Multiple timescales of sensory-evidence accumulation across the dorsal cortex

Article activity feed

1. 

   Version published to 10.7554/elife.70263 on eLife

   Jun 16, 2022
2. 

   eLife

   Apr 28, 2022

   Author Response

   Reviewer #1 (Public Review):

   Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. However, the functional significance of the difference in intrinsic timescales remains to be established. In this study, Pinto and colleagues addressed this question using optogenetic silencing of cortical areas in an evidence accumulation task in mice. While head-fixed mice performed in an accumulating-towers task in visual virtual reality, the authors silenced specific cortical regions by locally activating inhibitory neurons optogenetically. The weight of sensory evidence from different positions in the maze was estimated using logistic regressions. The authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also …

   Author Response

   Reviewer #1 (Public Review):

   Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. However, the functional significance of the difference in intrinsic timescales remains to be established. In this study, Pinto and colleagues addressed this question using optogenetic silencing of cortical areas in an evidence accumulation task in mice. While head-fixed mice performed in an accumulating-towers task in visual virtual reality, the authors silenced specific cortical regions by locally activating inhibitory neurons optogenetically. The weight of sensory evidence from different positions in the maze was estimated using logistic regressions. The authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in some cases. The authors also performed a wide-field calcium imaging and derived auto-regressive term based on a linear encoding model which include a set of predictors including various task events, coupling predictors from other brain regions in addition to auto-regressive predictors. The results indicated that inactivation of frontal regions reduced the weight of evidence accumulation on longer timescales than posterior cortical areas, and the autoregressive terms also supported the different timescales of integration.

   The question that this study addresses is very important, and the authors used elegant experimental and analytical approaches. While the results are of potential interest, some of the conclusions are not very convincing based on the presented data. Some of these issues need to be addressed before publication of this work.

   We thank the reviewer for their kind words and constructive feedback. In hindsight, we agree that some conclusions were unwarranted based on the original analysis. We have revamped our analytical approach to address these issues, as detailed below.

   Major issues:

   1. There are several issues that reduce the strength of the main conclusion regarding the timescale of integration using cortical silencing. 1a. The main analysis relied on the data pooled across multiple animals although individual animals exhibited a large amount of variability in the weights of integration across different time windows. Also, some mice which did not show a flat integration over time were excluded. This might also affect the interpretation of the analysis based on the pooled (and selected) data. How the individual variability affected the main conclusion needs to be discussed carefully.

   We have entirely replaced the pooled model for a mixed-effects logistic regression approach in which we explicitly modeled the variability introduced by individual animals (as well as different inactivation conditions). Because of this more principled approach, we added back the previously excluded mice. We also devised a shuffling procedure to further take that variability into account when reporting the statistical significance of the effects, as we now explain in Materials and Methods (line 652):

   “For the models in Figure 2, we also computed coefficients for shuffled data, where we randomized the laser-on labels 30 times while keeping the mouse and condition labels constant, such that we maintained the underlying statistics for these sources of variability. This allowed us to estimate the empirical null distributions for the laser-induced changes in evidence weighting terms.”

   Finally, we have also added text to be more explicit about this variability and how it informed the new analytical approach (line 169):

   “(...) to account for the inter-animal variability we observed, we used a mixed-effects logistic regression approach, with mice as random effects (see Materials and Methods for details), thus allowing each mouse to contribute its own source of variability to overall side bias and sensitivity to evidence at each time point, with or without the inactivations. We first fit these models separately to inactivation epochs occurring in the early or late parts of the cue region, or in the delay (y ≤ 100 cm, 100 < y ≤ 200 cm, y > 200 cm, respectively). We again observed a variety of effect patterns, with similar overall laser-induced changes in evidence weighting across epochs for some but not all tested areas (Figure 2–figure supplement 1). Such differences across epochs could reflect dynamic computational contributions of a given area across a behavioral trial. However, an important confound is the fact that we were not able to use the same mice across all experiments due to the large number of conditions (Figure 1–table supplement 1), such that epoch differences (where epoch is defined as time period relative to trial start) could also simply reflect variability across subjects. To address this, for each area we combined all inactivation epochs in the same model, adding them as additional random effects, thus allowing for the possibility that inactivation of each brain region at each epoch would contribute its own source of variability to side bias; different biases from mice perturbed at different epochs would then be absorbed by this random-effects parameter. We then aligned the timing of evidence pulses to laser onset and offset within the same models, as opposed to aligning with respect to trial start. This alignment combined data from mice inactivated at different epochs together, further ameliorating potential confounds from any mouse x epoch-specific differences. (...) This approach allowed us to extract the common underlying patterns of inactivation effects on the use of sensory evidence towards choice, while simultaneously accounting for inter-subject and inter-condition variability.”

   1b. The main conclusion that the frontal areas had longer integration windows largely depends on a few data points which relied on a very small number of samples (n = 4 or 3). This is, in part, because of the use of pooled data and because the number of samples comes from the alignment of the data with different timing of inactivation. This analysis also appears to suffer from the fact that the number of sample is biased toward the time of inactivation (y = 0 which had n = 6) compared to the preceding time windows (y = 50 and 100, which had n = 4 and 3, respectively).

   We agree with this assessment. As explained above, our new mixed-effects logistic regression approach explicitly models the variability introduced by mice and conditions, which allows us to focus on the effects that are common across mice and conditions. Because of these changes, we were now able to perform statistical analyses on coefficients using metrics based on their error estimates from the model fitting procedure, such that all estimates come from the same sample size and take into account the full data (t- and z-tests, as explained in more detail in Materials and Methods, line 665). This new analysis approach confirmed, and we believe strengthened, our main conclusions.

   1c. The clustering analysis uses only 7 data points corresponding to the cortical areas examined. The conclusions regarding the three clusters appear to be preliminary.

   We agree. The clustering analysis was more meant as a way to summarize the data rather than provide a strong statement of area groupings. Because this analysis requires clustering on only 7 data points, as the reviewer points out, and because it is in no way central to our claims, we have decided to drop it. Instead, we now present a direct comparison between frontal and posterior areas, which is more directly related to our claims (Figs. 2C, 3).

   1. The authors' conclusion that "the inactivation of different areas primarily affected the evidence-accumulation computation per se, rather than other decision-related processes" can be a little misleading. First, as the authors point out in the Results, the effect can be "the processing and/or memory of the evidence". Given that the reduction in the weight of evidence occurs during the inactivation period, the effect can be an impairment of passing the evidence to an integration process, and not accumulation process itself. Second, as discussed above (1b), the evidence supporting a longer timescale process (characterized as "memory" here) is not necessarily convincing. Additionally, the authors' analysis on "other decision-related processes" is limited (e.g. speed of locomotion), and it remains unclear whether the authors can make such a conclusion. Overall, whether the inactivation affected the evidence accumulation process and whether the inactivation did not affect other cortical functions remain unclear from the data.

   We agree with the reviewer that our previous modeling approach did not allow us to adequately separate between these different processes. However, we believe that our new approach addresses some of these shortcomings by being done in time rather than space (thus controlling for running speed effects), and separating evidence occurring before, during or after inactivation within the same model. As we now explain in the main text (line 156):

   “We reasoned that changes in the weighting of sensory evidence occurring before laser onset would primarily reflect effects on the memory of past evidence, while changes in evidence occurring while the laser was on would reflect disruption of processing and/or very short-term memory of the evidence. Finally, changes in evidence weighting following laser offset would potentially indicate effects on processes beyond accumulation per se, such as commitment to a decision. For example, a perturbation that caused a premature commitment to a decision would lead to towers that appeared subsequent to the perturbation having no weight on the animal’s choice. Although our inactivation epochs were defined in terms of spatial position within the maze, small variations in running speed across trials, along with the moderate increases in running speed during inactivation, could have introduced confounds in the analysis of evidence as a function of maze location (Figure 1–figure supplement 2). Thus, we repeated the analysis of Figure 1C but now with logistic regression models, built to describe inactivation effects for each area, in which net sensory evidence was binned in time instead of space. (...) We then aligned the timing of evidence pulses to laser onset and offset within the same models, as opposed to aligning with respect to trial start.”

   Throughout our description of results, we now more carefully outline whether the findings support a role in sensory-evidence processing, memory, or both, as well as post-accumulation processes manifesting as decreases in the weight of sensory evidence after laser offset. For example, our new analyses have more clearly shown prospective changes in evidence use when M1 and mM2 were silenced, compatible with the latter. We also agree with the reviewer that we cannot completely rule out other untested sources of behavioral deficits beyond the aforementioned decision processes. Thus, we have removed all statements to the effect that only evidence accumulation per se was affected. Importantly, though, we believe the new analyses do support the claims that the inactivation of all tested areas strongly affects the accumulation process, even if not exclusively.

   1. Different shapes of the autoregressive term may result from different sensory, behavioral or cognitive variables by which neurons in each brain area are modulated. In other words, if a particular brain area tracks specific variables that change on a slow timescale, the present analysis might not distinguish whether a slow autoregressive term is due to the intrinsic properties of neurons or circuits (as the authors conclude), or neuronal activities are modulated by a slowly-varying variable which was not included in the present model.

   We note that many of our task-related predictors, in particular ones related to sensory evidence, had lags that matched the timescales of the auto-regressive coefficients. Along with our regularization procedures, this would argue against variance misattribution to coefficients included in the model. We have now added an analysis of sensory-evidence coefficients to Figure 4–figure supplement 1, which did not reveal any significant differences between areas.

   Of course, as the reviewer suggests, it is possible that, despite our extensive parameterization of behavioral events, we failed to model some task component that would display timescale differences across areas. We have added a discussion to acknowledge this possibility (line 332):

   “Nevertheless, a caveat here is that the auto-regressive coefficients of the encoding model could conceivably be spuriously capturing variance attributable to other behavioral variables not included in the model. For example, our model parameterization implicitly assumes that evidence encoding would be linearly related to the side difference in the number of towers. Although this is a common assumption in evidence-accumulation models (e.g., Bogacz et al., 2006; Brunton et al., 2013), it could not apply to our case. At face value, however, our findings could suggest that the different intrinsic timescales across the cortex are important for evidence-accumulation computations.”

   Reviewer #2 (Public Review):

   Pinto et al use temporally specific optogenetic inactivation across the dorsal cortex during a navigation decision task to examine distinct contributions of cortical regions. Consistent with their previous findings (Pinto et al 2019), inactivation of most cortical regions impairs behavioral performance. A logistic regression is used to interpret the behavioral deficits. Inactivation of frontal cortical regions impairs the weighting of prior sensory evidence over longer timescale compared to posterior cortical regions. Similarly, the autocorrelation of calcium dynamics also increases across the cortical hierarchy. The study concludes that distributed brain regions participate in evidence accumulation and the accumulation process of each region is related to the hierarchy of timescales.

   Identify the neural substrate of evidence accumulation computation is a fundamentally important question. The authors assembled a large dataset probing the causal contributions of many cortical regions. The data is thus of interest. However, I have major concerns regarding the analysis and interpretation. I feel the results as presented currently do not fully support the conclusion that the behavioral deficit is related to evidence accumulation. Alternative interpretations should be ruled out. Another major concern is the variability of the inactivation effect across conditions. The assumptions for pooling inactivation conditions should be better justified. Finally, some framing in the text should more closely mirror the data. Most notably, the data does not casually demonstrate that the hierarchy of timescales across cortical regions is related to evidence accumulation since the experiments do not manipulate the timescales of cortical regions. The two phenomena might be related, but this is a correlation based on the present findings.

   We thank the reviewer for their thorough review and constructive suggestions. As we expand on below, we have changed our modeling approach to better account for data variability, and more explicitly justified the choice to pool across conditions. The modeling approach also allowed us to better pinpoint the different decision processes affected by cortical inactivation. Finally, we have also toned down our claims throughout the manuscript, and removed the claims of causality altogether.

   Reviewer #3 (Public Review):

   This study examines how the timescale over which sensory evidence is accumulated varies across cortical regions, and whether differences in timescales are causally relevant for sensory decisions. The authors leverage a powerful behavioral paradigm that they have previously described (Pinto et al., 2018; 2019) in which mice make a left vs. right decision in a virtual reality environment based on which side contains the larger number of visual cue "towers" passed by the "running" head-fixed mouse. The probability of tower presentation varies over time/space and between the left and right sides, requiring the mice to integrate tower counts over the course of the trial (several seconds/meters). To examine the contribution of a particular cortical region to sensory evidence accumulation, the authors optogenetically inactivated activity during several sub-epochs of the task, and examined the effect of inhibition on a) behavioral performance (% correct choices) and b) the strength of the contribution of sensory evidence to the decision as a function of time/space from the inhibition onset. Finally, the authors qualitatively compared the timescale of evidence accumulation identified for each region to the autocorrelation of activity in that region, calculated from reanalyzing the author's published calcium imaging data set (Pinto et al., 2019) with a more sophisticated regression model.

   The methodology and analyses are leading edge, ultimately allowing for a comparison of evidence accumulation dynamics across multiple cortical regions in a well-controlled behavioral task, and this is a nice extension of the authors' previous studies along these lines. The study can potentially be built on in two broad directions: a) examining how circuits within any of the regions studied here function to accumulate sensory evidence, and b) addressing how these regions coordinate to guide behavior. Overall, while the study is generally strong, addressing some points would increase confidence in the interpretation of the results.

   We thank the reviewer for their kind words and very helpful suggestions. As we expand on below, we now fit our model explicitly in the time domain and use mixed-effects regression to account for inter-mouse variability. We also expanded our discussion on interpretation caveats about the inactivation approach.

   Specifically:

   In describing the contribution of evidence to the decision, and how it is affected by inhibition (primarily Fig. 2), there is a confusing conflation of time and space. These are of course related by the mouse's running speed. But given that inactivation appears to consistently cause faster speeds (Fig. 2-Fig. S1), describing the effect of inhibition on the change of the weight of evidence as a function of space does not seem like the optimal way to examine how inactivation changes the timescale of evidence accumulation. The authors note in Fig. 2-Fig S1 that inactivation does not decrease speed, but it still would confound the results if inactivation increases speed (as appears to be the case; if not, it would be helpful for the authors to state it). Showing the data (e.g., in Fig. 2) as a function of time, and not distance, from laser on would allow the authors to achieve their aim of examining the timescale of evidence accumulation.

   Indeed, we do observe significant, though minor, increases in speed. We had originally only considered the confounds of decreases in speed, but we agree that increases could likewise confound the analysis. Following the reviewer’s suggestion, we devised a new model that bins evidence in time rather than in space. Moreover, the time of evidence occurrence is aligned to laser onset or offset within the same model, which allows us to compare more directly the changes in weighting of evidence occurring before, during or after inactivation. The results from these new models are now presented in Figs. 2, 3, 2-S1, 2-S2, and largely confirm the findings from our previous analysis in the space domain.

   Performing the analyses mouse by mouse, instead of on data aggregated across mice, would increase confidence in the conclusions and therefore strengthen the study. Mice clearly exhibit individual differences in how they weight evidence (Fig. 1C), as the authors note (line 81). It therefore would make sense to compare the effect of inactivation in a given mouse to its own baseline, rather than the average (flat) baseline. If the analyses must be performed on data aggregated across mice, some justification should be given, and the resulting limitations in how the results should be interpreted should be discussed. For example, perhaps there are an insufficient number of trials for such within-mouse comparisons (which would be understandable given the ambitious number of inactivated regions and epochs)?

   As the reviewer suggests, we prioritized the number of conditions and mice per condition rather than the number of trials each mouse had, which complicates a per-mouse analysis of changes in evidence weights. This is particularly true for fitting logistic regressions with multiple coefficients, as was our goal here. Regardless, we still agree that the inter-animal variability should be accounted for in the analysis. Rather than doing a per-mouse regression, however, we implemented a mixed-effects logistic regression, which estimates random effects for all mice together in the same model, accounting for that when estimating the fixed-effects coefficients. Indeed, this approach is recommended for statistical problems such as ours (e.g., Yu et al., Neuron, 2021, In press, https://doi.org/10.1016/j.neuron.2021.10.030). While the overall statistics were still computed from the estimates of the fixed effects, this allowed us to also display per-mouse data when reporting the models (e.g. Figures 2, 3), which hopefully will give readers a greater appreciation for inter-mouse variability in the data, showing variations in their baseline, as the reviewer suggests. Finally, in order to more explicitly account for non-flat baselines, we now report laser-induced changes in evidence weights normalized by the baseline, rather than simply subtracted, as we did previously.

   The method of inactivating cortical regions by activating local inhibitory neurons is quite common, and the authors' previous paper (Pinto et al., 2019) performed experiments to verify that light delivery produced the desired effect with minimal rebound or other off-target effects. Since this method is central to interpreting the results of the current study, adding more detail about these previous experiments and results would reassure the reader that the results are not due to off-target effects. Given that the cortical regions under study are interconnected, do the previous experiments (in Pinto et al., 2019) rule out the possibility that inactivating a given target region does not meaningfully affect activity in the other regions? This is particularly important given that activity is inhibited in multiple distinct epochs in this study.

   We agree that the issue of off-target effects is important to the interpretation of any inactivation experiment, and one that we have yet to adequately grapple with as a field. Our previous experiments only measured local spread of inactivation effects. Thus, while we did rule out rebound excitation, we cannot rule out possible off-target effects in distal regions that are connected with the region being inactivated. Experiments to measure this would involve measuring from a single area while systematically inactivating distal areas connected to it or not or, more ideally, measuring from multiple areas simultaneously while performing these systematic inactivations. These experiments themselves would constitute a whole project and therefore fall outside the scope of the present manuscript. Following the reviewer’s suggestion, we have expanded the discussion of these experiments and potential caveats.

   Line 145, Results: “Although our previous measurements indicate inactivation spreads of at least 2 mm (Pinto et al., 2019), we observed different effects even comparing regions that were in close physical proximity.”

   Line 223, Results: “However, the possibility remains that these effects are related to lingering effects of inactivation on population dynamics in frontal regions, which we have found to evolve on slower timescales (see below). Although we have previously verified in an identical preparation that our laser parameters lead to near-immediate recovery of pre-laser firing rates of single units, with little to no rebound (Pinto et al., 2019), these measurements were not done during the task, such that we cannot completely rule out this possibility.”

   Line 375, Discussion: “This could be in part due to technical limitations of the experiments. First, the laser powers we used result in large inactivation spreads, potentially encompassing neighboring regions. Moreover, local inactivation could result in changes in the activity of interconnected regions (Young et al. 2000), a possibility that should be evaluated in future studies using simultaneous inactivation and large-scale recordings across the dorsal cortex.”

   Line 516, Materials and Methods: “We used a 40-Hz square wave with an 80% duty cycle and a power of 6 mW measured at the level of the skull. This corresponds to an inactivation spread of ~ 2 mm (Pinto et al., 2019). While this may introduce confounds regarding ascribing exact functions to specific cortical areas, we have previously shown that the effects of whole-trial inactivations at much lower powers (corresponding to smaller spatial spreads) are consistent with those obtained at 6 mW. To minimize post-inactivation rebounds, the last 100 ms of the laser pulse consisted of a linear ramp-down of power (Guo et al., 2014; Pinto et al., 2019)”

   Read the original source
3. 

   eLife

   Jul 8, 2021

   Evaluation Summary:

   Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. In this study, Pinto and colleagues aimed at establishing the functional significance of intrinsic timescales across cortical regions by performing optogenetic silencing of cortical areas in an evidence accumulation task in mice. The results are of broad interest, but the reviewers identified a few important issues that need to be addressed to validate the authors' conclusions.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

   Read the original source
4. 

   eLife

   Jul 8, 2021

   Reviewer #1 (Public Review):

   Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. However, the functional significance of the difference in intrinsic timescales remains to be established. In this study, Pinto and colleagues addressed this question using optogenetic silencing of cortical areas in an evidence accumulation task in mice. While head-fixed mice performed in an accumulating-towers task in visual virtual reality, the authors silenced specific cortical regions by locally activating inhibitory neurons optogenetically. The weight of sensory evidence from different positions in the maze was estimated using logistic regressions. The authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in …

   Reviewer #1 (Public Review):

   Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. However, the functional significance of the difference in intrinsic timescales remains to be established. In this study, Pinto and colleagues addressed this question using optogenetic silencing of cortical areas in an evidence accumulation task in mice. While head-fixed mice performed in an accumulating-towers task in visual virtual reality, the authors silenced specific cortical regions by locally activating inhibitory neurons optogenetically. The weight of sensory evidence from different positions in the maze was estimated using logistic regressions. The authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in some cases. The authors also performed a wide-field calcium imaging and derived auto-regressive term based on a linear encoding model which include a set of predictors including various task events, coupling predictors from other brain regions in addition to auto-regressive predictors. The results indicated that inactivation of frontal regions reduced the weight of evidence accumulation on longer timescales than posterior cortical areas, and the autoregressive terms also supported the different timescales of integration.

   The question that this study addresses is very important, and the authors used elegant experimental and analytical approaches. While the results are of potential interest, some of the conclusions are not very convincing based on the presented data. Some of these issues need to be addressed before publication of this work.

   Major issues:

   1. There are several issues that reduce the strength of the main conclusion regarding the timescale of integration using cortical silencing.

   a) The main analysis relied on the data pooled across multiple animals although individual animals exhibited a large amount of variability in the weights of integration across different time windows. Also, some mice which did not show a flat integration over time were excluded. This might also affect the interpretation of the analysis based on the pooled (and selected) data. How the individual variability affected the main conclusion needs to be discussed carefully.

   b) The main conclusion that the frontal areas had longer integration windows largely depends on a few data points which relied on a very small number of samples (n = 4 or 3). This is, in part, because of the use of pooled data and because the number of samples comes from the alignment of the data with different timing of inactivation. This analysis also appears to suffer from the fact that the number of sample is biased toward the time of inactivation (y = 0 which had n = 6) compared to the preceding time windows (y = 50 and 100, which had n = 4 and 3, respectively).

   c) The clustering analysis uses only 7 data points corresponding to the cortical areas examined. The conclusions regarding the three clusters appear to be preliminary.

   1. The authors' conclusion that "the inactivation of different areas primarily affected the evidence-accumulation computation per se, rather than other decision-related processes" can be a little misleading. First, as the authors point out in the Results, the effect can be "the processing and/or memory of the evidence". Given that the reduction in the weight of evidence occurs during the inactivation period, the effect can be an impairment of passing the evidence to an integration process, and not accumulation process itself. Second, as discussed above (1b), the evidence supporting a longer timescale process (characterized as "memory" here) is not necessarily convincing. Additionally, the authors' analysis on "other decision-related processes" is limited (e.g. speed of locomotion), and it remains unclear whether the authors can make such a conclusion. Overall, whether the inactivation affected the evidence accumulation process and whether the inactivation did not affect other cortical functions remain unclear from the data.

   2. Different shapes of the autoregressive term may result from different sensory, behavioral or cognitive variables by which neurons in each brain area are modulated. In other words, if a particular brain area tracks specific variables that change on a slow timescale, the present analysis might not distinguish whether a slow autoregressive term is due to the intrinsic properties of neurons or circuits (as the authors conclude), or neuronal activities are modulated by a slowly-varying variable which was not included in the present model.

   Read the original source
5. 

   eLife

   Jul 8, 2021

   Reviewer #2 (Public Review):

   Pinto et al use temporally specific optogenetic inactivation across the dorsal cortex during a navigation decision task to examine distinct contributions of cortical regions. Consistent with their previous findings (Pinto et al 2019), inactivation of most cortical regions impairs behavioral performance. A logistic regression is used to interpret the behavioral deficits. Inactivation of frontal cortical regions impairs the weighting of prior sensory evidence over longer timescale compared to posterior cortical regions. Similarly, the autocorrelation of calcium dynamics also increases across the cortical hierarchy. The study concludes that distributed brain regions participate in evidence accumulation and the accumulation process of each region is related to the hierarchy of timescales.

   Identify the neural …

   Reviewer #2 (Public Review):

   Pinto et al use temporally specific optogenetic inactivation across the dorsal cortex during a navigation decision task to examine distinct contributions of cortical regions. Consistent with their previous findings (Pinto et al 2019), inactivation of most cortical regions impairs behavioral performance. A logistic regression is used to interpret the behavioral deficits. Inactivation of frontal cortical regions impairs the weighting of prior sensory evidence over longer timescale compared to posterior cortical regions. Similarly, the autocorrelation of calcium dynamics also increases across the cortical hierarchy. The study concludes that distributed brain regions participate in evidence accumulation and the accumulation process of each region is related to the hierarchy of timescales.

   Identify the neural substrate of evidence accumulation computation is a fundamentally important question. The authors assembled a large dataset probing the causal contributions of many cortical regions. The data is thus of interest. However, I have major concerns regarding the analysis and interpretation. I feel the results as presented currently do not fully support the conclusion that the behavioral deficit is related to evidence accumulation. Alternative interpretations should be ruled out. Another major concern is the variability of the inactivation effect across conditions. The assumptions for pooling inactivation conditions should be better justified. Finally, some framing in the text should more closely mirror the data. Most notably, the data does not casually demonstrate that the hierarchy of timescales across cortical regions is related to evidence accumulation since the experiments do not manipulate the timescales of cortical regions. The two phenomena might be related, but this is a correlation based on the present findings.

   Read the original source
6. 

   eLife

   Jul 8, 2021

   Reviewer #3 (Public Review):

   This study examines how the timescale over which sensory evidence is accumulated varies across cortical regions, and whether differences in timescales are causally relevant for sensory decisions. The authors leverage a powerful behavioral paradigm that they have previously described (Pinto et al., 2018; 2019) in which mice make a left vs. right decision in a virtual reality environment based on which side contains the larger number of visual cue "towers" passed by the "running" head-fixed mouse. The probability of tower presentation varies over time/space and between the left and right sides, requiring the mice to integrate tower counts over the course of the trial (several seconds/meters). To examine the contribution of a particular cortical region to sensory evidence accumulation, the authors …

   Reviewer #3 (Public Review):

   This study examines how the timescale over which sensory evidence is accumulated varies across cortical regions, and whether differences in timescales are causally relevant for sensory decisions. The authors leverage a powerful behavioral paradigm that they have previously described (Pinto et al., 2018; 2019) in which mice make a left vs. right decision in a virtual reality environment based on which side contains the larger number of visual cue "towers" passed by the "running" head-fixed mouse. The probability of tower presentation varies over time/space and between the left and right sides, requiring the mice to integrate tower counts over the course of the trial (several seconds/meters). To examine the contribution of a particular cortical region to sensory evidence accumulation, the authors optogenetically inactivated activity during several sub-epochs of the task, and examined the effect of inhibition on a) behavioral performance (% correct choices) and b) the strength of the contribution of sensory evidence to the decision as a function of time/space from the inhibition onset. Finally, the authors qualitatively compared the timescale of evidence accumulation identified for each region to the autocorrelation of activity in that region, calculated from reanalyzing the author's published calcium imaging data set (Pinto et al., 2019) with a more sophisticated regression model.

   The methodology and analyses are leading edge, ultimately allowing for a comparison of evidence accumulation dynamics across multiple cortical regions in a well-controlled behavioral task, and this is a nice extension of the authors' previous studies along these lines. The study can potentially be built on in two broad directions: a) examining how circuits within any of the regions studied here function to accumulate sensory evidence, and b) addressing how these regions coordinate to guide behavior. Overall, while the study is generally strong, addressing some points would increase confidence in the interpretation of the results. Specifically:

   In describing the contribution of evidence to the decision, and how it is affected by inhibition (primarily Fig. 2), there is a confusing conflation of time and space. These are of course related by the mouse's running speed. But given that inactivation appears to consistently cause faster speeds (Fig. 2-Fig. S1), describing the effect of inhibition on the change of the weight of evidence as a function of *space* does not seem like the optimal way to examine how inactivation changes the *time*scale of evidence accumulation. The authors note in Fig. 2-Fig S1 that inactivation does not decrease speed, but it still would confound the results if inactivation increases speed (as appears to be the case; if not, it would be helpful for the authors to state it). Showing the data (e.g., in Fig. 2) as a function of time, and not distance, from laser on would allow the authors to achieve their aim of examining the timescale of evidence accumulation.

   Performing the analyses mouse by mouse, instead of on data aggregated across mice, would increase confidence in the conclusions and therefore strengthen the study. Mice clearly exhibit individual differences in how they weight evidence (Fig. 1C), as the authors note (line 81). It therefore would make sense to compare the effect of inactivation in a given mouse to its own baseline, rather than the average (flat) baseline. If the analyses must be performed on data aggregated across mice, some justification should be given, and the resulting limitations in how the results should be interpreted should be discussed. For example, perhaps there are an insufficient number of trials for such within-mouse comparisons (which would be understandable given the ambitious number of inactivated regions and epochs)?

   The method of inactivating cortical regions by activating local inhibitory neurons is quite common, and the authors' previous paper (Pinto et al., 2019) performed experiments to verify that light delivery produced the desired effect with minimal rebound or other off-target effects. Since this method is central to interpreting the results of the current study, adding more detail about these previous experiments and results would reassure the reader that the results are not due to off-target effects. Given that the cortical regions under study are interconnected, do the previous experiments (in Pinto et al., 2019) rule out the possibility that inactivating a given target region does not meaningfully affect activity in the other regions? This is particularly important given that activity is inhibited in multiple distinct epochs in this study.

   Read the original source
7. 

   Version published to 10.1101/2020.12.28.424600v2 on bioRxiv

   May 11, 2021
8. 

   Version published to 10.1101/2020.12.28.424600v1 on bioRxiv

   Dec 29, 2020