Compiled U19 data reuse abstracts

BRAIN Initiative U19 Data Reuse Abstracts

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OXT - Oxytocin Group. Richard Tsien. Oxytocin Modulation of Neural Circuit Function and Behavior

Abstract 1:

Title: Oxytocin modulation of neural circuit function and behavior

Abstract: We are studying how oxytocin signaling and synaptic plasticity across multiple brain systems enables socio-spatial behavior in mice: how animals recognize and remember each other, with a particular focus on parenting.

We hypothesize that social stimuli are arousing and activate a number of modulatory systems, including oxytocin centers in the hypothalamus. Oxytocin is released in target areas such as lateral septum, hippocampal CA2, and auditory cortex, to affect responses to social stimuli and thus change behavior in C57Bl/6 mice. Our data collection spans a wide range of techniques, from molecular profiling of oxytocin receptor expression throughout the brain, the biophysical and biochemical signals and effectors directly and indirectly downstream of oxytocin receptor activation (immunocytochemistry, mass spectrometry, western blot), cellular and synaptic responses to socio-spatial stimuli and oxytocin signaling (electrophysiological recordings in vitro and in vivo, including whole-cell recordings and single-unit recordings), population-level responses to the same stimuli (with large-scale array recordings and 2-photon imaging), and behavioral data (documentary film-type footage, 1000s of hours of continuously recorded mouse homecage life and behavioral testing data).

Open questions include: what are the collection of molecular effectors of oxytocin receptor signaling in different brain areas, what are the large-scale multi-areal dynamics when animals interact socially or parentally and how plastic are these responses, what are the multi-modal receptive fields of oxytocin neurons in terms of the high-resolution moment to moment social interactions that occur during complex social encounters or the parental experience?

Abstract 2:

Title: Theta rhythm perturbation by focal cooling of the septal pacemaker in awake rats

Abstract: Hippocampal theta oscillations coordinate neuronal firing to support memory and spatial navigation. The medial septum (MS) is critical in theta generation by two possible mechanisms: either a unitary “pacemaker” timing signal is imposed on the hippocampal system, or it may assist in organizing target subcircuits within the phase space of theta oscillations. We used temperature manipulation of the MS to test these models. Cooling of the MS reduced both theta frequency and power and was associated with an enhanced incidence of errors in a spatial navigation task, but it did not affect spatial correlates of neurons. MS cooling decreased theta frequency oscillations of place cells and reduced distance-time compression but preserved distance-phase compression of place field sequences within the theta cycle. Thus, the septum is critical for sustaining precise theta phase coordination of cell assemblies in the hippocampal system, a mechanism needed for spatial memory.

Highlights

  • Cooling the medial septum slowed down theta oscillations in the hippocampus
  • The spatial representation in the hippocampus remained intact
  • Choice errors increased in a spatial task
  • Distance-time, but not distance-theta phase, compression was altered

Dataset details: It is a unique dataset in the sense that the animals had brain temperature manipulations and temperature probes implanted together with bilateral silicon probes while they were performing behavioral/spatial tasks.

Thermometer implanted in Medial Septum together with a thermal perturbation probe in freely awake Long Evans rats. 2000 single cells spike sorted, up to 150 bilateral simultaneous cells recorded from CA1, all sessions with behavior.

Behaviors

  • Circular track: up to 180 alternation trials, always with 40 control trials
  • Linear track
  • Wheel running.

Most sessions were recorded with Optitrack, a 3D tracking system (120Hz), and a ceiling-mounted video camera recorded at 10Hz. The animal's positional data was determined with Optitrack.

All data collected for this paper: Cooling of Medial Septum Reveals Theta Phase Lag Coordination of Hippocampal Cell Assemblies Peter Christian Petersen, György Buzsáki. Neuron, June 2020.

Further information in our databank with links for downloading: https://buzsakilab.com/wp/projects/entry/4919/

Abstract 3:

Title: Population Ca2+ activity across limibic system during social behaviors

Abstract: Oxytocin is an important neuropeptide for promoting the formation of social bond in animals of various species, including humans. Interestingly, oxytocin has also been implicated in promoting aggressive behaviors. A key site through which oxytocin can act to increase aggression is the VMHvl. VMHvl is an essential locus for male and female aggression and is enriched for oxytocin receptors. Furthermore, a cluster of oxytocin neurons are found right next to the VMHvl and may provide a specialized “local” source of oxytocin to the VMHvl. Thus, the overall goal of the project is to understand the oxytocin signaling in the VMHvl during aggressive encounters and its potential role in the aggression modulation. To achieve this goal, we have performed a series of in vivo optical recordings from the oxytocin receptor expressing neurons in the VMHvl and the oxytocin neurons neighboring the VMHvl during social behaviors. Additionally, we have recorded cell activity from multiple regions that are connected to the VMHvl during social behaviors using multi-channel fiber photometry systems. Thus, the main forms of our data are (1) behavioral data of mice during freely moving social interaction; (2) bulk Ca2+ activity from multiple limbic regions during social interaction. The behavioral data are both annotated manually and tracked with Deeplabcut while the recording data is processed and analyzed mainly using custom scripts written in Matlab. We are interested in understanding the relationship of activity in different brain regions and how they collective determine the timing of social behaviors. A second question we would be interested in exploring is how network activity in the limbic system varies with behavioral state (e.g. aggressive vs. non-aggressive state) and how oxytocin may contribute to the change in functional connectivity of the network.

SCC Martyn D Goulding Spinal Circuits for the Control of Dexterous Movement

Title: Spinal Circuits for the Control of Dexterous Movement

Abstract: Local networks within the spinal cord represent an essential computational layer for the control of limb-driven motor behaviors, integrating descending and sensory inputs to coordinate dexterous motor output. Significant advances have been made in characterizing the developmental programs that specify the core cardinal interneuron types that make up these motor networks. This knowledge has been used to develop a battery of mouse genetic reagents, which have been primarily used to study locomotion and spinal reflexes in the lumbar spinal cord.

Given the wider range of dexterous motor behaviors that are produced by cervical circuits and their modulation by descending motor pathways, the mouse cervical spinal cord provides a unique and tractable mammalian model system for understanding how coordinated movements are generated by local motor networks and how these motor behaviors are regulated by the brain. The functional interrogation and modeling of these circuits, based on real behavioral outcomes and detailed information about the cell types that generate these behaviors, will ensure that the overall project is greater than the sum of its parts. Specifically, we will address two overarching questions: 1) How do rhythmic spinal networks control non-rhythmic movements, which represent the majority of forelimb motor behaviors, and 2) How are these spinal circuits modified to control more complex joint movements to achieve forelimb dexterity? To address these questions, we will generate: (a) a pre-motor interneuron connectome that includes information on cell positions and synaptic weightings, (b) a comprehensive index of the physiological properties and molecular identities of genetically distinct neuronal subtypes within each cardinal interneuron class, (c) a functional description of spinal circuit control of natural forelimb motor behaviors, and (d) a working model of the motor network that describes how circuit connectivity and dynamics give rise to key elements of forelimb behavior. Ultimately, these data will be used to generate a searchable web-based portal with 3D visualization tools linked to the molecular, electrophysiological, functional, and network model databases. Together, this work will lead to a deeper understanding of the organization and function of cervical circuitry, which will be of great value to groups that are grappling with the issue of how motor centers in the brain communicate with spinal sensorimotor circuits to control movement.

Ripple - hREM, hippocampal Ripple related Episodic Memory. Ivan Soltesz. Towards a Complete Description of the Circuitry Underlying Sharp Wave-Mediated Memory Replay

Title: Cellular mechanisms of memory consolidation

Abstract: Our U19 group studies the cellular bases of memory consolidation, particularly how sharp wave ripples serve as a transfer mechanism between hippocampus and neocortex. We use cutting-edge large-scale electrophysiology and optophysiological recording technologies to study and manipulate identified cell types in behaving animals, coupled with data-driven simulations. Our goal is to elucidate the cellular mechanisms responsible for memory replay and its role in memory transfer and consolidation. The methods used by our group can be applied to many situations in which brain mechanisms of behavior and cognition are explored.

Our data spans several neurophysiological techniques that provide insights into the mechanisms sharp wave ripples:

  • Large-scale electrophysiological recordings of freely moving mice and rats in various learning and navigational tasks.
  • Behavioral measures span from classic position-and-heading-direction measures to a high-resolution continuous monitoring of the entire movement repertoire of the animal
  • Calcium imaging data and extraction of sharp wave ripples from the optical signal.
  • Novel fiber photometry and voltage imaging techniques that provide unprecedented information about cell types-specific contributions to population cooperativity.

We are particularly interested in application of analytical tools that could help determine specific cellular connectivity and contributions to the sharp wave ripple processes, and can assist our large-scale computational model building. Related analytical topics could include:

  • Tuning properties of hippocampal cells in various tasks
  • Examination and reliability of sharp wave ripples extracted from calcium imaging data
  • Novel analysis or visualization techniques applied to the simulated neural activity output by our large-scale computational model

ABC - Anything But Cortex. David Kleinfeld. Reverse Engineering the Brain Stem Circuits that Govern Exploratory Behavior

Title: The adaptive atlas

Abstract:

Scientific Topic: The anatomy and function of nerve circuits in the brain stem of the mouse.

Types of data: Image stacks from a scanning microscope. This includes both brightfield imaging as well as fluorescent imaging. Different channels in the fluorescent imaging. We are developing a computation and visualization pipeline for aligning an atlas with a stack of brain sections. The result is to overlay a standardized coordinate system on the stack. The coordinate system enables experimental results from multiple brains to be related to each other. Our current focus is on projections of the rabies retrovirus from muscles to locations in the mouse brain-stem.

Open Questions: what textures (microarchitectures) can be identified reliably in the brain? How can we efficiently find the best diffeomorphism to map the atlas to a brain?

FlyLoops - feedback loops of flies. Michael Dickinson. A Brain Circuit Program for Understanding the Sensorimotor Basis of Behavior

Title: Leg motor control in Drosophila

Abstract: To move the body, the brain must precisely coordinate patterns of activity among diverse populations of motor neurons. We used in vivo calcium imaging, electrophysiology, and behavior to understand how genetically-identified motor neurons control flexion of the fruit fly tibia. We found that leg motor neurons exhibit a coordinated gradient of anatomical, physiological, and functional properties. Large, fast motor neurons control high force, ballistic movements while small, slow motor neurons control low force, postural movements. Intermediate neurons fall between these two extremes. This hierarchical organization resembles the size principle, first proposed as a mechanism for establishing recruitment order among vertebrate motor neurons. Recordings in behaving flies confirmed that motor neurons are typically recruited in order from slow to fast. However, we also find that fast, intermediate, and slow motor neurons receive distinct proprioceptive feedback signals, suggesting that the size principle is not the only mechanism that dictates motor neuron recruitment.

Given the conservation of the size principle across species, one open theoretical question is whether hierarchical motor recruitment represents an optimal organization for neuromuscular control. If so, can violations of the recruitment order, seen in both vertebrate and invertebrate motor systems, help us understand the limits of hierarchical recruitment as a coding and control scheme? What can we infer about the structure of premotor networks from the coordinated activity of motor neurons controlling a particular joint?

We recently made the data from this project publicly available: https://doi.org/10.5061/dryad.76hdr7stb

Learning2Learn. Elizabeth Buffalo. Computational and Circuit Mechanisms Underlying Rapid Learning

Title: Learning2learn: Rapid learning in humans and non-human primates

Abstract:

Scientific topic: Our U19 collaboration studies rapid learning in primates (both human and non-human). We are interested in the ways in which flexible behavior arises from the apparent ability of primates to very rapidly learn complex tasks and rules, and to adjust these rules, as the environment changes.. This topic is particularly interesting and important, because the ability to learn how to learn seems to be a hallmark of human behavioral flexibility and high-level cognitive abilities. Moreover, while artificially-intelligent systems have evolved to perform many kinds of learning tasks at near-human performance levels, this kind of flexibility still largely eludes these systems altogether.

The types of data you collect (subjects, modalities, tasks): Multi-channel electrophysiological recordings of brain activity are obtained in awake behaving humans and non-human primates, while they perform complex behavioral tasks. In non-human primates, recordings are conducted using novel multi-channel drives that provide dense chronic high-quality recordings from multiple regions, including areas of the prefrontal cortex and the hippocampus. In human epilepsy patients, intracranial electrocorticography (ECoG) recordings are conducted in multiple areas on the cortical surface and stereoelectrocorticography (SEEG) recordings are conducted in the temporal and frontal cortices, as well as the hippocampus and amygdala. The recordings from deep structures capture both the local field potential and activity of single neurons.. The behavioral tasks used include the classic Wisconsin Card-Sorting Task (WCST), as well as a novel task of stimulus association. One of the remarkable strengths of these data is that very similar tasks were performed in both the human and non-human primate experiments.

Open questions that a theorist might be able to help you answer: We are interested in answering a host of questions about the representations of task information in brain circuits. For example, an open question is how rule representations emerge as a function of task performance, how rapid rule switching is implemented,, and how these flexible representations relate to coding of task information in distributed circuits. It would be potentially interesting to implement artificially-intelligent systems that can learn how to learn in ways that accurately emulate the behaviors exhibited in these tasks by biological systems.

brainCOGS - circuits of COGnitive Systems. Carlos Brody. Mechanisms of neural circuit dynamics in working memory and decision-making

Abstract 1:

Title: Multi-region calcium imaging during two decision making tasks

Abstract: I am using a two-photon mesoscope to record multi-region single-cell resolution calcium signals from 3 cortical regions while mice perform a decision making task in virtual reality. In this ‘Accumulating Towers task’ head-fixed mice are required to gradually accumulate visual evidence as they navigate in a virtual T-maze. The side on which the majority of the evidence appears informs them which maze arm the reward is located in. In an alternate version of the task (‘Visually Guided task’), mice navigate the same virtual T-maze and receive the same visual evidence cues but they do not have to accumulate evidence. Rather, they simply have to turn in the direction of a large visual guide. While mice perform these tasks, we record calcium signals simultaneously from the secondary motor area, retrosplenial cortex, and anterolateral visual cortex. This work provides a rare opportunity to explore how the neural underpinnings of decision making emerge on every level from single-cell responses to between region interactions.

Previous work from our lab comparing these tasks has shown that optogenetic inhibition of nearly any dorsocortical region impairs performance in the Accumulating Towers task whereas only inhibition of visual regions impairs performance in the Visually Guided task. Additionally, widefield calcium signals across cortical regions are less correlated in the Accumulating Towers task. Lower correlations were also observed during more difficult trials and more difficult task epochs. This seems to indicate that higher cognitive loads are supported by decreased neural correlations. A current objective is to replicate this last result with single-cell resolution data.

Questions that would benefit from theoretical modeling: If true, why are neuron-neuron correlations lower in the accumulating towers task? Is task-related information shared between brain regions (e.g. via a communication subspace)? How does neural activity coordinate between regions to produce behavior? In these tasks, an individual’s performance can vary from session to session and even between blocks of the same session. Can we identify performance-related neural correlates?

Abstract 2:

Title: The role of the hippocampus in context-dependent decision-making

Abstract: Many decisions depend on context; for example, “which shirt should I wear?” depends on whether you are going to work or a party. Decision-making (DM) thus often requires individuals to evaluate the consequences of multiple actions based on stored contextual memories. How does the mammalian brain make such context-dependent decisions? On the one hand, cellular recordings in rodents have characterized the neural circuit mechanisms involved in DM in multiple frontoparietal brain regions. On the other hand, lesion and inactivation studies have shown that the hippocampus (HC) is necessary for context-specific memory retrieval. Yet, the role of the HC in guiding context-dependent DM is unknown. We developed a virtual-reality T-maze navigation task in which head-fixed mice are required to make context-dependent spatial decisions. In one T-maze context, mice are trained to turn toward a visible turn guide; in the other context, mice are trained to turn away from a turn guide. This task is decomposable into sensory (e.g., visual cues), behavioral (e.g., running speed) and cognitive (e.g., context) components. While mice perform this task, we plan to conduct i) cellular-resolution two-photon imaging of the dentate gyrus (DG) HC subfield, ii) time-dependent optogenetic inactivation of the DG, iii) activity-dependent optogenetic reactivation of context-specific DG neural populations, and iv) electrophysiological recordings of prefrontal brain regions known to be involved in DM (e.g., premotor cortex) during simultaneous optogenetic inactivation or reactivation of DG. We hope these data could contribute to:

  1.  Models of HC contextual separation, such as attractor neural networks or autoassociative memory models.
  2. The development of models characterizing how HC contextual separation might drive DM-related activity in cortical brain regions.

With regard to the second point, for instance, HC context representations might gate or modulate the entire DM process itself, such that all DM computations occur in a context-dependent manner. Alternatively, HC contextual separation might gate or modulate only the output of DM-related processes, to decide the most suitable action given the current context. Adjudicating between these alternatives would strongly benefit from a flexible theoretical model that links HC contextual separation to DM computations at different stages of the DM process, which could then be constrained through targeted experimental manipulations.

Abstract 3:

Title: Participation of Edinger-Westphal Nucleus as an attentional gate to accumulate visual evidence in head-fixed mice

Abstract: Many decisions require individuals to accumulate evidence and preserve it in memory to guide their decisions towards the obtention of desirable consequences. This evidence accumulation process requires multiple components: a gate that tells the brain when to start and stop accumulating the evidence, update the information, and retain it in memory until a decision is made. Despite extensive prior research on the neural correlates of visual evidence accumulation, no conclusion about which brain region might support the mechanisms to initiate or stop the accumulation has been achieved. Visual information from the optical nerve reaches the Edinger-Westphal Nucleus (EWN), a midbrain region whose activation and inhibition improves and impairs attention, respectively. However, the role of EWN as a gate to start visual evidence accumulation remains unanswered. To address this issue, we will optogenetically silence EWN while already-trained head-fixed mice are performing a flashes accumulation task. During the task, flashes will appear on either lateral side and, after a delay epoch, the mice are required to emit a response to the side with a higher number of flashes. EWN optosilencing will be performed during whole-trial, cue-epoch (accumulation), or delay-epoch (memory), or starting at different times during the cue epoch (gate) to assess if evidence-leakage/open-gate could be induced. Pupillometry, an attentional measurement, will be tracked to serve as a gate-closing indicator: when accumulation starts. The aforementioned together with a passive version of the task, where the reward will be delivered in either lateral spout despite the evidence, will allow us to dissect the contribution of motor vs. attentive pupil diameter changes. Electrophysiological recordings of EWN and simultaneous optoinactivation of EWN plus electrophysiological recordings of the visual cortex will be obtained.

Data type: Mice, behavior, optogenetics, electrophysiology.

Open questions: Identify the components (gate, accumulation, memory) of the accumulation process that are being affected during optogenetic perturbations, via modeling behavior. Implement a Drift Diffusion Model that incorporates attentional open-close gate to account for choice and bias. Determine if these components could be encoded by population dynamics (e.g., manifolds). Quantify the contribution of pupil diameter changes over single-unit and population neural activity encoding of accumulation and gate closing.

Holobrain (Sensation) - coding, sensation, behavior. John Maunsell. Readout and control of spatiotemporal neuronal codes for behavior

Title: Sound encoding of neurons in input and associative layers of auditory cortex

Abstract:

Scientific Topic: Sound encoding of neurons in input and associative layers of auditory cortex

Data Types: 2-photon calcium imaging (GCaMP6s) in L2/3 and L4 of auditory cortex in awake head-fixed mice 3.

Open Questions: How is sound information encoded after stimulus offset? How is sound reliably encoded when trial-to-trial variability is so prevalent in these neuronal responses? What are the differences in population encoding from L4 to L2/3 of auditory cortex? The primary auditory cortex processes acoustic sequences for the perception of behaviorally meaningful sounds such as speech. Sound information arrives at its input layer 4 from where activity propagates to associative layer 2/3. It is currently not known whether there is a characteristic organization of neuronal population activity across layers and sound levels during sound processing. Here, we identify neuronal avalanches, which in theory and experiments have been shown to maximize dynamic range and optimize information transfer within and across networks, in primary auditory cortex. We used in vivo 2-photon imaging of pyramidal neurons in cortical layers L4 and L2/3 of mouse A1 to characterize the populations of neurons that were active spontaneously, i.e. in the absence of a sound stimulus, and those recruited by single-frequency tonal stimuli at different sound levels. This dataset allows for the calculation of robust receptive fields for each neuron and observation of the moment-to-moment activity of hundreds of neurons in two distinct areas of the auditory cortical circuit (L4 and L2/3). This dataset facilitates theorists to explore the differences in population encoding from L4 to L2/3 of auditory cortex, how sound information is encoded over time, and how sound is reliably encoded in the presence of overt trial-to-trial variability in responses.

Prior analysis: https://www.frontiersin.org/articles/10.3389/fnsys.2019.00045/full -- paper relies entirely on this dataset https://www.nature.com/articles/s41598-020-67819-4 -- paper uses the transgenic portion of the dataset as the C57BL/6 group

MSCZ - MultiScale Circuits of Zebrafish. Florian Engert. Sensorimotor processing, decision making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain

Title: The connectome of the larval zebrafish brain

Abstract: While the neural circuits that underlie behavior are of interest to a substantial part of the neuroscience community, there have been very few technical approaches that actually provide this kind of information across all levels at which circuits function, including the level of synaptic connections. We have established an electron microscopy core which is explicitly designed to provide the “wiring diagrams” of neural circuits in an efficient way. Much of our effort over the past 5 years has been to transform serial electron microscopy of large volumes (such as the fish nervous system) from a heroic to a more mundane enterprise. This transformation required innovations in hardware and software to abbreviate all the time-consuming steps in the connectomic pipeline. In particular we: 1) automated ultra-thin sectioning (using a tape-based approach), 2) automated image acquisition (using a custom multibeam serial electron microscope), 3) automated stitching and registration of the image data on high performance computing clusters, 4) automated segmentation of neurons and synapses on a GPU cluster, and 5) semi-automated proofreading and rendering of the neural circuits with custom software. Using this infrastructure we have collected tens of thousands of sections losslessly at 30 nm thickness and acquired images of them at lateral resolutions of 4 x 4 nanometers. This voxel size (480 nm3) provided enough detail for human or machine vision methods to trace out the finest aspects of neural connectivity. Acquiring these circuits is also relevant if neuronal connectivity can be associated with cells of particular types, hence the significant benefit of doing analysis of cell types that have been defined in the fish atlas associated with our overall project. Importantly, these circuit diagrams provide ground truth for testing and refining computational theories of brain function, and are therefore of obvious interest to theorists working on questions and constraints of circuit function.

Osmonauts - Dmitry Rinberg. Cracking the Olfactory Code

Abstract 1:

Title: High-speed volumetric imaging of piriform cortex during odor stimulation

Abstract: High-speed volumetric multiphoton imaging data of piriform cortex layers 2 and 3 were collected using a 16kHz resonant galvo in paralyzed but awake mice being exposed to different odor sets, in which each set parametrically varied odor distance at a particular scale (as measured in an odor space defined by more than 5,000 known odorants, using a PCA reduction of a set of more than 4,000 physiochemical features). These three sets were defined as “global” “tiled” and “clustered” depending upon inter-odor distances. Acquisition volumes spanned 210 mm in the Z axis across PCx L2 and L3. Volumes were split into 6 optical slices each spanning 35 mm of cortex. Volumes were positioned such that 2 slices resided in L2 and 4 slices resided in L3. This allowed us to monitor similarly sized populations of neurons in L2 and L3 given the approximately 3-fold lower cell density of L3 in posterior PCx. For experiments involving the global, clustered and tiled odor sets in odor-naïve animals, data was analyzed from 3 animals per odor set. In independent experiments, olfactory bulb inputs to the piriform (which reside in layer 1) were imaged in the same configuration (via homogenous viral delivery of GCaMP6s to bulb projection neurons); these experiments yielded >500 boutons per imaging field (x 3 mice) for the tiled odor set only.

Abstract 2:

Title: Automated segmentation of ROIs in odor-evoked glomerular imaging

Abstract: We collected spatiotemporal patterns of activity in the olfactory bulb glomeruli across multiple odors with one-photon calcium imaging. Data is the stack of 256 x 256 pixel fluorescent images collected with CCD or CMOS camera at 100hz. Odors used for a single set of experiments contains 8-10 monomolecular odors and 16-25 binary odor mixtures at two different concentration levels. Although there exist multiple algorithms used for automated segmentation of ROIs in calcium imaging data, application of those algorithms is challenging due to multiple factors that are unique to our experimental preparation. The challenges include densely packed spatial organization of glomeruli, scattering of fluorescence to neighboring ROIs and strong hemodynamics signal that contaminates neuronal activity dependent fluorescence changes.

DOPE Bernardo Sabatini Towards a unified framework for dopamine signaling in the striatum

Title: Dopamine recordings during a self-timed behavior

Abstract: 

GCaMP6f fiber photometry recordings from genetically-defined dopaminergic cell bodies in the substantia nigra pars compacta (SNc), the ventral tegmental area (VTA), and/or dopaminergic axon terminals in the dorsolateral striatum (DLS) were collected from water-deprived, head-fixed mice as they executed a self-timed movement task (n=12 mice). In a separate cohort of animals, dopamine release in the DLS was monitored during the self-timed movement task by fluorescence of one of the two novel dopamine indicators, dLight1.1 (n=5 mice) or DA2m (n=4 mice). We additionally co-expressed tdTomato as a control fluorophore to detect optical artifacts. Ongoing body movements were monitored by neck EMG, high speed video, and back-mounted accelerometer. Mice were given a 5 uL juice reward if the first lick following a start timing cue occurred within a reward window (3.333-7s after the cue). If the mice first-licked before or after the reward window, the mouse was not rewarded for the trial and had to wait the full trial duration before entering a 10 s intertrial interval. Each animal completed up to 26 behavioral sessions with 400-1500 trials each. Mice learned to target their licking toward the reward window, and we sought to relate the natural variability in the timing of these licks to the dopaminergic signal unfolding during the timing interval. Dopaminergic signals were analyzed by aligning to the cue and first-lick events. For analyses of averaged data, trials were pooled based on first-lick time, and we also repeated these analyses on single trial data, Two features were apparent in the data: a baseline offset in dopaminergic signal predictive of single-trial movement timing as well as a ramping signal reminiscent of a threshold process, in which the rate of ramping toward an apparent threshold level was likewise predictive of single-trial movement time. We quantified the relationship between the dopaminergic signal, ongoing body movements/artifacts and movement timing with a generalized linear encoding model. We quantified the predictive power of dopaminergic signals on movement timing with two complementary decoding models: 1) a single-trial threshold model, and 2) a generalized linear decoding model whose predictors included the dopaminergic signal as well as other task variables and movement signals.

MouseV1. Kenneth Miller. Understanding V1 circuit dynamics and computations

Title: Fine spatial organization of orientation tuning in mouse visual cortex

Abstract: 

The absence of spatial organization in orientation tuning had been thought as a major feature of the rodent primary visual cortex (V1). However, recent experimental discoveries have been revisiting and challenging this view. Population imaging studies have suggested that nearby neurons in the layer 2/3 (L2/3) of mouse V1 tend to have stronger tuning similarity than that of distant neuron pairs, indicating a localized spatial clustering of stimulus feature preference (Ringach et al. 2016, Jimenez et al. 2018, Kondo et al. 2016). However, the spatial scale of clustering is still in debate: either spread over hundreds of microns (Ringach et al. 2016), or limited to the scale of tens of microns (Kondo et al. 2016). Those differences could reflect distinct scales of local feedforward/recurrent cortical connectivity, so an accurate measurement of the spatial profile of local clustering will shed light on the underlying neuronal circuits, yielding way to circuit-based mechanisms of visual processing in rodent V1. Here using two-photon calcium imaging, we measured the orientation tuning properties of L2/3 neurons in mouse V1. We found a significant spatial clustering of tuning, but horizontally localized in only approximately 20 um, which is typically the average distance between horizontally neighboring neurons. To understand this narrow clustering, we explored a spiking neuron network model of L2/3 and L4 of mouse V1. Building on past models with broad recurrent wiring over 200 um (Rosenbaum et al., 2017; Huang et al., 2019) we additionally considered an excess connecting probability over a narrow 20 um range. A spatially narrow local tuning similarity matching our data emerges for even weak narrow connectivity, effectively adding only a few extra local connections per neuron. Our combined experimental and modeling work argue for a fine spatial scale of wiring between adjacent neurons in mouse V1.

MoC3. Rui Costa. Computational and circuit mechanisms underlying motor control

Title: Striatal correlates of locomotion

Abstract:

Our U19 group studies the functional and computational logic of connectivity between motor control centers and the spinal cord and muscle. We are anatomically and functionally characterizing the role of projection-specific populations of corticospinal neurons during particular modes of motor control based on cell-type specific connectivity between brain and spinal cord and employing novel imaging and electrophysiological techniques to measure and manipulate functionally and genetically-defined neural populations, and state-of-the-art computational tools. Because even the simplest motor program requires the activation of many neuronal populations across multiple brain areas, we are investigating the contribution of cortical and subcortical areas to the spinal cord and to muscle activity. We aim to dissect the contributions of activity in specific neural populations using closed-loop optogenetic manipulations and implement a dynamic back and forth between anatomical and functional mapping experiments, computational and conceptual models, and causal testing of predictions.
Previous work from our lab and others indicates that direct and indirect striatal projection pathways are concurrently active during movement initiation, this activity is action-specific, and needed for proper movement. However, this work was done by measuring activity in each pathway independently. As part of optimizing several imaging parameters, we have collected calcium imaging of striatal projection neurons during spontaneous locomotion in the mouse:
Dual-color 2-photon imaging of both direct- and indirect-pathway neurons from dorsolateral striatum             
Spontaneous locomotion on a running wheel with encoder for speed output
Simultaneous video of mouse on the wheel
These data are amenable for the application of computational models to help determine the neural dynamics and relation between neurons of both pathways during spontaneous locomotion.

CausalityInMotion. Gregory Deangelis. Neural basis of causal inference: representations, circuits, and dynamics

  • U19 – brief description overall project goals, competing circuit theories being developed/studied/integrated: see below Project Summary link.
  • Description of how Data Science Core is complying to the criteria of the FAIR principles: use of DataJoint, and collaboration with Vathes for data archiving ensure findable, accessible, interoperable & reusable data management.
  • List of Data Types in U19: Behavior and multi-unit neurophysiology recordings of behaving monkeys and mice.
  • Common Data Elements in U19: see Data Types.
  • Data Sharing goals in U19: Sharing of behavioral and spike-sorted neural activity data, and analysis code.
  • Data science tools being used in project: DataJoint for data sharing/analysis; MATLAB/Python scripts for customized detailed analysis and modeling.
  • Data science tools being developed for project: Discovering predicted latent variable dynamics underlying observed behavior in high-dimensional neurophysiology data.
  • Data science approaches to be shared with other U19’s: Any generic methods developed as part of the U19.
  • Data science challenges that could benefit from discussion with other U19’s: Model-free latent variable discovery.
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