scholarly journals Cell-Type-Specific Sensorimotor Processing in Striatal Projection Neurons during Goal-Directed Behavior

Neuron ◽  
2015 ◽  
Vol 88 (2) ◽  
pp. 298-305 ◽  
Author(s):  
Tanya Sippy ◽  
Damien Lapray ◽  
Sylvain Crochet ◽  
Carl C.H. Petersen
Keyword(s):  
Projection Neurons ◽  
Cell Type ◽  
Sensorimotor Processing ◽  
Striatal Projection Neurons ◽  
Cell Type Specific ◽  
Goal Directed Behavior

scholarly journals Cell type-specific plasticity of striatal projection neurons in parkinsonism and L-DOPA-induced dyskinesia

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Tim Fieblinger ◽  
Steven M. Graves ◽  
Luke E. Sebel ◽  
Cristina Alcacer ◽  
Joshua L. Plotkin ◽  
...  
Keyword(s):  
Projection Neurons ◽  
Cell Type ◽  
Striatal Projection Neurons ◽  
Cell Type Specific

scholarly journals Cell-type Specific Learning of Attentional Gating in Primate Striatum

2019 ◽  
Author(s):  
Kianoush Banaie Boroujeni ◽  
Mariann Oemisch ◽  
Seyed Alireza Hassani ◽  
Thilo Womelsdorf
Keyword(s):  
Visual Attention ◽  
Neural Mechanism ◽  
Projection Neurons ◽  
Cell Type ◽  
Behavioral Learning ◽  
Neural Learning ◽  
Striatal Projection Neurons ◽  
Feature Based ◽  
Cell Type Specific ◽  
Cortical Inputs

Cognitive flexibility depends on a fast neural learning mechanism for enhancing momentary relevant over irrelevant information. A possible neural mechanism realizing this enhancement uses fast-spiking interneurons (FSIs) in the striatum to train striatal projection neurons to gate relevant and suppress distracting cortical inputs. We found support for such a mechanism in nonhuman primates during the flexible adjustment of visual attention. FSIs gated visual attention cues during feature-based learning. One FSI population showed stronger inhibition during learning, while another FSI subpopulation showed weaker inhibition after learning signifying post-learning disinhibition. Additionally, a smaller neural subpopulation increased activity when salient distractor events were successfully suppressed. These findings highlight that fast behavioral learning of feature relevance is accompanied by fast neural learning of cell-type specific cortico-striatal gating.

scholarly journals Connectivity characterization of the mouse basolateral amygdalar complex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Houri Hintiryan ◽  
Ian Bowman ◽  
David L. Johnson ◽  
Laura Korobkova ◽  
Muye Zhu ◽  
...  
Keyword(s):  
Granular Cell ◽  
Cell Types ◽  
Projection Neurons ◽  
Cell Type ◽  
Connectivity Map ◽  
Analysis Techniques ◽  
Domain Specific ◽  
Cell Type Specific ◽  
Unique Domain

AbstractThe basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.

scholarly journals Periaqueductal efferents to dopamine and GABA neurons of the VTA

2017 ◽  
Author(s):  
Niels R. Ntamati ◽  
Meaghan Creed ◽  
Christian Lüscher
Keyword(s):  
Functional Analysis ◽  
Ventral Tegmental Area ◽  
Periaqueductal Gray ◽  
Retrograde Tracing ◽  
The Other ◽  
Projection Neurons ◽  
Cell Type ◽  
Gaba Neurons ◽  
Other Hand ◽  
Cell Type Specific

AbstractNeurons in the periaqueductal gray (PAG) modulate threat responses and nociception. Activity in the ventral tegmental area (VTA) on the other hand can cause reinforcement and aversion. While in many situations these behaviors are related, the anatomical substrate of a crosstalk between the PAG and VTA remains poorly understood. Here we describe the anatomical and electrophysiological organization of the VTA-projecting PAG neurons. Using rabies-based, cell type-specific retrograde tracing, we observed that PAG to VTA projection neurons are evenly distributed along the rostro-caudal axis of the PAG, but concentrated in its posterior and ventrolateral segments. Optogenetic projection targeting demonstrated that the PAG-to-VTA pathway is predominantly excitatory and targets similar proportions of Ih-expressing VTA DA and GABA neurons. Taken together, these results set the framework for functional analysis of the interplay between PAG and VTA in the regulation of reward and aversion.

scholarly journals Target-specific M1 inputs to infragranular S1 pyramidal neurons

2016 ◽  
Vol 116 (3) ◽  
pp. 1261-1274 ◽  
Author(s):  
Amanda K. Kinnischtzke ◽  
Erika E. Fanselow ◽  
Daniel J. Simons
Keyword(s):  
Primary Motor Cortex ◽  
Pyramidal Neurons ◽  
Projection Neurons ◽  
Cell Type ◽  
Intrinsic Properties ◽  
Subcortical Structures ◽  
Medial Nucleus ◽  
Cell Type Specific ◽  
Posterior Medial Nucleus

The functional role of input from the primary motor cortex (M1) to primary somatosensory cortex (S1) is unclear; one key to understanding this pathway may lie in elucidating the cell-type specific microcircuits that connect S1 and M1. Recently, we discovered that a subset of pyramidal neurons in the infragranular layers of S1 receive especially strong input from M1 (Kinnischtzke AK, Simons DJ, Fanselow EE. Cereb Cortex 24: 2237–2248, 2014), suggesting that M1 may affect specific classes of pyramidal neurons differently. Here, using combined optogenetic and retrograde labeling approaches in the mouse, we examined the strengths of M1 inputs to five classes of infragranular S1 neurons categorized by their projections to particular cortical and subcortical targets. We found that the magnitude of M1 synaptic input to S1 pyramidal neurons varies greatly depending on the projection target of the postsynaptic neuron. Of the populations examined, M1-projecting corticocortical neurons in L6 received the strongest M1 inputs, whereas ventral posterior medial nucleus-projecting corticothalamic neurons, also located in L6, received the weakest. Each population also possessed distinct intrinsic properties. The results suggest that M1 differentially engages specific classes of S1 projection neurons, thereby regulating the motor-related influence S1 exerts over subcortical structures.

scholarly journals Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning

Function ◽  
2021 ◽  
Author(s):  
Tanya Sippy ◽  
Corryn Chaimowitz ◽  
Sylvain Crochet ◽  
Carl C H Petersen
Keyword(s):  
Membrane Potential ◽  
Dopamine Receptors ◽  
Cell Types ◽  
Projection Neurons ◽  
Striatal Neurons ◽  
Cell Type ◽  
Indirect Pathway ◽  
Cell Type Specific ◽  
Striatal Membrane ◽  
Sensory Evoked

Abstract The striatum integrates sensorimotor and motivational signals, likely playing a key role in reward-based learning of goal-directed behavior. However, cell type-specific mechanisms underlying reinforcement learning remain to be precisely determined. Here, we investigated changes in membrane potential dynamics of dorsolateral striatal neurons comparing naïve mice and expert mice trained to lick a reward spout in response to whisker deflection. We recorded from three distinct cell types: i) direct pathway striatonigral neurons, which express type 1 dopamine receptors; ii) indirect pathway striatopallidal neurons, which express type 2 dopamine receptors; and iii) tonically active, putative cholinergic, striatal neurons. Task learning was accompanied by cell type-specific changes in the membrane potential dynamics evoked by the whisker deflection and licking in successfully-performed trials. Both striatonigral and striatopallidal types of striatal projection neurons showed enhanced task-related depolarization across learning. Striatonigral neurons showed a prominent increase in a short latency sensory-evoked depolarization in expert compared to naïve mice. In contrast, the putative cholinergic striatal neurons developed a hyperpolarizing response across learning, driving a pause in their firing. Our results reveal cell type-specific changes in striatal membrane potential dynamics across the learning of a simple goal-directed sensorimotor transformation, helpful for furthering the understanding of the various potential roles of different basal ganglia circuits.

scholarly journals Circuit organization of the excitatory sensorimotor loop through hand/forelimb S1 and M1

2021 ◽  
Author(s):  
Naoki Yamawaki ◽  
Martinna G. Raineri Tapies ◽  
Austin M. Stults ◽  
Gregory A. Smith ◽  
Gordon M. G. Shepherd
Keyword(s):  
Spinal Cord ◽  
Active Touch ◽  
Projection Neurons ◽  
Cuneate Nucleus ◽  
Cell Type ◽  
Wiring Diagram ◽  
Cortical Motor ◽  
Somatosensory Input ◽  
Ascending Pathways ◽  
Cell Type Specific

Sensory-guided limb control relies on communication across sensorimotor loops. For active touch with the hand, the longest loop is the transcortical continuation of ascending pathways, particularly the lemnisco-cortical and corticocortical pathways carrying tactile signals via the cuneate nucleus, ventral posterior lateral (VPL) thalamus, and primary somatosensory (S1) and motor (M1) cortices to reach corticospinal neurons and influence descending activity. We characterized excitatory connectivity along this pathway in the mouse. In the lemnisco-cortical leg, disynaptic cuneate→VPL→S1 connections excited mainly layer (L) 4 neurons. In the corticocortical leg, S1→M1 connections from L2/3 and L5A neurons mainly excited downstream L2/3 neurons, which excite corticospinal neurons. The findings provide a detailed new wiring diagram for the hand/forelimb-related transcortical circuit, delineating a basic but complex set of cell-type-specific feedforward excitatory connections that selectively and extensively engage diverse intratelencephalic projection neurons, thereby polysynaptically linking subcortical somatosensory input to cortical motor output to spinal cord.

scholarly journals Transcriptome analysis for the development of cell-type specific labeling to study olfactory circuits

2020 ◽  
Author(s):  
Anzhelika Koldaeva ◽  
Cary Zhang ◽  
Yu-Pei Huang ◽  
Janine Reinert ◽  
Seiya Mizuno ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Mrna Level ◽  
Sensory System ◽  
Cell Types ◽  
Projection Neurons ◽  
Dependent Manner ◽  
Cell Type ◽  
Cell Type Specific ◽  
Sensory Evoked ◽  
Driver Line

AbstractIn each sensory system of the brain, mechanisms exist to extract distinct features from stimuli to generate a variety of behavioural repertoires. These often correspond to different cell types at some stage in sensory processing. In the mammalian olfactory system, complex information processing starts in the olfactory bulb, whose output is conveyed by mitral and tufted cells (MCs and TCs). Despite many differences between them, and despite the crucial position they occupy in the information hierarchy, little is known how these two types of projection neurons differ at the mRNA level. Here, we sought to identify genes that are differentially expressed between MCs and TCs, with an ultimate goal to generate a cell-type specific Cre-driver line, starting from a transcriptome analysis using a large and publicly available single-cell RNA-seq dataset (Zeisel et al., 2018). Despite many genes showing differential expressions, we identified only a few that were abundantly and consistently expressed only in MCs. After further validating these putative markers using in-situ hybridization, two genes, namely Pkib and Lbdh2, remained as promising candidates. Using CRISPR/Cas9-mediated gene editing, we generated Cre-driver lines and analysed the resulting recombination patterns. This analysis indicated that our new inducible Cre-driver line, Lbhd2-CreERT2, can be used to genetically label MCs in a tamoxifen dose-dependent manner, as assessed by soma locations, projection patterns and sensory-evoked responses. Hence this line is a promising tool for future investigations of cell-type specific contributions to olfactory processing and demonstrates the power of publicly accessible data in accelerating science.

scholarly journals Basolateral amygdala parvalbumin neurons report aversive prediction error to constrain fear learning

2020 ◽  
Author(s):  
Joanna Oi-Yue Yau ◽  
Chanchanok Chaichim ◽  
John M. Power ◽  
Gavan P. McNally
Keyword(s):  
Prediction Error ◽  
Basolateral Amygdala ◽  
Projection Neuron ◽  
Fear Learning ◽  
Neuron Activity ◽  
Projection Neurons ◽  
Cell Type ◽  
Learning Mechanisms ◽  
Aversive Events ◽  
Cell Type Specific

AbstractAnimals, including humans, use prediction error to guide learning about danger in the environment. The basolateral amygdala (BLA) is obligatory for this learning and BLA excitatory projection neurons are instructed by aversive prediction error to form fear associations. Complex networks of inhibitory interneurons, dominated by parvalbumin (PV) expressing GABAergic neurons, form the intrinsic microcircuitry of the BLA to control projection neuron activity. Whether BLA PV interneurons are also sensitive to prediction error and how they use this error to control fear learning remains unknown. We used PV cell-type specific recording and manipulation approaches in male transgenic PV-Cre rats to address these issues. We show that BLA PV neurons control fear learning about aversive events but not learning about their omission. Furthermore, during fear learning BLA PV neurons express the activity signatures of aversive prediction error: greater activity to unexpected than expected aversive events and greater activity to better rather than poorer predictors of these events. Crucially, we show that BLA PV neurons act to limit fear learning across these variations in prediction error. Together, this demonstrates that prediction error instructs and regulates BLA fear association formation in a cell-type specific manner. Whereas BLA projection neurons use prediction error signals to form and store fear associations, BLA PV interneurons use prediction error signals to constrain fear association formation.Significance StatementThe capacity to predict sources of danger in the environment is essential for survival. This capacity is supported by associative learning mechanisms that are triggered when the danger experienced is greater than the danger expected. Here we show that the activity of parvalbumin positive GABAergic interneurons in the rat basolateral amygdala neurons report this difference between the danger expected and the danger experienced and that they use this difference to limit the amount of fear which is learned.

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