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Basal Ganglia Output Has a Permissive Non-Driving Role in a Signaled Locomotor Action Mediated by the Midbrain.

ABSTRACT: The basal ganglia are important for movement and reinforcement learning. Using mice of either sex, we found that the main basal ganglia GABAergic output in the midbrain, the substantia nigra pars reticulata (SNr), shows movement-related neural activity during the expression of a negatively reinforced signaled locomotor action known as signaled active avoidance; this action involves mice moving away during a warning signal to avoid a threat. In particular, many SNr neurons deactivate during active avoidance responses. However, whether SNr deactivation has an essential role driving or regulating active avoidance responses is unknown. We found that optogenetic excitation of SNr or striatal GABAergic fibers that project to an area in the pedunculopontine tegmentum (PPT) within the midbrain locomotor region abolishes signaled active avoidance responses, while optogenetic inhibition of SNr cells (mimicking the SNr deactivation observed during an active avoidance behavior) serves as an effective conditioned stimulus signal to drive avoidance responses by disinhibiting PPT neurons. However, preclusion of SNr deactivation, or direct inhibition of SNr fibers in the PPT, does not impair the expression of signaled active avoidance, indicating that SNr output does not drive the expression of a signaled locomotor action mediated by the midbrain. Consistent with a permissive regulatory role, SNr output provides information about the state of the ongoing action to downstream structures that mediate the action.SIGNIFICANCE STATEMENT During signaled active avoidance behavior, subjects move away to avoid a threat when directed by an innocuous sensory stimulus. Excitation of GABAergic cells in the substantia nigra pars reticulata (SNr), the main output of the basal ganglia, blocks signaled active avoidance, while inhibition of SNr cells is an effective stimulus to drive active avoidance. Interestingly, many SNr cells inhibit their firing during active avoidance responses, suggesting that SNr inhibition could be driving avoidance responses by disinhibiting downstream areas. However, interfering with the modulation of SNr cells does not impair the behavior. Thus, SNr may regulate the active avoidance movement in downstream areas that mediate the behavior, but does not drive it.

SUBMITTER: Hormigo S

PROVIDER: S-EPMC7896017 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Animals, including humans, readily learn to avoid harmful and threatening situations by moving in response to cues that predict the threat (e.g., fire alarm, traffic light). During a negatively reinforced sensory-guided locomotor action, known as signaled active avoidance, animals learn to avoid a harmful unconditioned stimulus (US) by moving away when signaled by a harmless conditioned stimulus (CS) that predicts the threat. CaMKII-expressing neurons in the pedunculopontine tegmentum area (PPT) of the midbrain locomotor region have been shown to play a critical role in the expression of this learned behavior, but the activity of these neurons during learned behavior is unknown. Using calcium imaging fiber photometry in freely behaving mice, we show that PPT neurons sharply activate during presentation of the auditory CS that predicts the threat before onset of avoidance movement. PPT neurons activate further during the succeeding CS-driven avoidance movement, or during the faster US-driven escape movement. PPT neuron activation was weak during slow spontaneous movements but correlated sharply with movement speed and, therefore, with the urgency of the behavior. Moreover, using optogenetics, we found that these neurons must discharge during the signaled avoidance interval for naive mice to effectively learn the active avoidance behavior. As an essential hub for signaled active avoidance, neurons in the midbrain tegmentum process the conditioned cue that predicts the threat and discharge sharply relative to the speed or apparent urgency of the avoidance (learned) and escape (innate) responses.SIGNIFICANCE STATEMENT During signaled active avoidance behavior, subjects move away to avoid a threat when directed by an innocuous sensory stimulus. Using imaging methods in freely behaving mice, we found that the activity of neurons in a part of the midbrain, known as the pedunculopontime tegmentum, increases during the presentation of the innocuous sensory stimulus that predicts the threat and also during the expression of the learned behavior as mice move away to avoid the threat. In addition, inhibiting these neurons abolishes the ability of mice to learn the behavior. Thus, neurons in this part of the midbrain code and are essential for signaled active avoidance behavior.

Project description:Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging. Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization-prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures. Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei. Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.

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Basal Ganglia Output Has a Permissive Non-Driving Role in a Signaled Locomotor Action Mediated by the Midbrain.

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