On 2021-02-04 03:34:40, user Sara Sims wrote:
Reviewer #2 (General assessment and major comments (Required)):
In this work, Sims and colleagues use resting-state functional connectivity and diffusion tractography in human connectome project data to examine the connectivity of the central and peripheral aspects of primary visual cortex. They find that central V1 connects more strongly to regions of prefrontal cortex interpreted as the Fronto-parietal network than does peripheral V1.
The idea that central V1 may be directly connected to control-related networks is an interesting one, and has fascinating implications for the study of top-down modulation of visual cortex function. However, I must say I am somewhat skeptical of these findings, for several reasons. <br />
First, I find the a priori anatomical basis for these proposed connections to be dubious. The authors themselves describe how Markov et al. explicitly conducted tract tracing with central V1 and found connections with posterior frontal and parietal cortex, but nothing with areas classically associated with the fronto-parietal cortex. The authors propose that the inferior fronto-occipital fasciculus may connect V1 with lateral prefrontal regions only in humans. However, they provide no evidence for this suggestion. Indeed, my understanding of the iFOF is that it connects to inferior and lateral occipital cortex (see e.g. figures from the Takemura study cited in this work). Can the authors better support the idea that the iFOF might be the route of connection between V1 and frontal cortex?
Thank you for your comments. We agree that while the data and methods we present here don’t address whether the iFOF is the route of connection between the inferior and lateral occipital cortex, more evidence from relevant literature would be helpful. The figures from the (Takemura et al., 2016) paper shows only inferior and lateral occipital cortex and are ambiguous for our regions of interest. However, other papers suggest that iFOF may be the route of connection between V1 and frontal cortex:
A paper by Wu and colleagues shows figures indicating that the IFOF does provide a connection between the medial occipital cortex and IFG. We now cite this in the paper. “Major white matter tracts that connect to the occipital lobe such as the inferior fronto-occipital fasciculus (connects occipital lobe to the lateral prefrontal cortex) and the inferior longitudinal fasciculus (connects occipital lobe to anterior temporal lobe) have been well documented using tractography methods in humans (Wu, Sun, Wang, & Wang, 2016).”
Second, I am concerned that both 1) the Central V1 ROI employed in this work and 2) the inferior frontal cortex region showing strong FC with that Central V1 ROI overlap very closely with regions where we have seen poor BOLD signal in our own fMRI data (I would like to attach a figure if possible). <br />
We are not confident what the source of the poor signal might be in posterior occipital or inferior frontal cortex; we suspect the presence of large veins (possibly the transverse sinus in V1; see Winawer et al., 2010, Journal of Vision). In any case, the data quality is low enough that we believe our data should not be considered to represent actual neural function in those regions. Can the authors demonstrate convincingly that this is not the case in their HCP data?
The reviewer suggests that based on their data, posterior occipital and inferior frontal cortex have relatively poor signal. They suggest that this poor signal would result in spurious correlations between the regions because of large veins. As described in our methods section for preprocessing of resting state scan data, white matter and CSF timecourses were regressed out, which aids in removing average venous artifact. Replication between 2 datasets (HCP and Griffis et al., 2017) and 2 modalities (DWI and resting state) further indicate the reliability of this effect.
The Winawer et al., 2010 article cites (Schira, Tyler, Breakspear, & Spehar, 2009) when discussing this issue; that paper suggests that poor signal in these regions may come largely from partial voluming (conflating signal from gray matter with signal from veins), and that these can be managed through increasing resolution with smaller voxel sizes. Our data are collected at resolutions finer than their recommendations, suggesting that such an effect should be minimal in this dataset. We have added the following text to the limitations section to address this comment: “We also acknowledge that large veins near posterior occipital cortex could impact our functional connectivity measurements in this area. However, we performed extensive pre-processing to reduce the impact of vessels on activity. In addition, the voxel size of our resting state scan is small (2mm isotropic), mitigating contributions from nearby veins due to partial voluming effects (Schira et al., 2009).”
Third, I have an issue with the localization of effects in this paper. The paper describes effects in the fronto-parietal network throughout the manuscript, including the title. How surprising, then, that the strongest effects are not in FP network at all! Figure 4A makes it very clear that the largest effects are in the IFG, which is outside the green outlines describing the extent of the fronto-parietal network, but inside the Default network. <br />
Figure 3A also supports this Default-centric localization, with Central V1 effects in posterior lateral parietal, medial parietal, and superior frontal cortex, all outside FP but inside Default. Since the FC effects are not actually primarily in FP, I see no reason why FP should be used as a mask in Figure 5. Indeed, the authors should show the localization of SC effects throughout the cortex, not just in FP. I also see no reason why these V1-Default connections should be characterized in any way as "attention" or "control".
We appreciate the reviewer’s comment and have made extensive modifications to the paper in response. The reviewer notes that some vertices of the effect we observed in left frontal cortex are in a portion of the IFG that is not classified by Yeo et al, 2011 as part of the frontoparietal network, but instead classified by that paper as the default mode network. We would like to note that most other papers that define DMN would not have included the IFG as part of that network, and in fact, Yeo’s 17-network parcellation from the same paper does not classify that portion of cortex as part of the default mode network. The inclusion of that parcel as part of the DMN is likely an artifact of the requirement of the algorithm in that paper to subdivide the brain into 7 discrete networks. However, the set of vertices can be described as being in the inferior frontal cortex, and we have reworked our discussion to de-emphasize the fronto-parietal network.
This said, we also quantified the similarities between the frontoparietal cortex and the functional connectivity patterns selective for V1, using Dice coefficients. This is now shown in Table 1. <br />
We have described this table within the text as follows: “Table 1 indicates high similarity between central V1 dominant regions and the FPN and partial similarity to portions of the CON and DMN, while the other V1 segments, mid- peripheral and far-peripheral are not strikingly similar to any networks.”
We have also added the following text to the article in reporting of Figure 4: “This inferior frontal gyrus region aligns well with the anterior portion of the FPN as defined by Yeo, but interestingly, it does expand somewhat beyond that border into the IFG (Inferior frontal gyrus) which is related to attention and control (Baldauf & Desimone, 2014; Chong, Williams, Cunnington, & Mattingley, 2008; Fassbender et al., 2004; Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010; Swick, Ashley, & Turken, 2008, 2011).”
The reviewer also suggests that localization of structural connectivity effects should be shown throughout the cortex. We have added a figure 5 that shows the effects in our three networks of interest on the same cortical sheet. This figure shows more clearly the delineations of the strong effects. For technical reasons, we cannot perform these analyses on the cortex’s entirety at once: as described in the methods section, probability tracking for each network was calculated separately. Interestingly, however, despite this, the patterns look continuous across the boundary.
Fourth, I feel that these FC and SC differences are wildly over-interpreted. From the scale, the actual strength of FC and SC between central V1 and lateral parietal cortex is extremely weak (around Z(r) = .1 for FC and p-track = .1 for SC). Under no circumstances would I believe that either of those values represents any sort of real connection. Cortical regions with direct structural connections have much stronger FC values than regions that indirectly influence each other via multi-step connections.
Functional connectivity magnitudes are always influenced by the preprocessing done to obtain them. In this case we regressed out the mean signal, and regressed out white matter and CSF. While this practice decreases the mean correlation strength (Shirer, Jiang, Price, Ng, & Greicius, 2015; Weissenbacher et al., 2009) it also improves across-subject reliability (Burgess et al., 2016). The debate about this practice, now a decade long, has focused on the interpretability of negative correlations, which we do not do here. All sides of the debate agree that the practice of mean signal regression should not influence relative correlations across brain areas.
We are looking at variability in connection strength between different portions of a single brain area, and we would expect roughly similar long-range connectivity between different parts of V1. We have incorporated this point into the discussion on page XX where we say “ While central and peripheral representation portions are still part of the same V1 area, and therefore we would expect similarity in their connectivity patterns, our results indicate that eccentricity differences do exist and are consistent with previously reported differences in information processing on central and peripheral visual information.”
In addition, we added to the limitations section a discussion of this:<br />
“Here, we show functional connectivity strengths on the order of r=0.1. While very reliable, these magnitudes are not as large as connections to other areas, for example, portions of the occipital lobe. Functional connectivity magnitudes are always influenced by the preprocessing done to obtain them. In this case, we regressed out the mean signal and regressed out white matter and CSF. While this practice decreases the mean correlation strength (Shirer et al., 2015; Weissenbacher et al., 2009) it also improves across-subject reliability (Burgess et al., 2016). The debate about this practice, now a decade long, has focused on the interpretability of negative correlations, which we do not do here to examine relative correlations across brain areas.
Further, very large portions of the brain probably have both stronger FC and SC to central V1 than these FP regions (the authors show this for FC but exclude this info for SC). <br />
We have included a new figure to show the SC patterns across more than just the FPN (now includes regions within FPN, DMN, and CON), now Figure 5. Along with the following text, “Next, we investigated similar comparisons between central and far-peripheral V1 in a different modality- structural connections. A t-test comparing the structural connection of central and far-peripheral V1 revealed significant effects (p<.001) in brain regions belonging to FPN, CON, and DMN functional networks (Figure 5). We chose these three networks to compare to functional connectivity findings from Figure 3. <br />
Notably, central representing V1 was preferentially connected (over far-peripheral V1) to regions associated with the FPN, including the mid orbitofrontal and inferior parietal regions of the FPN, as well as lateral portions of the DMN, and the insular portion of the CON. In contrast, far-peripheral representing V1 was preferentially connected (over central V1) to medial portions of the DMN (Figure 5).”
Most glaringly, I certainly don't believe there is a "direct structural connection" as is claimed in the discussion--a claim based, strangely, on the spatial correspondence between the structural and functional maps, which really has nothing to do with any evidence for a direct connection. <br />
As stated in the discussion limitations section “structural tractography analysis only identifies direct connections”. <br />
The probabilistic tractography method can only show connections between Region A and Region B. It cannot indicate if there were connections between Region A and Region B that traveled via Region C. Therefore if a connection is indicated by the method, it must be direct. <br />
The statement of a “direct structural connection” is not an interpretation of the correspondence between structural and functional maps, but an interpretation of the structural maps.
Finally, the authors must note that p values may not be used for spatial correlations between brain maps. This is because these maps are always highly autocorrelated, which violates the independence assumption of the correlation procedure. <br />
We have replaced spatial correlations between brain maps with Dice coefficients, a more field-standard method for comparing spatial maps. We thank the reviewer for the comments and think this new way of analyzing it is a better fit.
Reviewer #2 (Additional data files and statistical comments):
The authors should show the data (maps or scatterplots) going into their spatial correlation on page 13. <br />
Based on comments from reviewers, we changed this part of the analysis to dice coefficients with the following text : “A Dice Coefficient was calculated for comparison of the functional and structural connectivity differences of central vs far-peripheral V1 to the FPN, CON, and DMN. Across all 3 networks the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .707.<br />
Within the FPN the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .915. Within the CON the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .842. Within the DMN the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .85. These relationships indicate that the overall pattern of connectivity of central V1 greater than far peripheral V1 is consistent across modalities with an especially high overlap within the FPN.”