How did language evolve? Since the human lineage diverged from that of the other great apes millions of years ago, changes in the brain have given rise to behaviors that are unique to humans, such as language. Some of these changes involved alterations in the size and relative positions of brain areas, while others required changes in the connections between those regions. But did these changes occur independently, or can the changes observed in one actually explain the changes we see in the other?

One way to answer this question is to use neuroimaging to compare the brains of related species, using different techniques to examine different aspects of brain structure. Imaging a fatty substance called myelin, for example, can produce maps showing the size and position of brain areas. Measuring how easily water molecules diffuse through brain tissue, by contrast, provides information about connections between areas.

Eichert et al. performed both types of imaging in macaques and healthy human volunteers, and compared the results to existing data from chimpanzees. Computer simulations were used to manipulate the myelin-based images so that equivalent brain areas in each species occupied the same positions. In most cases, the distortions – or 'warping' – needed to superimpose brain regions on top of one another also predicted the differences between species in the connections between those regions. This suggests that movement of brain regions over the course of evolution explain the differences previously observed in brain connectivity.

But there was one notable exception, namely a bundle of fibers with a key role in language called the arcuate fasciculus. This structure follows a slightly different route through the brain in humans compared to chimpanzees and macaques. Eichert et al. show that this difference cannot be explained solely by changes in the positions of brain regions. Instead, the arcuate fasciculus underwent additional changes in its course, which may have contributed to the evolution of language.

The framework developed by Eichert et al. can be used to study evolution in many different species. Interspecies comparisons can provide clues to how brain structure and activity relate to each other and to behavior, and this knowledge could ultimately help to understand and treat brain disorders.

The temporal lobe is a morphological adaptation of the brain that is unique to primates (Bryant and Preuss, 2018). Its origins likely include expansion of higher-order visual areas to accompany the primate reliance on vision (Allman, 1982). Temporal association cortex contains areas devoted to higher-level visual processing and social information processing (Rushworth et al., 2013; Sallet et al., 2011) that, in turn, rely strongly on visual information in primates (Perrett et al., 1992). The expanded temporal cortex in apes and humans contains several multimodal areas and areas associated with semantics and language (Dronkers et al., 2004; Hickok and Poeppel, 2007; Price, 2000). As such, understanding the evolution of temporal cortex across the primate order is a vital step to understanding primate behavioral adaptations.

Two lines of evidence are often brought to bear on differences in temporal lobe organization across humans and other primates. The first line emphasizes selective local expansions of temporal cortex and subsequent relocation of areas. Morphologically, great apes possess an extra sulcus in the temporal cortex, suggesting at the very least expansion of this part of cortex. Mars et al. (2013) reported a region in the middle part of the superior temporal sulcus of the macaque that shares anatomical features of the human temporo-parietal junction area located at the caudal end of the temporal cortex, suggesting a major relocation of this area. In a similar vein, Patel et al. (2019) suggest that expansion of the temporo-parietal junction and superior temporal sulcus gave rise to a modified ventral visual processing stream to support increased social abilities in humans. The second line of evidence emphasizes changes in the connectivity of the temporal lobe. Rilling et al. (2008) first suggested dramatic expansion of the arcuate fasciculus temporal cortex projections in the human, but more recent studies also emphasize increased projections of the middle longitudinal and inferior fronto-occipital fasciculi and their role in language-related processes in the human (Catani and Bambini, 2014; Makris et al., 2013; Makris and Pandya, 2009; Saur et al., 2008).

These different schools place different emphasis on what happened to temporal cortex across different primate lineages. Their results, however, should be interpreted in relation to one another as species differences in brain organization can come in many forms that can interact in unpredictable ways (Krubitzer and Kaas, 2005; Mars et al., 2018a; Mars et al., 2017). Dissociating such different types of species differences is challenging (Figure 1A,B). For instance, given an ancestral or reference state, local expansions of the cortical sheet can lead to the relocation of homologous areas between two species. As a case in point, human MT+ complex is located much more ventrally in posterior temporal cortex than its macaque homolog (Huk et al., 2002). Such cortical relocations also affect the location of connections of these areas, but this situation is distinct from the scenario in which a tract extends into new cortical territory.

Figure 1

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These two scenarios are illustrated in Figure 1B. The red area in the top panel has expanded, leading to a relocation of the blue area with respect to the purple and yellow area. The connections of the areas do not change in this scenario, resulting in a relocation of the connections of the blue area. In the bottom panel, a yellow area’s tract terminations have invaded the neighboring purple territory, but this change is independent from cortical expansion. Thus, in both cases connections are located in a different place from those in the reference state, but the causes are different.

In this study we investigate to which extent species differences in temporal lobe organization are due to cortical relocation and tract extension. To be able to do this, we propose a framework to test among different forms of cortical reorganization by registering brains together into a single shared coordinate system (Figure 1C,D). Such an approach allows us to place different brains into a common space based on one feature and then compare the results to registration based on another feature. This ‘common space’ concept proved feasible in a previous study testing whether the extension of the human arcuate fasciculus (AF) compared with the macaque AF could be accounted for by differential cortical expansion between the two brains (Eichert et al., 2019). In the present study, we generalize this approach to develop a cross-species registration based on a multimodal surface matching algorithm (MSM, Robinson et al., 2018; Robinson et al., 2013; Robinson et al., 2014) to derive a cortical registration between different species.

We based our registration framework on whole brain neuroimaging data of macaque, chimpanzee and human brains. Neuroimaging allows one to acquire high-resolution data from the same brains using different modalities within a short time. The digital nature of the data allows easy manipulation, making it ideal for the present purposes (Le et al., 1985; Grannell and Mansfield, 1975; Lauterbur, 1973; Le Bihan et al., 1986; Thiebaut de Schotten et al., 2019). As primary modality we use surface maps derived from the cortical ribbon of T1- and T2-weighted scans, which have been shown to correlate well with cortical myelinization and which are available for all three species (Glasser et al., 2014; Glasser and Van Essen, 2011). Such ‘myelin maps’ can be used to identify homologous areas across brains and species, such as primary sensory and motor cortex, which is high in myelin, and association cortex, which is low in myelin (Glasser et al., 2014; Large et al., 2016). As second modality, we use diffusion MRI tractography to reconstruct long range white matter fibers of the temporal and parietal lobes to establish its connections (Bryant et al., 2019; Mars et al., 2018c).

Given data from these two modalities, we developed an approach to reveal different types of cortical reorganization. We argue this approach is particularly suitable to study the temporal lobe, as it has well described myelin markers and cortical connections (Glasser et al., 2014; Large et al., 2016; Mars et al., 2013; Ruschel et al., 2014). First, we register the cortical surfaces of the different species to one another based on myelin maps (Figure 1C). This cortical alignment uses the distinction of primary and higher order areas in myelin maps as anchor points across all three species. Next, we apply this registration to overlay homologous parts of the cortex and to calculate the underlying distortions of the cortical sheet. This registration field effectively models areal expansion or contraction underlying cortical relocation of homologous areas. We then apply this registration to the cortical projection maps of temporal and inferior parietal lobe white matter tracts to assess how well the myelin-based registration can predict changes in tract projection patterns across species (Figure 1D). A good prediction, i.e. a high overlap of the tract maps, indicates that cortical expansion and relocation of targets zones alone can predict tact projections (Figure 1D, top), a poor prediction indicates that the tract is reaching new cortical territory (Figure 1D, bottom).

Here, we distinguish different scenarios of cortical evolution for a set of temporal and parietal white matter tracts. By applying a cross-species registration we can infer if only cortical relocation was affecting a tract’s connectivity profile or if a tract is reaching into new cortical territory. A deeper understanding of species differences in brain reorganization is essential for our understanding how evolutionary specializations of the temporal lobe underlie uniquely human cognitive functions.

We set out to investigate different types of cortical reorganization affecting the temporal lobe across macaque, chimpanzee, and human brains. First, we investigated cortical relocation of brain areas by registering myelin maps of the cortical surface to one another and derived the local distortions required. Second, we used the resulting mesh deformations to transform maps of cortical projections of major white matter tracts that terminate in temporal and inferior parietal lobe. This allowed us to assess how well the myelin registration predicts actual projection maps across species or whether it cannot capture them, providing an index of tract extensions in the human brain.

Myelin registration

We developed a surface registration between species based on myelin maps using multimodal surface matching (MSM, Robinson et al., 2018). Figure 2 shows the final results of chimpanzee-to-human, macaque-to-chimpanzee, and macaque-to-human brain registrations. The cross-species registration aligns the myelin maps well, with the predicted human maps showing most of the distinctive features of the actual human myelin map (Figure 2, top row). Posterior areas such as V1 are well aligned, with the highest myelin evident on the medial part of the occipital cortex, having relocated quite substantially from a more lateral orientation in the macaque. The prominent myelin hot spot in the location of the MT+ complex is also noticeable. Areas where the myelin maps showed fewer distinctive features to guide the registration, such as in the prefrontal cortex, showed some differentiation between the predicted and actual human maps. Spatial correlation maps of the human myelin maps and the predicted myelin maps as well as the deformation fields underlying the registrations are provided in Appendix 2—figure 1.

Figure 2

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Tract maps

We constructed the cortical projection maps of the following tracts in all three species: Middle longitudinal fasciculus (MDLF), inferior longitudinal fasciculus (ILF), the third branch of the superior longitudinal fasciculus (SLF3), the inferior fronto-occipital fasciculus (IFO), and the arcuate fasciculus (AF) (Figure 3A,C,E). The human and macaque tract maps resemble those obtained in previous studies (Mars et al., 2018c; Schmahmann and Pandya, 2009) and the chimpanzee SLF3 and AF are similar to previous reports (Hecht et al., 2015; Rilling et al., 2008). The other chimpanzee tracts are reported here for the first time, apart from a previous exploratory study (Mars et al., 2019).

Figure 3

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We applied the myelin-based surface registration to assess whether the cortical relocation demonstrated in the myelin registration above fully explains the changes in tracts. Figure 3 shows actual and predicted tract maps. For visual assessment, a thresholded overlay of actual human and predicted tract maps is shown in Figure 4A. As described above, we focus on a description of temporo-parietal cortex given the multiple competing theories of its reorganization in different primate lineages.

Figure 4

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We assessed the success of the myelin registration in predicting the tract projections in a number of ways. First, weighted correlation maps provide a visualization of the local quality of the prediction (Figure 4B,C). A high value means that the myelin registration alone is sufficient to predict a tract’s projection in this part of the brain. A low correlation value indicates that reorganization of a tract’s connectivity pattern took place in addition to cortical relocation modelled by the myelin registration. Second, the Dice coefficient of similarity provides a more general measure of similarity between the predicted and actual tract maps, where a Dice coefficient of ‘1’ indicates perfect overlap and thus no tract extension into areas other than would be predicted by cortical relocation assessed using the myelin map registration. Finally, we calculated a ‘tract extension ratio’ that indicates how much of the actual human tract projections extends into parts of the surface not predicted, where a value of >1 indicates a tract extension into novel territory. Both the Dice coefficients and tract extension ratios were computed for thresholded tract maps defined by the human tract map covering 40% of the brain’s surface, but the resulting pattern of values is robust across a range of thresholds (see Appendix 3—figure 2).

In general, it can be observed that the myelin-based registration can predict the tract maps well in both hemispheres, with the notable exception of AF and to a lesser extent ILF and SLF3 (Figure 4). AF in particular shows the lowest Dice coefficient and the highest extension ratio (Figure 5A,B), indicating that this tract’s differential projections in the human brain are not merely due to relocation of areas. The maps for macaque and chimpanzee are overall predicted to a similar degree, as can be seen in the overlay and correlation maps, with the exception of AF (Figure 4). The effect for AF is captured in the Dice coefficients and tract extension measures (Figure 5A,B).

Figure 5

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Figure 5—source data 1

Numerical data underlying the connectivity fingerprint shown in Figure 5C.

The values are organized as data array that stores the individual tract map intensities. The dimensions of the array are 3 × 1 × 7 × 2 × 20 for n_species, n_hemispheres (only left hemisphere), n_tracts, n_vertices, n_subjects.

https://cdn.elifesciences.org/articles/53232/elife-53232-fig5-data1-v2.mat

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A two-way statistical analysis was performed in both hemispheres to assess the effect of species and tract on the extension ratios. In the left hemisphere, there was no significant main effect of species (F(1, 179)=1.38, p=0.47), but a highly significant main effect of tract (F(4, 792)=565.00, p<0.001) and a highly significant interaction effect of species and tract (F(4, 792)=207.73, p<0.001). In the right hemisphere, we found a significant main effect of species (F(1, 179)=16.76, p<0.001) as well as a highly significant effect of tract (F(4, 792)=261.94, p<0.001) and a highly significant interaction effect (F(4, 792)=225.70, p<0.001). We will discuss the various tracts in more detail below.

The myelin-based registration results in good prediction for tract projections in the temporo-parietal cortex. The actual human tract terminations of MDLF span the superior temporal gyrus and reach the inferior parietal cortex (Figure 4A). In the macaque and chimpanzee, the actual MDLF terminates in superior temporal gyrus but reaches only to a small part of the inferior parietal cortex. When applying the myelin-based registration, macaque and chimpanzee MDLF are both predicted to reach a comparable portion of the human temporal lobe and parts of both angular and supramarginal gyri of the inferior parietal lobe (Makris et al., 2013). This overlap is captured in the weighted correlation maps, which have high values in the temporal lobe (Figure 4B,C). The Dice coefficients for the chimpanzee and macaque MDLF are high and the extension ratio is close to one indicating no tract extension in addition to cortical expansion (Figure 5A,B).

A similar observation can be made for the posterior terminations of SLF3 and IFO. The myelin-based registration can predict the parietal cortical projections to a large degree. The predicted cortical terminations of the tract show a strong overlap with the actual human tract terminations. In line with the overlay maps and the weighted correlation maps, the Dice coefficients are relatively high. Taken together, this suggests that expansion and relocation of brain areas are largely sufficient to model the posterior cortical terminations of SLF3 and IFO, while extension of the tract’s connectivity pattern plays a minor role in explaining the species differences.

Predicted ILF terminations show that the expected occipito-temporal connection can be modelled well (Catani and Thiebaut de Schotten, 2008). The extension ratio for ILF is elevated indicating that there is some remaining tract extension that has not been modelled by cortical relocation. This is also reflected in the tract overlay (Figure 4A), which shows that human ILF has more extended posterior projections than predicted by the myelin registration. Thus, although the overall architecture of the tracts is well predicted, this tract seems to have extended into new cortical territory in the human lineage.

The clearest case of a tract extension in the human brain was presented by AF. Human AF reaches anteriorly to inferior and dorsal frontal areas. The posterior projections of human AF reach into middle and inferior temporal cortex. The chimpanzee posterior terminations are in inferior parietal lobe and superior temporal cortex and in the macaque, the temporal projections reach superior temporal areas. For AF, the myelin-based registration does not provide a good prediction of the tract map across species, especially for the macaque. The correlation map for the chimpanzee shows low correlation along the temporal lobe and for the macaque, correlation values in temporal lobe are extremely low. AF has the lowest Dice coefficient and the extension ratio is high, especially in the macaque, which is in line with overlay and correlation maps. The ‘failure’ of the myelin registration in the temporal lobe indicates that extension and relocation of cortical areas is not sufficient to explain the posterior tract projections of AF, but that the tract extended into new cortical territory in the temporal lobe.

The goal of this study was to study brain specializations of the temporal lobe across multiple primate species in the context of two forms of cortical reorganization: cortical relocation due to local expansions and extensions of tracts into new cortical territory (Figure 1). For this reason, we developed a cross-species surface registration method based on cortical myelin content, which gives us an index of how cortical areas have relocated during evolution. In a subsequent step, we tested if cortical relocation can predict the connectivity patterns of a set of tracts across species. We showed that cortical expansion and resulting relocation of brain areas alone provide a good prediction of several tracts’ terminations in posterior temporal and parietal cortex. In the case of AF in particular, we showed that an additional change in brain architecture was extension of the tract into new cortical areas independent of cortical expansion and resulting relocation.

As pointed out in the introduction, different lines of evidence on temporal reorganization across primate species have emphasized either the expansion and subsequent relocation of brain areas or changes in temporal lobe connectivity. Both approaches are valuable and we here demonstrate that both are applicable to different parts of the temporal lobe. Previous work has suggested expansion and relocation of areas in posterior temporal cortex and adjacent parietal cortex (Mars et al., 2013; Patel et al., 2019), which is consistent with the ventral location of MT+ complex in humans compared to other primates. Similar reorganizations have been suggested by Haak et al. (2018), who proposed modifications of visual-temporal pathways in the human. These major relocations were captured by our myelin registration and demonstrated to predict certain features of white matter tracts, such as the posterior projections of MDLF.

The aim of our research is not to find the ‘best’ species registration, but to shed light on brain evolution by studying where registrations based on different modalities disagree. Many aspects of brain architecture can be modified during the course evolution and each individual aspect would provide us with a unique registration. The present study represents only one example of how cortical specializations can be studied by comparing cross-species registrations of different modalities. Testing the effect of a myelin registration on connectivity is not meant to imply pre-eminence of myelin over connectivity. In fact, the reversed approach, i.e. to derive a registration based on individual tract maps and then test their differential effect on transformed myelin maps, would be highly informative. We chose myelin here as the primary feature, because we wanted to test the specific hypothesis that some tracts expanded while others simply followed relocation of areas across the cortex. Thus, we used a measure that could index the relocation (myelin) and test it on our measure of interest (the tracts).

Separate from cortical relocation, changes in connectivity between humans and other species have been described for a variety of association tracts, including the temporal projections of AF into middle temporal gyrus (Eichert et al., 2019; Rilling et al., 2008), the frontal projections of SLF3 (Hecht et al., 2015), and expansion of the ventral route consisting of MDLF and IFO (Forkel et al., 2014; Makris et al., 2013; Makris et al., 2009). Both of these latter tracts have been suggested to play a role in language functions in the human brain (Catani and Bambini, 2014; Hagoort, 2016; Makris et al., 2009; Makris and Pandya, 2009; Saur et al., 2008).

In the case of MDLF, the human tract projections can be predicted well by the cortical relocation model. This indicates that the pattern of cortical terminations changed according to the general expansion and relocation of brain areas, without additional extension into new regions of the brain. For other tracts reaching to temporo-parietal areas, such as ILF, IFO and SLF3, the posterior tract projections can also be modelled well, despite the large distortions of target areas within the cortical sheet. These tracts seem to follow the evolutionary scenario described in the upper row in Figure 1B,D, where a tract’s extension in the human brain can be explained by relocation of areas along the cortical sheet. This does not necessarily mean that the tracts have not been recruited for new functions, but the type of change is different from that of tracts such as AF.

AF showed the lowest consistency across species when applying the myelin-based registration. Dice ratio and extension ratio reflect the increased tract termination especially into the temporal lobes, which can be observed in the tract maps. This points to further evolutionary adaptations that specifically affected the connectivity pattern of AF independent of cortical relocation, the scenario described in the bottom figure in Figure 1B,D. Our result is consistent with previous accounts in the literature (Ardesch et al., 2019; Eichert et al., 2019; Rilling et al., 2008), but the approach described here enabled us to formally test this hypothesis in the wider context of cortical reorganization across three primate species and to quantify the species differences. Importantly, extension of a tract into new cortical territories alters the unique connectivity fingerprint of the innervated areas, which profoundly changes the computational capabilities that area supports (Mars et al., 2018b). Being able to dissociate different modifications of brain architecture can inform us about how temporal lobe specializations link to uniquely human higher cognition (Qi et al., 2019; Roelofs, 2014; Schomers et al., 2017).

Apart from modifications of AF, we also noted some minor extensions of ILF into temporo-parietal cortex. ILF’s extension in the human brain is consistent with reports that that showed a split of this tract into multiple subtracts due to the expansion of parts of the temporal cortex, including the fusiform gyrus (Latini et al., 2017; Roumazeilles et al., 2019). This extensions could be related to the increase of cortical territory related to processing social information, such as social networks and faces (Noonan et al., 2018; Sallet et al., 2011). Previously, it has been shown that parietal SLF3 projections are most prominent in the human brain, which has been linked to our unique capacity of social learning (Hecht et al., 2013). We show that species differences in the posterior projections of SLF3 can be mostly explained by local expansion of the posterior temporal and parietal cortex. Similarly, we show that cortical expansion can model the terminations of MDLF, a tract, which has been linked to visuospatial and integrative audiovisual function (Makris et al., 2013). Our results thus suggest that SLF3 and MDLF didn’t undergo additional evolutionary modifications that affected their posterior terminations.

The MSM framework we adopted is ideally suited to work with multimodal descriptors of the cerebral cortex. It has become a vital tool for human surface registration (Abdollahi et al., 2014; Garcia et al., 2018; Glasser et al., 2016) and here we demonstrated its utility for cross-species research. With the presented surface matching method, we showed that a registration based on T1w/T2w MRI data can match critical landmarks across species. We have referred to these maps as ‘myelin maps’ in accordance with other studies in the literature (Glasser et al., 2014; Large et al., 2016) but it should be noted that this is a heuristic. T1w/T2w maps are sensitive to other features than myelin and other sequences are sensitive to aspects of cortical myelin (Lutti et al., 2014). The crucial point is that the maps we employed here are similar across species, allowing us to compare like with like (Glasser et al., 2014). Projecting data of different modalities to a surface representation is a useful tool for comparative neuroscience. It allows us to visualize the different modalities within the same cortical sheet and to compare topologies on this 2D surface, which opens a wide array of mathematical tools. A similar approach has been taken to investigate the relationship between gene expression and myelin content of the cortex (Burt et al., 2018) and gradients of change across multiple modalities of brain organization (Blazquez Freches et al., 2020; Huntenburg et al., 2018).

The presented approach can be flexibly modified to include a variety of cortical features, which can be compared across species. Myelin does not provide high contrast in the large human frontal cortex and, as such, it is difficult to provide a good registration in frontal areas. Furthermore, the effects we report can only be reliably interpreted within the spatial resolution of brain areas. More fine scale species differences and homology assignments are not possible with the data shown here. However, the current method can be generalized to any modality of cortical organization, so future studies can incorporate modalities that have greater contrast in this part of the brain such as neurite orientation dispersion and density imaging (NODDI) measures (Zhang et al., 2012) and resting state fMRI networks (Vincent et al., 2007).

In sum, here we present a framework for analyzing structural reorganization of the temporo-parietal cortex across different primate brains. We dissociated cortical relocation of areas due to local expansion and modifications of white matter tract connectivity. Future work will expand this approach not only to different modalities, but also to a much wider range of species, which is now becoming increasingly possible due to the availability of multi-species datasets (Heuer et al., 2019; Milham et al., 2018). This provides a crucial step towards the understanding of phylogenetic diversity across the primate brain.

This study used previously published datasets and availability of source data for the different datasets is provided in an overview table in the main manuscript ('Data Availability Overview'). The anonymised human MRI dataset that was generated for the present study is available via OpenNeuro under the accession code ds002634 (version 1.0.1). Result scene files are openly available from the Wellcome Centre for Integrative Neuroimaging's GitLab at https://git.fmrib.ox.ac.uk/neichert/project_MSM (copy archived at https://github.com/elifesciences-publications/project_msm). Group-level myelin-maps and tract surface maps of the three species are openly accessible as part of the result scene files. Numerical data underlying Figure 5 and Appendix 3—figure 2 are provided as source data with the article. All further derived data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Author details

1. Nicole Eichert

   Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Resources, Formal analysis, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   nicole.eichert@psy.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7818-5787

2. Emma C Robinson

   Biomedical Engineering Department, King’s College London, London, United Kingdom

   Contribution

   Software, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Katherine L Bryant

   Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1045-4543

4. Saad Jbabdi

   Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3234-5639

5. Mark Jenkinson

   Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6043-0166

6. Longchuan Li

   Marcus Autism Center, Children's Healthcare of Atlanta, Emory University, Atlanta, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0559-0754

7. Kristine Krug

   1. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
   2. Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
   3. Leibniz-Insitute for Neurobiology, Magdeburg, Germany

   Contribution

   Resources, Writing - review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0001-7119-9350

8. Kate E Watkins

   Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2621-482X

9. Rogier B Mars

   1. Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
   2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Software, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6302-8631

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