Unraveling the impact of congenital deafness on individual brain organization

Reviewer #1 (Public review):

This experiment sought to determine what effect congenital/early-onset hearing loss (and associated delay in language onset) has on the degree of inter-individual variability in functional connectivity to the auditory cortex. Looking at differences in variability rather than group differences in mean connectivity itself represents an interesting addition to the existing literature. The sample of deaf individuals was large, and quite homogeneous in terms of age of hearing loss onset, which are considerable strengths of the work. The experiment appears well conducted and the results are certainly of interest.

Reviewer #2 (Public review):

Summary:

This study focuses on changes in brain organization associated with congenital deafness. The authors investigate differences in functional connectivity (FC) and differences in the variability of FC. By comparing congenitally deaf individuals to individuals with normal hearing, and by further separating congenitally deaf individuals into groups of early and late signers, the authors can distinguish between changes in FC due to auditory deprivation and changes in FC due to late language acquisition. They find larger FC variability in deaf than normal-hearing individuals in temporal, frontal, parietal, and midline brain structures, and that FC variability is largely driven by auditory deprivation. They suggest that the regions that show a greater FC difference between groups also show greater FC variability.

Strengths:

The manuscript is well-written, and the methods are clearly described and appropriate. Including the three different groups enables the critical contrasts distinguishing between different causes of FC variability changes. The results are interesting and novel.

Weaknesses:

Analyses were conducted for task-based data rather than resting-state data. The authors report behavioral differences between groups and include behavioral performance as a nuisance regressor in their analysis. This is a good approach to account for behavioral task differences, given the data. Nevertheless, additional work using resting-state functional connectivity could remove the potential confound fully.

The authors have addressed my concerns well.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

(1) This experiment sought to determine what effect congenital/early-onset hearing loss (and associated delay in language onset) has on the degree of inter-individual variability in functional connectivity to the auditory cortex. Looking at differences in variability rather than group differences in mean connectivity itself represents an interesting addition to the existing literature. The sample of deaf individuals was large, and quite homogeneous in terms of age of hearing loss onset, which are considerable strengths of the work. The experiment appears well conducted and the results are certainly of interest. I do have some concerns with the way that the project has been conceptualized, which I share below.

Thank you for acknowledging the strengths and novelty of our study. We have now addressed the conceptual issues raised; please see below in the specific comments.

(2) The authors should provide careful working definitions of what exactly they think is occurring in the brain following sensory deprivation. Characterizing these changes as 'largescale neural reorganization' and 'compensatory adaptation' gives the impression that the authors believe that there is good evidence in support of significant structural changes in the pathways between brain areas - a viewpoint that is not broadly supported (see Makin and Krakauer, 2023). The authors report changes in connectivity that amount to differences in coordinated patterns of BOLD signal across voxels in the brain; accordingly, their data could just as easily (and more parsimoniously) be explained by the unmasking of connections to the auditory cortex that are present in typically hearing individuals, but which are more obvious via MR in the absence of auditory inputs.

We thank the Reviewer for the suggestion to clarify and better support our stance regarding reorganization. We indeed believe that the adaptive changes in the auditory cortex in deafness represent real functional recruitment for non-auditory functions, even in the relatively limited large-scale anatomical connectivity changes. This is supported by animal works showing causal evidence for the involvement of deprived auditory cortices in non-auditory tasks, in a way that is not found in hearing controls (e.g., Lomber et al., 2010, Meredith et al., 2011, reviewed in Alencar et al., 2019; Lomber et al., 2020). Whether the word “reorganization” should be used is indeed debated recently (Makin and Krakauer, 2023). Beyond terminology, we do agree that the basis for the changes in recruitment seen in the brains of people with deafness or blindness is largely based on the typical anatomical connectivity at birth. We also agree that at the group level, there is poor evidence of large-scale anatomical connectivity differences in deprivation. However, we think there is more than ample evidence that the unmasking and more importantly re-weighting of non-dominant inputs gives rise to functional changes. This is supported by the relatively weaker reorganization found in late-onset deprivation as compared to early-onset deprivation. If unmasking of existing connectivity without any functional additional changes were sufficient to elicit the functional responses to atypical stimuli (e.g., non-visual in blindness and non-auditory in deafness), one would expect there to be no difference between early- and late-onset deprivation in response patterns. Therefore, we believe that the fact that these are based on functions with some innate pre-existing inputs and integration is the mechanism of reorganization, not a reason not to treat it as reorganization. Specifically, in the case of this manuscript, we report the change in variability of FC from the auditory cortex, which is greater in deafness than in typically hearing controls. This is not an increase in response per se, but rather more divergent values of FC from the auditory cortex, which are harder to explain in terms of ‘unmasking’ alone, unless one assumes unmasking is particularly variable. The mechanistic explanation for our findings is that in the absence of auditory input’s fine-tuning and pruning of the connectivity of the auditory cortex, more divergent connectivity strength remains among the deaf. Thus, auditory input not only masks non-dominant inputs but also prunes/deactivates exuberant connectivity, in a way that generates a more consistently connected auditory system. We have added a shortened version of these clarifications to the discussion (lines 351-372).

(3) I found the argument that the deaf use a single modality to compensate for hearing loss, and that this might predict a more confined pattern of differential connectivity than had been previously observed in the blind to be poorly grounded. The authors themselves suggest throughout that hearing loss, per se, is likely to be driving the differences observed between deaf and typically-hearing individuals; accordingly, the suggestion that the modality in which intentional behavioral compensation takes place would have such a large-scale effect on observed patterns of connectivity seems out of line.

Thank you for your critical insight regarding our rationale on modality use and its impact on connectivity patterns in the deaf compared to the blind. After some thought, we agree that the argument presented may not be sufficiently strong and could distract from the main findings of our study. Therefore, we have decided to remove this claim from our revised manuscript.

(4) The analyses highlighting the areas observed to be differentially connected to the auditory cortex and areas observed to be more variable in their connectivity to the auditory cortex seem somewhat circular. If the authors propose hearing loss as a mechanism that drives this variability in connectivity, then it is reasonable to propose hypotheses about the directionality of these changes. One would anticipate this directionality to be common across participants and thus, these areas would emerge as the ones that are differently connected when compared to typically hearing folks.

We are a little uncertain how to interpret this concern. If the question was about the logic leading to our statement that variability is driven by hearing loss, then yes, we indeed were proposing hearing loss as a mechanism that drives this variability in connectivity to the auditory cortex; we regret this was unclear in the original manuscript. This logic parallels the proposal made with regard to the increased variability in FC in blindness; deprivation leads to more variable outcomes, due to the lack of developmental environmental constraints (Sen et al., 2022). Specifically, we first analyzed the differences in within-group variability between deaf and hearing individuals (Fig. 1A), followed by examining the variability ratio (Fig. 1B) in the same regions that demonstrated differences. The first analysis does not specify which group shows higher variability; therefore, the second analysis is essential to clarify the direction of the effect and identify which group, and in which regions, exhibits greater variability. We have clarified this in the revised manuscript (lines 125-127): “To determine which group has larger individual differences in these regions (Figure 1B), we computed the ratio of variability between the two groups (deaf/hearing) in the areas that showed a significant difference in variability (Figure 1A)”. Nevertheless, this comment can also be interpreted as predicting that any change in FC due to deafness would lead to greater variability. In this case, it is also important to mention that while we would expect regions with higher variability to also show group differences between the deaf and the hearing (Figure 2), our analysis demonstrates that variability is present even in regions without significant group mean differences. Similarly, many areas that show a difference between the groups in their FC do not show a change in variability (for example, the bilateral anterior insula and sensorimotor cortex). In fact, the correlation between the regions with higher FC variability (Figure 1A) and those showing FC group differences (Figure 2B) is significant but rather modest, as we now acknowledge in our revised manuscript (lines 324-328). Therefore, increased FC and increased variability of FC are not necessarily linked.

(5) While the authors describe collecting data on the etiology of hearing loss, hearing thresholds, device use, and rehabilitative strategies, these data do not appear in the manuscript, nor do they appear to have been included in models during data analysis. Since many of these factors might reasonably explain differences in connectivity to the auditory cortex, this seems like an omission.

We thank the Reviewer for their comment regarding the inclusion of these variables in our manuscript. We have now included additional information in the main text and a supplementary table in the revised manuscript that elaborates further on the etiology of hearing loss and all individual information that characterizes our deaf sample. Although we initially intended to include individual factors (e.g., hearing threshold, duration of hearing aid use, and age of first use) in our models, this was not feasible for the following reasons: 1) for some subjects, we only have a level of hearing loss rather than specific values, which we could not use quantitatively as a nuisance variable (it was typical in such testing to ascertain the threshold of loss as belonging to a deafness level, such as “profound” and not necessarily go into more elaborate testing to identify the specific threshold), and 2) this information was either not collected for the hearing participants (e.g., hearing threshold) or does not apply to them (e.g., age of hearing aid use), which made it impossible to use the complete model with all these variables. Modeling the groups separately with different variables would also be inappropriate. Last, the distribution of the values and the need for a large sample to rigorously assess a difference in variability also precluded sub-dividing the group to subgroup based on these values.

Therefore, we opted for a different way to control for the potential influence of these variables on FC variability in the deaf. We tested the correlation between the FC from the auditory cortex and each of these parameters in the areas that showed increased FC in deafness (Figures 1A, B), to see if it could account for the increased variability. This ROI analysis did not reveal any significant correlations (all p > .05, prior to correction for multiple comparisons; see Figures S4, S5, and S6 for scatter plots). The maximal variability explained in these ROIs by the hearing factors was r2=0.096, whereas the FC variability (Figure 1B) was increased by at least 2 in the deaf. Therefore, it does not seem like these parameters underlie the increased variability in deafness. To test if these variables had a direct effect on FC variability in other areas in the brain, we also directly computed the correlation between FC and each factor individually. At the whole-brain level, the results indicate a significant correlation between AC-FC and hearing threshold, as well as a correlation between AC-FC and the age of hearing aid use onset, but not for the duration of hearing aid use (Figure S3). While these may be interesting on their own, and are added to the revised manuscript, the regions that show significant correlations with hearing threshold and age of hearing aid use are not the same regions that exhibit FC variability in the deaf (Figures 1A, B).

Overall, these findings suggest that although some of these factors may influence FC, they do not appear to be the driving factors behind FC variability. Finally, in terms of rehabilitative strategies, only one deaf subject reported having received long-term oral training from teachers. This participant started this training at age 2, as now described in the participants’ section. We thank the reviewer for raising this concern and allowing us to show that our findings do not stem from simple differences ascribed to auditory experience in our participants.

Reviewer #2 (Public Review):

(1) The paper has two main merits. Firstly, it documents a new and important characteristic of the re-organization of the brains of the deaf, namely its variability. The search for a welldefined set of functions for the deprived auditory cortex of the deaf has been largely unsuccessful, with several task-based approaches failing to deliver unanimous results. Now, one can understand why this was the case: most likely there isn't a fixed one well-defined set of functions supported by an identical set of areas in every subject, but rather a variety of functions supported by various regions. In addition, the paper extends the authors' previous findings from blind subjects to the deaf population. It demonstrates that the heightened variability of connectivity in the deprived brain is not exclusive to blindness, but rather a general principle that applies to other forms of deprivation. On a more general level, this paper shows how sensory input is a driver of the brain's reproducible organization.

We thank the Reviewer for their observations regarding the merits of our study. We appreciate the recognition of the novelty in documenting the variability of brain reorganization in deaf individuals.

(2) The method and the statistics are sound, the figures are clear, and the paper is well-written. The sample size is impressively large for this kind of study.

We thank the Reviewer for their positive feedback on the methodology, statistical analysis, clarity of figures, and the overall composition of our paper. We are also grateful for the acknowledgment of our large sample size, which we believe significantly strengthens the statistical power and the generalizability of our findings.

(3) The main weakness of the paper is not a weakness, but rather a suggestion on how to provide a stronger basis for the authors' claims and conclusions. I believe this paper could be strengthened by including in the analysis at least one of the already published deaf/hearing resting-state fMRI datasets (e.g. Andin and Holmer, Bonna et al., Ding et al.) to see if the effects hold across different deaf populations. The addition of a second dataset could strengthen the evidence and convincingly resolve the issue of whether delayed sign language acquisition causes an increase in individual differences in functional connectivity to/from Broca's area. Currently, the authors may not have enough statistical power to support their findings.

We thank the Reviewer for their constructive suggestion to reinforce the robustness of our findings. While we acknowledge the potential value of incorporating additional datasets to strengthen our conclusions, the datasets mentioned (Andin and Holmer, Bonna et al., Ding et al.) are not publicly available, which limits our ability to include them in our analysis. Additionally, datasets that contain comparable groups of delayed and native deaf signers are exceptionally rare, further complicating the possibility of their inclusion. Furthermore, to discern individual differences within these groups effectively, a substantially larger sample size is necessary. As such, we were unfortunately unable to perform this additional analysis. This is a challenge we acknowledge in the revised manuscript (lines 442-445), especially when the group is divided into subcategories based on the level of language acquisition, which indeed reduces our statistical power. We have however, now integrated the individual task accuracy and reaction time parameters as nuisance variables in calculating the variability analyses; all the results are fully replicated when accounting for task difficulty. We also report that there was no group difference in activation for this task between the groups which could affect our findings.

We would like to note that while we would like to replicate these findings in an additional cohort using resting-state, we do not anticipate the state in which the participants are scanned to greatly affect the findings. FC patterns of hearing individuals have been shown to be primarily shaped by common system and stable individual features, and not by time, state, or task (Finn et al., 2015; Gratton et al., 2018; Tavor et al., 2016). While the task may impact FC variability, we have recently shown that individual FC patterns are stable across time and state even in the context of plasticity due to visual deprivation (Amaral et al., 2024). Therefore, we expect that in deafness as well there should not be meaningful differences between resting-state and task FC networks, in terms of FC individual differences. That said, we are exploring collaborations and other avenues to access comparable datasets that might enable a more powerful analysis in future work. This feedback is very important for guiding our ongoing efforts to verify and extend our conclusions.

(4) Secondly, the authors could more explicitly discuss the broad implications of what their results mean for our understanding of how the architecture of the brain is determined by the genetic blueprint vs. how it is determined by learning (page 9). There is currently a wave of strong evidence favoring a more "nativist" view of brain architecture, for example, face- and object-sensitive regions seem to be in place practically from birth (see e.g. Kosakowski et al., Current Biology, 2022). The current results show what is the role played by experience.

We thank the Reviewer for highlighting the need to elaborate on the broader implications of our findings in relation to the ongoing debate of nature vs. nurture. We agree that this discussion is crucial and have expanded our manuscript to address this point more explicitly. We now incorporate a more detailed discussion of how our results contribute to understanding the significant role of experience in shaping individual neural connectivity patterns, particularly in sensory-deprived populations (lines 360-372).

Reviewer #3 (Public Review):

Summary:

(1) This study focuses on changes in brain organization associated with congenital deafness. The authors investigate differences in functional connectivity (FC) and differences in the variability of FC. By comparing congenitally deaf individuals to individuals with normal hearing, and by further separating congenitally deaf individuals into groups of early and late signers, the authors can distinguish between changes in FC due to auditory deprivation and changes in FC due to late language acquisition. They find larger FC variability in deaf than normal-hearing individuals in temporal, frontal, parietal, and midline brain structures, and that FC variability is largely driven by auditory deprivation. They suggest that the regions that show a greater FC difference between groups also show greater FC variability.

Strengths:

- The manuscript is well written.

- The methods are clearly described and appropriate.

- Including the three different groups enables the critical contrasts distinguishing between different causes of FC variability changes.

- The results are interesting and novel.

We thank the Reviewer for their positive and detailed feedback. Their acknowledgment of the clarity of our methods and the novelty of our results is greatly appreciated.

Weaknesses:

(2) Analyses were conducted for task-based data rather than resting-state data. It was unclear whether groups differed in task performance. If congenitally deaf individuals found the task more difficult this could lead to changes in FC.

We thank the Reviewer for their observation regarding possible task performance differences between deaf and hearing participants and their potential effect on the results. Indeed, there was a difference in task accuracy between these groups. To account for this variation and ensure that our findings on functional connectivity were not confounded by task performance, we now included individual task accuracy and reaction time as nuisance variables in our analyses. This approach allowed us to control for any performance differences. The results now presented in the revised manuscript account for the inclusion of these two nuisance variables (accuracy and reaction time) and completely align with our original conclusions, highlighting increased variability in deafness, which is found in both the entire deaf group at large, as well as when equating language experience and comparing the hearing and native signers. The correlation between variability and group differences also remains significant, but its significance is slightly decreased, a moderate effect we acknowledge in the revised manuscript (see comment #4). The differences between the delayed signers and native signers are also retained (Figure 3), now aligning better with language-sensitive regions, as previously predicted. The inclusion of the task difficulty predictors also introduced an additional finding in this analysis, a significant cluster in the right aIFG. Therefore, the inclusion of these predictors reaffirms the robustness of the conclusions drawn about FC variability in the deaf population.

We would like to note that while we would like to replicate these findings in an additional cohort using resting-state if we had access to such data, we do not anticipate the state in which the participants are scanned to greatly affect the findings. FC patterns of hearing individuals have been shown to be primarily shaped by common system and stable individual features, and not by time, state, or task (Finn et al., 2015; Gratton et al., 2018; Tavor et al., 2016). While the task may impact FC variability, we have recently shown that individual FC patterns are stable across time and state even in the context of plasticity due to visual deprivation (Amaral et al., 2024). Therefore, we expect that in deafness as well there should not be meaningful differences between resting-state and task FC networks, in terms of FC individual differences. We have also addressed this point in our manuscript (lines 442-451).

(3) No differences in overall activation between groups were reported. Activation differences between groups could lead to differences in FC. For example, lower activation may be associated with more noise in the data, which could translate to reduced FC.

We thank the reviewer for noting the potential implications of overall activation differences on FC. In our analysis of the activation for words, we found no significant clusters showing a group difference between the deaf and hearing participants (p < .05, cluster-corrected for multiple comparisons) - we also added this information to the revised manuscript (lines 542-544). This suggests that the differences in FC observed are not confounded by variations in overall brain activation between the groups under these conditions.

(4) Figure 2B shows higher FC for congenitally deaf individuals than normal-hearing individuals in the insula, supplementary motor area, and cingulate. These regions are all associated with task effort. If congenitally deaf individuals found the task harder (lower performance), then activation in these regions could be higher, in turn, leading to FC. A study using resting-state data could possibly have provided a clearer picture.

We thank the Reviewer for pointing out the potential impact of task difficulty on FC differences observed in our study. As addressed in our response to comment #2, task accuracy and reaction times were incorporated as nuisance variables in our analysis. Further, these areas showed no difference in activation between the groups (see response to comment #3 above). Notably, the referred regions still showed higher FC in congenitally deaf individuals even when controlling for these performance differences. Additionally, these findings are consistent with results from studies using resting-state data in deaf populations, further validating our observations. Specifically, using resting-state data, Andin & Holmer (2022), have shown higher FC for deaf (compared to hearing individuals) from auditory regions to the cingulate cortex, insular cortex, cuneus and precuneus, supramarginal gyrus, supplementary motor area, and cerebellum. Moreover, Ding et al. (2016) have shown higher FC for the deaf between the STG and anterior insula and dorsal anterior cingulated cortex. This suggests that the observed FC differences are likely reflective of genuine neuroplastic adaptations rather than mere artifacts of task difficulty. Although we wish we could augment our study with resting-state data analyzed similarly, we could not at present acquire or access such a dataset. We acknowledge this limitation of our study (lines 442-451) in the revised manuscript and intend to confirm that similar results will be found with resting state data in the future.

(5) The correlation between the FC map and the FC variability map is 0.3. While significant using permutation testing, the correlation is low, and it is not clear how great the overlap is.

We acknowledge that the correlation coefficient of 0.3, while statistically significant, indicates a moderate overlap. It's also worth noting that, using our new models that include task performance as a nuisance variable, this value has decreased somewhat, to 0.24 (which is still highly significant). It is important to note that the visual overlap between the maps is not a good estimate of the correlation, which was performed on the unthresholded maps, to estimate the link not only between the most significant peaks of the effects, but across the whole brain patterns. This correlation is meant to suggest a trend rather than a strong link, but especially due to its consistency with the findings in blindness, we believe this observation merits further investigation and discussion. As such, we kept it in the revised manuscript while moderating our claims about its strength.

Reviewer #1 (Recommendations For The Authors):

(1) Page 4: Does auditory cortex FC variability..." FC is not yet defined.

Corrected, thanks.

(2) Page 4: "It showed lower variability..." What showed this?

Clarified, thanks.

(3) Page 11: "highlining the importance" should read "highlighting the importance".

Corrected, thanks.

(4) Page 11: Do you really mean to suggest functional connectivity does not vary as a function of task? This would not seem well supported.

We do not suggest that FC doesn’t vary as a function of task, and have revised this section (lines 447-451).

(5) Page 12: "there should not to be" should read "there should not be".

Corrected, thanks.

(6) Page 12: "and their majority" should read "and the majority".

Corrected, thanks.

Reviewer #2 (Recommendations For The Authors):

Major

(1) Although this is a lot of work, I nonetheless have another suggestion on how to test if your results are strong and robust. Perhaps you could analyze your data using an ROI/graph-theory approach. I am not an expert in graph theory analysis, but for sure there is a simple and elegant statistic that captures the variability of edge strength variability within a population. This approach could not only validate your results with an independent analysis and give the audience more confidence in their robustness, but it could also provide an estimate of the size of the effect size you found. That is, it could express in hard numbers how much more variable the connections from auditory cortex ROI's are, in comparison to the rest of the brain in the deaf population, relative to the hearing population.

We thank the Reviewer for suggesting the use of graph theory as a method to further validate our findings. While we see the potential value in this approach, we believe it may be beyond the scope of the current paper, and merits a full exploration of its own, which we hope to do in the future. However, we understand the importance of showing the uniqueness of the connectivity of the auditory cortex ROI as compared to the rest of the brain. So, in order to bolster our results, we conducted an additional analysis using control regions of interest (ROIs). Specifically, we calculated the inter-individual variability using all ROIs from the CONN Atlas (except auditory and language regions) as the control seed regions for the FC. We showed that the variability of connectivity from the auditory cortex is uniquely more increased on deafness, as compared to these control ROIs (Figure S1). This additional analysis supports the specificity of our findings to the auditory cortex in the deaf population. We aim to integrate more analytic approaches, including graph theory methods, in our future work.

Minor

(1) Some citations display the initial of the author in addition to the last name, unless there is something I don't know about the citation system, the initial shouldn't be there.

This is due to the citation style we're using (APA 7th edition, as suggested by eLife), which requires including the first author's initials in all in-text citations when citing multiple authors with the same last name.

Reviewer #3 (Recommendations For The Authors):

(1) I recommend that the authors provide behavioral data and results for overall neural activation.

Thanks. We have added these to the revised manuscript. Specifically, we report that there was no difference in the activation for words (p < .05, cluster-corrected for multiple comparisons) between the deaf and hearing participants. Further, we report the behavioral averages for accuracy and reaction time for each group, and have now used these individual values explicitly as nuisance variables in the revised analyses.

(2) For the correlation between FC and FC variability, it seemed a bit odd that the permuted data were treated additionally (through Gaussian smoothing). I understand the general logic (i.e., to reintroduce smoothness), but this approach provides more smoothing to the permutation than the original data. It is hard to know what this does to the statistical distribution. I recommend using a different approach or at least also reporting the p-value for non-smoothed permutation data.

In response to this suggestion and to ensure transparency in our results, we have now included also the p-value for the non-smoothed permutation data in our revised manuscript (still highly significant; p < .0001). Thanks for this proposal.

(3) For the map comparison, a plot with different colors, showing the FC map, the FC variability map, and one map for the overlap on the same brain may be helpful.

We thank the Reviewer for their suggestion to visualize the overlap between the maps. However, we performed the correlation analysis using the unthresholded maps, as mentioned in the methods section of our manuscript, specifically to estimate the link not only between the most significant peaks of the effects, but across the whole brain patterns. This is why the maps displayed in the figures, which are thresholded for significance, may not appear to match perfectly, and may actually obscure the correlation across the brain. This methodological detail is crucial for interpreting the relationship and overlap between these maps accurately but also explains why the visualization of the overlap is, unfortunately, not very informative.