Evidence-based practice (EBP) is a process of making practice decisions and evaluating effectiveness through identifying, selecting, and applying the best scientific evidence Rubin, . RandomiScholars in these health-related fields have accumulated a broad range of research on science-based health promotion programs, which paid more attention to repairing the weakness or problems of individuals and communities. However, it has been recognized that the traditional approach is insufficient or inefficient enough to help individuals and communities achieve sustained outcomes since the emergence of positive psychology in the United States about two decades ago , illustrating a snapshot of the latest progress of evidence-based strength interventions.Although positive psychology is a relatively young branch of psychology, a few strengths-based intervention studies were conducted  of adolescents with high levels of trait anger. Another quasi-experimental trial was conducted by Corbu et al. to test the effect of a Positive Psychological Micro-Coaching program on non-executive workers' psychological capital. Results implicated that short-term positive psychological coaching is a valuable way to develop personal resources in improving goal achievement and then work-related goals in non-executive employees.This section contains nine articles investigating producing positive psychological, social, cultural, and health-based outcomes. Three experimental studies were collected in this section, including one RCT and three quasi-experiment studies. A three-group RCT designed by Cheng et al. investigated the impact of objective isolation and subjective social isolation on the mental health of older Chinese adults and the mediating effect of aging attitudes. Using the sample from the 2014 Chinese Longitudinal Aging Social Survey, the research showed that aging attitudes play a significant mediating role between social isolation and mental health. Yang et al. used a sample from the 2013 Chinese General Social Survey to examine the relationship between life satisfaction and lifestyle, the number of children, and widowhood status. The established moderated mediation model illustrated that lifestyle partly mediated the relationship between widowhood and life satisfaction while the number of children moderated the relationship between widowhood and lifestyle and between lifestyle and life satisfaction. Zhang and Tang's qualitative study explored factors that impact Chinese students' choice of study destination and choice of subject and program. It is shown that cultural capital, gender, class, and family involvement all influenced Chinese female students' aspiration to study in the United Kingdom, and despite the fact these students have the privilege to study abroad, female students from the middle class are constrained by Chinese gender norms and class background when making educational choices.Two studies conducted mediation analysis among older adults in the Chinese context, and one study conducted content analysis among Chinese female students in the United Kingdom. Duan et al.'s study was the first to examine the factor structure of the Physical Disability Resilience Scale (PDRS) in the Chinese context based on the Multiple Sclerosis Resiliency Scale. The revised PDRS with four subscales  showed good reliability and validity in assessing resilience among Chinese people with a physical disability. To et al. developed the Parent Empowerment via Transformative Learning Questionnaire (PETLQ) and confirmed it as a scale with sufficient factorial validity and internal consistency for assessing parents' attitudes and competence in parent empowerment and for evaluating the effectiveness of parenting intervention programs. It is notable that even though these two studies were classified in psychology contexts, social workers were involved in the research process as key members.Two studies in this section provided evidence-based strength intervention with reliable and valid methods of assessment tools. Fulone et al.) and 117 different coaching tools (18 overarching coaching techniques) used in the different phases of the Positive Psychological Coaching model . To evaluate the mediatory role of sense of coherence, Hori et al. conducted a survey among healthcare professionals in a Japanese general hospital and found that sense of coherence mediated the relations between empathy and both self-vigor mood and self-depression mood. This research indicates that more effective empathy performance interventions need to be developed for healthcare professionals.Three articles were included in this section with a psychiatric focus on evidence-based strength intervention, with two systematic review articles and one correlation study. Two systematic reviews summarized six strategies used to improve community services for deinstitutionalized patients with severe mental disorders (In the public health context, researchers paid attention to the wellbeing of patients with physical or mental health issues and the fidelity of nurses-delivered healthcare programs, including four articles.Gao et al. conducted a network meta-analysis to examine the safety and effectiveness of surgical interventions for pure cervical radiculopathy. This research illustrates that all surgical interventions can achieve satisfactory results and surgeons can choose appropriate surgical interventions based on their strengths and patient-related factors. Zhao et al.'s systematic review of RCTs on the effects of the tourniquet on pain and return to function showed that the routine use of a tourniquet during total knee arthroplasty was not recommended due to more pain, slower functional recovery, and more complications. Another systematic review and meta-analysis conducted by Zhang et al. found that horticultural therapy had a significant positive effect on depressive symptom reductions in the elderly. To enable the systematic evaluation of parenting program delivery and to better identify the therapeutic components that enable targeted efforts at improvement, Anis et al. developed a fidelity assessment checklist to make the program-delivery evidence-based.Three articles are systematic reviews studying the wellbeing of patients. The collection of this Research Topic presents the features of the broadening scope and high level of evidence (APA Presidential Task Force on Evidence-Based Practice, Despite the progress made through these studies, more work is needed further to develop positive psychology in the evidence-based strength intervention field. It is worth noticing that this Research Topic had not attracted any study from social work, and the number of articles from the psychiatric and public health field was much smaller than psychology. Thus, from the evidence-based perspective, there is still a need for more high-quality empirical evidence to provide a theoretical rationale for the exploration in broadening scope and the establishment of a stronger and broader evidence base, especially from but not limited to social work, public health, and psychiatry professional perspectives. From the strength-based perspective, the current research still mainly focused on individual-level phenomena and did not deeply explore contextual factors. Future research might focus more on contextual and structural factors that impact personal, group, and communal wellbeing in multiple contexts and explore more mediators and moderators that can explain the effectiveness of evidence-based strengths interventions and the underlying mechanisms of positive changes.XS wrote the draft of this editorial. WD finalized the version and submitted it to the journal. All authors contributed to the article and approved the submitted version.The East China University of Science and Technology Supporting Funds for Scientific Research Strengths-Based Interventions in Multiple Contexts.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Our findings suggest that the intrinsic timescale hierarchy of distributed cortical areas is an important component of evidence-accumulation mechanisms.Cortical areas seem to form a hierarchy of intrinsic timescales, but the relevance of this organization for cognitive behavior remains unknown. In particular, decisions requiring the gradual accrual of sensory evidence over time recruit widespread areas across this hierarchy. Here, we tested the hypothesis that this recruitment is related to the intrinsic integration timescales of these widespread areas. We trained mice to accumulate evidence over seconds while navigating in virtual reality and optogenetically silenced the activity of many cortical areas during different brief trial epochs. We found that the inactivation of all tested areas affected the evidence-accumulation computation. Specifically, we observed distinct changes in the weighting of sensory evidence occurring during and before silencing, such that frontal inactivations led to stronger deficits on long timescales than posterior cortical ones. Inactivation of a subset of frontal areas also led to moderate effects on behavioral processes beyond evidence accumulation. Moreover, large-scale cortical Ca The cerebral cortex of both rodents and primates appears to be organized in a hierarchy of intrinsic integration timescales, whereby frontal areas integrate input over longer time windows than sensory areas . AlthougIn particular, the decisions we make in our daily lives often unfold over time as we deliberate between competing choices. This raises the possibility that decisions co-opt the cortical timescale hierarchy such that different cortical areas integrate decision-related information on distinct timescales. A commonly studied type of time-extended decision-making happens under perceptual uncertainty, which requires the gradual accrual of sensory evidence. This involves remembering a running tally of evidence for or against a decision, updating that tally when new evidence becomes available, and making a choice based on the predominant evidence . Neural Here, we hypothesized that the pattern of widespread recruitment of cortical areas during prolonged evidence accumulation can be in part explained by their underlying timescale hierarchy. To test this, we trained mice to accumulate evidence over seconds toward navigational decisions and used brief optogenetic inactivation of single or combined cortical areas, restricted to one of six epochs of the behavioral trials. We show that the inactivation of widespread areas in the dorsal cortex strongly affects the processing and memory of sensory evidence, and that inactivating a subset of frontal areas also results in prospective behavioral deficits. Further, the inactivation of different areas affects accumulation over distinct timescales, such that, to an approximation, frontal areas contribute to evidence memory over longer temporal windows than posterior areas. In agreement with this, we show that cortical activity during the accumulation task displays a gradient of intrinsic timescales, which are longer in frontal areas. Our findings thus suggest the existing cortical hierarchy of temporal integration windows is important for evidence-accumulation computations.We trained mice to accumulate evidence over relatively long timescales while navigating in VR . The micOur previous results have shown that cortical contributions to the performance of this task are widespread , but ourCompatible with our previous whole-trial inactivation experiments , we founWe next assessed directly whether the inactivation of different cortical areas led to changes in how much the mice based their final decision on evidence from different times in the trial with respect to inactivation. 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 . Thus, wThis modeling approach revealed that inactivation of different areas led to deficits in the use of sensory evidence from distinct time points within the behavioral trials. For instance, PPC inactivation only led to significant decreases in the use of sensory evidence occurring during inactivation or immediately preceding it  led to profound deficits in the use of long-past evidence , but only aM2 inactivation led to changes in the use of evidence occurring while the laser was on (p<0.001). Interestingly, inactivation of mM2 and M1 also led to significant changes in the use of sensory evidence occurring after laser offset (p<0.001), although to a lesser extent than pre-laser evidence . This co5,15 = 3.09, p[time]=0.047, F[area]1,3 = 33.93, p[area]=0.010, F[interaction]5,15 = 3.60, p[interaction]=0.025).Thus, while we observed diverse temporal profiles of evidence-weighting deficits resulting from the inactivation of different areas of the dorsal cortex, to an approximation they could be broadly divided according to whether they belonged to the frontal or posterior cortex. Indeed, frontal and posterior areas differed significantly in terms of the magnitude and time course of evidence-weighting deficits induced by their inactivation . Both posterior and frontal significantly deviated from the even-combination prediction  = 1994.85, p=6.2 × 10–13, two-way ANOVA with repeated measures).Our inactivation results thus suggest that different regions of the dorsal cortex contribute to distinct aspects of evidence-accumulation-based decisions. In particular, while all areas we tested appear to have a role in evidence accumulation, they do so on distinct timescales. This is reminiscent of the findings that cortical areas display a hierarchy of intrinsic timescales, such that primary sensory areas tend to integrate over shorter time windows than frontal and other association areas . While t neurons . To do t neurons  by inclu neurons . Crucial neurons . Adding 6,30 = 4.49, p=0.006, one-way ANOVA with repeated measures), being larger for frontal than posterior areas. Note that, while it is possible that these coefficients capture autocorrelations introduced by intrinsic GCaMP6f dynamics, there is no reason to believe that this affects our conclusions, as indicator dynamics should be similar across regions. Thus, during the evidence-accumulation task, cortical regions display increasing intrinsic timescales going from visual to frontal areas. This is consistent with previous reports for spontaneous activity and other behavioral tasks  and were performed in accordance with the Guide for the Care and Use of Laboratory Animals . We used both male and female VGAT-ChR2-EYFP mice aged 2–16 months . Part of the inactivation data from some of these animals was collected in the context of previous work , but the2+ imaging) is from data collected in the context of a previous study (tm93.1(tetO-GCaMP6f)Hze Tg[Camk2a-tTA]1Mmay/J, Jackson Laboratories, stock # 024108] and Emx1-IRES-Cre . These animals also underwent the surgical procedure described above.The analysis reported in us study , althoughttps://github.com/sakoay/AccumTowersTools/tree/master/OpticalSensorPackage, https://pni.princeton.edu/pni-software-tools/virmen), running on Matlab  on a PC.The mice were trained in a VR environment  describe–1 and 2.3 m–1 on the rewarded and non-rewarded sides, respectively (or 8.0 and 1.6 m–1 in some sessions), with a 12 cm refractory period and an overall density of 5 m–1. The towers appeared when the mice were 10 cm away from their drawn locations and disappeared 200 ms later . After the maze stem, the mice turned into one of the two arms , and received a reward if they turned to the arm corresponding to the highest tower count (4–8 µL of 10% v/v sweet condensed milk). This was followed by a 3 s inter-trial interval, consisting of 1 s of a frozen frame of the VR environment and 2 s of a black screen. An erroneous turn resulted in a loud sound and a 12 s timeout.We trained the mice in the accumulating towers task . The micEach daily behavioral session  started with warm-up trials of a visually guided task in the same maze, in which towers appeared only on the rewarded side and additionally a 30 cm tall visual guide visible from the start of the trial was placed in the arm corresponding to the reward location. The animals progressed to the main task when they achieved at least 85% correct trials over a running window of 10 trials in the warm-up task. During the accumulating-towers task, performance was evaluated over a 40-trial running window, both to assess side biases and correct them using an algorithm described elsewhere  and to tWe used a scanning laser setup described in detail elsewhere . BrieflyWe targeted a total of nine area combinations, either consisting of homotopic bilateral pairs or multiple bilateral locations. The galvanometers alternated between locations at 200 Hz (20 mm travel time: ~250 µs) and in the case of more than two locations, the sequence of visited locations was chosen to minimize travel distance. The inactivated locations were defined based on stereotaxic coordinates using bregma as reference, as follows:Primary visual cortex (V1): –3.5 AP, 3 MLMedial secondary visual cortex : –2.5 AP, 2.5 MLPosterior parietal cortex (PPC): –2 AP, 1.75 MLRetrosplenial cortex (RSC): –2.5 AP, 0.5 MLPosteromedial portion of the premotor cortex (mM2): 0.0 AP, 0.5 MLAnterior portion of the premotor cortex (aM2): +3 AP, 1 MLPrimary motor cortex (M1): +1 AP, 2 MLPosterior cortex: V1, mV2, PPC, and RSCFrontal cortex: mM2, aM2, and M1To ensure consistency in bregma location across behavioral sessions, the experimenter set bregma on a reference image and for each session the current image of the mouse’s skull was registered to this reference using rigid transformations. Different sessions contained different combinations of areas and inactivation epochs, resulting in partially overlapping mice and sessions for each condition. The probability of inactivation trials, therefore, varied across sessions, ranging from a total of 0.15–0.35 across conditions, and from 0.02 to 0.15 per condition. In our experience, capping the probability at ~0.35 is important to maintain motivation throughout the behavioral session.vF) and 470 nm excitation (bF). After applying a heuristic correction to vF . We first performed retinotopic mapping to define visual areas and used the obtained maps to find, for each mouse, the optimal affine transformation to the Allen framework.Details on the experimental setup and data preprocessing can be found elsewhere . Brieflyon to Fv , we calcAll analyses of the behavioral effects of cortical inactivations were performed in Python 3.8. Linear encoding model fitting of widefield data was performed in Matlab, and the results were analyzed in Python.Because of the warm-up and easy-block trials, the sessions are naturally organized into a block structure, such that the duration of each block of the accumulating-towers task is of at least 40 trials (see above). We selected all trials from blocks in which the control (laser off) performance was at least 60% correct, collapsed over all levels of sensory evidence. After block selection, we excluded trials in which the animals failed to reach the end of the maze, or in which the total traveled distance exceeded the nominal maze length by more than 10% . These sWe calculated overall performance as the percentage of trials in which the mice turned to the side with the highest tower counts, separately for control and inactivation trials.Speed was calculated for each inactivation segment using the total x-y displacement. We compared laser-induced changes in speed to control trials from the same maze segment.We computed psychometric curves separately for control and inactivation trials by plotting the percentage of right-choice trials as a function of the difference in the number of right and left towers . ∆ was binned in increments of five between –15 and 15, and its value defined as the average ∆ weighted by the number of trials. We fitted the psychometric curves using a four-parameter sigmoid:To assess how mice weighted sensory evidence from different segments of the cue region, we performed a logistic regression analysis in which the probability of a right choice was predicted from a logistic function of the weighted sum of the net amount of sensory evidence from each of four equally spaced segments :where ∆ = # right – # left towers calculated separately for each segment. These weighting functions were calculated separately for ‘laser on’ and ‘laser off’ trials. To quantify the laser-induced changes in evidence weighting, we simply subtracted the ‘laser on’ from the ‘laser off’ curves, such that negative values indicate smaller evidence weights in the ‘laser on’ condition. Bin sizes were chosen to match the resolution of our inactivation epochs.To directly quantify how our manipulations impacted the weighting of sensory evidence in time relative to inactivation onset and offset, while accounting for variability introduced by different mice and epochs in the trial, we fitted a mixed-effects logistic regression model to the mice’s choices using the Pymer4 package for Python :(1)pR=11Rp is the probability of making a right side choice, R denotes random effects (see below), and XE is the sensory-evidence fixed-effects terms for control (X=C) and laser-on (X=L) trials:where i, aligned by the laser onset or offset depending on superscript Y. Y=Pre captures evidence occurring before inactivation; thus, evidence time is aligned to laser onset and binned at increasingly negative time values. Y=During captures evidence occurring while the laser is on; thus, evidence time is aligned to laser offset and binned at negative time values. Finally, Y=Post captures evidence occurring after the laser is off; thus, evidence time is aligned to laser offset and binned at positive time values. For control trials, we used dummy laser onset times defined as the time in the control trial corresponding to when the animal crossed the y position value where the laser was on in the nearest inactivation trial. where We included two classes of random effects, mouse identity, and inactivation epoch.  or laser (L) trials, and R is modeled as a multivariate Gaussian distribution with mean = 0. In other words, the per-mouse random effects are modeled as a noise sample from this zero-mean gaussian, with the fixed effect of equations (1) and (2) capturing tendencies that are common across the mice. The model is fitted using maximum likelihood estimation as described in detail elsewhere  limited to 0≤y≤ 300 cm . Activity was then z-scored across all trials. ∆F/F of each area was modeled as a linear combination of different predictors at different time lags. In addition to the previously used task-event predictors , where x is either a task event or the activity of the ROI at a previous time point, and j . In the task term, x at time t-i. Specifically, tR indicates the occurrence of a right tower, tL of a left tower, ∆=cumulative #R – #L towers, θ is the view angle, dθ/dt is the virtual view angle velocity, sp is the running speed, y is the spatial position in the maze stem (no lags), and ch, pch, and prw are constant offsets for a given trial, indicating upcoming choice, previous choice (+1 for right and –1 for left) and previous reward (1 for reward and –1 otherwise), respectively.In the above equations, λ, we trained the model using two-thirds of the trials (both correct and wrong choices) and tested it on the remaining one-third of trials. We picked the value of λ that maximized accuracy and used median accuracy and weight values across all 10 × 3 runs for that λ. Model accuracy was defined as the linear correlation coefficient between actual ∆F/F and that predicted by the model in the test set.The model was fitted using threefold cross-validation. For each of 20 values of the penalty term T, another one adding the auto-regressive term A, and the other with the coupling term C in addition to A and T. All versions were fitted using exactly the same cross-validation data partitioning to allow for direct comparison. We averaged cross-validated predictions over hemispheres and sessions for each mouse, performing the comparison with mouse-level data. Statistical significance of the differences between the accuracy of different models was computed using a two-way ANOVA with repeated measures with factors ROI and model type, and individual model comparisons were made using Tukey’s post hoc test. Coefficient analysis in We tested three versions of the encoding model, one with just the task term To quantify the timescales from the fitted auto-regressive coefficients, for each behavioral session we fitted an exponential decay function to the coefficients between 0.1 and 2 s in the past, normalized to the coefficient at 0.1 s (first bin):B is the offset term, A controls the amplitude of the curve, x is the vector of normalized coefficients, and τ is the decay time constant. Fits were performed using the non-linear least squares algorithm. The extracted time constants (τ) were first averaged over hemispheres and sessions for each mouse, and statistics were performed on mouse averages. The significance of the differences in the time constants across regions was assessed by performing a one-way ANOVA with repeated measures, with cortical regions as the factor.where ith ranked p-value, iP, was deemed significant if it satisfied iP ≤ (αi)/n, where n is the number of comparisons and α is the significance level. In our case, α=0.050 and 0.025 for one-sided and two-sided tests, respectively.We corrected for multiple comparisons using a previously described method for false discovery rate correction . Brieflyhttps://github.com/BrainCOGS/PintoEtAl2020_sub-trial_inact  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Michael Frank as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Gidon Felsen (Reviewer #3).Thank you for submitting your article "Multiple timescales of sensory-evidence accumulation across the dorsal cortex" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions: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 authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in some cases, suggesting that inactivation of frontal cortical regions had long-lasting effects than that of posterior cortical regions. This study provides important results addressing the relation between cortical functions and intrinsic timescales.eLife.The reviewers agreed that this study addresses an important question, and the authors performed sophisticated experiments, and collected a large amount of data. The results are presented clearly and the manuscript is well-written. All the reviewers thought that the results are potentially of great interest to a wide audience. However, the reviewers found several substantive issues which reduced the confidence on the authors' conclusions. These issues need to be addressed before publication of this study at In particular, the following points have been identified as essential issues:1. The presented analysis does not consider a large variability that exists at the level of individual animals. There is also some variability across conditions (e.g. photoinhibition of different epochs). Furthermore, the statistical analyses presented in the manuscript often rely on a small number of samples, and the sample size is not equal across the conditions . Because of these issues, we felt that the main conclusion needs to be supported by further analysis investigating these variabilities, and careful discussions of these potential caveats.2. The authors claim that the optogenetic silencing primarily affected the evidence-accumulation computation, but not other decision-related processes. The reviewers found this claim to be not strongly supported by the data. From the presented data, whether silencing specifically affected the evidence-accumulation process, not just passing the evidence to an accumulation process, remains unclear. Furthermore, silencing affects running speed . Also, the reviewers thought that alternative possibilities have not been fully examined.3. Optogenetic silencing sometimes increased the running speed. This can potentially reduce the time spent in each location, and may affect the acquisition sensory information. It is important that the reduced regression weight is not the side effect of reduce time spent in each location. Furthermore, some analysis based on time, not just locations, would be very helpful.More detailed comments and suggestions on the above issues are included in the individual reviewers' comments.Reviewer #2 (Recommendations for the authors):1) Overall, the inactivation effect is highly variable across brain regions and conditions. For example, in Figure 1-Supp 2, silencing mV2 and RSC during the 3rd quarter of the cue region reduce weighting 100 cm back, but the effect is not replicated when silencing is extended in time . The effect is yet different when silencing the posterior cortical regions, which covers mV2 and RSC. There are many cases like this. What is this variability due to? Is this degree of variability expected from behavioral variability? It is difficult to evaluate how robust the behavioral deficits are without an estimate of the expected variability and false positive rate.2) The conclusion that inactivation primarily affects evidence accumulation is based on weights from the logistic regression. A drop in weights of the sensory evidence presumably means the stimulus information is lost. However, there could be other reasons weights could drop. For example, if mice stop engage in the task after photostimulation, this could presumably lower the weights since mice no longer base their choice on the sensory stimulus. The analysis of weights after photostimulation provides a nice control . However, several areas do show prospective deficits in weighting of future evidence, although this is not observed in all areas. Prospective deficits could be consistent with mice stop performing the task. This possibility should be ruled out.3) Some additional analyses could further corroborate the interpretation that the deficit is specifically in evidence accumulation. For example, if the inactivation selectively abolishes the memory of prior evidence, stimuli presented thereafter should still be integrated and a model based the evidence after the photostimulus should predict choice. If so, this could strengthen the interpretation that the deficits are specific to the accumulated evidence. Otherwise, it could suggest inactivation is degrading performance for other reasons.4) In general, I could not find information on how well the logistic regression predicts choice.5) The main result of the paper  is based on effects averaged across different inactivation conditions (different epochs). However, I wonder if it makes sense to combine conditions like this. One, I wonder if this could hide areas that are involved during specific epochs of the task. The text states that "…aligned curves from different epochs were fairly consistent ", but it is not clear how this is quantified and compared to what reference. Two, I wonder if this pooling would violate assumptions of statistical tests given data now comes from distinct sources, rather than being repeated observations.6) The analysis of calcium dynamics are based on the autoregressive component of the GLM model. This is counterintuitive because that component is not related to the stimulus or the task. If the claim is that evidence accumulation is related to the timescale of neural dynamics, shouldn't the analysis focus on the coefficients for E_δ (cumulative #R – #L towers), i.e. the component of the dynamics that encodes the stimulus?7) In a couple of places in the text, I feel the claims should be weakened as they go beyond the data. For example,a. Intro: "… provide the first casual demonstration that this hierarchy [of timescale] is important for cognitive behavior." A similar statement is in the 2nd paragraph of discussion. I suggest changing the framing. The experiments do not manipulate the timescale of cortical regions. The relationship with the observed behavioral deficit is correlative.b. Page 11, "This suggests that signals from the different dorsal cortical areas could be combined by downstream regions in a near-linear fashion. Candidate regions include … " The following paragraph is perhaps more suitable for discussion since the experiments do not probe subcortical regions. Also see comment 8 below. The effects of combined-area inactivation in fact appear to be qualitatively different from the average of single area silencing.c. Page 13, "…the different intrinsic timescales across the cortex support evidence integration over time windows of different durations." For the same reason as in comment (a) above, I suggest rephrasing or removing this framing.d. Abstract and intro, "inactivation of different areas primarily affected the evidence-accumulation per se, rather than other decision-related process". It seems the results do not examine other decision-related process besides the weighting of sensory evidence.eLife 2019), which suggests that the spread of inactivation at 6 mW extends well beyond 2 mm in radius.e. The text claims the spatial resolution of inactivation is 1.5-2mm. This is somewhat misleading. In Figure S2 of Pinto 2019, 60% of neurons are silenced at this light intensity at 2mm from light center. This broad inactivation is also consistent with the characterization from the Svoboda lab  In Figure 2-Supp 3, the effects of posterior vs frontal cortex inactivation do not appear to be very different from each other. This is somewhat different from the averages of single area effects. In general, the statistical tests in the paper do not directly compare the effects of posterior cortex inactivation vs. frontal cortex inactivation. A more appropriate test for the key conclusion should be an interaction of y-position dependence with cortical regions.9) The explanation of power analysis is not very clear (page 26-27). How are the control trials subsampled at different number of inactivation trials? What does it mean to bootstrap all the inactivation conditions together? At what effect size is n=250 sufficient to detect the effect?10) The non-monotonic effect of cluster 3 (V1 and RSC) in Figure 2c is counterintuitive. The effect seems to be present in several individual conditions in Figure 1-Supp 2. However, other conditions don't show this (e.g. delay epoch inactivation). The text states that the effect is potentially compatible with findings that multiple timescales exist in a single region. Please explain this notion more clearly and how it could lead to no deficit for recent stimulus information but deficits for distant stimulus memory.11) Mice speed up during photostimulation in nearly all conditions . Are mice responding to the light? Ideally, a negative control could be included to show there are no non-specific effects of photostimulation when analyzed in the logistic regression. This could be done by photostimulation in GFP mice or by inactivation a cortical region not involved in the behavior.Reviewer #3 (Recommendations for the authors):Related to the above comment on aggregating data across mice, the presentation of the data would be more transparent if mouse-by-mouse results were shown, where possible . For example, symbols for individual mice could be shown in Figure 1E instead of (or in addition to) the mean across mice. Presumably change in performance was calculated within mice and then averaged, rather than averaging laser on and laser off performance across mice and then taking the difference between the two. But the description  could apply to either analysis. Essential revisions: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 authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in some cases, suggesting that inactivation of frontal cortical regions had long-lasting effects than that of posterior cortical regions. This study provides important results addressing the relation between cortical functions and intrinsic timescales.The reviewers agreed that this study addresses an important question, and the authors performed sophisticated experiments, and collected a large amount of data. The results are presented clearly and the manuscript is well-written. All the reviewers thought that the results are potentially of great interest to a wide audience. However, the reviewers found several substantive issues which reduced the confidence on the authors' conclusions. These issues need to be addressed before publication of this study at eLife.We are glad the reviewers found our work to be of potentially great interest, and we thank them for their valuable, thorough and constructive critique of our manuscript. We believe they raised a number of important issues, which prompted us to significantly revise our analytical approaches and writing. Specifically, we have made the following major changes to the manuscript:– We have replaced the analysis of inactivation effects on evidence weighting for a mixed-effects logistic regression model that is parameterized in time instead of space. This new parameterization better separates the effects on sensory evidence that occurs before, during or after the inactivation, all within the same models. Moreover, variability from individual mice is now explicitly accounted for as random effects. Further, we also combine different epochs in the same model by adding them as additional random effects. These changes simultaneously address major concerns about inter-mouse variability, low-n statistics on conditions rather than mice, and potential confounds from laser-induced changes and inter-trial variability in running speed. Overall, the results from the new analysis largely match our previous analysis, but the increased statistical power and better parameterization allowed us to clear up some findings.– Individual mouse data are now displayed where relevant.– We have added analyses of stimulus-related coefficients in the linear encoding models of widefield data.– We have removed our claims of causality and exclusive inactivation effects on evidence accumulation, and added more thorough discussions of the caveats around the interpretation of our findings.We hope the reviewers and editors will agree that we have satisfactorily addressed their concerns, and as a result our manuscript is much improved. We provide detailed responses to the individual points below.In particular, the following points have been identified as essential issues:1. The presented analysis does not consider a large variability that exists at the level of individual animals. There is also some variability across conditions (e.g. photoinhibition of different epochs). Furthermore, the statistical analyses presented in the manuscript often rely on a small number of samples, and the sample size is not equal across the conditions . Because of these issues, we felt that the main conclusion needs to be supported by further analysis investigating these variabilities, and careful discussions of these potential caveats.We have entirely replaced this analysis for a mixed-effects logistic regression approach in which the fixed effects are weights of sensory evidence in time, and random effects are mice and conditions. Thus, we now explicitly model the variability introduced by these two factors, which allows us to focus on the effects that are common across mice and conditions. Additionally, we now 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 . Finally, to further estimate variability introduced by mice and conditions (epochs), we have devised a shuffling procedure where laser-on labels are shuffled while maintaining mouse and condition statistics. We use this procedure to estimate the empirical null distributions of inactivation-induced changes in evidence weights, which we use to further assess statistical significance of the effects. These new analyses are presented in Figures 2, 3 and corresponding supplements. We have also added text to be more explicit about these sources of variability (line 169):“(…) to account for the inter-animal variability we observed, we used a mixed-effects logistic regression approach, with mice as random effects , 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 . 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 . 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 , such that epoch differences  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. Each fixed-effects data point in figures below  thus reflects tendencies common across mice, not individual mouse effects; the latter are shown as the random effects (faded colors). 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.”2. The authors claim that the optogenetic silencing primarily affected the evidence-accumulation computation, but not other decision-related processes. The reviewers found this claim to be not strongly supported by the data. From the presented data, whether silencing specifically affected the evidence-accumulation process, not just passing the evidence to an accumulation process, remains unclear. Furthermore, silencing affects running speed . Also, the reviewers thought that alternative possibilities have not been fully examined.We agree with the reviewers that our previous modeling approach did not allow us to adequately separate between these different processes, both because it confounded sensory processing and sensory memory and because effects on post-laser sensory evidence were part of a separate analysis that did not take into account pre-laser evidence weights. To address these shortcomings, not only did we fit a new model in time rather than space, but we also separated evidence occurring before, during or after inactivation within the same model. We did so by aligning evidence time to either laser onset or offset . 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 . 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.”We would also like to emphasize that we consider the evidence-accumulation process to involve both “passing evidence to the accumulator” and “accumulating / remembering it.” To make this more explicit, we have added the following sentence to the introduction (line 48):“[evidence accumulation] involves remembering a running tally of evidence for or against a decision, updating that tally when new evidence becomes available, and making a choice based on the predominant evidence.”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 reviewers 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.3. Optogenetic silencing sometimes increased the running speed. This can potentially reduce the time spent in each location, and may affect the acquisition sensory information. It is important that the reduced regression weight is not the side effect of reduce time spent in each location. Furthermore, some analysis based on time, not just locations, would be very helpful.The increases in running speed are small in magnitude, averaging only ~8% from control levels across conditions (we have changed the units in the speed figure from cm/s to % of control to highlight that). However, we do agree with the reviewers that this potentially introduces confounds when analyzing the effects in space rather than time. Thus, along with the changes described above, we have replaced the analysis in time by a single model that parametrizes evidence in time, aligned to either laser onset or offset. These analyses are now presented in Figures 2 and 3, and corresponding supplements, and largely confirm our main findings.More detailed comments and suggestions on the above issues are included in the individual reviewers' comments.Reviewer #2 (Recommendations for the authors):1) Overall, the inactivation effect is highly variable across brain regions and conditions. For example, in Figure 1-Supp 2, silencing mV2 and RSC during the 3rd quarter of the cue region reduce weighting 100 cm back, but the effect is not replicated when silencing is extended in time . The effect is yet different when silencing the posterior cortical regions, which covers mV2 and RSC. There are many cases like this. What is this variability due to? Is this degree of variability expected from behavioral variability? It is difficult to evaluate how robust the behavioral deficits are without an estimate of the expected variability and false positive rate.We have now changed our modeling approach to better account for the variability across mice and conditions present in the data. We now use a single mixed-effects model to fit all conditions and mice for a given area, using evidence weights as fixed effects and mice and conditions as random effects. Because of the large number of different conditions (54), not all mice were exposed to all area-epoch combinations. Thus, it is unfortunately not possible to dissociate these two sources of variability. For example, it could be the case that inter-condition variability genuinely reflects some dynamic process, such that the effect of inactivating a given area is different depending on the inactivation epoch. While this possibility is interesting, we cannot probe it conclusively because inter-condition variability could trivially arise from differences between mice exposed to different conditions. We now discuss this explicitly in the Results section (line 174):“(…) 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 . 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 , such that epoch differences  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. (…) Thus, 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.”Following the reviewer's excellent suggestion, we have also devised a procedure to estimate the empirical null distribution of laser effects, taking into account the variability in our data. We then used that distribution to further constrain effect significance. This is explained in Materials and methods :“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. (…) Significance of the coefficients in the mixed-effects model of evidence in time were calculated using a t-test based on the coefficient estimate and its standard error. Additionally, for the models in Figure 2 we only considered coefficients to be significant if their standard error did not overlap the ± 1 SD intervals from the coefficients extracted from the shuffled models.”Finally, we note that all of our p-values are in fact corrected for a false discovery rate using Bejamini and Hochberg's method (1995), as described in Materials and methods (line 739):ith ranked p-value, iP, was deemed significant if it satisfied π ≤ (αi)/n, where n is the number of comparisons and α is the significance level. In our case, α = 0.050 and 0.025 for one-sided and two-sided tests, respectively.""We corrected for multiple comparisons using a previously described method for false discovery rate (FDR) correction . Briefly, p-values were ranked in ascending order, and the 2) The conclusion that inactivation primarily affects evidence accumulation is based on weights from the logistic regression. A drop in weights of the sensory evidence presumably means the stimulus information is lost. However, there could be other reasons weights could drop. For example, if mice stop engage in the task after photostimulation, this could presumably lower the weights since mice no longer base their choice on the sensory stimulus. The analysis of weights after photostimulation provides a nice control . However, several areas do show prospective deficits in weighting of future evidence, although this is not observed in all areas. Prospective deficits could be consistent with mice stop performing the task. This possibility should be ruled out.3) Some additional analyses could further corroborate the interpretation that the deficit is specifically in evidence accumulation. For example, if the inactivation selectively abolishes the memory of prior evidence, stimuli presented thereafter should still be integrated and a model based the evidence after the photostimulus should predict choice. If so, this could strengthen the interpretation that the deficits are specific to the accumulated evidence. Otherwise, it could suggest inactivation is degrading performance for other reasons.within the same model. Thus, because choice can be predicted based on both pre- and post-laser evidence, we can now compare the effects on these directly . With two exceptions, we did not observe any significant deficits in prospective evidence weighting. As the reviewer points out, this suggests that the mice perform the task normally after the inactivation in these cases. We did observe post-inactivation changes in evidence use for M1 and mM2, although these were milder than the decreases in pre-laser evidence weighting . We believe that this suggests a role for these areas in both accumulation and post-accumulation processes. Of course, this does suggest that evidence accumulation is not the only affected computation. We also agree that we have not completely ruled out other potential sources of behavioral deficits. 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.We will address points 2 and 3 together. We agree with the reviewer that our previous modeling approach did not allow us to adequately separate between these different processes, both because it confounded sensory processing and sensory memory and because effects on post-laser sensory evidence were part of a separate analysis that did not take into account pre-laser evidence weights. However, we believe that our new modeling approach addresses these shortcomings by separating evidence occurring before, during or after inactivation 4) In general, I could not find information on how well the logistic regression predicts choice.Thank you for catching this oversight. We have added cross-validated choice-prediction accuracy distributions to Figure 2B (~70% accuracy across models).5) The main result of the paper  is based on effects averaged across different inactivation conditions (different epochs). However, I wonder if it makes sense to combine conditions like this. One, I wonder if this could hide areas that are involved during specific epochs of the task. The text states that "…aligned curves from different epochs were fairly consistent ", but it is not clear how this is quantified and compared to what reference. Two, I wonder if this pooling would violate assumptions of statistical tests given data now comes from distinct sources, rather than being repeated observations.The reviewer makes a good point. However, as detailed in our response to comment 1, we cannot be certain that those are legitimate differences between epochs, rather than just an artifact of inter-animal variability. Thus, we would rather make the more conservative choice of presenting them together, while still showing more granular per-condition analyses in the supplements. We also justify these choices more explicitly in the main text now. Finally, while our previous method of pooling data may have violated statistical assumptions, the fact that we are now fitting all data in the same model and explicitly modeling that variability as random effects should address that concern.6) The analysis of calcium dynamics are based on the autoregressive component of the GLM model. This is counterintuitive because that component is not related to the stimulus or the task. If the claim is that evidence accumulation is related to the timescale of neural dynamics, shouldn't the analysis focus on the coefficients for E_δ (cumulative #R – #L towers), i.e. the component of the dynamics that encodes the stimulus?We have now added the analysis suggested by the reviewer to Figure 4—figure supplement 1. Interestingly, we did not observe systematic timescale differences in E_δ, or in the responses locked to sensory-evidence pulses. This is now described in the Results section (line 315):“We first wondered whether different timescales would be reflected in model coefficients related to sensory evidence. Interestingly, however, we did not observe any significant differences across areas in the time course of coefficients for contralateral tower stimuli or cumulative sensory evidence . Thus, we next focused our analysis on the auto-regressive coefficients of the model.”At face value, this would suggest that the auto-regressive coefficients capture temporal components of neural dynamics that are not locked to any task events. Of course, the possibility remains that, despite our extensive parameterization of behavioral events, we failed to capture 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 , it could not apply to our case."7) In a couple of places in the text, I feel the claims should be weakened as they go beyond the data. For example,a. Intro: "… provide the first casual demonstration that this hierarchy [of timescale] is important for cognitive behavior." A similar statement is in the 2nd paragraph of discussion. I suggest changing the framing. The experiments do not manipulate the timescale of cortical regions. The relationship with the observed behavioral deficit is correlative.Following the reviewer's suggestion, we have removed these claims altogether.b. Page 11, "This suggests that signals from the different dorsal cortical areas could be combined by downstream regions in a near-linear fashion. Candidate regions include … " The following paragraph is perhaps more suitable for discussion since the experiments do not probe subcortical regions. Also see comment 8 below. The effects of combined-area inactivation in fact appear to be qualitatively different from the average of single area silencing.This has been moved to the discussion as suggested. Also please note that, as we elaborate in the response to comment 8 below, our new analysis methods did reveal significant differences between simultaneous silencing and single-area averages. The discussion has been updated accordingly.c. Page 13, "…the different intrinsic timescales across the cortex support evidence integration over time windows of different durations." For the same reason as in comment (a) above, I suggest rephrasing or removing this framing.We have rewritten this sentence as: “our findings could suggest that the different intrinsic timescales across the cortex are important for evidence-accumulation computations.” Note that this now also immediately follows our discussion on the caveats about the auto-regressive coefficients (see response to comment 6).d. Abstract and intro, "inactivation of different areas primarily affected the evidence-accumulation per se, rather than other decision-related process". It seems the results do not examine other decision-related process besides the weighting of sensory evidence.Following the reviewer’s suggestion, we have changed this framing throughout. While we do believe that our revised analyses indicate disruptions to the accumulation process (see response to comments 2 and 3 above), we agree that we did not fully examine other alternatives, so we removed our claims that evidence accumulation is the only affected process.e. The text claims the spatial resolution of inactivation is 1.5-2mm. This is somewhat misleading. In Figure S2 of Pinto 2019, 60% of neurons are silenced at this light intensity at 2mm from light center. This broad inactivation is also consistent with the characterization from the Svoboda lab , which suggests that the spread of inactivation at 6 mW extends well beyond 2 mm in radius.The original estimate referred to full inactivation, but the reviewer is of course correct that we still saw partial inactivation at 2 mm. We have therefore replaced these statements in the text by "≥ 2 mm" .8) In Figure 2-Supp 3, the effects of posterior vs frontal cortex inactivation do not appear to be very different from each other. This is somewhat different from the averages of single area effects. In general, the statistical tests in the paper do not directly compare the effects of posterior cortex inactivation vs. frontal cortex inactivation. A more appropriate test for the key conclusion should be an interaction of y-position dependence with cortical regions.The reviewer is correct that simultaneous inactivation appears to yield qualitatively different results than individual area averages. Our previous statistical procedures did not capture significant differences given their low power. However, our new analysis indeed revealed those differences to be significant . Despite this, it remains the case that frontal inactivation caused larger deficits on longer timescales, either when comparing single-area averages  or simultaneous inactivation . We have expanded the description of these findings in the Results section, copied below for the reviewer's convenience (line 232):5,15 = 3.09, p(time) = 0.047, F(area)1,3 = 33.93, p(area) = 0.010, F(interaction)5,15 = 3.60, p(interaction) = 0.025)."(…) Indeed, frontal and posterior areas differed significantly in terms of the magnitude and time course of evidence-weighting deficits induced by their inactivation To further explore the different contributions of posterior and frontal cortical areas to the decision-making process, we next analyzed the effect of inactivating these two groups of areas simultaneously, using the same mixed-effects modeling approach as above. Compatible with our previous analysis, we found significant differences in how these two manipulations impacted the use of sensory evidence . In particular, compared to posterior areas, frontal inactivation resulted in a significantly larger decrease in the use of sensory evidence occurring long before laser onset . Moreover, it led to decreases in the use of sensory evidence occurring after inactivation , lending further support for a role of these regions in post-accumulation processes.Finally, we wondered whether evidence information from different areas is evenly combined, at least from a behavioral standpoint. To do this, we compared the effects of simultaneously inactivating all frontal or posterior areas to that expected by an even combination of the effects of inactivating areas individually (i.e. their average). Both posterior and frontal significantly deviated from the even-combination prediction . This could suggest that signals from the different dorsal cortical areas are combined with different weights towards a final decision."9) The explanation of power analysis is not very clear (page 26-27). How are the control trials subsampled at different number of inactivation trials? What does it mean to bootstrap all the inactivation conditions together? At what effect size is n=250 sufficient to detect the effect?We regret that this section was not written clearly enough. We have rewritten it to clarify the reviewer’s questions (line 672):“To estimate statistical power, we performed a bootstrapping-based power analysis based on the one described by Guo et al. . We randomly subsampled the full dataset containing all inactivation conditions. In each subsample, we selected different numbers of inactivation trials regardless of area-epoch combination , and added a random subset of control trials such that the relative proportion of control to laser trials was preserved in the subsampled dataset. We then ran the bootstrapping procedure described above to compute laser-induced changes in overall performance combined across all inactivation conditions, extracting p-values for each of the values of n subsamples. We repeated this procedure ten times. Power was defined as the minimum number of trials required to observe p < 0.05 at the empirical effect size pooled across conditions, as defined by the first n where the 2 x SEM across the 10 repeats is below 0.05. We obtained an aggregate power of n = 250.”10) The non-monotonic effect of cluster 3 (V1 and RSC) in Figure 2c is counterintuitive. The effect seems to be present in several individual conditions in Figure 1-Supp 2. However, other conditions don't show this (e.g. delay epoch inactivation). The text states that the effect is potentially compatible with findings that multiple timescales exist in a single region. Please explain this notion more clearly and how it could lead to no deficit for recent stimulus information but deficits for distant stimulus memory.We share the reviewer's puzzlement about these findings. However, they have remained true even after we accounted for inter-subject and inter-condition variability. Following the reviewer's suggestion, we have expanded the discussion of the findings, including possible technical artifacts leading to them :"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 , a possibility that should be evaluated in future studies using simultaneous inactivation and large-scale recordings across the dorsal cortex. At face value, however, the findings could be a reflection of the fact that diverse timescales exist at the level of individual neurons within each region . For example, inactivating an area with multimodal distributions of intrinsic timescales across its neurons could conceivably result in non-monotonic effects of inactivation."11) Mice speed up during photostimulation in nearly all conditions . Are mice responding to the light? Ideally, a negative control could be included to show there are no non-specific effects of photostimulation when analyzed in the logistic regression. This could be done by photostimulation in GFP mice or by inactivation a cortical region not involved in the behavior.We performed the suggested controls in the context of a recent paper, in which we used identical photostimulation parameters . We did not observe the significant effects on running speed for any of the locations tested here when we performed the experiments in mice not expressing channelrhodopsin (and only a minor effect in 1/29 tested locations). We have added a statement to this effect when describing these results (line 151):“Importantly, we have previously shown that these effects are specific to mice expressing ChR2, ruling out a non-specific light effect .”Reviewer #3 (Recommendations for the authors):Related to the above comment on aggregating data across mice, the presentation of the data would be more transparent if mouse-by-mouse results were shown, where possible . For example, symbols for individual mice could be shown in Figure 1E instead of (or in addition to) the mean across mice. Presumably change in performance was calculated within mice and then averaged, rather than averaging laser on and laser off performance across mice and then taking the difference between the two. But the description  could apply to either analysis.combined across mice” to “pooled across mice” where relevant.We have now added individual mouse data where applicable . However, performance was in fact calculated on data pooled across mice and averaged across bootstrapping iterations  to account for different numbers of trials across mice. Thus, the displayed averages are closer to trial-count-weighted averages across mice. We now realize that the wording was unclear, and have changed “


Background: Neuromodulation using epidural electrical stimulation (EES) has shown functional restoration in humans with chronic spinal cord injury (SCI). EES during body weight supported treadmill training (BWSTT) enhanced stepping performance in clinical trial participants with paraplegia. Unfortunately, tools are lacking in availability to quantify clinician assistance during BWSTT with and without EES. Force sensitive resistors (FSRs) have previously quantified clinician assistance during static standing; however, dynamic tasks have not been addressed.Objective: To determine the validity of FSRs in measurements of force and duration to quantify clinician assistance and participant progression during BWSTT with EES in participants with SCI.Design: A feasibility study to determine the effectiveness of EES to restore function in individuals with SCI.Methods: Two male participants with chronic SCI were enrolled in a pilot phase clinical trial. Following implantation of an EES system in the lumbosacral spinal cord, both participants underwent 12 months of BWSTT with EES. At monthly intervals, FSRs were positioned on participants' knees to quantity forces applied by clinicians to achieve appropriate mechanics of stepping during BWSTT. The FSRs were validated on the benchtop using a leg model instrumented with a multiaxial load cell as the gold standard. The outcomes included clinician-applied force duration measured by FSR sensors and changes in applied forces indicating progression over the course of rehabilitation.Results: The force sensitive resistors validation revealed a proportional bias in their output. Loading required for maximal assist training exceeded the active range of the FSRs but were capable of capturing changes in clinician assist levels. The FSRs were also temporally responsive which increased utility for accurately assessing training contact time. The FSRs readings were able to capture independent stance for both participants by study end. There was minimal to no applied force bilaterally for participant 1 and unilaterally for participant 2.Conclusions: Clinician assistance applied at the knees as measured through FSRs during dynamic rehabilitation and EES (both on and off) effectively detected point of contact and duration of forces; however, it lacks accuracy of magnitude assessment. The reduced contact time measured through FSRs related to increased stance duration, which objectively identified independence in stepping during EES-enabled BWSTT following SCI. Spinal cord injury (SCI) affects nearly 300,000 individuals in the United States each year , and sevThe delivery of BWSTT has included numerous approaches since its inception, but most models require four highly skilled clinicians experienced in providing tactile cues and support for individuals with SCI. Detailed recovery models, designed to progressively regain lost functions, are an alternative option to current guidelines of standard-of-care therapy. The BWSTT principles instruct clinicians on optimal hands-on support at the legs, progressively increasing limb loading and repetition of steps for maximal sensory input . The rehThe tools to measure independence and progression of functional recovery with the use of EES are imperative to provide support for continued therapeutic care and are necessary to demonstrate progression of function. Other forms of stimulation, such as non-invasive transcutaneous stimulation, have shown improvement in independence through measurement of interactive applied forces with force sensitive resistors (FSRs). The FSRs are commercially available and manufactured from thin, flexible polymers that allow easy adaptation to locations where forces may be applied. The internal resistance of the FSR changes when force is applied to the active area of the sensor, which affects the output voltage and current passing through it . The FSRThe studies that report functional return with the use of EES and rehabilitation interventions have yet to classify clinician assistance using objective measures during BWSTT to quantify performance progress. In this study, the FSRs were incorporated during dynamic BWSTT stepping assessments, with the sensors overlying the tibial tuberosity of participants to assess applied forces during the stance phase of the gait cycle to monitor progression toward maximal independence and minimal clinician assistance. Therefore, the goal of this study was to validate the FSRs for application during dynamic stepping activities, and to verify timing and force application of clinician assistance, which may indicate participant progression toward independence during BWSTT.1 and c0  (Equation 1).Square FSR sensors  were soldered to 10-foot leads with a Bayonet Neill-Concelman (BNC) connector on the terminal end. The FSRs were connected to an operational amplifier circuit powered with a direct current (DC) supply voltage of 5 V . Before An instrumented lower leg model (ILL) was constructed from polyvinyl chloride (PVC) tubing (5-inch outer diameter), extruded structural aluminum, metal and polymer hardware, and a six-axis load cell  to document applied forces to the leg to serve as a gold standard . The ILLTwo clinicians, experienced in locomotor training, participated in this validation study. The clinicians applied load to the patella surface of the ILL to simulate contact with a recipient of locomotor training. Each clinician applied loads to the ILL at three different load levels  that were based on their tactile feel for the range of loading applied clinically. Sets of 10 repeated loads (each representing a leg step) were applied at each load level with short breaks between each set. To drive a frequency of load application that would be consistent and similar to real locomotor training conditions, clinicians applied loads in cadence with a metronome set at a rate of 70 beats per minute. Both clinicians applied loading sets to the ILL positioned on the framework to represent a recipient's right-side leg; the ILL was then repositioned to represent a recipient's left-side leg. Each clinician applied a total of 60 loads to the ILL. The FSR and ILL loads were recorded at a sample rate of 4 kHz .Two male participants, both diagnosed with traumatic, sensorimotor complete SCI and American Spinal Injury Association (ASIA) Impairment Scale Grade A (AIS-A)  were enrThe participants completed 6 months of locomotor training following protocols laid out by Locomotor Training Principles and Practice . FollowiOptimal parameters were systematically determined through testing of active electrode configurations and adjustments in stimulation parameters that have been described in our previous publication . These Ep < 0.05). Pearson Correlation Coefficients were calculated, and straight lines were fit to the data using linear regression to characterize any proportional bias.The steps for which the FSR load on the ILL did not exceed the measurement range were identified for analysis. Peak FSR load and peak resultant applied force on the load cell were calculated for these steps. The duration of contact was also assessed for all steps. Bland-Altman plots were generated for applied force and duration data to identify the presence of any offset or proportional biases in the FSR response. Offset biases were tested using a Wilcoxon signed rank test . Each gait cycle duration was identified by Tekscan in-shoe sensors. The stance and swing phase of the gait cycle were identified similar to our previous publication . The durOf the 120 loads applied to the ILL by the two clinicians who participated in the validation study, 52 attempts resulted in loads that did not exceed the capacity of the FSR sensor. For the low-level loading, 40 of 40 loads registered on the FSR (20 per clinician), 12 of 40 medium-level loads registered (5 for clinician 1 and 7 for clinician 2), and 0 of 40 high-level loads registered. The gold standard load cell indicated that the median (range) of low-level applied force by the clinicians was 61.8 N (29.1–102.9 N), the median (range) of medium-level applied force was 124.7 N (82.3–261.0 N), and the median (range) of high-level applied force was 304.7 N (230.9–385.5 N).p < 0.001; p < 0.001; The Bland-Altman plot for applied force results indicated the presence of an offset bias between the response of the FSR and that of the gold standard; the FSR response magnitude consistently falls below that of the gold standard, and the bias was confirmed by the Wilcoxon signed rank test (N) for both legs at each timepoint during the stance phase of the gait cycle are presented in Table 2.Participant 1 data including the median range of the maximum applied forces (The median peak applied forces reported between FSRs (right and left) and between timepoints are highly variable. During stance phase with “no EES” condition, force was applied for all steps within all trials indicating the need for assistance during knee extension. Overall, the median applied forces for the left leg, during “no EES,” were always higher than “EES on.” However, the right leg had a single trial during month 12 where the median applied force was higher during “EES on” . The oveThe median peak applied forces reported between FSRs (right and left) and between timepoints are highly variable. Overall, the median peak applied forces were higher during “EES on” for the left leg. The right leg, on the other hand, had similar applied forces for both conditions . During Due to the nature of locomotor training being highly variable and relying heavily on hands-on cueing and support for individuals with SCI, it is plausible that the level of assistance during step training with EES can be related to the level of independence during stepping. During BWSTT, applied force was used to assist in stance phase of stepping to prevent knee buckling and to properly cue and engage the lower extremity extensor muscles. Two participants, both with sensorimotor complete SCI graded AIS-A, were able to improve independence of the stance phase in BWS treadmill stepping with optimal EES detected by FSRs positioned at the tibial tuberosity, which measured applied forces between clinicians' hands and participants' knees. During dynamic training with “no EES” and “EES on,” the FSRs were used to detect changes in force and duration of assistance during the stance or extension phase of the gait cycle to determine progression toward independence while using minimal BWS and slow speeds by month 18. The FSRs detected accurate point of contact and duration of assisted force at the knee related to increasing stance phase over the course of EES for both participants. Currently, there no clinical tools available to quantify assistance during BWSTT. The NeuroRecovery Scale assesses progression during step training in terms of independence from BWS, however, hands-on assistance is not graded . The FunThe force was detected for most trials without a plateau except for participant 2 during month 12, where the FSR readings plateaued at ~80 N . During The FSR validation determined that we can accurately assess contact initiation and duration by the clinicians compared to the gold standard load cell. Reduced clinician assistance was evidenced by decreased durations of applied force on the FSRs at the knee during the use of EES. Participant 1 did not require hands-on application at the knee for consecutive steps bilaterally with the use of EES by month 18 . ParticiPreviously, Sayenko et al. reported FSR data from static standing demonstrating that external assistance at the knees decreased with increasing transcutaneous stimulation intensities, compared to no stimulation. Similar to observations in our study, the assistance during static activities was needed consistently during conditions with no stimulation . PreviouSeveral strategies for improved force magnitude accuracy are apparent. One is to optimize the calibration procedure. All calibrations in this study were performed quasi-statically with the FSR laid flat on a hard surface; the environments in which the sensors are applied during participant assessments required them to be slightly curved and laid over a soft substrate. Additional experimentation to recreate the form, material interactions, and dynamics of the application environment may improve output. Another strategy may involve engineering the manner in which clinician forces are directed. The area of these FSRs is smaller than the trainer hands, and we cannot say with certainty that all forces are directed to the sensor and not shared with the surrounding tissue. A “handle” or some other fixture attached to the FSR may be engineered to better capture the transfer of force from the clinicians' hand to the sensor. Additionally, a sensor of a different, customized coverage area may be considered. Lastly, since we observed that the measurement range of these off-the-shelf sensors was not sufficient to capture the entire domain of forces for this activity, customizing the properties of a FSR to alter sensitivity and saturation load may be beneficial.For the validation study, the 6-mm-thick Akton pad used may have had greater compliance than that of the soft tissue located at the knee on a human limb. A pad with lesser thickness may be more representative of tissue properties at the tibial tuberosity. The use of a single FSR between the two clinicians for the validation may have resulted in a degradation of quality of the sensor over time between testing bouts and limited the accuracy of grading low, medium, and high applied forces. Also, grading applied force simply as low, medium, and high may not create enough resolution to generate true differences. Application of this methodology during a participant's session with more trials, and where no EES, suboptimal EES, and optimal EES are used, may provide more concrete descriptions of assistance levels. Finally, the FSRs each have their own inherent range making it difficult to detect true trends between data collection timepoints.The FSRs applied to rigid surfaces produced accuracy in measurements of timing of assistance; however, there are limitations in surface placement of device and consistent device performance requires consistent placement of hands on the sensors by trained clinicians. The conditions of use of the FSR in the clinical study were not strictly controlled as this was not a priority of the study, and this could have been a limitation on data analysis. Sensors composed of a more flexible material, which would mimics the skin texture, could be useful on less rigid surfaces allowing for movement with skin surface during dynamic activities. The FSRs were not applied to the medial hamstring for assessment of the swing phase. Alternate instrumentation is recommended for application to the medial hamstring and ankle region for swing evaluation. The shape and fixation method of the current FSRs would potentially impede the sensory cues necessary for the level of movement and variability in tissue density during swing phase. Force sensitive gloves could be an option; however, hands-on application of sensory cues during BWSTT are intended to be skin-to-skin contact. Further development of FSRs with a more pliable material may enable application to other locations where force is applied during step training, including the hamstrings and tibialis anterior and would enable a more complete picture of assistance throughout the gait cycle.Based on the results of this study, we conclude that FSRs could be applied to effectively measure clinicians' assistance and their corresponding applied force duration during dynamic rehabilitation combined with EES on and off. The FSR sensors effectively detected clinician assistance applied at the knees during dynamic rehabilitation with EES (both on and off) in terms of initial point of contact and duration of forces. However, accuracy of force magnitude was less impressive with this configuration. With this tool, we were able to observe reduced contact time by the clinicians that was related to increased stance duration, which objectively identified independence in stepping with the use of EES-enabled BWSTT following SCI. Future studies using FSRs to assess clinician assistance including other landmarks may provide a more complete picture of overall assistance during dynamic tasks. The FSRs have been demonstrated to provide accurate feedback on frequency and duration of clinician contact. With modifications to calibration techniques and custom-design strategies, absolute measures of force magnitude may be used detect clinician assistance and indicate progression during a variety of dynamic therapeutic interventions.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Mayo Clinic Institutional Review Board. The participants provided their written informed consent to participate in this study.KDZ, MLG, PJG, and DGS initiated the project. ART designed the validation experiment. ART, MBL, and MLG performed the validation experiment. MBL, MLG, DDV, and KJF performed the rehabilitation for participants. ART, PJG, and JSC contributed to participant data collection. MBL, ART, and CL drafted the manuscript as well as completed revisions, analysis, and interpretation of data with subsequent contributions from all authors. MLG, DDV, RFH, JSC, DGS, and KJF assisted in revisions. KDZ supervised all aspects of this project and provided approval for the publication and content. All authors approved the final submission.KDZ received funding from the Jack Jablonski Bel13ve in Miracles Foundation, Mayo Clinic Rehabilitation Medicine Research Center, Mayo Clinic Transform the Practice, and Craig H. Neilsen Foundation. PJG was supported by Regenerative Medicine Minnesota and the Mayo Clinic Center for Regenerative Medicine. JSC was supported by the Mayo Clinic Graduate School of Biomedical Sciences.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.