Abstract
Cognitive inhibitory control, the ability to rapidly suppress responses inappropriate for the context, is essential for flexible and adaptive behavior. Although most studies on inhibitory control have focused on the fronto-basal-ganglia circuit, we found that rapid behavioral stopping is enabled by neuronal inhibition in the basal forebrain (BF). In rats performing the stop signal task, putative noncholinergic BF neurons with phasic bursting responses to the go signal were nearly completely inhibited by the stop signal. The onset of BF neuronal inhibition was tightly coupled with and temporally preceded the latency to stop, the stop signal reaction time. Artificial inhibition of BF activity in the absence of the stop signal was sufficient to reproduce rapid behavioral stopping. These results reveal a previously unknown subcortical mechanism of rapid inhibitory control by the BF, which provides bidirectional control over the speed of response generation and inhibition.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Boucher, L., Palmeri, T.J., Logan, G.D. & Schall, J.D. Inhibitory control in mind and brain: An interactive race model of countermanding Saccades. Psychol. Rev. 114, 376–397 (2007).
Aron, A.R. From reactive to proactive and selective control: developing a richer model for stopping inappropriate responses. Biol. Psychiatry 69, e55–e68 (2011).
Schall, J.D. & Godlove, D.C. Current advances and pressing problems in studies of stopping. Curr. Opin. Neurobiol. 22, 1012–1021 (2012).
Stuphorn, V. Neural mechanisms of response inhibition. Curr. Opin. Behav. Sci. 1, 64–71 (2014).
Aron, A.R. et al. Converging evidence for a fronto-basal-ganglia network for inhibitory control of action and cognition. J. Neurosci. 27, 11860–11864 (2007).
Logan, G.D. & Cowan, W.B. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295–327 (1984).
Mayse, J.D., Nelson, G.M., Park, P., Gallagher, M. & Lin, S.-C. Proactive and reactive inhibitory control in rats. Front. Neurosci. 8, 104 (2014).
Gauggel, S., Rieger, M. & Feghoff, T.-A. Inhibition of ongoing responses in patients with Parkinson's disease. J. Neurol. Neurosurg. Psychiatry 75, 539–544 (2004).
Mirabella, G. et al. Deep brain stimulation of subthalamic nuclei affects arm response inhibition in Parkinson's patients. Cereb. Cortex 22, 1124–1132 (2012).
McAlonan, G.M. et al. Age-related grey matter volume correlates of response inhibition and shifting in attention-deficit hyperactivity disorder. Br. J. Psychiatry 194, 123–129 (2009).
Andrés, P., Guerrini, C., Phillips, L.H. & Perfect, T.J. Differential effects of aging on executive and automatic inhibition. Dev. Neuropsychol. 33, 101–123 (2008).
Coxon, J.P., Van Impe, A., Wenderoth, N. & Swinnen, S.P. Aging and inhibitory control of action: cortico-subthalamic connection strength predicts stopping performance. J. Neurosci. 32, 8401–8412 (2012).
Hu, S., Chao, H.H., Zhang, S., Ide, J.S. & Li, C.S. Changes in cerebral morphometry and amplitude of low-frequency fluctuations of BOLD signals during healthy aging: correlation with inhibitory control. Brain Struct. Funct. 219, 982–994 (2014).
Eagle, D.M. et al. Stop-signal reaction-time task performance: role of prefrontal cortex and subthalamic nucleus. Cereb. Cortex 18, 178–188 (2008).
Baunez, C. et al. Effects of STN lesions on simple vs choice reaction time tasks in the rat: preserved motor readiness, but impaired response selection. Eur. J. Neurosci. 13, 1609–1616 (2001).
Duann, J.R., Ide, J.S., Luo, X. & Li, C.S. Functional connectivity delineates distinct roles of the inferior frontal cortex and presupplementary motor area in stop signal inhibition. J. Neurosci. 29, 10171–10179 (2009).
Li, C.S., Yan, P., Sinha, R. & Lee, T.W. Subcortical processes of motor response inhibition during a stop signal task. Neuroimage 41, 1352–1363 (2008).
Schmidt, R., Leventhal, D.K., Mallet, N., Chen, F. & Berke, J.D. Canceling actions involves a race between basal ganglia pathways. Nat. Neurosci. 16, 1118–1124 (2013).
Freund, T.F. & Gulyás, A.I. GABAergic interneurons containing calbindin D28K or somatostatin are major targets of GABAergic basal forebrain afferents in the rat neocortex. J. Comp. Neurol. 314, 187–199 (1991).
Gritti, I., Mainville, L., Mancia, M. & Jones, B.E. GABAergic and other noncholinergic basal forebrain neurons, together with cholinergic neurons, project to the mesocortex and isocortex in the rat. J. Comp. Neurol. 383, 163–177 (1997).
Richardson, R.T. & DeLong, M.R. Context-dependent responses of primate nucleus basalis neurons in a go/no-go task. J. Neurosci. 10, 2528–2540 (1990).
Lin, S.-C. & Nicolelis, M.A.L. Neuronal ensemble bursting in the basal forebrain encodes salience irrespective of valence. Neuron 59, 138–149 (2008).
Avila, I. & Lin, S.-C. Distinct neuronal populations in the basal forebrain encode motivational salience and movement. Front. Behav. Neurosci. 8, 421 (2014).
Avila, I. & Lin, S.-C. Motivational salience signal in the basal forebrain is coupled with faster and more precise decision speed. PLoS Biol. 12, e1001811 (2014).
Lin, S.-C., Gervasoni, D. & Nicolelis, M.A.L. Fast modulation of prefrontal cortex activity by basal forebrain noncholinergic neuronal ensembles. J. Neurophysiol. 96, 3209–3219 (2006).
Nguyen, D.P. & Lin, S.-C. A frontal cortex event-related potential driven by the basal forebrain. Elife 3, e02148 (2014).
Stuphorn, V., Taylor, T.L. & Schall, J.D. Performance monitoring by the supplementary eye field. Nature 408, 857–860 (2000).
Matsumoto, M. & Hikosaka, O. Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature 459, 837–841 (2009).
Bromberg-Martin, E.S., Matsumoto, M. & Hikosaka, O. Distinct tonic and phasic anticipatory activity in lateral habenula and dopamine neurons. Neuron 67, 144–155 (2010).
Butovas, S. & Schwarz, C. Spatiotemporal effects of microstimulation in rat neocortex: a parametric study using multielectrode recordings. J. Neurophysiol. 90, 3024–3039 (2003).
Butovas, S., Hormuzdi, S.G., Monyer, H. & Schwarz, C. Effects of electrically coupled inhibitory networks on local neuronal responses to intracortical microstimulation. J. Neurophysiol. 96, 1227–1236 (2006).
Long, J.D. & Carmena, J.M. Dynamic changes of rodent somatosensory barrel cortex are correlated with learning a novel conditioned stimulus. J. Neurophysiol. 109, 2585–2595 (2013).
Watanabe, K., Lauwereyns, J. & Hikosaka, O. Neural correlates of rewarded and unrewarded eye movements in the primate caudate nucleus. J. Neurosci. 23, 10052–10057 (2003).
Jin, X., Tecuapetla, F. & Costa, R.M. Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences. Nat. Neurosci. 17, 423–430 (2014).
Jahfari, S. et al. How preparation changes the need for top-down control of the basal ganglia when inhibiting premature actions. J. Neurosci. 32, 10870–10878 (2012).
Jin, X. & Costa, R. Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).
Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates (Academic Press, 2007).
Wiest, M.C., Bentley, N. & Nicolelis, M.a.L. Heterogeneous integration of bilateral whisker signals by neurons in primary somatosensory cortex of awake rats. J. Neurophysiol. 93, 2966–2973 (2005).
Acknowledgements
We thank P.R. Rapp, J. Long, S.M. Raver and V. Stuphorn for critical discussions and reading of the manuscript, and H.M.V. Manzur and B. Brock for technical support. This research was funded by the Intramural Research Program of the National Institute on Aging and by a NARSAD Young Investigator Award to S.-C.L., and grants from the US National Institutes of Health (P01 AG09973 to M.G. and F31 AG045039 to J.D.M.).
Author information
Authors and Affiliations
Contributions
J.D.M., G.M.N. and S.-C.L. designed the study. J.D.M. and G.M.N. performed experiments and collected data. I.A. performed the electrical stimulation experiment. J.D.M., G.M.N. and S.-C.L. analyzed data. J.D.M. and S.-C.L. wrote the manuscript with input from G.M.N, I.A. and M.G.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Comparison of behavioral performance in two variants of SST
(a) Distributions of SSRT estimates in the two SST variants (Stop Reward: n = 19 rats, 543 sessions; Stop No Reward: n = 8 rats, 466 sessions). While rats were not required to stop in the Stop No Reward Task, they nonetheless showed rapid behavioral stopping to the stop signal, with similar SSRTs as in the Stop Reward Task. (b) The proportion (mean ± s.e.m.) of failure-to-stop (FS) and successful stop (SS) trials in the two SST variants at each of the five SSDs. SSD1 is always 0ms such that the tone and light were presented simultaneously. The remaining four SSDs were evenly spaced in 40ms steps and adjusted by experimenters between sessions. Rats failed to stop more often at longer SSDs in both variants of the SST. The similarity of SSRTs in both SST variants suggests that the rapid behavioral stopping in the SST does not depend on the knowledge that successful stopping is rewarded. (c) Wait time, the latency between stop signal onset and fixation port exit, was significantly longer in successful stop trials when stopping was rewarded (independent samples t-test). Individual animals (points) are plotted along with group mean (red), ± 1.96 s.e.m. (red shaded area) and ± 1 s.d. (blue) (n=19, 8; number of animals in the two SST variants) (d). The proportion of stop trials in which rats approached the reward port and licked at least three times was significantly higher in the Stop Reward Task than in the Stop No Reward Task. Conventions as in panel (c).
Supplementary Figure 2 Histological reconstruction of BF recording electrode locations
Summary of the histological reconstruction in the current study, with each pair of colored boxes representing the locations of BF recording electrodes in one rat. Electrodes are located throughout multiple subregions including the ventral part of globus pallidus (GP), caudal ventral pallidum (VP), substantia innominata (SI), nucleus basalis of Meynert (NBM, or B), magnocellular preoptic nucleus (MCPO) and horizontal limb of the diagonal band (HDB), consistent with the widespread spatial distribution of cortically-projecting BF neurons.
Supplementary Figure 4 Comparison of reentry behavior in the two SST variants and in the BF electrical stimulation experiment
(a) The distributions of fixation port exit and reentry events in all reentry trials in the three task conditions (Stop Reward, Stop No Reward, and BF electrical stimulation) relative to SSRT, reproducing Fig. 6b and Fig. 8e. In all cases, fixation port exit and reentry events were most common just before and after SSRT, respectively. These distributions were normalized by the number of reentry trials in each task, so the area under each distribution summed to one. (b) The distributions from (a) were normalized by the number of stop trials (or stimulated trials) in each task. The distributions of fixation port exit times in all stop trials were shown in black, and the areas under each black trace summed to one. (c) The distributions of fixation port exit in reentry trials (blue traces in (b)) were normalized by the distributions of fixation port exit in all stop trials (black traces in (b)) in each task at each 25ms bin. Fixation port exits at or around SSRT in stop trials were most likely to become reentry trials in all three task conditions.
Results in (b) and (c) show that fixation port reentries were most frequent in the Stop Reward Task, followed by BF electrical stimulation, and least frequent in the Stop No Reward Task. The difference of reentry frequency between the two variants of SST likely reflected the modulation of task contingency, such that there was a stronger motivation to reenter when successful stopping leads to reward. Despite the lower frequency of reentry events in the Stop No Reward Task, the reversal of accelerometer signals at SSRT was similarly evident in most failure-to-stop trials in the Stop No Reward Task (Fig. 6c, 6d; 13/15 sessions from the Stop No Reward Task). The reentry behaviors and accelerometer signals together suggest that, in both SST variants, movement patterns in failure-to-stop trials began to differ from latency-matched go trials at SSRT. The difference between the two SST variants, however, was that the reversals of accelerometer signals at SSRT were converted at a lower rate to fixation port reentries in the Stop No Reward Task compared to the Stop Reward Task.
Supplementary Figure 5 Additional information related to the BF electrical stimulation experiments
(a) Response of bursting (n=21) and other (n=23) BF neurons to the go sound (left panel) and brief BF electrical stimulation (middle and right panels show different time scales, 11 sessions from 4 rats). Responses of individual neurons are color-coded in the middle and lower rows for bursting and other neurons, respectively. BF bursting neurons, but not other neurons, demonstrated near complete inhibition in response to BF electrical stimulation after a brief rebound excitation. (b) Schematic of a variant of the electrical stimulation experiment, which was the same as the version shown in Fig.8a except that the stimulated trials were rewarded as the non-stimulated trials, so that the presence of BF stimulation did not change reward contingency. In this task configuration, even if rats can perceive BF stimulation as a sensory cue, there was no reason to use perceived BF stimulation to modify behavior. (c) Distribution of estimated SSRT in two variants of the stimulation experiment. Stimulation slowed down fixation port exits in 18/20 sessions (7 rats) when stimulated trials were not rewarded (gray), and in 9/18 sessions (3 rats) when stimulated trials were rewarded (blue). SSRT estimates in the two variants of the stimulation experiment were not different (mean SSRT 111ms vs. 108ms, F(1,25)=0.01, p=0.91).
Supplementary Figure 6 A model for bidirectional control of the speed of response generation and inhibition by BF neurons
(a) Simple decision-making processes, such as those measured by RT, are commonly modeled by an activity accumulation process of a hypothesized decision unit that is likely located in the fronto-basal-ganglia circuit. In response to the go signal, a behavioral response is initiated once the activity of the decision unit reaches a threshold. Given the same go signal, the activity accumulation slope is variable from trial to trial (middle panel), resulting in variable RTs (top panel). This activity accumulation process likely depends on BF bursting activity (bottom panel) because successful detection in a near-threshold auditory detection task is coupled with the presence of BF bursting, while failed detection is coupled with the absence of BF bursting23. (b) Blue and green traces depict neuronal and behavioral responses to two go signals that differ only in the associated amount of reward. A stronger BF bursting responses to the go signal is coupled with a faster and more precise RT distribution, suggesting that the strength of BF bursting modulates the slope of activity accumulation in the decision unit25. (c) In the SST, the stop signal elicits near complete BF neuronal inhibition (red trace, bottom panel), which is coupled with and slightly precedes rapid behavioral stopping and SSRT (top panel). We hypothesize that BF neuronal inhibition leads to rapid behavioral stopping by preventing further activity accumulation in trials that have not reached threshold (middle panel). The short delay (~10ms) between the onset of BF neuronal inhibition and SSRT requires that BF can modulate cortical activity within the same delay. Consistent with such a requirement, BF bursting neurons can rapidly modulate cortical activity within 5-10ms to generate an event-related potential28. (d) When the stop signal is presented earlier (compared to panel (c)), BF neuronal inhibition also occurs earlier and prevents more trials from reaching the threshold, resulting in more successful stop trials. Our results also suggest that the BF bursting response and BF neuronal inhibition can be independently controlled by the go signal and the stop signal, respectively, such that SSRT is not affected by the delay between go and stop signals. Together, these results support the unified hypothesis that BF bursting neurons serve as a bidirectional gain modulation signal for the decision-making process.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–6 (PDF 865 kb)
Rights and permissions
About this article
Cite this article
Mayse, J., Nelson, G., Avila, I. et al. Basal forebrain neuronal inhibition enables rapid behavioral stopping. Nat Neurosci 18, 1501–1508 (2015). https://doi.org/10.1038/nn.4110
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nn.4110
This article is cited by
-
The behavioral signature of stepwise learning strategy in male rats and its neural correlate in the basal forebrain
Nature Communications (2023)
-
Electrical stimulation of the nucleus basalis of meynert: a systematic review of preclinical and clinical data
Scientific Reports (2021)
-
Basal forebrain subcortical projections
Brain Structure and Function (2019)
-
Parallel processing by cortical inhibition enables context-dependent behavior
Nature Neuroscience (2017)
-
Response inhibition in Parkinson’s disease: a meta-analysis of dopaminergic medication and disease duration effects
npj Parkinson's Disease (2017)