For goal-directed behavior it is critical that we can both select the appropriate action and learn to modify the underlying movements (e.g. the pitch of a note or velocity of a reach) to improve outcomes. The basal ganglia are a critical nexus where circuits necessary for the production of behavior, such as neocortex and thalamus, are integrated with reward signaling 1 to reinforce successful, purposive actions 2. Dorsal striatum, a major input structure of basal ganglia is composed of two opponent pathways, direct and indirect, thought to select actions that elicit positive outcomes or suppress actions that do not, respectively 3,4. Activity-dependent plasticity modulated by reward is thought to be sufficient for selecting actions in striatum 5,6. Although perturbations of basal ganglia function produce profound changes in movement 7, it remains unknown whether activity-dependent plasticity is sufficient to produce learned changes in movement kinematics, such as velocity. Here we used cell-type specific stimulation delivered in closed-loop during movement to demonstrate that activity in either the direct or indirect pathway is sufficient to produce specific and sustained increases or decreases in velocity without affecting action selection or motivation. These behavioral changes were a form of learning that accumulated over trials, persisted after the cessation of stimulation, and were abolished in the presence of dopamine antagonists. Our results reveal that the direct and indirect pathways can each bidirectionally control movement velocity, demonstrating unprecedented specificity and flexibility in the control of volition by the basal ganglia.

Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.

Establishing the circuitry underlying attentional and oculomotor control is a longstanding goal of systems neuroscience. The macaque lateral intraparietal area, LIP, has been implicated in both processes, but numerous studies have produced contradictory findings. Anatomically, LIP consists of a dorsal and ventral subdivision, yet the functional significance of this division remains unclear. We co-injected muscimol, a GABAa agonist, and manganese, an MR-lucent paramagnetic ion, into different portions of LIP, testing the effect of the resulting reversible inactivation on saccade planning and attention, and visualized each injection using anatomical MRI. We find that LIPd is primarily an oculomotor planning region, while LIPv contributes to both attentional and oculomotor processes. Additional testing reveals that, even within LIPv, the two functions are dissociable. Thus our novel muscimol+Mn-MRI technique establishes clear structure-function relationships that distinguish LIPv from LIPd, and reveals dissociable circuits for attention and eye movements in the posterior parietal cortex.

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.