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  1. Author Response:

    Reviewer #1 (Public Review):

    In this paper, the authors describe a MRI-based functional connectivity mode for the striatum and attempt to show that it is related to dopaminergic input from the midbrain. Currently, dopaminergic input can only be assessed in humans with radionuclide imaging modalities (PET and SPECT), which have poor spatial resolution, relatively long acquisition times, and require radioactive tracers. The MRI-based method would provide higher resolution and greater accessibility, and moreover, can be applied retrospectively to data that has already been collected. The authors use multiple lines of study to build the case: comparison to DaT SPECT, which shows the distribution of dopamine transporters; alteration in Parkinson's Disease, where dopaminergic input is known to be reduced; and relation to alcohol and tobacco use in healthy volunteers, where dopamine signalling in the brain's reward processing pathway is altered. The combination of clinical, behavioral and imaging experiments to validate the MRI biomarker of dopamine input is the major strength of this study. Not only is the biomarker altered as expected in each case, but the alterations also exhibit regional specificity that is consistent with prior reports often obtained with invasive measurements. A direct validation of the biomarker would require invasive histology that is clearly impossible in healthy humans, but while any single finding from one modality would be less convincing, taken together, they provide sufficient circumstantial evidence to motivate further use and investigation of the biomarker. The authors use quantitative techniques to characterize the change in the functional connectivity mode and find truly impressive correspondence with the SPECT measurements of DaT at the group level. As expected, the correspondence is weaker at the individual level, but still respectable. The authors show substantial individual data throughout the manuscript in addition to the group data, which increases confidence in both their results and the potential utility of the biomarker in the clinic. For example, the relationship between symptom severity after L-DOPA and changes in the biomarker at the individual level is very encouraging. The least convincing aspect of the manuscript was the relationship between the connectivity mode and the amount of tobacco use (Fig 6, top) where the line fit looks as if it may have been driven by two very high use points. Given the strength of the other findings, even if the relationship with tobacco use does not completely hold up, it detracts very little from the overall study. The lack of a difference in the biomarker between the left-dominant Parkinson's group and the control group is also a bit surprising. Given the discussion about flooring effects, it may be a power effect, but it definitely warrants more investigation in the future.

    We thank the reviewer for providing feedback to improve this manuscript and for acknowledging the many strengths and importance of our work. To make sure that the relationship between the connectivity mode and the amount of tobacco use (Figure 6) was not driven by outliers, we first determined if the two high use points of 175 and 195 cigarettes a week with TSM (linear X) values of 1.395 and 1.440 respectively, are outliers. The median and interquartile range (IQR) of this distribution are 1.272 and 0.122 respectively. Accordingly, both high-value points just fall outside the Q1-Q3 IQR of 1.150 – 1.394, but the first datapoint of 1.395 is still within 2 standard deviations from the mean (1.268+2*0.0803=1.429) and the second datapoint (1.440) is still within 3 standard deviations from the mean (1.509). As such, we do not consider these data points as extreme outliers that need to be removed from our analysis. We nevertheless repeated the GLM analyses testing for associations between the amount of tobacco use and second-order connectivity mode without these two subjects and the association was still significant (X 2 =46.14, p=0.004). It is also important to keep in mind that our sample is population-based. While the corresponding usage of cigarettes (175 and 195 cigarettes a week corresponding to 25 and 28 cigarettes a day) is at the high end in this particular population-based sample, this amount of use is not uncommon in regular smokers. As for the lack of findings with respect to left-dominant PD patients we agree that here we may be suffering from a lack of power. Nevertheless we feel that this is worth reporting for the sake of completeness, if only to indicate that as a straight-forward hypothesis, it did indeed get tested.

    Reviewer #2 (Public Review):

    This is an excellent paper with an excellent outstanding methodology and sequence of steps which contains many strengths

    • First, they apply a novel fMRI resting state functional connectivity method, connectopic mapping (CM). This is validated in a large standard data set, the HCP fMRI, in around 800 healthy subjects.
    • Secondly, they use the measurement of a striatal DA transporter, DaT SPECT, in a large number of subjects (around 200) to establish spatial correlation with fMRI connectopic mapping.
    • Thirdly, they measure subjects where striatal dysfunction is known to be altered. Parkinson disease (PD) with L-Dopa therapy; this serves the purpose to the direct impact of dopamine deficiency (D2-receptors) and dopamine replacement therapy (L-Dopa) on striatal connectopic mapping
    • Fourthly, they further support that by scanning people with daily alcohol or nicotine consumption whose degree of substance use corelates with the striatal connectopic mapping.

    We thank the reviewer for providing feedback to improve our manuscript and for acknowledging the excellence of our methodology and the strengths of our work.

    Some weaknessness shall be mentioned.

    • I was wondering how their striatal DA connectopic marker stands in relation to others like melatonin-sensitive MRI (Cassidy et al.PNAS and others). This should at least be discussed. Ideally, they do melatonin scanning in their sample and correlate it with their striatal connectopic marker. This would provide the opportunity to more directly validate their marker.
    • Another issue is the biochemical specificity. The striatum contains also many glutamatergic (medium spiny) and gaba-ergic neurons which are key in mediating DA effects as the latter (as far as I know) terminate on the former. Moreover, it is known that rsFC is related to excitation-inhibition balance and thus to glutamate-GABA. How can the authors make sure that their cortical conenctopic maps are really related specifically to DA rather than glutamate and/or GABA? This is even more urgent given that we know glutamate changes in alcohol and/or smoking and also in PD to be prevalent.
    • It would be good if this issue of specificity could be addressed. Like in people who receive ketamine (anti-NMDA): if the authors' connectopic marker is specific for striatal DA, it should not be changed under NMDA treatment.
    • Another way is to conduct computational modelling: modulation of glutamate/GABA should ideally not affect the striatal cortical connectopic marker....
    • Some key literature should be cited and discussed: Conio et al. 2020 establishes a model of DA projects and their implications for psychiatric disorders
    • Yet another issue is the question for serotonin. Various papers by Marinto/Magioncalda in especially bipolar disorder recently established modulation of nigral D2 by raphe-based serotonin. This should be discussed at least: Could the connectopic marker be related to such modulation? How could they make sure that their marker is related exclusively to cortical D2 projections rather than cortical serotonine effects? I am aware that these are tough questions but they should at least be addressed in the discussion...
    • Moreover, the striatum is a complex region with subdivisions like dorsal and ventral which again can be featured by different dopamine systems (D2 vs D1/5) - this should be probed in their data to enhance specificity for nigral-based D2 of their connectopic marker....

    The above points nearly all relate to the specificity of the second-order connectivity mode to dopaminergic projections. We refer the reviewer therefore to our response to comment 1 of the essential revisions. Here we conducted additional analyses demonstrating that the mapping of the second-order connectivity mode onto the DaT SPECT scan is far superior compared to the PET tracers available for other neurotransmitter systems, such as the serotonin and GABA system. Further and in addition to our response above it appears that our sensitivity analysis does not suggest a strong differentiation of the second connectivity mode relative to D1 or D2 receptor distribution but instead segregates either of these from the distribution of the DaT. We unfortunately do not have access to melatonin-sensitive MRI data or high-resolution fMRI data of patients. While the reviewer has many excellent suggestions these therefore need to remain the subject of future studies.

    Reviewer #3 (Public Review):

    The study provides an impressive breadth of analyses, including comparisons to SPECT imaging, Parkinson's patients, drug manipulation and behavior, which build to form a compelling case that the identified patterns of functional connectivity. The surface modeling approach employed provides an interesting alternative to more standard parcellation approaches, which highlights the possibility that organization with the striatum occurs along gradients, rather than within functionally or anatomically circumscribed regions. Importantly, the findings have potentially wide-ranging implications and applications, since striatal dopamine (DA) and cortico-striatal connectivity are of great interest across a wide variety of fields, including their variation across the lifespan, disruption in various clinical populations, and contribution to normative behaviors.

    We thank the reviewer for providing feedback to improve our manuscript and for commenting on the breadth of analyses and potential wide-ranging implications of our work.

    While the surface modeling approach has some appealing features, it is a rather complex approach that is hard to understand intuitively. The difficultly to grasp its nuances limited my ability to follow some of the interpretations provided. For example, an important aspect of the results is that only the second order mode of the functional connectivity profile (and not the 0th or 1st order modes) are associated with dopamine measures and manipulations, but I found it difficult to assess what these different modes are capturing. Are these overlapping modes of distinct aspects of connectivity (each of which is expressed to a different extend), or different characterizations of the same pattern? Do the modes represent the extent to which different striatal regions exhibit the same pattern of cortical connectivity, or is the connectivity pattern also shifting? Some additional clarity on these patterns would have greatly helped me understand the subsequent results. Similarly, in the results of PD patients, it is stated "we can interpret the observed alteration in the connection topography as a decrease in dopaminergic projections to striatum." (l. 242). A decrease in the quadratic term of the TSM would seem to indicate less spatial variability, but not obviously an overall decrease, which would seem instead to be reflected by the 0th order term (if I understand these modes correctly). Some clarification on this interpretation, and more description of the modes in general, would be helpful.

    We acknowledge that our connectopic mapping method and the subsequently applied trend surface modeling (TSM) approach might not be as intuitive and easy to understand as more traditional functional connectivity approaches. This is largely due to classical approaches neglecting the presence of functional multiplicity, i.e., the fact that within brain regions neural computations can contribute to multiple cognitive processes. In short, connectopic mapping yields a set of overlapping, but independent connection topographies or “connectivity modes” that together describe the functional organization of a brain region. In Haak et al 2016, we demonstrated for example that in V1 we can detect separate gradients that reflect sensitivity to orientation and eccentricity– cortical organisations that can also be probed experimentally using retinotopic mapping procedures. Likewise, when applying connectopic mapping to the striatum, the obtained connection topographies indicate how the connectivity profile with the rest of the brain changes across striatum. Voxels that have similar colours in these connectivity modes have similar connectivity patterns with the rest of the brain. Which aspects of functional connectivity these modes are precisely capturing depends on the region of interest investigated and is furthermore difficult to predict beforehand, especially for the higher-order connectivity modes. Regarding the striatum, we showed in previous work (Marquand et al., 2017) that the dominant (zerothorder) mode represents its basic anatomical subdivisions, while the first-order mode maps on to a ventromedial-to-dorsolateral gradient associated with goal-directed behaviour in cortex that has been described previously on the basis of tract-tracing work in non-human primates. In this manuscript we subsequently provide evidence that the second-order striatal connectivity mode maps onto dopaminergic projections.

    We have now clarified our approach in the legend of Figure 1: “Then similarity between voxels is computed using the η2 coefficient, resulting in matrix S. Manifold learning using Laplacian eigenmaps is then applied to this matrix, yielding a set of overlapping, but independent connection topographies or “connectivity modes” that together describe the functional organization of the striatum. These connection topographies indicate how the connectivity profile with the rest of the brain changes across striatum. Voxels that have similar colours in these connectivity modes have similar connectivity patterns with the rest of the brain.”

    Further, we have also clarified the trend surface modeling (TSM) approach in the Materials and Methods section:

    “Finally, to enable statistical analysis over these connection topographies, we fitted spatial statistical models to obtain a small number of coefficients summarizing the second-order connectivity mode of each striatal subregion in the X, Y, and Z axes of MNI152 coordinate space. For this, we use ‘trend surface modelling’ (TSM; 27), an approach originally developed in the field of geostatistics, but that has wide ranging applications due to its ability to model the overall distribution of properties throughout space as a simplified surface. Here we use the TSM approach to predict each individual subject’s connection topography by fitting a set of polynomial basis functions defined by the coordinates of each striatal location. …. This criterion strongly favoured a polynomial of degree 2 for the putamen subregion and a polynomial of degree 4 for the caudate-NAcc subregion. This means that the connectivity mode in putamen was modelled with linear and quadratic functions in the X, Y, and Z direction of MNI152 coordinate space (6 TSM coefficients) and the connectivity mode in the caudate-NAcc region with linear, quadratic, cubic and quartic functions in the X, Y and Z direction of MNI152 coordinate space (12 TSM coefficients). The TSM coefficients of the fitted polynomial basis functions describe the rate at which the connectivity modes changes along a given spatial dimension and can be used for statistical analysis.”

    Regarding the following statement: "we can interpret the observed alteration in the connection topography as a decrease in dopaminergic projections to striatum.", we would like to point out that we first used a GLM omnibus test of all TSM coefficients modelling the second-order connectivity mode to investigate whether an association with UPDRS symptom severity was present. Post-hoc Pearson correlations then revealed that this association was driven by the quadratic TSM coefficients modelling the putamen region in the Y and Z direction of MNI space. Next, we plotted the association with UPDRS symptom severity for the quadratic Y coefficient as well as show five of the connectivity modes at varying UPDRS symptom severity for visualization and interpretation purposes in Figure 4B. The interpretation above is based on visual inspection of these five connectivity modes shown in figure 4B (in light of the similarity between the second-order connectivity mode and the DaT SPECT scan shown in Figure 2). We hope that this answer sufficiently clarifies our interpretation.

    Several common confounds for rsFC analyses, especially head motion, are not sufficiently well addressed as to ensure that they do not contribute to the spatial patterns reported. Specifically, the second-order fit would seem to capture some sense of the "sharpness" of the spatial connectivity profile in the striatum. This seems like it could be driven either by neurophysiological features regarding the functional segregation of these regions, or data quality features regarding the smoothness of the data. Since one effect of head motion (in both resting state fMRI and other domains such as PET/SPECT) can be to change the spatial smoothness of data, it would be important to characterize how much of the variance in this measure can be accounted for by head motion (or other confounds). This is especially true since such confounds are known to be greater in, e.g., patient populations, which could affect the analyses performed later.

    We agree that head motion can indeed have a profound impact on resting-state functional connectivity analyses. We have now added several post-hoc sensitivity analyses to the supplementary materials strongly demonstrating that our findings are unlikely to be confounded by head motion. For a more extensive description, we refer to our detailed response to comment 2 in the essential revisions section of this document.

    Finally, the findings are at various points referred to as a potential biomarker for dopamine (dys)function. While this term has been used in a wide range of contexts, such claims generally require a greater burden of proof than the presence of statistically significant associations, e.g., including classification and/or sensitivity/specificity analyses. These assertions do not yet seem well supported by the included statistics, and may need clarification.

    Indeed, to ultimately proof that our connectivity mode can be used as a biomarker for dopamine (dysfunction) would require invasive histology, which is impossible in healthy humans and in the context of this study. As such, we cautiously refer to our connectivity mode as a ‘potential’ biomarker for dopaminergic (dys)function and also state in the discussion that more research and out of sample replication is needed. We believe that, while each of our findings in isolation would be insufficient to claim that the second-order striatal connectivity mode could be used a potential biomarker, all our findings together provide sufficient circumstantial evidence to motivate the further use and investigation of this connectivity mode as a biomarker. In particular, the direct within-subject mapping of the connectivity mode onto the DaT SPECT scan (acknowledged as being a biomarker) and the finding that our connectivity mode is sensitive to acute dopaminergic modulation suggest specificity to dopaminergic function. Furthermore, we also conducted an additional analysis (see Figure 2–figure supplement 1) comparing the spatial mapping of DaT SPECT to the second-order striatal connectivity mode, to various other PET derived neurotransmitter systems. This analysis revealed that the TSM coefficients describing the DaT SPECT scan provide a much better fit to the data than TSM coefficients describing any other PET derived neurotransmitter system.

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  3. Evaluation Summary:

    This paper provides a novel method for characterizing the functional topography of the striatum based on functional connectivity profiles. Importantly, a series of ambitious analyses provide compelling (if somewhat indirect) evidence via associations to SPECT imaging, in patient populations (Parkinson's Disease), and under drug manipulation (L-DOPA), that this organization is strongly associated with the distribution of dopamine transporter concentrations. Markers of dopamine neurophysiology and signaling, especially those available in standard, non-invasive imaging acquisitions, are of great interest across a wide number of research domains.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer # 2 and Reviewer #3 agreed to share their name with the authors.)

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  4. Reviewer #1 (Public Review):

    In this paper, the authors describe a MRI-based functional connectivity mode for the striatum and attempt to show that it is related to dopaminergic input from the midbrain. Currently, dopaminergic input can only be assessed in humans with radionuclide imaging modalities (PET and SPECT), which have poor spatial resolution, relatively long acquisition times, and require radioactive tracers. The MRI-based method would provide higher resolution and greater accessibility, and moreover, can be applied retrospectively to data that has already been collected. The authors use multiple lines of study to build the case: comparison to DaT SPECT, which shows the distribution of dopamine transporters; alteration in Parkinson's Disease, where dopaminergic input is known to be reduced; and relation to alcohol and tobacco use in healthy volunteers, where dopamine signalling in the brain's reward processing pathway is altered.

    The combination of clinical, behavioral and imaging experiments to validate the MRI biomarker of dopamine input is the major strength of this study. Not only is the biomarker altered as expected in each case, but the alterations also exhibit regional specificity that is consistent with prior reports often obtained with invasive measurements. A direct validation of the biomarker would require invasive histology that is clearly impossible in healthy humans, but while any single finding from one modality would be less convincing, taken together, they provide sufficient circumstantial evidence to motivate further use and investigation of the biomarker. The authors use quantitative techniques to characterize the change in the functional connectivity mode and find truly impressive correspondence with the SPECT measurements of DaT at the group level. As expected, the correspondence is weaker at the individual level, but still respectable. The authors show substantial individual data throughout the manuscript in addition to the group data, which increases confidence in both their results and the potential utility of the biomarker in the clinic. For example, the relationship between symptom severity after L-DOPA and changes in the biomarker at the individual level is very encouraging.
    The least convincing aspect of the manuscript was the relationship between the connectivity mode and the amount of tobacco use (Fig 6, top) where the line fit looks as if it may have been driven by two very high use points. Given the strength of the other findings, even if the relationship with tobacco use does not completely hold up, it detracts very little from the overall study. The lack of a difference in the biomarker between the left-dominant Parkinson's group and the control group is also a bit surprising. Given the discussion about flooring effects, it may be a power effect, but it definitely warrants more investigation in the future.

    Read the original source
    Was this evaluation helpful?
  5. Reviewer #2 (Public Review):

    This is an excellent paper with an excellent outstanding methodology and sequence of steps which contains many strengths:

    - First, they apply a novel fMRI resting state functional connectivity method, connectopic mapping (CM). This is validated in a large standard data set, the HCP fMRI, in around 800 healthy subjects.
    - Secondly, they use the measurement of a striatal DA transporter, DaT SPECT, in a large number of subjects (around 200) to establish spatial correlation with fMRI connectopic mapping.
    - Thirdly, they measure subjects where striatal dysfunction is known to be altered. Parkinson disease (PD) with L-Dopa therapy; this serves the purpose to the direct impact of dopamine deficiency (D2-receptors) and dopamine replacement therapy (L-Dopa) on striatal connectopic mapping
    - Fourthly, they further support that by scanning people with daily alcohol or nicotine consumption whose degree of substance use corelates with the striatal connectopic mapping.

    Some weaknesses shall be mentioned:

    - I was wondering how their striatal DA connectopic marker stands in relation to others like melatonin-sensitive MRI (Cassidy et al.PNAS and others). This should at least be discussed. Ideally, they do melatonin scanning in their sample and correlate it with their striatal connectopic marker. This would provide the opportunity to more directly validate their marker.
    - Another issue is the biochemical specificity. The striatum contains also many glutamatergic (medium spiny) and gaba-ergic neurons which are key in mediating DA effects as the latter (as far as I know) terminate on the former. Moreover, it is known that rsFC is related to excitation-inhibition balance and thus to glutamate-GABA. How can the authors make sure that their cortical conenctopic maps are really related specifically to DA rather than glutamate and/or GABA? This is even more urgent given that we know glutamate changes in alcohol and/or smoking and also in PD to be prevalent.
    - It would be good if this issue of specificity could be addressed. Like in people who receive ketamine (anti-NMDA): if the authors' connectopic marker is specific for striatal DA, it should not be changed under NMDA treatment.
    - Another way is to conduct computational modelling: modulation of glutamate/GABA should ideally not affect the striatal cortical connectopic marker....
    - Some key literature should be cited and discussed: Conio et al. 2020 establishes a model of DA projects and their implications for psychiatric disorders
    - Yet another issue is the question for serotonin. Various papers by Marinto/Magioncalda in especially bipolar disorder recently established modulation of nigral D2 by raphe-based serotonin. This should be discussed at least: Could the connectopic marker be related to such modulation? How could they make sure that their marker is related exclusively to cortical D2 projections rather than cortical serotonine effects? I am aware that these are tough questions but they should at least be addressed in the discussion...
    - Moreover, the striatum is a complex region with subdivisions like dorsal and ventral which again can be featured by different dopamine systems (D2 vs D1/5) - this should be probed in their data to enhance specificity for nigral-based D2 of their connectopic marker....

    Read the original source
    Was this evaluation helpful?
  6. Reviewer #3 (Public Review):

    The study provides an impressive breadth of analyses, including comparisons to SPECT imaging, Parkinson's patients, drug manipulation and behavior, which build to form a compelling case that the identified patterns of functional connectivity. The surface modeling approach employed provides an interesting alternative to more standard parcellation approaches, which highlights the possibility that organization with the striatum occurs along gradients, rather than within functionally or anatomically circumscribed regions. Importantly, the findings have potentially wide-ranging implications and applications, since striatal dopamine (DA) and cortico-striatal connectivity are of great interest across a wide variety of fields, including their variation across the lifespan, disruption in various clinical populations, and contribution to normative behaviors.

    While the surface modeling approach has some appealing features, it is a rather complex approach that is hard to understand intuitively. The difficultly to grasp its nuances limited my ability to follow some of the interpretations provided. For example, an important aspect of the results is that only the second order mode of the functional connectivity profile (and not the 0th or 1st order modes) are associated with dopamine measures and manipulations, but I found it difficult to assess what these different modes are capturing. Are these overlapping modes of distinct aspects of connectivity (each of which is expressed to a different extend), or different characterizations of the same pattern? Do the modes represent the extent to which different striatal regions exhibit the same pattern of cortical connectivity, or is the connectivity pattern also shifting? Some additional clarity on these patterns would have greatly helped me understand the subsequent results. Similarly, in the results of PD patients, it is stated "we can interpret the observed alteration in the connection topography as a decrease in dopaminergic projections to striatum." (l. 242). A decrease in the quadratic term of the TSM would seem to indicate less spatial variability, but not obviously an overall decrease, which would seem instead to be reflected by the 0th order term (if I understand these modes correctly). Some clarification on this interpretation, and more description of the modes in general, would be helpful.

    Several common confounds for rsFC analyses, especially head motion, are not sufficiently well addressed as to ensure that they do not contribute to the spatial patterns reported. Specifically, the second-order fit would seem to capture some sense of the "sharpness" of the spatial connectivity profile in the striatum. This seems like it could be driven either by neurophysiological features regarding the functional segregation of these regions, or data quality features regarding the smoothness of the data. Since one effect of head motion (in both resting state fMRI and other domains such as PET/SPECT) can be to change the spatial smoothness of data, it would be important to characterize how much of the variance in this measure can be accounted for by head motion (or other confounds). This is especially true since such confounds are known to be greater in, e.g., patient populations, which could affect the analyses performed later.

    Finally, the findings are at various points referred to as a potential biomarker for dopamine (dys)function. While this term has been used in a wide range of contexts, such claims generally require a greater burden of proof than the presence of statistically significant associations, e.g., including classification and/or sensitivity/specificity analyses. These assertions do not yet seem well supported by the included statistics, and may need clarification.

    Read the original source
    Was this evaluation helpful?