Dynamics of mesoscale brain network during visual discrimination learning revealed by chronic, large-scale single-unit recording
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China
- University of Chinese Academy of Sciences, China
Figures
Figure 1 with 2 supplements
High-throughput recording in mice performing a visual Go/No-Go task.
(A) Schematic of the task. (B) Average correct rate during training (mean ± SEM, n = 7 mice). (C) Photos showing the uFINE-M shanks and recording sites. (D) Schematic showing the implantation sites of uFINE-M arrays, along with example single-unit waveforms recorded from each brain region. Brain section images are taken from The Scalable Brain Atlas (Bakker et al., 2015) derived from data in Lein et al., 2007. (E) Example spike rasters during two trials. (F) Top, the number of single units recorded in each brain region. Each data point represents data from an individual recording session. Bottom, the total number of single units recorded during training (n = 5 mice). Each symbol represents data from an individual mouse.
Figure 1—figure supplement 1
Implantation depth accuracy test.
(A) Example section showing recovered array shanks in the cortex. The actual implantation depth was defined according to the location of the guiding hole at the tip of the shanks. (B) Errors in implantation depth, as the difference between actual depth and original target depth (negative values represent shallower depth). A slight tendency for deeper errors in cortical implantation (1100 μm) and shallower errors in subcortical implantation (2100 μm) was observed.
Figure 1—figure supplement 2
Behavioral performance in daily sessions.
(A-C) D-prime curves for the three mice in the learning group. Each data point represents the behavioral discriminability (Wickens, 2001) calculated from the 10 trials before and 10 trials after the corresponding trial. Although the d-prime overall increased with training, the task performance showed nonnegligible fluctuations in each session. The trials after the last licking (indicated by vertical dashed lines) in each session were excluded. Colored segments mark the data used in subsequent analyses. For data at the early training stage, trials in early sessions with d-prime < 2 (orange lines) were used, and for expert stage data, trials in late sessions with d-prime > 3 (green lines) were used.
Figure 2 with 2 supplements
Activity changes throughout task learning.
(A) Averaged firing rate aligned to the visual stimulus onset for all CR trials and Hit trials in the early and expert stages (n = 118 early CR and 828 early Hit trials from 7 sessions of 3 mice, 610 expert CR trials and 677 expert Hit trials from the same mice). Shading, SEM. (B) Left, distribution of activity onset timing across time. Right, activity onset timing of each region. Each data point represents data from a neuron. (C) Same as B but for Hit trials. (D) Comparison of activity onset timing between the early and expert stages. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. Error bars, SEM.
Figure 2—figure supplement 1
Average firing rate from the bootstrap-resampled datasets.
The mean traces of all bootstrap-resampled datasets (n = 500 datasets) were plotted, and were highly similar to original data. Firing rate traces were aligned to the visual stimulus onset. Shading, SEM.
Figure 2—figure supplement 2
Spread of peak activation times across brain regions.
Violin plot showing the distribution of differences between peak activation times of each region pairs at different learning stages. Thick lines in the boxes indicate medians, thin lines in the boxes indicate quartiles. ***p < 0.001, t-test with Sidak correction.
Figure 3
Definition of functional connection.
(A) Schematic of data processing flow of calculating functional connectivity. For each 200-ms time window (t), cross-correlation scores were calculated between spike trains of neuron pairs and the percentage of neuron pairs that showed significant cross-correlations was treated as functional connection strength between brain regions. The regional connection matrix was then ranked from 1 to 10 to evaluate the relative importance of regional connection compared with other connections within the same time window of the same trial. (B) Details of processing stages. (C) Connection rank matrix calculated from the example data in A. The individual regional connection strength measurement (left) was used in Figure 6, Figure 6—figure supplement 1. The summed regional connection strength measurement (middle and right) was used in Figures 3—5 and 7, Figure 4—figure supplement 1, Figure 5—figure supplements 1–3.
Figure 4 with 2 supplements
Ranking dynamics in CR trials during learning.
(A) Input/output ranking dynamics during early and expert CR trials. (B) Average rank of each brain region in the early stimulus period (0–400 ms after stimulus onset), late stimulus period (400–800 ms after stimulus onset), early response period (800–1800 ms after stimulus onset), and late response period (1800–2800 ms after stimulus onset) of early and expert CR trials, mapped on brain atlas (Bakker et al., 2015). ΔRank represents the rank change between the expert and early stages. n = 118 early CR trials from 7 sessions of 3 mice, and 610 expert CR trials from 6 sessions of same mice. Error bars, SEM.
Figure 4—figure supplement 1
Ranking dynamics in CR trials during learning.
(A) Average input rank of each brain region in the early stimulus period (0-400 ms after stimulus onset), late stimulus period (400–800 ms after stimulus onset), early response period (800–1800 ms after stimulus onset), and late response period (1800–2800 ms after stimulus onset) of early and expert CR trials. (B) Same as A but for output ranks. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. # indicates the rank value significantly different (p < 0.05, t-test) from 5 (average level of random data, indicated by dashed lines). n = 118 early CR trials from 7 sessions of 3 mice, and 610 expert CR trials from 6 sessions of same mice. Error bars, SEM.
Figure 4—figure supplement 2
Motion energy in CR trials during learning.
(A) Example video frame showing regions of interest (ROIs) used in oral-facial movement analysis to measure motion energy (Ramseyer, 2020). (B) Example showing the absolute value of brightness difference between consecutive frames. (C-E) Average motion energy of mouse oral-facial movements in CR, Hit and FA trials. n = 466, 1422, 1184 early CR, Hit, FA trials and 730, 774, 92 expert CR, Hit, FA trials from 3 mice. *, p < 0.05, t-test with Sidak correction. Error bars, SEM.
Figure 5 with 3 supplements
Ranking dynamics in Hit trials during learning.
(A) Input/output ranking dynamics in early and expert Hit trials. (B) Average rank of each brain region in the early stimulus period (0–400 ms after stimulus onset), late stimulus period (400–800 ms after stimulus onset), early response period (800–1800 ms after stimulus onset), and late response period (1800–2800 ms after stimulus onset) of early and expert Hit trials, mapped on brain atlas (Bakker et al., 2015). ΔRank represents the rank change between the expert and early stages. n = 828 early Hit trials from 7 sessions of 3 mice, and 677 expert Hit trials from 6 sessions of 3 mice. Error bars, SEM.
Figure 5—figure supplement 1
Ranking dynamics in Hit trials during learning.
(A) Average input rank of each brain region in the early stimulus period (0–400 ms after stimulus onset), late stimulus period (400–800 ms after stimulus onset), early response period (800–1800 ms after stimulus onset), and late response period (1800–2800 ms after stimulus onset) of early and expert Hit trials. (B) Same as A but for output ranks. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. # indicates the rank value significantly different (p < 0.05, t-test) from 5 (average level of random data, indicated by dashed lines). n = 828 early Hit trials from 7 sessions of 3 mice, and 677 expert Hit trials from 6 sessions of 3 mice. Error bars, SEM.
Figure 5—figure supplement 2
Ranking dynamics in Hit trials during learning, with spike time aligned to the first lick of each trial.
Input/output ranking dynamics in early and expert Hit trials, with spike time aligned to the first lick of each trial and the cross-correlation analyses recalculated. Horizontal dashed lines indicate the average level of random data.
Figure 5—figure supplement 3
Ranking dynamics in fruitless-learning Hit trials compared to expert Hit trials.
(A) Input/output ranking dynamics in the fruitless-learning Hit trials and expert Hit trials. (B) Average input rank of each brain region in the early stimulus period (0–400 ms after stimulus onset), late stimulus period (400–800 ms after stimulus onset), early response period (800–1800 ms after stimulus onset), and late response period (1800–2800 ms after stimulus onset) of the fruitless-learning and expert Hit trials. (C) Same as B but for output ranks. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. # indicates the rank value is significantly different (p < 0.05, t-test) from 5 (average level of random data, indicated by dashed lines). n = 844 Hit trials from 8 sessions of 2 fruitless-learning group mice, and 677 expert Hit trials from 6 sessions of 3 normal-learning group mice. Error bars, SEM.
Figure 6 with 1 supplement
Rank increase in CR trials was attributed to elevated input/output rank from/to other regions.
(A) Average functional connection rank changes for the four regions (V1, V2M, M2, and OFC) that showed increased rank values in the stimulus period of CR trials during visual learning. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. (B) Same as A but for the response period of CR trials. n = 118 early CR trials from 7 sessions of 3 mice, and 610 expert CR trials from 6 sessions of 3 mice. Dashed lines at rank 5 indicate the average level of random data. Error bars, SEM. Stim, stimulus period. Res, response period.
Figure 6—figure supplement 1
Rank decrease in CR trials was attributed to reduced input/output rank from/to other regions.
(A) Average input rank changes for the three regions (V2L, MDTh, and striatum) that showed decreased rank values in CR trials during visual learning. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. (B) Same as A but for the response period of CR trials. (C-D) Same as A-B but for output ranks. n = 118 early CR trials from 7 sessions of 3 mice, and 610 expert CR trials from 6 sessions of 3 mice. Dashed lines at rank 5 indicate the average level of random data. Error bars, SEM. Stim, stimulus period. Res, response period.
Figure 7
Encoding of visual stimulus information during task learning.
(A) Schematic of the ROC analyses and example data from a neuron preferring the No-Go stimulus. (B) Percentage of stimulus-selective neurons in each brain region during the early and late training stages. (C) Mean percentage of stimulus-selective neurons in the early (0–400 ms after stimulus onset) and late (400–800 ms after stimulus onset) stimulus periods. n = 10 time bins for the early training stage and 30 time bins for the expert training stage. (D) Same as C, but for the early (800–1800 ms after stimulus onset), and late (1800–2800 ms after stimulus onset) response periods. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, t-test with Sidak correction. n = 34 time bins for the early training stage and 102 time bins for late training stage. (E) Correlation between stimulus encoding peak time and input/output rank in expert CR trials. A significant correlation was observed during the stimulus period but not the response period (Pearson’s correlation).
Figure 8
The effects of bilateral optogenetic inhibition on task performance.
(A) Expression of AAV2/9-mCaMKIIa-eJaws3.0-mRuby3-WPRE-pA in the OFC. VO: ventral orbitofrontal cortex; MO: medial orbitofrontal cortex. Regions were named according to the Paxinos atlas (Paxinos, 2019). (B) Correct rejection rate for the OFC-stimulus period inhibition group (eJaws 3.0 Stim), and the control group (mCherry Stim). Shading, SEM. n = 8 and 14 mice for the eJaws 3.0 and mCherry group, respectively. ****p < 0.0001, significance for the group factor in two-way ANOVA. (C) Same as B, but for the OFC-response period inhibition group (n = 8 mice) and control group (n = 16 mice). (D) Average miss rate for each mouse in the OFC manipulation group and control group. (E-H): Same as A to D but for V2M inhibition. n = 8 mice for each manipulation group. ***p < 0.001, significance for the group factor in two-way ANOVA. The control group here was the same group of mice in A-D.
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Dynamics of mesoscale brain network during visual discrimination learning revealed by chronic, large-scale single-unit recording
eLife 14:RP108083.
https://doi.org/10.7554/eLife.108083.3
