary for the flight responses: we found that VGluT2+ neurons in the deeper medial superior colliculus (dmSC) increase their activity during a repeated threatening stimulus, while VGluT2+ neurons of the dorsolateral periaqueductal gray (dPAG) are silent until just before the initiation of escape, and are maximally active during escape. These results suggest that the dmSC accumulates evidence of threat which dPAG neurons threshold. This interpretation is supported by optogenetic activation of mSC-VGluT2+ neurons in vivo, which recapitulates the statistics of escape probability evoked with a visual stimulus, while activation of VGluT2+ neurons in the dPAG evokes an all-or-nothing escape response. Finally, using channelrhodopsin-2-assisted circuit mapping and monosynaptic viral tracing, we reveal that over half of dPAG-VGluT2+ neurons receive monosynaptic connections from mSC-VGluT2+ neurons with a low probability of release, allowing this synapse to act as a high-pass filter and providing a mechanism for the computation of an escape decision. These findings advance our understanding of how defensive behaviours are generated at circuit and single-cell level, and of how neurons process information in a circuit critical for implementing basic behaviours. Acknowledgments First and foremost, I would like to thank Tiago Branco for his supervision, mentoring and encouragement throughout my PhD, and for taking me on to start with. Thank you for the opportunity to do this project and learn from you. I would also like to thank the following people: Sabine Rühle, Ruben do Vale and Vanessa Stempel, to whom I am greatly indebted for the work we’ve done together on a demanding project, and making it fun no matter how the experiments were going. It has been a great pleasure to be lucky enough to work with friends. Past and present Members of the Branco Lab, for discussions (both scientific and less-so), coffee, fun, and an amazingly friendly and cooperative lab atmosphere. Gregory Jefferis for his continued support throughout my PhD. Ian Duguid, Adam Packer, Paavo Huoviala, Sina Tootoonian, Kevin Lloyd, Henry Dalgleish and Christoph Schmidt-Hieber for experimental advice and discussions. Steve Scotcher and Phil Heard of the LMB Mechanical Workshop for building the arenas used in this study. Kostas Betsios for programming the data acquisition software. Florian Rau for imparting his typesetting prowess. And last but not least; my parents, sister, and the rest of my family for their infallible support and encouragement, and in particular Vanessa for proofreading. vii Contents Statement of contributions iii Acknowledgments vii List of Illustrations xv List of Abbreviations xvii 1 Introduction 1 1.1 The neuroethology of instinctive defensive behaviours . 1 1.1.1 A historical account of instinct . 1 1.1.2 Scientific perspective . 4 1.1.3 The anti-predation strategies of prey . 4 1.2 Escaping from predators . 7 1.2.1 Theory . 7 1.2.2 Ethology of escape . 10 1.3 Circuits for defensive behaviour . 12 1.3.1 The amygdala and hypothalamic systems . 12 1.4 The midbrain defensive system . 16 1.4.1 Overview of the periaqueductal gray . 16 1.4.2 The periaqueductal gray and defensive behaviour . 18 1.4.3 Overview of the superior colliculus . 20 1.4.4 The role of the superior colliculus in defensive behaviour . 24 1.5 Aims of this study . 27 2 Methods 29 2.1 Animals . 29 2.2 Behavioural assay . 29 2.2.1 Experimental set-up . 29 2.2.2 Protocols . 31 2.2.3 Sensory stimuli . 31 2.2.4 Analysis . 32 2.2.5 Behavioural Model . 33 2.3 General surgical procedures . 33 2.4 Viruses . 34 2.5 Neural activity manipulations in behaving animals . 35 2.5.1 Optogenetic activation . 35 2.5.2 Genetic ablation . 36 2.5.3 Optogenetic inhibition . 37 2.5.4 Pharmacological inactivation . 37 2.6 Calcium imaging during escape behaviour . 38 2.6.1 Data acquisition . 38 xi Contents 2.6.2 Image analysis . 39 2.7 In vitro electrophysiology and circuit mapping . 40 2.7.1 Data acquisition . 40 2.7.2 Data analysis . 41 2.8 Retrograde circuit tracing . 42 2.9 General data analysis . 42 3 A behavioural assay for controlling escape behaviour in mice suggests an underlying de- cision process 45 3.1 Escape behaviour is controlled by threat intensity . 45 3.2 A theoretical model for computing escape . 50 3.3 Plasticity of defensive behaviour against visual threats . 50 3.4 Summary . 53 4 Two midbrain circuit elements are necessary for escape behaviour 55 4.1 Glutamatergic neurons in the medial superior colliculus are required for escape from visual threats . 55 4.2 The dorsal PAG is necessary for initiating escape . 58 4.3 Inactivation of additional brain regions implicated in visually-evoked and defensive behaviour . 60 4.3.1 Visual cortex and amygdala are not vital circuit elements . 60 4.3.2 Evidence that the parabigeminal nucleus is dispensable in flight behaviour 60 4.4 Summary . 63 5 Neural activity during escape behaviour 65 5.1 Computational roles of excitatory neurons in the medial superior colliculus and dorsal periaqueductal gray . 65 5.2 dmSC neurons are predictive of conditioned escape . 69 5.3 Activity in dPAG VGluT+ neurons is strongly correlated with escape vigour . 70 5.3.1 A subset of dPAG neurons are active during freezing . 71 5.4 Summary . 71 6 A neural mechanism for computing escape behaviour 73 6.1 Optogenetic activation of mSC and dPAG produces escape to a shelter. 73 6.2 Optogenetic investigation of escape behaviour supports different escape roles for the mSC and dPAG . 75 6.3 Identification and characterisation of an excitatory SC-PAG circuit . 77 6.3.1 An excitatory convergence of deep SC neurons onto dPAG neurons . 77 6.3.2 Biophysics and synaptic properties of dPAG neurons . 80 6.3.3 Investigation of the SC-PAG connection reveals weak, unreliable transmission 81 6.3.4 Recurrent dmSC connectivity helps overcome the synaptic threshold . 84 6.4 Summary . 85 7 Discussion 87 7.1 Behavioural evidence of a decision process in control of escape .
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