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Gated feedforward inhibition in the frontal cortex releases goal-directed action

Abstract

Cortical circuits process both sensory and motor information in animals performing perceptual tasks. However, it is still unclear how sensory inputs are transformed into motor signals in the cortex to initiate goal-directed actions. In this study, we found that a visual-to-motor inhibitory circuit in the anterior cingulate cortex (ACC) triggers precise action in mice performing visual Go/No-go tasks. Three distinct features of ACC neurons—visual amplitudes of sensory neurons, suppression times of motor neurons and network activity from other neurons—predicted response times of the mice. Moreover, optogenetic activation of visual inputs in the ACC, which drives fast-spiking sensory neurons, prompted task-relevant actions in mice by suppressing ACC motor neurons and disinhibiting downstream striatal neurons. Notably, when mice terminated actions in response to stop signals, both motor neuron and network activity increased. Collectively, our data demonstrate that visual inputs to the frontal cortex trigger gated feedforward inhibition to initiate goal-directed actions.

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Fig. 1: Neurons in the ACC and the V1 represent sensory and motor signals in mice performing a visual GNG task.
Fig. 2: Response amplitudes of SInc neurons and suppression times of MDec neurons in the ACC predict trial-by-trial variations in response times of mice.
Fig. 3: Neighboring neurons in the ACC gate sensory-to-motor suppression.
Fig. 4: Optogenetic identification of ACC←V2M neurons representing fast-spiking SInc neurons.
Fig. 5: Optogenetic activation of V2M inputs to the ACC triggers licking response and suppression of motor neurons in the ACC.
Fig. 6: Photostimulation of V2M axons in the ACC activates task-relevant motor neurons in the DMS.
Fig. 7: Role of the ACC in inhibitory control during the stop-signal task.
Fig. 8: Schematic illustration of neural mechanisms underlying gated sensory-to-motor transformation in the ACC.

Data availability

All the representative data that support the main findings are publicly available at https://github.com/seungheelee1789/ACC_Kim. Further requests for data used in this study can be directed to the corresponding author (shlee1@kaist.ac.kr).

Code availability

All custom MATLAB codes to analyze and visualize the representative data that support the main findings are publicly available at https://github.com/seungheelee1789/ACC_Kim. Further requests for data used in this study can be directed to the corresponding author (shlee1@kaist.ac.kr).

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Acknowledgements

We thank V. Stuphorn, D. Lee, M. W. Jung and all the other Lee lab members for helpful discussions. We also thank E. Wheeler for editorial assistance. This work was supported by grants to S.-H.L. from the National Research Foundation funded by the Korea Ministry of Science and ICT (2017M3C7A1030798, 2021R1A2C3012159 and 2021R1A4A2001803), the KAIST Global Singularity Program for 2020 and the ETRI grant (19ZS1500).

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Authors

Contributions

J.-H.K. and S.-H.L. conceived and designed the experiments. J.-H.K. performed all the experiments and analyzed data. D.-H.M. and E.J. performed some of the behavior training and histological experiments. I.C. established an infrared recording system. J.-H.K. and S.-H.L. wrote the manuscript.

Corresponding author

Correspondence to Seung-Hee Lee.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Alex Kwan, Jeremy Seamans, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Performance changes of all mice used in this study across the training and test sessions of the visual detection task.

a, Learning curves (correct rates) of all the mice that were trained to perform the visual detection task in this study. Among 113 mice, 107 mice learned the task within 20 sessions (blue lines), but 6 mice failed (gray lines). Blue solid lines indicate correct rates (>70%) of mice across the final three consecutive sessions. b, The number of sessions the mice were trained for to become experts (correct rates >70%) (total 107 mice, blue). Among these, 38 mice (magenta) were used for experiments without recording and 69 mice (red) were used for experiments with recording. Individual dots denote individual mice. c, Performance of mice during the main experiments after the training. Magenta, without recording experiments; red, with recording experiments. Thin and thick lines indicate individual and averaged data. d, % of HitIL, HitPL, Miss, FA, and CR trials on the first session and the last session of the training from 107 mice. ***p < 0.05, two-sided Wilcoxon signed-rank test. Error bars show ± SEM. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 2 Effects of V2M, M2, and S1 inactivation on task performance.

a, Schematic of MUS injection (top) and histological confirmation of MUS injection (bottom) into the V2M (a1), M2 (a2), and S1 (a3). Scale bars, 1 mm. b, Example lick raster plots of mice at each injection condition (V2M MUS (b1), M2 MUS (b2), and S1 MUS (b3)). gray shade, visual stimuli; blue shade, lick response window. c, % changes of HitIL, HitPL, Miss, FA, and CR rates by MUS injection compared with no injection (No inj.). d, % of HitPL trials across the trials with different levels of luminance. Dotted lines with squares, MUS injection; solid lines with circles, no injection controls. e, Same as d, but for HitIL trials. Color represents trial types. n.s. (not significant), *p < 0.05; two-sided Wilcoxon signed-rank test with Bonferroni correction for the statistics in c-e. Error bars show ± SEM. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 3 Task-related activity in V2M and M2 neurons.

a, Schematic of in vivo multichannel recording in the V2M or M2 of mice performing the visual detection task. b, Categorization of neuronal types in the M2 (top; n = 147, 4 mice) and the V2M (bottom; n = 76, 2 mice). c-e, Same as Fig. 2j but for V2M SInc neurons (c, n = 12, 2 mice), M2 SInc neurons (d, n = 12, 4 mice), and M2 SDec neurons (e, n = 14, 4 mice). f-g, Same as Fig. 2l,n but for M2 MInc (top, n = 13, 4 mice) and MDec neurons (bottom, n = 23, 4 mice). Error bars, ± SEM. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 4 Sensory and motor signals in the population of V1 neurons.

a-c, Same as Fig. 2a-c, but for V1 SInc (n = 40) and SDec neurons (n = 27). d-g, Same as Fig. 2d-g, but for V1 MInc (red, n = 13) and MDec neurons (blue, n = 9). 11 mice. n.s. (not significant); *p < 0.05; two-sided Wilcoxon signed-rank test. Error bars, ± SEM. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 5 Analysis of orofacial movements in mice performing the visual 936 detection task.

a, Original image of the head of an example mouse taken by the infrared high-resolution camera (top left) and its video motion energy (VME, top right). Dotted rectangles, analyzed regions of interest (ROIs) for pupil size estimation and VME of the orofacial movements (moving nose, whiskers, and licking). Bottom, the estimated pupil boundary of three representative images of the mouse eye (dotted ellipses). b, Fluctuation of pupil size and VME of three orofacial movements during visual detection task. Green solid lines and green dotted lines indicate the onset of Go and middle luminance stimuli, respectively. c, Pairwise correlation coefficient (r) between licking and other behavioral movements. Black line, mean; gray lines, individual mouse. Total 6 mice. d,e, Normalized (z-scored) fluctuations of four behavioral movements around the onset of spontaneous licking (d, magenta arrowheads and vertical lines) and the onset of perceptual licking (e, brown arrowheads and vertical lines). f, High-resolution infrared images of three example mice around the lick times detected by a beam-break lickometer system during the visual detection task. The time-stamps are relative times from the detected lick times. Images in the center with red boxes indicate the images at the closest times from the detected lick times. Sampling rates of infrared imaging and beam break lickometer systems are 30 Hz and 30 kHz, respectively. See also Supplementary Video 1.

Extended Data Fig. 6 Correlation between licking response times (lick latency) and activities of SInc and SDec neurons in the ACC and the V1.

a, Fraction of ACC SInc and SDec neurons that show significant correlation (Pearson correlation, p < 0.05) between visually evoked activities and lick latencies. Filled circles indicate neurons with significant correlation b, Same as a, but for V1 SInc and SDec neurons. c-g, Same as Fig. 2j, but for total population of ACC SInc neurons (c), total population of ACC SDec neurons (d), example V1 SInc neuron (e), total population of V1 SInc neurons (f), and total population of V1 SDec neurons (g). Inset in e, averaged spike waveform of the example neuron. Scale bar, 1 ms. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 7 Learning-induced changes in ACC activities.

a, Identification of task-relevant neurons in the ACC of untrained (left; n = 111, 3 mice) and trained mice (right; n = 550, 21 mice). b, z-scored population activity of ACC neurons showing visual responses from untrained (left) and trained mice (right). Green arrowheads indicate the time of stimuli onset. c, Cumulative distribution (left) and population average (right) of absolute visual signals (|z|, 0 ~ 0.5 s after stimuli onset) in ACC neurons from untrained (gray) and trained mice (black). 111 neurons from 3 untrained mice and 550 neurons from 21 trained mice d,e, Same as b,c, but for motor signals of ACC neurons from untrained and trained mice. Absolute motor signals are calculated during −0.5 ~ 0.5 s from lick onset. Magenta arrowheads indicate lick onset time. n.s. (not significant); *p < 0.05, **p < 0.01, ***p < 0.001; two-sample Kolmogorov-Smirnov test (CDFs in panels c,e) and two-sided Wilcoxon rank-sum test (bar graphs in panel c,e). Error bars, ± SEM. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 8 Correlation between licking response times (lick latency) and ramping activities of motor neurons in the ACC and the V1.

a, Fraction of MInc and MDec neurons that show ramp-to-threshold premotor activity in ACC and V1. Red and blue, neurons that show significant correlation between time to cross the lower threshold and lick latencies. b-d, Color-coded PSTHs (b), scatter plots (c), and ramp rates (d) of ACC MDec neurons that show significant correlation in a (neurons in the blue box) across trials with early-to-late lick latencies. Note that time to cross thresholds (either 1 or 8) in ramping-down activity of ACC MDec neurons showed a linear relationship with lick latency at a slope near 1, while ramping rates (the slopes of ramping-down activity) were constant. e-m, Same as Fig. 2l-n, but for ACC MInc (e-g), V1 MDec (h-j) and MInc neurons (k-m). For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 9 Photostimulation of V2M axons in the ACC outside the task, and effects of photostimulation of DMS-projecting ACC neurons on the neural activities in neighboring ACC neurons compared to photostimulation of V2M-recipient ACC neurons.

a, Left, mean lick rate (Hz) during photostimulation of V2M axons in the ACC outside the visual detection task (17 sessions with 7 mice). Right, mean lick rate during pre (2 s), on (1 s), and post period (2 s) from the onset of blue light. b, Activity changes in ACC neurons by photostimulation of the DMS-projecting ACC neurons (left) and the V2M-recipient ACC neurons (right). c, Normalized activity (z) changes (Δactivity, laserON – laserOFF) by photostimulation of the DMS-projecting (left) and the V2M-recipient ACC neurons (right). Red and blue circles indicate increased and suppressed neurons, respectively. Note that photostimulation of the V2M-recipient ACC neurons drives much suppressive influence. d, Cumulative distribution of Δactivity in ACC neurons from the DMS-projecting (solid line) and V2M-recipient ACC neurons (dotted line). Data about photostimulation of V2M-recipient ACC neurons are the same as in Fig. 5f,g. n.s. (not significant); ***p < 0.001; two-sided Wilcoxon signed-rank test (a); two-sample Kolmogorov-Smirnov test (d). Error bars show ± SEM. For detailed statistics information, see Supplementary Table 5.

Extended Data Fig. 10 Learning the stop-signal task.

a, Learning curves of mice performing the visual detection task (correct rates; 15 mice). All mice learned the visual detection task. Blue, mice that learned the stop-signal task; gray, mice that did not learn the stop-signal task. Dark lines indicate correct rates of final 3 consecutive sessions (> 70 %) at the end of the visual detection task. b, Correct rates of the mice that learned (light blue solid lines) and did not learn (gray dotted lines) the stop-signal task. c, Successful stop rates (%) during stop-signal trials. 12 mice learned the task (light blue solid lines), but 3 mice failed to learn the task (gray dotted lines). Blue solid lines indicate successful stop rates (> 50%) of final 2 consecutive sessions. d, The number of sessions required to learn the visual detection task (15 mice) and the stop-signal task (12 mice). Individual dots denote individual mice. Error bars show ± SEM.

Supplementary information

Supplementary Information

Supplementary Tables 1–6.

Reporting Summary

Supplementary Video 1

Orofacial movements of task-performing mouse. Infrared imaging of four orofacial movements (pupil size, whisker movement, nose movement and licking) from a mouse performing the visual detection task. Colored boxes indicate ROIs measured for each orofacial movement (black for pupil size, magenta for whisking movement, red for nose movement and cyan for licking). White ellipse, pupil boundary; white cross, center of pupil; green vertical lines, onsets of visual stimuli; blue dots, licking.

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Kim, JH., Ma, DH., Jung, E. et al. Gated feedforward inhibition in the frontal cortex releases goal-directed action. Nat Neurosci 24, 1452–1464 (2021). https://doi.org/10.1038/s41593-021-00910-9

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