Primate Nigrostriatal Dopamine System Regulates Saccadic Response Inhibition

Ogasawara, Takaya; Nejime, Masafumi; Takada, Masahiko; Matsumoto, Masayuki

doi:10.1016/j.neuron.2018.10.025

Cited by 30 publications

(36 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frontal Eye Field decrease activity in less than 10 ms before SSRT (Hanes et al, 1998), dopaminergic neurons in rodent substantia nigra and striatum increase activity only 12 ms before SSRT (Ogasawara et al, 2018), TMS at ~25 ms before SSRT over human Intraparietal Sulcus prolongs SSRT (Osada et al, 2019), and P300 human EEG activity ~300 ms after the Stop signal relates to the stopping latency (Wessel and Aron, 2015). Whereas the rather late timing of some of these results might be related to processes such as monitoring and feedback (Huster et al, 2019) as has been ascribed to brain signatures that modulate after SSRT (Logan et al, 2015;Schall and Boucher, 2007), our earlier latencies for prefrontal bursts, TMS-MEP and muscle…”

Section: Discussionmentioning

confidence: 99%

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Jana

Hannah

Muralidharan

et al. 2020

eLife

131

161

View full text Add to dashboard Cite

Action-stopping is a canonical executive function thought to involve top-down control over the motor system. Here we aimed to validate this stopping system using high temporal resolution methods in humans. We show that, following the requirement to stop, there was an increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade supports a physiological model of action-stopping, and partitions it into subprocesses that are isolable to different nodes and are more precise than the behavioral latency of stopping. Variation in these subprocesses, including at the single-trial level, could better explain individual differences in impulse control.

show abstract

Section: Discussionmentioning

confidence: 99%

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Jana

Hannah

Muralidharan

et al. 2020

eLife

131

161

View full text Add to dashboard Cite

show abstract

“…This model specifies the chronometrics of stopping in more detail than extant human models, and, more generally, raises questions about the timing reported in some other studies. For example, other research has shown that movement neurons in monkey Frontal Eye Field decrease activity <10 ms before SSRT 42 , dopaminergic neurons in rodent substantia nigra and striatum increase activity 12 ms prior to SSRT 43 , TMS at ∼25 ms before SSRT over human Intraparietal Sulcus prolongs SSRT 44 , and that P300 human EEG activity ∼300 ms after the Stop signal relates to stopping speed 45 . Our observations of shorter latencies for prefrontal bursts, TMS-MEP and muscle CancelTime, raise questions about what is reflected in these late neural activities.…”

Section: Discussionmentioning

confidence: 99%

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Jana

Hannah

Muralidharan

et al. 2019

Preprint

View full text Add to dashboard Cite

15Action-stopping is a canonical executive function thought to involve top-down control 16 over the motor system. Here we aimed to validate this stopping system using high temporal 17 resolution methods in humans. We show that, following the requirement to stop, there was an 18 increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal 19 gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact 20 of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected 21 in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade 22 confirms a detailed model of action-stopping, and partitions it into subprocesses that are isolable 23 to different nodes and are more precise than the behavioral speed of stopping. Variation in these 24 subprocesses, including at the single-trial level, could better explain individual differences in 25 impulse control. 26 27 28The ability to control one's actions and thoughts is important for our daily lives; for 29 example: changing gait when there is an obstacle in the path 1 , resisting the temptation to eat 30 when on a diet 2 , overcoming the tendency to say something hurtful 3 . While many processes 31 contribute to such forms of control, one important process is response inhibition -the prefrontal 32 (top-down) stopping of initiated response tendencies 4 . In the laboratory, response inhibition is 33 often studied with the stop-signal task 5 . On each trial, the participant initiates a motor response, 34 and then, when a subsequent Stop signal occurs, tries to stop. From the behavioral data one can 35 estimate a latent variable; the speed of stopping known as Stop Signal Reaction Time (SSRT), 36 which is typically 200-250 ms in healthy adults 5 . SSRT has been useful in neuropsychiatry 37where it is often longer for patients vs. controls [6][7][8][9][10][11] . The task has also provided a rich test-bed, 38 3 across species, for mapping out a putative neural architecture of prefrontal-basal-ganglia-regions 39 for rapidly suppressing motor output areas 6,12,13 . Given this rich literature, this task is one of the 40 few paradigms included in the longitudinal Adolescent Brain Cognitive Development study 14 of 41 10,000 adolescents over 10 years. 42Against this background, a puzzle is that the relation between SSRT and 'real-world' 43 self-reported impulsivity is often weak 15-20 . One explanation is that SSRT may not accurately 44 index the brain's true stopping speed. Indeed, recent mathematical modelling of behavior during 45 the stop-signal task suggests that standard calculations of SSRT may overestimate the brain's 46 stopping speed by ~100 ms 15 [also see 21 ]. Further, in a recent study 22 , electromyographic (EMG) 47 recordings revealed an initial increase in EMG activity in response to the Go cue, followed by a 48 sudden decline at ~150 ms after the Stop signal. This decline in EMG could be because of the 49 Stop process 'kicking in'...

show abstract

“…While many studies have focused on the role of the striatum in selecting rewarded actions (16,18,25) and stimuli (49), fewer have studied its role in avoiding low-value actions (50)(51)(52)(53) and stimuli (54). Here, our goal was to understand how activity in DMS dSPNs and iSPNs influences the ability to suppress an initially encountered low-value choice in order to make a subsequent high-value choice.…”

Section: Discussionmentioning

confidence: 99%

Choice suppression is achieved through opponent but not independent function of the striatal indirect pathway in mice

Delevich

Hoshal

Collins

et al. 2019

Preprint

View full text Add to dashboard Cite

SignificanceThere is significant clinical value to understanding how we reject or suppress making a choice, and the dorsomedial striatum (DMS) is a critical arbiter of this process. While optogenetic stimulation of DMS indirect pathway spiny projection neurons (iSPNs) can inhibit movement, it is unclear how iSPNs contribute to suppression of choices. A simple 'no go' function has been proposed for iSPNs, suggesting their activity enables choice suppression, but we found that chemogenetic activation of iSPNs impaired suppression of low value choices. This effect was explained by an algorithmic model in which the relative output of direct pathway (dSPNs) and iSPNs determines choice. Our findings have important implications for designing interventions to improve maladaptive decision-making in psychiatric disorders and addiction. AbstractThe dorsomedial striatum (DMS) plays a key role in action selection, but little is known about how direct and indirect pathway spiny projection neurons (dSPNs and iSPNs) contribute to serial decision-making. A popular 'select/suppress' heuristic proposes that dSPNs encode selected actions while iSPNs encode the suppression of alternate actions. Here, we used pathway-specific chemogenetic manipulation during serial choice behavior to test predictions generated by the 'select/suppress' heuristic versus a network inspired OpAL (Opponent Actor Learning) model of basal ganglia function in which the relative balance of dSPN and iSPN output determines choice. In line with OpAL predictions, chemogenetic activation, not inhibition, of iSPNs disrupted learned suppression of nonrewarded choices. These results cannot be explained by the classic view that choice suppression is an extension of iSPN stopping or 'no go' function.Together, our computational and empirical data challenge the 'select/suppress' interpretation of striatal function in the context of choice behavior and highlight the ability of iSPNs to modulate choice exploration.

show abstract

Primate Nigrostriatal Dopamine System Regulates Saccadic Response Inhibition

Cited by 30 publications

References 70 publications

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

Choice suppression is achieved through opponent but not independent function of the striatal indirect pathway in mice

Contact Info

Product

Resources

About