Author response:
The following is the authors’ response to the original reviews.
Reviewer 1:
Comment 1: Indirect Estimates of White Matter Connections: While dMRI is a valuable tool, it inherently provides indirect and inferred information about neural pathways. The accuracy and specificity of tractography can be influenced by various factors, including fiber crossing, partial volume effects, and algorithmic assumptions. A potential limitation in the accuracy of indirect estimates might affect the precision of spatial extent measurements, introducing uncertainty in the interpretation of cortico-thalamic connectivity patterns. Addressing the methodological limitations associated with indirect estimates and considering complementary approaches could strengthen the overall robustness of the findings.
We appreciate the reviewer’s comment and agree tractography is an indirect estimate and subject to limitations. Regarding this manuscript, the key question is not whether the anatomical tracts are without false positives or negatives, and in fact we argue that this question is outside the scope of this manuscript and has been addressed in several previous studies (e.g. Thomas et al. 2015, Schilling et al., 2020, Grisot et al. 2021, and many others). Instead, the key question for this manuscript is whether the focality of termination patterns within the thalamus is systematically biased in a way that the observation of a hierarchy effect is artifactual. The many supplementary analyses in this manuscript do help address this question and increase our confidence that the indirect nature of tractography does not systematically bias the EDpc1 measure such that association areas only appear to have more diffuse connectivity patterns relative to sensorimotor areas.
Comment 2: An over-arching theme of my review is that, each time I found myself wondering about a detail, a null, or a reference, I had only to read the next sentence or paragraph to find my concern handled in a clear and concise fashion. This is, in my opinion, the mark of work of the highest order. I congratulate the authors on their excellent work, which I believe will be impactful and well-received.
I have no notes that I feel can help improve what is already an impeccable piece of work.
We thank the reviewer for the kind comment.
Reviewer #2:
Comment 1: Structural thalamocortical connectivity was estimated from diffusion imaging data obtained from the HCP dataset. Consequently, the robustness and accuracy of the results depend on the suitability of this data for such a purpose. Conducting tractography on the cortical-thalamic system is recognized as a challenging endeavor for several reasons. First, diffusion directions lose their clearly defined principal orientations once they reach the deep thalamic nuclei, rendering the tracking of structures on the medial side, such as the medial dorsal (MD) and pulvinar nuclei difficult. Somewhat concerning is those are regions that authors found to show diffuse connectivity patterns. Second, the thalamic radiata diverge into several directions, and routes to the lateral surface often lack the clarity necessary for successful tracking. It is unclear if all cortical regions have similar levels of accuracy, and some of the lateral associative regions might have less accurate tracking, making them appear to be more diffuse, biasing the results.
As mentioned in the weakness section, it is crucial to address the need for better validation or the inclusion of control analyses to ensure that the results are not systematically biased due to known issues, such as the difficulty in tracking the medial thalamus and the potential for higher false positives when tracking the lateral frontal cortex.
We thank that reviewer for bringing up an important point. To determine if some areas of the thalamus were more difficult to track and, in turn, biased the EDpc1 measure we added an additional supplemental figure (S31). In this figure, shown below, we calculate the total SC of all ipsilateral cortical areas to each thalamic voxel. We show that, indeed, medial thalamic voxels have a lower total streamline count to ipsilateral cortex, and we see reduced total streamline counts to lateral thalamic areas and the very posterior end of the thalamus. We determined if some cortical areas preferentially projected to parts of the thalamus with lower ipsilateral total SC (i.e. by calculating the overlap between SC and total cortical SC for each thalamic voxel) and found only a weak relationship with our measure. Furthermore, we regressed each voxel’s mean ipsilateral cortical SC from streamline count matrix. We found that the EDpc1 measure didn’t significantly change after the regression.
Additionally, we note that this analysis assumes that all thalamic voxels should have equal strength of connectivity (i.e., total SC) to the ipsilateral cortex and that such a measure is a proxy for “accuracy.” While both of these assumptions may not be entirely valid, this figure does demonstrate that potential reductions in tracking from the medial thalamus does not significantly affect the EDpc1 measure.
Comment 2: While the methodology employed by the authors appears to be state-of-the-art, there exists uncertainty regarding its appropriateness for validation, given the well-documented issues of false positives and false negatives in probabilistic diffusion tractography, as discussed by Thomas et al. 2014 PNAS. Although replicating the results in both humans and non-human primates strengthens the study, a more compelling validation approach would involve demonstrating the method's ability to accurately trace known tracts from established tracing studies or, even better, employing phantom track data. Many of the control analyses the authors presented, such as track density, do not speak to accuracy.
In addition to or response to Reviewer 1 Comment 1, we would like to add the following:
We agree with the reviewer that tractography methods have known limitations. We would also like to point out that several studies have already performed the studies suggested by the reviewer. Many studies have compared tracts reconstructed from diffusion data using tractography methods to tracer-derived connections (eg. Thomas et al., 2014, as mentioned by the reviewer; Donahue et al., 2016, J Neurosci; Dauguet et al., 2007 NeuroImage; Gao et al., 2013 PloS One; van den Heuvel et al., 2015, Hum Brain Map; Azadbakht et al., 2015 Cereb Cortex; Ambrosen et al., 2020 NeuroIamge). Notably, studies comparing tractography and tracer-derived white matter tracts in the same animal (e.g. Grisot et al., 2021; Gao et al., 2013 PloS One) have demonstrated that tractography errors may be inflated in studies comparing tractography and tracer-derived connections in different animals.
Additionally, others have employed phantoms to assess the validity of tractography methods (e.g. Drobnjak et al., 2021). For the purposes of this manuscript, phantom data would not be an adequate control because phantom data would likely not capture the biological complexities of tracking subcortical white matter tracts and identifying projections within subcortical grey matter.
While a comparison of our tractography-derived ED measure to ED calculated on terminations from tracer studies within the thalamus from several somatomotor and associative regions in macaques would provide additional confidence for our results, such a control is certainly outside the scope of this study. Additionally, such a study would not provide a ground truth comparison for the human data. Even if this hypothetical experiment was performed, a negative finding would not refute our results, as any differences could be attributed to evolutionary differences. Unfortunately, there exists no ground truth to compare human white matter connectivity patterns to, which is why we stress-tested our results in as many ways as possible. These stress tests revealed that our main findings are very robust.
Specifically, as the key validity question of our study was whether there was a confound that systematically biased the ED measure as to make the hierarchy effect artifactual, the control analyses we performed to determine if track density, cortical geometry, bundle integrity, etc in fact do speak the robustness of the results. Regarding the track density analyses we argue that these control analyses do speaks to accuracy. The reviewer mentioned above that some cortical areas may be biased because their anatomical tracts may be more difficult to reconstruct using tractography. The mean streamline count is meant to reflect the density of a fiber bundle, but corticothalamic tracts that are more difficult to track will, by nature, have fewer streamline counts. So, the mean streamline not only reflects the density of a fiber bundle but also how easily that tract is to reconstruct. Therefore, if it was the case that cortical areas with more difficult to reconstruct white matter tracts to the thalamus are also more diffuse, then we should observe a strong positive correlation between the ED measure and the mean streamline count, which we tested directly and found only a weak correlation (Fig. S11). This is true for tracking to the entire thalamus, and the additional supplemental Figure S31 shows that reduced tracking to specific parts of the thalamus (e.g. the medial portion) also does not strongly relate to the ED measure. So, tracts that are more difficult to reconstruct may also be more diffuse, but this seems to add only a little noise and does not account for the strong relationship between the ED measure and T1w/T2w and RSFCpc1 measures the reflect the cortical hierarchy.
Comment 3: If tracking the medial thalamus is indeed less accurate, characterized by higher false positives and false negatives, it could potentially lead to increased variability among individual subjects. In cases where results are averaged across subjects, as the authors have apparently done, this could inadvertently contribute to the emergence of the "diffuse" motif, as described in the context of the associative cortex. This presents a critical issue that requires a more thorough control analysis and validation process to ensure that the main results are not artifacts resulting from limitations in tractography.
Additionally, conducting a control analysis to demonstrate that individual variability in tracking endpoints within the thalamus, when averaged across subjects, does not artificially generate a more diffuse connectivity pattern, is essential.
We thank the reviewer for bringing up this point, and the reviewer is correct that a simple group average of streamline counts across that thalamus could make some thalamic patterns appear more diffuse if those patterns vary slightly in location across people. The simplest way to address this concern is to show that diffuse patterns are present in individual subjects. Fig. 2 panels B, C, H, and I are all subject-level figures, which show that we can replicate the group level findings in Fig. 2 panels F, G. Specifically, Fig 2. Panels H and I show that the effect of association areas exhibiting more diffuse connectivity patterns within the thalamus relative to sensorimotor areas is generalizable across subjects.
To the reviewer’s point, the other way that averaged streamline counts could make focal connections seem diffuse is by averaging within cortical areas (e.g. to test the possibility that association areas may have highly variability focal patterns, and when averaged within the cortical area it makes these focal patterns appear more diffuse). To test this, we show that we can replicate the hierarchy effect at the vertex level, by calculating the extent of connectivity patterns for every cortical vertex and correlated vertex-level EDpc1 values to vertex-level T1w/T2w and RSFC_pc1 values (Fig S20).
Hopefully the data shown in Fig. 2 (replication at the individual level) and Fig. S20 (replication at the vertex level) ameliorate the reviewer’s concerns that averaging highly variable focal connectivity patterns within the thalamus (either across people or across vertices) does not artifactually produce diffuse thalamic connectivity patterns for associative cortical areas.
Comment 4: Because the authors included data from all thresholds, it seems likely that false positive tracks were included in the results. The methodology described seems to unavoidably include anatomically implausible pathways in the spatial extent analyses.
The thresholding approach taken in the manuscript aimed to control for inter-areal differences in anatomical connection strength that could confound the ED estimates. Here I am not quite clear why inter-areal differences in anatomical connection strength have to be controlled. A global threshold applied on all thalamic voxels might kill some connections that are weak but do exist. Those weak pathways are less likely to survive at high thresholds. In the meantime, the mean ED is weighted, with more conservative thresholds having higher weights. That being said, isn't it possible that more robust pathways might contribute more to the mean ED than weaker pathways?
This is a good point from the reviewer, and we appreciate them bringing up these points about our thresholding rationale. We would like to clarify two points: why it was appropriate for our question to threshold thalamic voxels for each cortical area separately and why we iteratively thresholded thalamic voxels.
Regarding thalamic connectivity differences between cortical areas: a global threshold would indeed exclude weak, but potentially true, connections. This was part of our rationale for thresholding thalamic voxels for each cortical area separately. Too conservative of a global threshold would exclude all thalamic voxels for some cortical areas and too liberal of a threshold would include many potentially false positive connections for other cortical areas. Our method of thresholding each cortical area’s thalamic voxels separately ensured that we were sampling thalamic voxels in an equitable manner across cortical areas. We updated the text to clarify this:
Methods section, pg. 11, section Framework to quantify the extent of thalamic connectivity patterns via Euclidean distance (ED)
“We used Euclidean distance (ED) to quantify the extent of each cortical area's thalamic connectivity patters. Probabilistic tractography data require thresholding before the ED calculation. To avoid the selection of an arbitrary threshold (Sotiropoulos et al., 2019, Zhang et al., 2022), we calculated ED for a range of thresholds (Figure 1a). Our thresholding framework uses a tractography-derived connectivity matrix as input. We iteratively excluded voxels with lower streamline counts for each cortical parcel such that the same number of voxels was included at each threshold. At each threshold, ED was calculated between the top x% of thalamic voxels with the highest streamline counts. This produced a matrix of ED values (360 cortical parcels by 100 thresholds). This matrix was used as input into a PCA to derive a single loading for each cortical parcel. While alternative thresholding approaches have been proposed, this framework optimizes the examination of spatial patterns by proportionally thresholding the data, enabling equitable sampling of each cortical parcel's streamline counts within the thalamus.
This approach controlled for inter-areal differences in anatomical connection strength that could confound the ED estimates. In contrast, a global threshold, which is applied to all cortical areas, may exclude all thalamic streamline counts for some cortical areas that are more difficult to reconstruct, thus making it impossible to calculate ED for that cortical area, as there are no surviving thalamic voxels from which to calculate ED. This would be especially problematic for white matter tracts are more difficult to reconstruct (e.g. the auditory radiation), and cortical areas connected to the thalamus by those white matter tracts would have a disproportionate number of thalamic voxels excluded when using a global threshold.”
Regarding thalamic connectivity differences across the thalamus for a given cortical area, the thresholding method we use does include anatomically implausible connections in the ED calculation because we sample voxels iteratively, and as more and more thalamic voxels are included in the ED analysis the likelihood that they reflect spurious connections increases. This approach made the most sense to us, because there is no way to identify a threshold that only includes true positive connections. And since this method does not exist, we sampled all thresholds and leveraged the behavior of the ED metric across thresholds to quantify the spread of a connectivity pattern. As the reviewer points out, since the measure is effectively “weighted,” more “robust” or anatomically plausible pathways should contribute more to the EDpc1 rather than weaker pathways. This is exactly the balanced approach we aimed for: a measure that is driven by connections that have the highest likelihood of being a true positive but does not rely on an arbitrary threshold.
We did also replicate our main findings after thresholding and binarizing the data for separate thresholds, which show that our main effect was strongest only when thalamic voxels with the highest streamline counts (which are assumed to have a lower chance of being false positives) are included in the ED calculation (Fig. S5). This more traditional method of thresholding also supported our results, and increases our overall confidence that associative cortical areas have more diffuse connectivity patterns within the thalamus relative to somatomotor areas.
Comment 5: In the introduction, there is a bit of ambiguity that needs clarification. The overall goal of the study appears to be the examination of anatomical connectivity from the cortex to the thalamus, specifically whether a cortical region projects to a single thalamic subregion or multiple thalamic subregions. However, certain parts of the introduction also suggest an exploration of the concept of thalamic integration, which typically means a single thalamic region integrating input from multiple cortical regions (converging input). These two patterns, many cortical regions to one thalamic region versus one cortical region to many different thalamic regions, represent distinct and fundamentally different concepts that should be clarified in the manuscript.
We thank the reviewer for pointing out this ambiguity and have edited the introduction to clarify this point:
Our argument for a potential mechanism for integration is the following: because corticothalamic connectivity is topographically organized, if a cortical area has a more diffuse anatomical projection across the thalamus that means its connections overlap with more cortical areas. To the reviewer’s point, our argument is simply that one cortical area targeting multiple thalamic nuclei inherently suggests that such a cortical area has overlapping connectivity patterns with many other cortical areas in the same thalamic subregion. We have updated the introduction to clarify this further.
Intro, pg 1.
“Studies of cortical-thalamic connectivity date back to the early 19th century, yet we still lack a comprehensive understanding of how these connections are organized (see 13 and 14 for review). The traditional view of the thalamus is based on its histologically-defined nuclear structure (6). This view was originally supported by evidence that cortical areas project to individual thalamic nuclei, suggesting that the thalamus primarily relays information (15). However, several studies have demonstrated that cortical connectivity within the thalamus is topographically organized and follows a smooth gradient across the thalamus (16–21). Additionally, some cortical areas exhibit extensive connections within the thalamus, which target multiple thalamic nuclei (22? ). These extensive connections may enable information integration within the thalamus through overlapping termination patterns from different cortical areas, a key mechanism for higher-order associative thalamic computations (23– 25). However, our knowledge of how thalamic connectivity patterns vary across cortical areas, especially in humans, remains incomplete. Characterizing cortical variation in thalamic connectivity patterns may offer insights into the functional roles of distinct cortico-thalamic loops (6, 7).”
Discussion, pg 9. Section: The spatial properties of thalamic connectivity pat- terns provide insight into the role of the thalamus in shaping brain-wide information flow.
“In this study, we demonstrate that association cortical areas exhibit diffuse anatomical connections within the thalamus. This may enable these cortical areas to integrate information from distributed areas across the cortex, a critical mechanism supporting higher-order neural computations. Specifically, because thalamocortical connectivity is organized topographically, a cortical area that projects to a larger set of thalamic subregions has the potential to communicate with many other cortical areas. We observed that anterior cingulate cortical areas had some of the most diffuse thalamic connections. This observation aligns with findings from Phillips et al. that area 24 exhibited the most diffuse anatomical terminations across the mediodorsal nucleus of the thalamus relative to other prefrontal cortical area…”
Reviewer 3:
Comment 1: Potential weaknesses of the study are that it seems to largely integrate aspects of the thalamus that have been already described before. The differentiation between sensory and association systems across thalamic subregions is something that has been described before (see: Oldham and Ball, 2023; Zheng et al., 2023; Yang et al., 2020 Mueller, 2020; Behrens, 2003).
It is true that previous studies have shown that corticothalamic systems vary between sensory and associative cortical areas. Furthermore, there is much evidence that indicates that the sensory-association hierarchy is a major principle of brain organization in general. However, how and why these circuits are different is still not fully known, both across the whole brain and in corticothalamic circuits specifically.
Our study is the first to compare patterns of anatomical connectivity within the thalamus and determine if cortical areas vary in the extent of those patterns. So our main finding isn't that sensory and association cortical areas show differences in thalamic connectivity, it is that they specifically show differences in their pattern of connectivity within the thalamus. This provides a unique insight into how sensory and associative systems differ in their thalamic connectivity in primates.
Additionally, we show evidence that provides some insight into why these differences may exist. Although we cannot provide causal evidence, our data suggest that differences in patterns of anatomical connectivity within the thalamus were related to how different cortical areas process information via the thalamus, which aligns with speculations from Phillips et al 2021.
So our main finding isn't that sensory and association cortical areas show differences in thalamic connectivity, is it that they specifically show differences in their pattern of connectivity within the thalamus and these differences may help us understand how these cortical areas process information and, in turn, how they may support different types of computations, both of which are major goals in neuroscience. To better clarify this in the manuscript, we made the following changes:
Discussion, Paragraph 1, pg 8:
“This study contributes to the rich body of literature investigating the organization of cortico-thalamic systems in human and non-human primates. Prior research has shown that features of thalamocortical connectivity differ between sensory and association systems, and our work advances this understanding by demonstrating that these systems also differ in the pattern and spatial extent of their anatomical connections within the thalamus. Using dMRI-derived tractography across species, we show that these connectivity patterns vary systematically along the cortical hierarchy in both humans and macaques. These findings are critical for establishing the anatomical architecture of how information flows within distinct cortico-thalamic systems. Specifically, we identify reproducible tractography motifs that correspond to sensorimotor and association circuits, which were consistent across individuals and generalize across species. Collectively, this study offers convergent evidence that the spatial pattern of anatomical connections within the thalamus differs between sensory and association cortical areas, which may support distinct computations across cortico-thalamic systems.”
Comment 2: (1) Why not formally test the association between humans and macaques by bringing the brains to the same space?
We thank the reviewer for this query. We were primarily interested in using the macaque data as a validation of the human data, because it was acquired at a much higher resolution, there are no motion confounds, and it provides a bridge with the tract tracing literature in macaques. We are currently studying interspecies differences in patterns of thalamic connectivity, as well as extensions of our approach into structure-function coupling, and we believe these topics warrant their own paper.
Comment 3: (2) Possibly flesh out the differences between this study and other studies with related approaches a bit further.
We updated the discussion section to better clarify the differences in this study from previous research. See response to Reviewer 3 Comment 1 for text changes.
Comment 4: (3) The current title entails 'cortical hierarchy' but would 'differentiation between sensory and association regions' not be more correct? Or at least a reflection on how cortical hierarchy can be perceived?
We treat these phrases as synonymous terms. Our definition of cortical hierarchy is a smooth transition in features between sensory and motor areas to higher-order associative areas. The use of cortical hierarchy is meant to reflect that our measure continuously varies across the cortex. We updated the manuscript to make this clearer:
Abstract, pg 1.
“Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy, from sensory and motor to multimodal associative cortical areas.”
Comment 5: (4) For the core-matrix map, there is a marked left-right differences and also there are only two donors in the right hemisphere, possibly note this as a limitation?
We thank the reviewer for this observation. We updated Fig. S28 Panel D to show that the correspondence between EDpc1 and the Core-Matrix (CPc) cortical maps holds when the correlation was done for left and right cortex, separately.
