Efficient retrograde access to projection neurons for the delivery of sensors and effectors constitutes an important and enabling capability for neural circuit dissection. Such an approach would also be useful for gene therapy, including the treatment of neurodegenerative disorders characterized by pathological spread through functionally connected and highly distributed networks. Viral vectors, in particular, are powerful gene delivery vehicles for the nervous system, but all available tools suffer from inefficient retrograde transport or limited clinical potential. To address this need, we applied in vivo directed evolution to engineer potent retrograde functionality into the capsid of adeno-associated virus (AAV), a vector that has shown promise in neuroscience research and the clinic. A newly evolved variant, rAAV2-retro, permits robust retrograde access to projection neurons with efficiency comparable to classical synthetic retrograde tracers and enables sufficient sensor/effector expression for functional circuit interrogation and in vivo genome editing in targeted neuronal populations. VIDEO ABSTRACT.

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ( 2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.

In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance based on prior experience, which we refer to as a history-dependent gain computation. Finally, we discuss how insights from the history-dependent gain model might translate from the bench to the bedside, primarily the implications for the design of adaptive deep brain stimulation. Thus, we explicate the key empirical and conceptual support for a normative, computational model with substantial explanatory power for the broad role of basal ganglia circuits in health and disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The thalamus is the central communication hub of the forebrain and provides the cerebral cortex with inputs from sensory organs, subcortical systems and the cortex itself. Multiple thalamic regions send convergent information to each cortical region, but the organizational logic of thalamic projections has remained elusive. Through comprehensive transcriptional analyses of retrogradely labeled thalamic neurons in adult mice, we identify three major profiles of thalamic pathways. These profiles exist along a continuum that is repeated across all major projection systems, such as those for vision, motor control and cognition. The largest component of gene expression variation in the mouse thalamus is topographically organized, with features conserved in humans. Transcriptional differences between these thalamic neuronal identities are tied to cellular features that are critical for function, such as axonal morphology and membrane properties. Molecular profiling therefore reveals covariation in the properties of thalamic pathways serving all major input modalities and output targets, thus establishing a molecular framework for understanding the thalamus.

UNLABELLED: Midbrain dopamine (DA) neurons are thought to be a critical node in the circuitry that mediates reward learning. DA neurons receive diverse inputs from regions distributed throughout the neuraxis from frontal neocortex to the mesencephalon. While a great deal is known about changes in the activity of individual DA neurons during learning, much less is known about the functional changes in the microcircuits in which DA neurons are embedded. Here we used local field potentials recorded from the midbrain of behaving mice to show that the midbrain evoked potential (mEP) faithfully reflects the temporal and spatial structure of the phasic response of midbrain neuron populations during conditioning. By comparing the mEP to simultaneously recorded single units, we identified specific components of the mEP that corresponded to phasic DA and non-DA responses to salient stimuli. The DA component of the mEP emerged with the acquisition of a conditioned stimulus, was extinguished following changes in reinforcement contingency, and could be inhibited by pharmacological manipulations that attenuate the phasic responses of DA neurons. In contrast to single-unit recordings, the mEP permitted relatively dense sampling of the midbrain circuit during conditioning and thus could be used to reveal the spatiotemporal structure of multiple intermingled midbrain circuits. Finally, the mEP response was stable for months and thus provides a new approach to study long-term changes in the organization of ventral midbrain microcircuits during learning.

SIGNIFICANCE STATEMENT: Neurons that synthesize and release the neurotransmitter dopamine play a critical role in voluntary reward-seeking behavior. Much of our insight into the function of dopamine neurons comes from recordings of individual cells in behaving animals; however, it is notoriously difficult to record from dopamine neurons due to their sparsity and depth, as well as the presence of intermingled non-dopaminergic neurons. Here we show that much of the information that can be learned from recordings of individual dopamine and non-dopamine neurons is also revealed by changes in specific components of the local field potential. This technique provides an accessible measurement that could prove critical to our burgeoning understanding of the molecular, functional, and anatomical diversity of neuron populations in the midbrain.

A fundamental goal in neuroscience is to uncover common principles by which different modalities of information are processed. In the mammalian brain, thalamus acts as the essential hub for forebrain circuits handling inputs from sensory, motor, limbic, and cognitive pathways. Whether thalamus imposes common transformations on each of these modalities is unknown. Molecular characterization offers a principled approach to revealing the organization of thalamus. Using near-comprehensive and projection-specific transcriptomic sequencing, we found that almost all thalamic nuclei fit into one of three profiles. These profiles lie on a single axis of genetic variance which is aligned with the mediolateral spatial axis of thalamus. Genes defining this axis of variance include receptors and ion channels, providing a systematic diversification of input/output transformations across the topography of thalamus. Single cell transcriptional profiling revealed graded heterogeneity within individual thalamic nuclei, demonstrating that a spectrum of cell types and potentially diverse input/output transforms exist within a given thalamic nucleus. Together, our data argue for an archetypal organization of pathways serving diverse input modalities, and provides a comprehensive organizational scheme for thalamus.

Behavior is readily classified into patterns of movements with inferred common goals-actions. Goals may be discrete; movements are continuous. Through the careful study of isolated movements in laboratory settings, or via introspection, it has become clear that animals can exhibit exquisite graded specification to their movements. Moreover, graded control can be as fundamental to success as the selection of which action to perform under many naturalistic scenarios: a predator adjusting its speed to intercept moving prey, or a tool-user exerting the perfect amount of force to complete a delicate task. The basal ganglia are a collection of nuclei in vertebrates that extend from the forebrain (telencephalon) to the midbrain (mesencephalon), constituting a major descending extrapyramidal pathway for control over midbrain and brainstem premotor structures. Here we discuss how this pathway contributes to the continuous specification of movements that endows our voluntary actions with vigor and grace. Expected final online publication date for the , Volume 43 is July 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Summary The interplay between two major forebrain structures—cortex and subcortical striatum—is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated, while the primary motor cortex is involved in specifying the continuous parameters of an upcoming/ongoing movement. Recent data indicate that striatum may also be involved in specification. These alternatives have been difficult to reconcile because comparing very distinct actions, as is often done, makes essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity across similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify continuous parameters governing movement execution.

The interplay between two major forebrain structures - cortex and subcortical striatum - is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated while the primary motor cortex is involved in the online control of movement execution. Recent data indicates that striatum may also be critical for specifying movement execution. These alternatives have been difficult to reconcile because when comparing very distinct actions, as in the vast majority of work to date, they make essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity during similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar, but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify flexible parameters of action during movement execution.

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1–3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5–7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Naïve mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.