Striatonigral control of movement velocity in mice

Bartholomew, Ryan A.; Li, Haofang; Gaidis, Erin; Stackmann, Michelle; Shoemaker, Charles T.; Rossi, Mark A.; Yin, Henry H.

doi:10.1111/ejn.13187

Cited by 42 publications

(41 citation statements)

References 47 publications

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“…Recent work has shown that sensorimotor SPN activity is often highly correlated with movement velocity (Kim et al, 2014;Yttri and Dudman, 2016), and stimulation of striatal output frequency-dependently determines movement velocity (Bartholomew et al, 2016). Yet it remains unclear how, during target-driven pursuit behavior, the striatal output is commanded by representation of distance to target.…”

Section: Introductionmentioning

confidence: 99%

A striatal interneuron circuit for continuous target pursuit

Kim

Hughes

et al. 2018

Preprint

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25Most adaptive behaviors require precise tracking of targets in space. In pursuit 26 behavior with a moving target, mice use distance to target to guide their own movement 27 continuously. Here we show that in the sensorimotor striatum, parvalbumin-positive fast-28 spiking interneurons (FSIs) can represent the distance between self and target during 29 pursuit behavior, while striatal projection neurons (SPNs), which receive FSI projections, 30 can represent self-velocity. FSIs are shown to regulate velocity-related SPN activity during 31 pursuit, so that movement velocity is continuously modulated by distance to target. 32 Moreover, bidirectional manipulation of FSI activity can selectively disrupt performance 33 by increasing or decreasing the self-target distance. Our results reveal a key role of the 34 FSI-SPN interneuron circuit in pursuit behavior, and elucidate how this circuit implements 35 distance to velocity transformation required for the critical underlying computation. 36 37 38 39 40 41 42 43 44 45 46 47 48 50Whether pursuing a prey or approaching a mate, natural behaviors often involve 51 continuous tracking of targets in space. Yet the neural substrates of such pursuit behavior remain 52 poorly understood. Technical limitations have prevented the study of natural pursuit behavior in 53 freely moving animals [1][2][3][4] . In this study we designed a new behavioral task for freely moving 54 mice. In this task, mice follow a continuously moving target that delivers sucrose reward. Using 55 3D motion capture, we were able to track not only the position of the animal but also the distance 56 to target, a crucial variable for accurate pursuit. This task allows us to compare, for the first 57 time, continuous behavioral variables and neural activity recorded at the same time in freely 58 moving animals. 59 We studied the contribution of the inhibitory interneuron circuit in the sensorimotor 60 striatum to pursuit behavior. The striatum is a major basal ganglia (BG) nucleus that has been 61 implicated in motor control, compulsive behavior, and habit formation [5][6][7][8][9] . A critical circuit in 62 the striatum is formed by the parvalbumin positive GABAergic fast-spiking interneurons (FSIs) 63 and striatal projection neurons (SPNs) 10, 11 . FSIs, which constitute less than 1% of the striatal 64 neuronal population, receive glutamatergic inputs from the cerebral cortex and project to many 65 SPNs, which make up over 90% of the population 12, 13 . The sensorimotor striatum is 66 characterized by the highest expression of FSIs. Reduced numbers of FSIs is associated with 67 neuropsychiatric disorders such as Tourette's syndrome and obsessive-compulsive disorder 14, 15 . 68 Behavioral studies have also implicated the FSIs in choice behavior and habitual lever pressing 69 13, 16 . 70 Recent work has shown that sensorimotor SPN activity is often highly correlated with 71 movement velocity 17, 18 , and stimulation of striatal output alters movement velocity frequency-72 dependently 19 ....

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Section: Introductionmentioning

confidence: 99%

A striatal interneuron circuit for continuous target pursuit

Kim

Hughes

et al. 2018

Preprint

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“…The neural activity that occurs in between experimenter-defined events is usually neglected. However, recent work has shown that continuous behavioral measurements with high spatial and temporal resolution can reveal previously neglected relationships between neural activity and behavior (Barter et al, 2014(Barter et al, , 2015aKim et al, 2014Kim et al, , 2019Bartholomew et al, 2016;Hughes et al, 2019). Our system demonstrates that, with continuous behavior measures, neural activity outside of these observed defined timepoints becomes intelligible.…”

Section: Discussionmentioning

confidence: 79%

A Head-Fixation System for Continuous Monitoring of Force Generated During Behavior

Hughes

Bakhurin

Barter

et al. 2020

Front. Integr. Neurosci.

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Many studies in neuroscience use head-fixed behavioral preparations, which confer a number of advantages, including the ability to limit the behavioral repertoire and use techniques for large-scale monitoring of neural activity. But traditional studies using this approach use extremely limited behavioral measures, in part because it is difficult to detect the subtle movements and postural adjustments that animals naturally exhibit during head fixation. Here we report a new head-fixed setup with analog load cells capable of precisely monitoring the continuous forces exerted by mice. The load cells reveal the dynamic nature of movements generated not only around the time of taskrelevant events, such as presentation of stimuli and rewards, but also during periods in between these events, when there is no apparent overt behavior. It generates a new and rich set of behavioral measures that have been neglected in previous experiments. We detail the construction of the system, which can be 3D-printed and assembled at low cost, show behavioral results collected from head-fixed mice, and demonstrate that neural activity can be highly correlated with the subtle, whole-body movements continuously produced during head restraint.

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“…Although there is significant literature supporting a role for these inputs in action selection or initiation, there are certainly other possibilities. One such idea is that Str inputs encode movement velocity and the resulting SNr firing rate encodes spatial position ( Kim et al, 2014 ; Bartholomew et al, 2016 ; Barter et al, 2015 ). If we apply our modeling results to this view, then we predict that the Cl − load from the GPe, by tuning SNr responses to GABAergic inputs from Str, could impact velocity with which selected movements are performed.…”

Section: Discussionmentioning

confidence: 99%

A computational model explains and predicts substantia nigra pars reticulata responses to pallidal and striatal inputs

Phillips

Rosner

Gittis

et al. 2020

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A computational model explains and 1 predicts substantia nigra pars 2 reticulata responses to pallidal and 3 striatal inputs 4 Abstract As a rodent basal ganglia (BG) output nucleus, the substantia nigra pars reticulata (SNr) 10 is well positioned to impact behavior. SNr neurons receive GABAergic inputs from the striatum 11 (direct pathway) and globus pallidus (GPe, indirect pathway). Dominant theories of action selection 12 rely on these pathways' inhibitory actions. Yet, experimental results on SNr responses to these 13 inputs are limited and include excitatory e ects. Our study combines experimental and 14 computational work to characterize, explain, and make predictions about these pathways. We 15 observe diverse SNr responses to stimulation of SNr-projecting striatal and GPe neurons, including 16 biphasic and excitatory e ects, which our modeling shows can be explained by intracellular 17 chloride processing. Our work predicts that ongoing GPe activity could tune the SNr operating 18 mode, including its responses in decision-making scenarios, and GPe output may modulate 19 synchrony and low-frequency oscillations of SNr neurons, which we con rm using optogenetic 20 stimulation of GPe terminals within the SNr. 21 22 29 also receives converging GABA A -receptor mediated synaptic inputs associated with the two major 30 transmission channels through the BG, the direct and indirect pathways. Thus, the behavioral 31 in uence of the BG is ultimately regulated by how the SNr integrates these inputs. 32 Although dominant theories of action selection strongly rely on the inhibitory actions of these 33 pathways on SNr, the details of this integration process have not been thoroughly investigated 34 and remain poorly understood. Interestingly, the inputs to SNr from the two pathways feature 35 distinct characteristics. Indirect pathway GABAergic projections to SNr arise from the external 36 segment of the globus pallidus (GPe), which engages in tonic spiking activity; occur via basket-like 37 synapses around the soma of SNr neurons; and exhibit short-term depression. Direct pathway 38 inputs are delivered by striatal (Str) neurons, which spike much more sparsely; are located on distal 39 1 of 35 Manuscript submitted to eLife dendrites; and exhibit short-term facilitation (Smith and Bolam, 1991; Von Krosigk et al., 1992; 40 Connelly et al., 2010; Lavian and Korngreen, 2016). The complexity of how these aspects interact 41 may have hindered the study of the convergence of these inputs to the SNr, yet there may be an 42 additional, easily overlooked factor in uencing the process as well: GABA dynamics (Raimondo 43 et al., 2012; Doyon et al., 2011 Doyon et al., , 2016b. The ongoing activity of GPe neurons would likely induce a 44 large tonic chloride load on SNr neurons, potentially depolarizing the GABA reversal potential, E GABA . 45 Although striatal inputs are less frequent, their impacts would be a ected by chloride accumulation, 46 which could be exaggerated in smaller dendritic co...

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Striatonigral control of movement velocity in mice

Cited by 42 publications

References 47 publications

A striatal interneuron circuit for continuous target pursuit

A striatal interneuron circuit for continuous target pursuit

A Head-Fixation System for Continuous Monitoring of Force Generated During Behavior

A computational model explains and predicts substantia nigra pars reticulata responses to pallidal and striatal inputs

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