Although bradykinesia, tremor, and rigidity are hallmark motor defects in Parkinson’s disease (PD) patients, they also experience motor learning impairments and non-motor symptoms such as depression. The neural basis for these different PD symptoms are not well understood. While current treatments are effective for locomotion deficits in PD, therapeutic strategies targeting motor learning deficits and non-motor symptoms are lacking. We found that distinct parafascicular (PF) thalamic subpopulations project to caudate putamen (CPu), subthalamic nucleus (STN), and nucleus accumbens (NAc). While PF-->CPu and PF-->STN circuits are critical for locomotion and motor learning respectively, inhibition of the PF-->NAc circuit induced a depression-like state. While chemogenetically manipulating CPu-projecting PF neurons led to a long-term restoration of locomotion, optogenetic long-term potentiation at PF-->STN synapses restored motor learning behavior in PD model mice. Furthermore, activation of NAc-projecting PF neurons rescued depression-like PD phenotypes. Importantly, we identified nicotinic acetylcholine receptors capable of modulating PF circuits to rescue different PD phenotypes. Thus, targeting PF thalamic circuits may be an effective strategy for treating motor and non-motor deficits in PD.

Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally com- plementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple subpopulations. Our analyses revealed that such a subpopulation structure enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, for inactivation experiments and for the implication of different neurons in multi-tasking.

In behaving rodents, CA1 pyramidal neurons receive spatially-tuned depolarizing synaptic input while traversing a specific location within an environment called its place. Midbrain dopamine neurons participate in reinforcement learning, and bursts of action potentials riding a depolarizing wave of synaptic input signal rewards and reward expectation. Interestingly, slice electrophysiology in vitro shows that both types of cells exhibit a pronounced reduction in firing rate (adaptation) and even cessation of firing during sustained depolarization. We included a five state Markov model of NaV1.6 (for CA1) and NaV1.2 (for dopamine neurons) respectively, in computational models of these two types of neurons. Our simulations suggest that long-term inactivation of this channel is responsible for the adaptation in CA1 pyramidal neurons, in response to triangular depolarizing current ramps. We also show that the differential contribution of slow inactivation in two subpopulations of midbrain dopamine neurons can account for their different dynamic ranges, as assessed by their responses to similar depolarizing ramps. These results suggest long-term inactivation of the sodium channel is a general mechanism for adaptation.

How psychedelic drugs change the activity of cortical neuronal populations is not well understood. It is also not clear which changes are specific to transition into the psychedelic brain state and which are shared with other brain state transitions. Here, we used Neuropixels probes to record from large populations of neurons in prefrontal cortex of mice given the psychedelic drug TCB-2. The primary effect of drug ingestion was stretching of the distribution of log firing rates of the recorded population. This phenomenon was previously observed across transitions between sleep and wakefulness, which prompted us to examine how common it is. We found that modulation of the width of the log-rate distribution of a neuronal population occurred in multiple areas of the cortex and in the hippocampus even in awake drug-free mice, driven by intrinsic fluctuations in their arousal level. Arousal, however, did not explain the stretching of the log-rate distribution by TCB-2. In both psychedelic and intrinsically occurring brain state transitions, the stretching or squeezing of the log-rate distribution of an entire neuronal population were the result of a more close overlap between log-rate distributions of the upregulated and downregulated subpopulations in one brain state compared to the other brain state. Often, we also observed that the log-rate distribution of the downregulated subpopulation was stretched, whereas the log-rate distribution of the upregulated subpopulation was squeezed. In both subpopulations, the stretching and squeezing were a signature of a greater relative impact of the brain state transition on the rates of the slow-firing neurons. These findings reveal a generic pattern of reorganisation of neuronal firing rates by different kinds of brain state transitions.

Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. Compared to standard algorithms such as t-SNE and UMAP, Rastermap finds finer and higher dimensional patterns of neural variability, as measured by quantitative benchmarks. We applied Rastermap to a variety of datasets, including spontaneous neural activity, neural activity during a virtual reality task, widefield neural imaging data during a 2AFC task, artificial neural activity from an agent playing atari games, and neural responses to visual textures. We found within these datasets unique subpopulations of neurons encoding abstract properties of the environment.

The main pillars of social behaviors involve (1) mating, where males copulate with female partners to reproduce, and (2) aggression, where males fight conspecific male competitors in territory guarding. Decades of study have identified two key regions in the hypothalamus, the medial preoptic nucleus (MPN) and the ventrolateral part of ventromedial hypothalamus (VMHvl) , that are essential for male sexual and aggressive behaviors, respectively. However, it remains ambiguous what area directs excitatory control of the hypothalamic activity and generates the initiation signal for social behaviors. Through neural tracing, in vivo optical recording and functional manipulations, we identified the estrogen receptor alpha (Esr1)-expressing cells in the posterior amygdala (PA) as a main source of excitatory inputs to the MPN and VMHvl, and key hubs in mating and fighting circuits in males. Importantly, two spatially-distinct populations in the PA regulate male sexual and aggressive behaviors, respectively. Moreover, these two subpopulations in the PA display differential molecular phenotypes, projection patterns and in vivo neural responses. Our work also observed the parallels between these social behavior circuits and basal ganglia circuits to control motivated behaviors, which Larry Swanson (2000) originally proposed based on extensive developmental and anatomical evidence.

The serotonin system is the most frequently targeted neural system pharmacologically for treating psychiatric disorders, including depression and anxiety. Serotonin neurons of the dorsal and median raphe nuclei (DR, MR) collectively innervate the entire forebrain and midbrain, modulating diverse physiology and behaviour. By using viral-genetic methods, we found that DR serotonin system contains parallel sub-systems that differ in input and output connectivity, physiological response properties, and behavioural functions. To gain a fundamental understanding of the molecular heterogeneity of DR and MR, we used single-cell RNA - sequencing (scRNA-seq) to generate a comprehensive dataset comprising eleven transcriptomically distinct serotonin neuron clusters. We generated novel intersectional viral-genetic tools to access specific subpopulations. Whole-brain axonal projection mapping revealed that the molecular features of these distinct serotonin groups reflect their anatomical organization and provide tools for future exploration of the full projection map of molecularly defined serotonin groups. The molecular architecture of serotonin system lays the foundation for integrating anatomical, neurochemical, physiological, and behavioural functions.

How can neural networks exhibit persistent activity on time scales much larger than allowed by cellular properties? We address this question in the context of larval zebrafish, a model vertebrate that is accessible to brain-scale neuronal recording and high-throughput behavioral studies. We study in particular the dynamics of a bilaterally distributed circuit, the so-called ARTR, including hundreds neurons. ARTR exhibits slow antiphasic alternations between its left and right subpopulations, which can be modulated by the water temperature, and drive the coordinated orientation of swim bouts, thus organizing the fish spatial exploration. To elucidate the mechanism leading to the slow self-oscillation, we train a network graphical model (Ising) on neural recordings. Sampling the inferred model allows us to generate synthetic oscillatory activity, whose features correctly capture the observed dynamics. A mean-field analysis of the inferred model reveals the existence several phases; activated crossing of the barriers in between those phases controls the long time scales present in the network oscillations. We show in particular how the barrier heights and the nature of the phases vary with the water temperature.

Corticospinal neurons (CSN) are the cortical projection neurons that innervate the spinal cord and some brainstem targets with segmental precision to control voluntary movement of specific functional motor groups, limb sections, or individual digits, yet molecular regulation over CSN segmental target specificity is essentially unknown. CSN subpopulations exhibit striking axon targeting specificity from development into maturity: Evolutionarily newer rostrolateral CSN exclusively innervate bulbar-cervical targets (CSNBC-lat), while evolutionarily older caudomedial CSN (CSNmed) are more heterogeneous, with distinct subpopulations extending axons to either bulbar-cervical or thoraco-lumbar segments. The cervical cord, with its evolutionarily enhanced precision of forelimb movement, is innervated by multiple CSN subpopulations, suggesting inter-neuronal interactions in establishing corticospinal connectivity. I identify that Lumican, previously unrecognized in axon development, controls the specificity of cervical spinal cord innervation by CSN. Remarkably, Lumican, an extracellular matrix protein expressed by CSNBC-lat, non-cell-autonomously suppresses axon collateralization in the cervical cord by CSNmed. Intersectional viral labeling and mouse genetics further identify that Lumican controls axon collateralization by multiple subpopulations in caudomedial sensorimotor cortex. These results identify inter-axonal molecular crosstalk between CSN subpopulations as a novel mechanism controlling corticospinal connectivity and competitive specificity. Further, this mechanism has potential implications for evolutionary diversification of corticospinal circuitry with finer scale precision. "" Complementing this work, to comprehensively elucidate related axon projection mechanisms functioning at tips of growing CSN axons in vivo, I am currently applying experimental and analytic approaches recently developed in my postdoc lab (Poulopoulos*, Murphy*, Nature, 2019) to quantitatively and subcellularly “map” RNA and protein molecular machinery of subtype-specific growth cones, in parallel to their parent somata, isolated directly in vivo from developing subcerebral projection neurons (SCPN; the broader cortical output neuron population targeting both brainstem and spinal cord; includes CSN). I am investigating both normal development and GC-soma dysregulation with mutation of central CSN-SCPN transcriptional regulator Ctip2/Bcl11b.