Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

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1. 

   Version published to 10.7554/elife.78620 on eLife

   Oct 12, 2023
2. 

   eLife

   May 25, 2023

   eLife assessment

   Using chemogenetic manipulation, the authors induce or suppress activity in D1 spiny neurons in the dorsomedial striatum of mice. The results effectively demonstrate that excitation or inhibition of this class of neurons results in a consistent behavioral effect that is linked to an impact on local dynamics in thalamic regions that project to this part of the thalamus, as well as cortical regions that can be more readily defined as unimodal as identified by a classification approach. This work has clear relevance to the field of neuroimaging, getting at the broader hemodynamic signatures of direct pathway stimulation in the striatum, but requires critical revisions to justify their main conclusions.

   Using chemogenetic manipulation, the authors induce or suppress activity in D1 spiny neurons in the dorsomedial striatum …

   eLife assessment

   Using chemogenetic manipulation, the authors induce or suppress activity in D1 spiny neurons in the dorsomedial striatum of mice. The results effectively demonstrate that excitation or inhibition of this class of neurons results in a consistent behavioral effect that is linked to an impact on local dynamics in thalamic regions that project to this part of the thalamus, as well as cortical regions that can be more readily defined as unimodal as identified by a classification approach. This work has clear relevance to the field of neuroimaging, getting at the broader hemodynamic signatures of direct pathway stimulation in the striatum, but requires critical revisions to justify their main conclusions.

   Using chemogenetic manipulation, the authors induce or suppress activity in D1 spiny neurons in the dorsomedial striatum of mice. The results effectively demonstrate that excitation or inhibition of this class of neurons results in a consistent behavioral effect that is linked to an impact on local dynamics in thalamic regions that project to this part of the thalamus, as well as cortical regions that can be more readily defined as unimodal as identified by a classification approach. This work has clear relevance to the field of neuroimaging, getting at the broader hemodynamic signatures of direct pathway stimulation in the striatum, but requires critical revisions to justify their main conclusions.

   Read the original source
3. 

   eLife

   May 25, 2023

   Reviewer #1 (Public Review):

   This manuscript reports the results of a clever chemogenetic manipulation study designed to probe how stimulation (excitation and inhibition) of D1-expressing spiny projection neurons in the striatum (direct pathway) influences hemodynamic characteristics of local and global regions in the brain in mice. Stimulating in the dorsal caudate, the authors found alteration of the local hemodynamic BOLD responses in adjacent areas of the striatum, regions of the thalamus known to project back to the striatum, and more unimodal cortical regions. The authors also observed a decrease in functional connectivity between several cortical regions and the striatum with direct pathway excitation, and the opposite effect with direct pathway inhibition. Put together the results begin to paint a picture of the macroscopic …

   Reviewer #1 (Public Review):

   This manuscript reports the results of a clever chemogenetic manipulation study designed to probe how stimulation (excitation and inhibition) of D1-expressing spiny projection neurons in the striatum (direct pathway) influences hemodynamic characteristics of local and global regions in the brain in mice. Stimulating in the dorsal caudate, the authors found alteration of the local hemodynamic BOLD responses in adjacent areas of the striatum, regions of the thalamus known to project back to the striatum, and more unimodal cortical regions. The authors also observed a decrease in functional connectivity between several cortical regions and the striatum with direct pathway excitation, and the opposite effect with direct pathway inhibition. Put together the results begin to paint a picture of the macroscopic signatures of direct pathway stimulation (and inhibition) that could be used to help infer global BOLD patterns in task-related experiments.

   Overall, this appears to be a timely study. The rise of papers using chemo/optogenetic methods to probe the underlying mechanisms of the BOLD response is crucial for the neuroimaging field to continue moving forward. The methods are, for the most part, rigorous and clear. There are, however, a few open issues that require addressing in order for readers to reach the same conclusion as the authors.

   MAJOR CONCERNS

   1. Classifier as an evaluation method.

   The authors largely rely on a support vector machine (SVM) classifier to predict whether BOLD dynamics within atlas-defined regions reflect stimulation-on or stimulation-off windows. While in one way this is a conservative method for evaluating stimulation effects in the resting BOLD fluctuations, the authors largely report their findings as accuracies of the classifier. Figures 3-5 largely only report model accuracy effects, but we get no sense as to what exactly is happening to the BOLD dynamics in each region. The autocorrelation analysis (Fig. 6) somewhat tries to get at this, but only for a subset of regions and the results are largely unclear (see comment below). As a result, a key goal of the study is left largely unaddressed for the reader: i.e. how do intra-region BOLD dynamics change with direct pathway stimulation? The study needs more effort put into this descriptive level of analysis to complement the rigorous classifier analysis.

   Also, the classifier method itself seems highly parameterized. The hctsa method returns 7702 features for each time series. It is unclear exactly how many were in the final set used to run the classification, but even if half of the features were removed, it would still make the classification problem highly overparameterized (e.g., 23 and 25 observations against thousands of features for the excitation and inhibition classifiers respectively). Assuming the authors used cross-validation correctly (which we need more information to support), the risk of inflated classification performance is mitigated. However, we need the details to be able to vet that the bias-variance tradeoff was resolved effectively in this model. In addition, it would be nice to know the features that loaded highly on the final model to resolve the questions about what changes in the local BOLD dynamics from excitation and inhibition of the direct pathway.

   2. Local stimulation effects in the striatum

   Figure 3 is quite confusing. The classifier is supposed to predict stimulation (excitation or inhibition) on vs. stimulation off (control) periods. This would predict a single number (balanced prediction accuracy) per striatal nuclei. Yet the heat maps shown in this figure show classification accuracies for both stimulation and control conditions. Where do the two numbers come from? Also, given the extremely limited short-range lateral connectivity in the striatum, why are the only stimulation effects observed not in the subnucleus being stimulated (Cpl,dm,cd), but for adjacent subnuclei (CPre, CPivmv) and *only* for excitation conditions? This lack of direct change in BOLD dynamics at the stimulated site seems important and largely ignored.

   3. Autocorrelation findings.

   The one attempt to characterize what happens in the intra-region BOLD dynamics is the autocorrelation analysis reported on page 11 and in Figure 6. However this analysis a) only focuses on the thalamic nuclei (not also the cortical and the single striatal site shown to exhibit stimulation effects) and b) only focuses on a few time series measures. Why this limited focus of both target (thalamic nuclei) and measure (first lag of the autocorrelation)? There are many measures for characterizing the temporal characteristics of autocorrelated series from the hctsa analysis. This selective focus seems both narrow and incomplete.

   4. Connectivity results

   The changes in functional connectivity as a result of direct pathway stimulation (excitation and inhibition) are both fascinating and limited. There is a clear excitation/inhibition difference in effects, as shown in Figure 7 B-C. However, Figure 7B suggests something different than the change results shown in Figure 7C. It appears that the application of clozapine increases functional connectivity in the control mice (black line Fig. 7B). This effect is exaggerated in the inhibition condition, but (most importantly) direct pathway excitation does not really reveal a significant change in the BOLD connectivity patterns. Now this does not change the authors' overall conclusions (connectivity is suppressed with direct pathway excitation relative to control mice), but the nuance of what is happening in the control mice is important for interpretation purposes: direct pathway excitation does not necessarily decrease functional connectivity but does not express the increase in connectivity observed from the application of clozapine. This needs to be elaborated more.

   Along the same lines, there is an interesting disconnect between the intra-region results and the inter-region (connectivity) results. It is clear that resting BOLD dynamics in thalamic nuclei that project back to the striatum, as well as more unimodal cortical areas, change from direct pathway stimulation in the dorsal caudate. Yet, only one cortical region (MOp) with significant functional connectivity changes overlaps with the set of nuclei that exhibit intra-region BOLD changes. This suggests that local BOLD dynamics and global connectivity are largely disconnected effects. Yet this seems to be largely ignored in the current work. It would be nice to see more analysis, and discussion, of the intra-region and inter-region stimulation effects.

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4. 

   eLife

   May 25, 2023

   Reviewer #2 (Public Review):

   In their manuscript, Markicevic et al. report that manipulation of D1 spiny neurons in the right dorsomedial striatum results in a behavioral effect observed in motor movement. This behavioral effect is accompanied by changes in BOLD fMRI changes as estimated by a classification approach and pairwise regional correlation. These brain-wide analyses reveal a number of important outcomes. First, alterations in signal dynamics are observed in the striatum most dominantly in the injection site when contrasting excitation to inhibition. Second, thalamic regions that have reciprocal anatomical connections with the injection site show greater classification accuracy. Third, evaluation of cortical regions demonstrates increased classification accuracy for unimodal regions including primary motor, visual, primary …

   Reviewer #2 (Public Review):

   In their manuscript, Markicevic et al. report that manipulation of D1 spiny neurons in the right dorsomedial striatum results in a behavioral effect observed in motor movement. This behavioral effect is accompanied by changes in BOLD fMRI changes as estimated by a classification approach and pairwise regional correlation. These brain-wide analyses reveal a number of important outcomes. First, alterations in signal dynamics are observed in the striatum most dominantly in the injection site when contrasting excitation to inhibition. Second, thalamic regions that have reciprocal anatomical connections with the injection site show greater classification accuracy. Third, evaluation of cortical regions demonstrates increased classification accuracy for unimodal regions including primary motor, visual, primary somatosensory, and posterior parietal association regions. Lastly, using pairwise correlations, a decrease is observed when comparing excitation to either inhibition or no modulation of activity in the primary motor cortex, anterior cingulate, and retrosplenial cortices.

   This report effectively demonstrates that excitation or inhibition of a large population of D1 spiny neurons results in disruption of basic motor behavior. The greatest strength of the work is derived from identifying that features in the time-series of regions in the thalamus that project and receive projections to the injected site are impacted as well unimodal cortical regions. Moreover, a differential effect is observed for excitatory drive relative to both no drive and inhibition. The use of the approach by Fulcher and Jones (2017) provides an important addition to the more commonly used pairwise correlation approach as it relies on the dynamics of the fMRI signal.

   While the methods adopted by the authors to acquire the data and evaluate the experimental manipulations are robust and the obtained results are compelling, the current analysis comes short of relating whether variation that can be estimated across the animals has an impact on these results. Specifically, the authors do not leverage the individual animal viral expression or impact on behavior to constrain and estimate the observed responses reported subsequently. Several reports in humans have used individual variability to estimate the relation between behavior and changes in the BOLD fMRI responses at rest, and a basic demonstration of this type of result has been achieved in mice. Applying a similar approach here would further strengthen the result reported here by identifying which regions are linked to the behavioral deficit (e.g., whether the primary motor cortex is linked to contraversive/ipsiversive rotations at the individual level).

   Complementing linking the behavior of individual animals to changes in the fMRI signal, an estimation of structure-function that is driven by each individual animal's expression map may enhance the current analysis approach by leveraging potential subtle expression variations to reveal whether the observed changes can be explained by the extent to which expression is different across animals. In addition, a quantification of the difference between the excitatory and inhibitory cohorts will rule out that differences in the impact on the fMRI signal were a result of unintentional group differences in expression extent.

   A significant weakness in the current version of the manuscript is the lack of quantification of the viral expression. Currently, the authors do not provide enough information on the extent of coverage of viral expression on average or at the individual level. In particular, while the authors are careful to use the Allen Mouse Brain Connectivity atlas to constrain the fMRI results, they do not relate the specific expression extent, to clearly communicate to the readers, which regions within the striatum are likely to have better representation given the actual expression levels. Moreover, the authors do not use their own nor the Allen Institute data to carry out a formal structure-function analysis (following Stafford et al., 2014 PNAS, for example). This is critical since the authors wish to infer on the impact of their manipulation on both cortical and thalamic regions while the precise region in the striatum that they affect is never quantified.

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5. 

   eLife

   May 25, 2023

   Reviewer #3 (Public Review):

   By using chemogenetic manipulations of direct pathway neurons in the dorsomedial part of the striatum (DMS) of anesthetized mice combined with fMRI, Markicevic et al explore changes in BOLD dynamics at local (striatum) and macro-scale (brain-wide) levels. The article is appropriately written, and the main findings are well organized and presented in 7 figures. Figures 1 and 2 schematize the techniques and document the motor effects of chemogenetic manipulations. Figures 3-7 describe neural changes induced by these manipulations. The main strength of this work is the level of specificity of the chemogenetic manipulations, which combined with brain-wide functional exploration, provide a very useful map of the consequences of activating a specific striatal subpopulation. In my opinion, the main weakness of this …

   Reviewer #3 (Public Review):

   By using chemogenetic manipulations of direct pathway neurons in the dorsomedial part of the striatum (DMS) of anesthetized mice combined with fMRI, Markicevic et al explore changes in BOLD dynamics at local (striatum) and macro-scale (brain-wide) levels. The article is appropriately written, and the main findings are well organized and presented in 7 figures. Figures 1 and 2 schematize the techniques and document the motor effects of chemogenetic manipulations. Figures 3-7 describe neural changes induced by these manipulations. The main strength of this work is the level of specificity of the chemogenetic manipulations, which combined with brain-wide functional exploration, provide a very useful map of the consequences of activating a specific striatal subpopulation. In my opinion, the main weakness of this work is that the results are under-discussed and not appropriately contextualized in the current views of the functions of the basal ganglia. My main concerns are exposed in the following lines:

   1. In the first finding the authors show that D1 activation/inactivation produces reliable changes in the infected region (DMS), but most importantly, also produced changes in adjacent areas, suggesting intra-striatal communication. The way the data is presented and discussed appears to be confirmatory of what has been previously described with electrophysiological recordings. In my opinion, the most important part of this section would be to fully describe the differences between activation and inactivation groups. Is interesting that opposite manipulations of D1 receptors produced very similar maps of discrimination (Fig. 3). Therefore, it would be necessary to discuss the meaning of obtaining similar classification accuracy indices with opposite manipulations. Perhaps, the use of SVM classifiers can be complemented with other analytical techniques to further disentangle the consequences of manipulating intrastriatal D1 receptors.

   2. The second finding (Fig. 4) indicates that thalamic regions forming "closed loops" with the striatum were more affected by chemogenetic manipulations. We knew from anatomical studies that the BG are part of anatomically segregated cortico-BG-thalamic loops. Therefore, it would be expected that these anatomical boundaries would somehow limit functional connectivity maps. Here again, I consider that the manuscript would be improved with further analysis or discussion. For example, it would be interesting to perform further analysis relating the previous section (local striatal connectivity) with this one. In this section, several thalamic nuclei presented higher levels of classification accuracy, but in the previous section, the authors showed that DMS manipulation also produced the same effects in different intrastriatal regions. Therefore, it is not possible to know if the thalamic effects are related to the manipulation of D1 in the DMS or its adjacent regions.

   3. In the third finding (Fig. 5) the authors show that the most "sensitive" cortical regions to the manipulations were classified as "unimodal". This is an interesting result; however, it would be necessary to at least provide further discussion on its potential meaning. It is important to consider that the cortical regions with significant changes, for example, primary sensorimotor cortices, mainly target the dorsolateral, not the dorsomedial striatum. In this context, would it be possible to establish a new analysis to characterize potential correlations between cortical regions and striatal subregions?

   4. The fourth finding (Figure 6) is that thalamic but not cortical regions presented low-frequency fluctuations. What is the meaning of an increase in slow fluctuations? Why did D1 activation (and not inactivation) induced this effect? Are striatal sub-regions also presenting these slow fluctuations?

   5. In the last finding (Figure 7), the authors explored potential changes in functional connectivity (FC) between the striatum and cortical and subcortical regions. Contrary to the results obtained with the SVM-based analytical tool, FC analysis revealed that D1 activation and inactivation produced opposite results, while D1 activation decreased FC in several cortical and subcortical regions, D1 inactivation increased it. While this set of data is clearly described, the implications of these relationships could be further discussed. For example, how do the authors explain that FC with SSp was not significantly changed with this analytical method, but was one of the most affected regions with the Balanced Classification Accuracy method?

   6. Finally, there is no section in the discussion where the behavioral effects observed in figure 2 are contextualized in the massive set of BOLD results presented in the following sections.

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6. 

   Version published to 10.1101/2022.03.11.483972v1 on bioRxiv

   Mar 12, 2022