Using multi-modal neuroimaging to characterise social brain specialisation in infants
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This important study provides a state-of-the-art framework to explore the coupling of complementary cerebral measures (neural, hemodynamic, and metabolic) during development by providing an interesting roadmap for multimodal neuroimaging in infants. The methodological contribution is compelling with an original setup for simultaneous EEG and NIRS recording and solid data analyses. However, the claims about functional specialization and the role of the temporal-parietal junction in social processing are only partially supported by the results. This work will be of interest to a broad audience of scientists interested in multimodal neuroimaging and cognitive development.
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Abstract
The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.
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Author Response
Reviewer #3 (Public Review):
This manuscript proposes to tackle a very interesting and methodologically challenging topic: the mechanistic underpinnings of neural specialization in the infant brain. The authors presented 4- to 7-month-old infants with social and non-social stimuli while their neural, hemodynamic, and metabolic activity was monitored, and they report a complex pattern of relationships between neural and metabolic or hemodynamic responses during social processing on the one hand, and during non-social processing on the other hand.
The approach described in this manuscript is very interesting and the combined use of EEG and bNIRS data appears very promising. However, there is some confusion between the initial aims of the study, and the analyses performed, which jeopardizes the clarity and the impact …
Author Response
Reviewer #3 (Public Review):
This manuscript proposes to tackle a very interesting and methodologically challenging topic: the mechanistic underpinnings of neural specialization in the infant brain. The authors presented 4- to 7-month-old infants with social and non-social stimuli while their neural, hemodynamic, and metabolic activity was monitored, and they report a complex pattern of relationships between neural and metabolic or hemodynamic responses during social processing on the one hand, and during non-social processing on the other hand.
The approach described in this manuscript is very interesting and the combined use of EEG and bNIRS data appears very promising. However, there is some confusion between the initial aims of the study, and the analyses performed, which jeopardizes the clarity and the impact of this manuscript. Besides, the predictions of the authors are often underspecified which complexifies the interpretation of the results.
Based on its abstract, the goal of this work is to "combine simultaneous measures of coordinated neural activity metabolic rate and oxygenated blood supply to measure emerging specialization in the infant brain". The introduction nicely elaborates on the "interactive specialization theory" and the potential role of the interplay between brain energy consumption and neural activity in the emergence of functionally specialized brain regions during development. The authors present a novel multimodal approach, with potentially important implications for the study of brain specialization as a function of experience or maturation. Yet the experimental procedure presented in this manuscript only assesses specialized brain activity in response to social processing in 4- to 7-month-old infants, using multimodal neuroimaging.
Indeed, the authors presented 4- to 7-month-old infants with social and non-social stimuli while their neural, hemodynamic, and metabolic activity was monitored. The authors report significant differences between the two conditions in terms of neural activity in the delta, alpha, beta, and gamma bands; as well as in the pattern of hemodynamic to metabolic coupling. Using a GLM approach, the authors report on fNIRS channels and EEG sensors showing significant relationships between the evoked neural activity in the beta and gamma frequency bands, and each of the bNIRS signals (HbO, HbR & CCO), in the social and in the non-social conditions. The authors identify a particular fNIRS channel overlaying posterior STS, showing a positive relationship between Pz EEG beta activity and HbO, as well as CCO, together with a negative relationship between that same neural activity and HbR, in the social condition. This pattern of activity was not observed in the non-social condition.
Overall, these results indicate differential neural responses to social and non-social stimuli, coupled metabolic and hemodynamic activity in response to social as well as nonsocial stimuli.
These results additionally indicate coordinated metabolic, hemodynamic, and neural responses in brain regions selective for social processing, but it does not allow us to conclude that this coordinated activity is actually related to the functional specialization process (e.g. last sentence of the abstract).
We would like to thank the reviewer for their detailed comments. Based on their suggestions, we have made several changes to the manuscript. This study was the first to combine EEG and broadband NIRS and therefore served as a proof of principle study. At the onset of this work, there were many elements to develop such as the technical aspect of simultaneous bNIRS – EEG measurements as well as the methodology to combine the signals from both techniques with such different time resolutions. Therefore, we focused on one age group of infants rather than performing a study involving multiple age groups. The 4-to-7-month-old age group has been studied extensively using fNIRS, particularly to look at social brain development using similar stimuli as those used in the present study. Previous studies have demonstrated that social selectivity can be detected at 4 – 8 months of age (Grossmann et al., 2010; Lloyd-Fox et al., 2012, 2013, 2017). As this was a proof of principle study, we wanted to ensure that we were able to replicate results from previous studies with this new methodology. We therefore used one age group of 4-to-7-months. This has also been added to the introduction of the manuscript to provide clearer reasoning for using this age group.
The reviewer is correct that the current study does not provide direct evidence of developmental change in functional specialisation or the hypothesised interactive process through which functional specialisation may occur. Rather, we are measuring the status of functional specialisation (the idea that different areas in the brain are specialised for different functions) at the age we study, by testing whether the signals we observe are selective to social but not non-social stimuli. We have reframed the abstract and introduction of the manuscript to ensure this is clear, and we additionally now focus more on the methodology developed to answer such questions. Future studies can leverage our methodology to study different age groups to establish how the relationships between neural and vascular/metabolic signals changes over developmental time, which may provide greater insight into the specialisation process.
Grossmann, T., Oberecker, R., Koch, S. P., & Friederici, A. D. (2010). The Developmental Origins of Voice Processing in the Human Brain. Neuron, 65(6), 852–858. https://doi.org/https://doi.org/10.1016/j.neuron.2010.03.001
Lloyd-Fox, S., Begus, K., Halliday, D., Pirazzoli, L., Blasi, A., Papademetriou, M., Darboe, M. K., Prentice, A. M., Johnson, M. H., Moore, S. E., & Elwell, C. E. (2017). Cortical specialisation to social stimuli from the first days to the second year of life: A rural Gambian cohort. Developmental Cognitive Neuroscience, 25, 92–104. https://doi.org/10.1016/j.dcn.2016.11.005
Lloyd-Fox, S., Blasi, A., Elwell, C. E., Charman, T., Murphy, D., & Johnson, M. H. (2013). Reduced neural sensitivity to social stimuli in infants at risk for autism. Proceedings of the Royal Society B: Biological Sciences, 280(1758), 20123026. https://doi.org/10.1098/rspb.2012.3026
Lloyd-Fox, S., Blasi, A., Mercure, E., Elwell, C. E., & Johnson, M. H. (2012). The emergence of cerebral specialization for the human voice over the first months of life. Social Neuroscience, 7(3), 317–330. https://doi.org/10.1080/17470919.2011.614696
Another weakness of this manuscript relates to the unclear or underspecified motivations behind some of the performed analyses. For example, the authors contrast brain responses to social vs. baseline, non-social vs. baseline, and social vs. non-social. For clarity in the manuscript, the authors should specify the motivation behind each of these contrasts and their predictions.
We thank the reviewer for their suggestion. We have added the predictions for each of the analyses in the introduction section, lines 436 – 527. We have removed the “social minus non-social” comparison for the EEG topographical maps from Figure 2 as there was no value added by including this comparison.
Another example is in the analysis of the hemodynamic and metabolic coupling analysis, here the authors analyze only the social vs. baseline and non-social vs. baseline contrast, and they do not analyze the social vs non-social contrast. It would be useful for the reader to understand why only these two contrasts are performed and not the social vs. non-social, and what are the predictions of the authors.
We have now added this into the manuscript and the results can be seen in Figure 3c. We have clarified our predictions both at the end of the introduction (lines 436 - 527) and at the beginning of the discussion (lines 685 – 755).
The following has been added to the introduction:
For EEG, we expected an increase in neural activity in response to the social condition and a decrease in neural activity in response to the non-social condition. Based on previous work, this was expected to be strongest in the theta frequency band [3]. Moreover, for the combined bNIRS-EEG analyses, we hypothesised differentiated haemodynamic/metabolic coupling with neural activity for the social and non-social stimulus conditions. We performed two types of statistical tests: a) individual comparisons of the social and non-social conditions and b) comparison of the social condition versus the non-social condition. The individual condition tests were performed to show the scale and spatial location/sensitivity of the coupling between haemodynamics/metabolism and neural activity for each condition. Meanwhile, the social versus non-social comparison was performed to show where there was a significant difference in the coupling between the two conditions. With comparison (a) we aimed to identify regions involved in the processing of social and non-social stimuli by identifying the regions where the coupling was significant. With comparison (b) we aimed to identify regions where coupling was significantly different between conditions. We predicted that for the individual comparison of the social condition, we would observe positive associations between bNIRS and EEG measures, i.e. coordinated increases in haemodynamics/metabolism and neural oscillatory activity in the beta and gamma frequency bands (based on previous combined EEG – fMRI studies [16], [18]–[21], [23], [30]) which would be localised to core social brain regions. We hypothesised that for the non-social condition, over the same brain regions, positive associations would be observed between bNIRS and EEG measures, but they would be coordinated decreases in haemodynamics/metabolism and oscillatory activity. We also expected coordinated increases in haemodynamics/metabolism and oscillatory activity localised to the parietal brain region. These predictions are based on our previous work [29] where we demonstrated that stronger coupling between haemodynamics and metabolism was observed in the temporo-parietal regions for the social condition and in parietal region for the non-social condition which is known to play an important role in object processing [31], [32]. For the social versus the non-social contrast, we predicted that haemodynamic activity and metabolism would be coupled with neuronal oscillatory activity more strongly for the social stimuli in comparison to the non-social stimuli, with significant differences being observed in the temporo-parietal regions.
The following has been added to the discussion:
As a proof of principle, we examined the relationship between these measures to identify regional selectivity to social versus non-social stimuli. To first demonstrate the scale and spatial sensitivity of the coupling between haemodynamic/metabolic activity and neuronal oscillatory activity, comparisons were performed individually for the social and non-social conditions. For this, we predicted coordinated increases in haemodynamics/metabolism and neural activity in the beta and gamma frequency band. We predicted that for the social condition this would be localised to the core social brain regions (temporo-parietal region) while for the non-social condition, we expected the coupling to be localised to parietal regions, known to be involved in object processing [31], [32]. We additionally expected coordinated decreases in haemodynamic/metabolic activity and neural activity over the temporo-parietal region for the non-social condition, in accordance with our previous work [29]. Next, to demonstrate differential coupling for social and non-social stimuli, we performed a comparison of the social condition versus the non-social condition. For this, we hypothesised that in the beta and gamma frequency bands, there would be stronger coupling between haemodynamics/metabolism and neural activity for the social condition over the temporo-parietal region.
Finally, the core result of this work derives from the final GLM analysis which relates EEG activity to hemodynamic or metabolic responses. This analysis implies the inspection of interactions between 3 neuroimaging modalities, with 4 EEG measures, 2 hemodynamic measures, and 1 metabolic measure, which represents a very rich and relatively complex analytic approach. Unfortunately, the predictions are not clearly specified, which makes results interpretation difficult.
We appreciate that the methods are complex, and the hypotheses should be stated more clearly. The hypotheses have now been explicitly stated both at the end of the introduction (lines 436 - 527) and at the beginning of the discussion (lines 685 – 755).
Based on the results (L160-162) and discussion (L233-235) sections, it appears that the authors aim at identifying brain regions showing a precise pattern of activity, with a positive relationship between EEG activity and HbO/CCO responses together with a concurrent negative relationship between EEG and HbR responses in response to social events, but not in response to non-social events. Importantly, the social vs. non-social contrast seems crucial to assess the selectivity of the response. Yet, the authors analyze the 3 chromophores separately, and they do not contrast the two conditions (figure 3). As a result, the authors are limited to reporting a descriptive pattern of relationships between EEG and HbO/HbR/CCO activations for the social condition. And another one for the non-social condition. Overall, the authors conclude that channel 14, overlaying the right TPJ, shows the expected pattern of activity, specifically in response to social stimuli. Yet, this statement is only supported by visual inspection/comparison of the results between the social vs baseline and non-social vs baseline conditions. The authors do not assess analytically the differential patterns of activations between the two conditions. Instead, a GLM including all 3 chromophores and contrasting the two experimental conditions would allow us to directly test the predicted pattern of activity, and the selectivity of the activity for social stimuli.
As per the reviewer’s comment, we have now included the comparison of the social and non-social conditions, shown in Figure 3c. The results from this comparison showed that haemodynamics and metabolic activity at channels 11 and 14 (located spatially close to one another) had a significantly greater association to EEG electrode “Pz” for the social condition, in comparison to the non-social condition for the beta and gamma bands. These results support/indicate the selectivity of the response to the social condition, analytically.
We have kept the results showing the individual comparison of the social and non-social conditions. The individual condition tests were performed to show the scale and spatial location/sensitivity of the coupling between haemodynamics/metabolism and neural activity for each condition. Meanwhile, the social versus non-social comparison was performed to show where there was a significant difference in the coupling between the two conditions. With comparison (a) we aimed to identify regions involved in the processing of social and non-social stimuli by identifying the regions where the coupling was significant. With comparison (b) we aimed to identify regions where coupling was significantly different between conditions. The following has been added on line 533 – 541 to explain the reasoning behind the comparisons performed.
We performed two types of statistical tests: a) individual comparisons of the social and non-social conditions and b) comparison of the social condition versus the non-social condition. The individual condition tests were performed to show the scale and spatial location/sensitivity of the coupling between haemodynamics/metabolism and neural activity for each condition. Meanwhile, the social versus non-social comparison was performed to show where there was a significant difference in the coupling between the two conditions. With comparison (a) we aimed to identify regions involved in the processing of social and non-social stimuli by identifying the regions where the coupling was significant. With comparison (b) we aimed to identify regions where coupling was significantly different between conditions.
As our interest was in looking at the selectivity of the response and not comparing the chromophores, we did not perform a comparison between chromophores.
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eLife assessment
This important study provides a state-of-the-art framework to explore the coupling of complementary cerebral measures (neural, hemodynamic, and metabolic) during development by providing an interesting roadmap for multimodal neuroimaging in infants. The methodological contribution is compelling with an original setup for simultaneous EEG and NIRS recording and solid data analyses. However, the claims about functional specialization and the role of the temporal-parietal junction in social processing are only partially supported by the results. This work will be of interest to a broad audience of scientists interested in multimodal neuroimaging and cognitive development.
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Reviewer #1 (Public Review):
An interesting combination of simultaneous broadband NIRS and EEG was acquired in 5-month-old infants (N=42) while they watched social and non-social videos. This substantial undertaking yielded a valuable dataset. The analysis was well developed, including a metabolic measure (COO) as well as haemoglobin measures; localisation of the NIRS signal; and an investigation of the EEG frequency bands correlated with the NIRS. The results, that the temporoparietal junction is engaged by social stimuli, are consistent and reassuring.
The contributions of the manuscript seem largely methodological, which is valuable, but in places the authors oversell the implications of the work - both theoretically and methodologically.
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Reviewer #2 (Public Review):
This work uses broadband NIRS to investigate metabolic and hemodynamic changes in the brains of infants watching social or non-social stimuli, with simultaneous EEG providing the reference for specialization. The authors postulate that metabolic changes and neurovascular coupling will correlate better with power in the high-frequency beta and gamma band, but this is only justified by references to adult work. I suggest to justify better this assumption at the end of the introduction line 115 and discussing why this should be the case in infants as well.
The authors test the hypothesis that metabolic, hemodynamic, and high-frequency EEG activity will show similar spatial localization. The results support the claim. The methods are sound and thoroughly described, graphics are excellent.
At the moment though, …
Reviewer #2 (Public Review):
This work uses broadband NIRS to investigate metabolic and hemodynamic changes in the brains of infants watching social or non-social stimuli, with simultaneous EEG providing the reference for specialization. The authors postulate that metabolic changes and neurovascular coupling will correlate better with power in the high-frequency beta and gamma band, but this is only justified by references to adult work. I suggest to justify better this assumption at the end of the introduction line 115 and discussing why this should be the case in infants as well.
The authors test the hypothesis that metabolic, hemodynamic, and high-frequency EEG activity will show similar spatial localization. The results support the claim. The methods are sound and thoroughly described, graphics are excellent.
At the moment though, the GitHub repository for code is empty and could not be used (sentence "All code used to analyse the NIRS data and the integration of the NIRS and EEG data is available on GitHub (https://github.com/maheensiddiqui91/NIRS-EEG)" line 346.
The Discussion is appropriate, although limitations could be more elaborate, particularly concerning spatial coverage issues and the methodological improvements required for improved fNIRS spatial resolution.
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Reviewer #3 (Public Review):
This manuscript proposes to tackle a very interesting and methodologically challenging topic: the mechanistic underpinnings of neural specialization in the infant brain. The authors presented 4- to 7-month-old infants with social and non-social stimuli while their neural, hemodynamic, and metabolic activity was monitored, and they report a complex pattern of relationships between neural and metabolic or hemodynamic responses during social processing on the one hand, and during non-social processing on the other hand.
The approach described in this manuscript is very interesting and the combined use of EEG and bNIRS data appears very promising. However, there is some confusion between the initial aims of the study, and the analyses performed, which jeopardizes the clarity and the impact of this manuscript. …
Reviewer #3 (Public Review):
This manuscript proposes to tackle a very interesting and methodologically challenging topic: the mechanistic underpinnings of neural specialization in the infant brain. The authors presented 4- to 7-month-old infants with social and non-social stimuli while their neural, hemodynamic, and metabolic activity was monitored, and they report a complex pattern of relationships between neural and metabolic or hemodynamic responses during social processing on the one hand, and during non-social processing on the other hand.
The approach described in this manuscript is very interesting and the combined use of EEG and bNIRS data appears very promising. However, there is some confusion between the initial aims of the study, and the analyses performed, which jeopardizes the clarity and the impact of this manuscript. Besides, the predictions of the authors are often underspecified which complexifies the interpretation of the results.
Based on its abstract, the goal of this work is to "combine simultaneous measures of coordinated neural activity metabolic rate and oxygenated blood supply to measure emerging specialization in the infant brain". The introduction nicely elaborates on the "interactive specialization theory" and the potential role of the interplay between brain energy consumption and neural activity in the emergence of functionally specialized brain regions during development. The authors present a novel multimodal approach, with potentially important implications for the study of brain specialization as a function of experience or maturation. Yet the experimental procedure presented in this manuscript only assesses specialized brain activity in response to social processing in 4- to 7-month-old infants, using multimodal neuroimaging.
Indeed, the authors presented 4- to 7-month-old infants with social and non-social stimuli while their neural, hemodynamic, and metabolic activity was monitored. The authors report significant differences between the two conditions in terms of neural activity in the delta, alpha, beta, and gamma bands; as well as in the pattern of hemodynamic to metabolic coupling. Using a GLM approach, the authors report on fNIRS channels and EEG sensors showing significant relationships between the evoked neural activity in the beta and gamma frequency bands, and each of the bNIRS signals (HbO, HbR & CCO), in the social and in the non-social conditions. The authors identify a particular fNIRS channel overlaying posterior STS, showing a positive relationship between Pz EEG beta activity and HbO, as well as CCO, together with a negative relationship between that same neural activity and HbR, in the social condition. This pattern of activity was not observed in the non-social condition.
Overall, these results indicate differential neural responses to social and non-social stimuli, coupled metabolic and hemodynamic activity in response to social as well as nonsocial stimuli. These results additionally indicate coordinated metabolic, hemodynamic, and neural responses in brain regions selective for social processing, but it does not allow us to conclude that this coordinated activity is actually related to the functional specialization process (e.g. last sentence of the abstract).Another weakness of this manuscript relates to the unclear or underspecified motivations behind some of the performed analyses. For example, the authors contrast brain responses to social vs. baseline, non-social vs. baseline, and social vs. non-social. For clarity in the manuscript, the authors should specify the motivation behind each of these contrasts and their predictions.
Another example is in the analysis of the hemodynamic and metabolic coupling analysis, here the authors analyze only the social vs. baseline and non-social vs. baseline contrast, and they do not analyze the social vs non-social contrast. It would be useful for the reader to understand why only these two contrasts are performed and not the social vs. non-social, and what are the predictions of the authors.
Finally, the core result of this work derives from the final GLM analysis which relates EEG activity to hemodynamic or metabolic responses. This analysis implies the inspection of interactions between 3 neuroimaging modalities, with 4 EEG measures, 2 hemodynamic measures, and 1 metabolic measure, which represents a very rich and relatively complex analytic approach. Unfortunately, the predictions are not clearly specified, which makes results interpretation difficult.
Based on the results (L160-162) and discussion (L233-235) sections, it appears that the authors aim at identifying brain regions showing a precise pattern of activity, with a positive relationship between EEG activity and HbO/CCO responses together with a concurrent negative relationship between EEG and HbR responses in response to social events, but not in response to non-social events. Importantly, the social vs. non-social contrast seems crucial to assess the selectivity of the response. Yet, the authors analyze the 3 chromophores separately, and they do not contrast the two conditions (figure 3). As a result, the authors are limited to reporting a descriptive pattern of relationships between EEG and HbO/HbR/CCO activations for the social condition. And another one for the non-social condition. Overall, the authors conclude that channel 14, overlaying the right TPJ, shows the expected pattern of activity, specifically in response to social stimuli. Yet, this statement is only supported by visual inspection/comparison of the results between the social vs baseline and non-social vs baseline conditions. The authors do not assess analytically the differential patterns of activations between the two conditions. Instead, a GLM including all 3 chromophores and contrasting the two experimental conditions would allow us to directly test the predicted pattern of activity, and the selectivity of the activity for social stimuli.
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