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An investigation across 45 languages and 12 language families reveals a universal language network

Nature Neuroscience volume 25, pages 1014–1019 (2022)Cite this article

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Abstract

To understand the architecture of human language, it is critical to examine diverse languages; however, most cognitive neuroscience research has focused on only a handful of primarily Indo-European languages. Here we report an investigation of the fronto-temporo-parietal language network across 45 languages and establish the robustness to cross-linguistic variation of its topography and key functional properties, including left-lateralization, strong functional integration among its brain regions and functional selectivity for language processing.

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Fig. 1: The language network in native speakers of diverse languages.
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Data availability

The data that support the findings of this study are available at https://osf.io/cw89s.

Code availability

The code used to analyze the data in this study is available at https://osf.io/cw89s.

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Acknowledgements

We thank Z. Fan, F. Frank and J. Vera-Rebollar for help with finding and recording the speakers; Z. Fan, J. Vera-Rebollar, F. Frank, A. Verkerk, the Max Planck Institute in Nijmegen, C. Kidd and M. Xiang for help with locating the texts of Alice in Wonderland in different languages; I. Blank, A. Paunov, B. Lipkin, D. Greve and B. Fischl for help with some of the analyses; J. McDermott for letting us use the sound booths in his laboratory for the recordings; J. Wu, N. Jhingan and B. Lipkin for creating a website for disseminating the localizer materials and script; M. Lewis for allowing us to use the linguistic family maps from the GeoCurrents website; B. A. Cabrera for help with figures; EvLab and TedLab members and collaborators; the audiences at the Neuroscience of Language Conference at NYU-AD (2019) and at the virtual Cognitive Neuroscience Society conference (2020) for helpful feedback; T. Gibson, D. Blasi, M. Seghier and two anonymous reviewers for comments on earlier drafts of the manuscript; Y. Diachek for collecting the data for the Russian speakers (used in Supplementary Fig. 4); J. Pryor and S. Lall for promoting this work when it was still at the early stages; and our participants. The authors would also like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT and the support team (S. Shannon and A. Takahashi). S.M.-M. was supported by la Caixa Fellowship LCF/BQ/AA17/11610043, a Friends of McGovern Fellowship and the Dingwall Foundation Fellowship. E.F. was supported by NIH awards R00-HD057522, R01-DC016607 and R01-DC-NIDCD and research funds from the Brain and Cognitive Sciences Department, the McGovern Institute for Brain Research and the Simons Center for the Social Brain.

Author information

Author notes

  1. These authors contributed equally: Saima Malik-Moraleda, Dima Ayyash.

Authors and Affiliations

  1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Saima Malik-Moraleda, Dima Ayyash, Josef Affourtit, Zachary Mineroff, Olessia Jouravlev & Evelina Fedorenko

  2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Saima Malik-Moraleda, Dima Ayyash, Josef Affourtit, Zachary Mineroff, Olessia Jouravlev & Evelina Fedorenko

  3. Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA

    Saima Malik-Moraleda, Jeanne Gallée & Evelina Fedorenko

  4. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

    Malte Hoffmann

  5. Department of Radiology, Harvard Medical School, Boston, MA, USA

    Malte Hoffmann

  6. Eberly Center, Carnegie Mellon University, Pittsburgh, PA, USA

    Zachary Mineroff

  7. Department of Cognitive Science, Carleton University, Ottawa, ON, Canada

    Olessia Jouravlev

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  1. Saima Malik-Moraleda
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Contributions

Conceptualization, project administration and supervision: E.F. Methodology: S.M.-M., D.A., J.G. and E.F. Investigation (data collection): S.M.-M., D.A., J.G., J.A., Z.M. and O.J. Data curation: S.M.-M., D.A. and J.A. Formal analysis: S.M.-M. Validation: S.M.-M. and J.A. Visualization: S.M.-M. and M.H. Software: S.M.-M., D.A., J.A. and Z.M. Writing—original draft: S.M.-M., D.A. and E.F. Writing—review and editing: J.G., J.A., M.H., Z.M. and O.J.

Corresponding authors

Correspondence to Saima Malik-Moraleda or Evelina Fedorenko.

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The authors declare no competing interests.

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Nature Neuroscience thanks M. Florencia Assaneo, Narly Golestani and Mohamed Seghier for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of the individual activation maps for the Sentences > Nonwords contrast and the Native-language > Degraded-language contrast in the two native-English-speaking participants.

The two maps are voxel-wise (within the union of the language parcels) spatially correlated at r = 0.77 and r = 0.99 for participants 492 and 502, respectively (the correlations are Fisher-transformed). Across the full set of participants, the average Fisher-transformed spatial correlation between the maps for the Sentences > Nonwords contrast in English and the Native-language > Degraded-language contrast in the participant’s native language (again, constrained to the language parcels) is r = 0.88 (SD = 0.43) for the left hemisphere and 0.73 (SD = 0.38) for the right hemisphere. (Note that using the union of the language parcels rather than the whole brain is conservative for computing these correlations; including all the voxels would inflate the correlations due to the large difference in activation levels between voxels that fall within the language parcels vs. outside their boundaries. Instead, we are zooming in on the activation landscape within the frontal, temporal, and parietal areas that house the language network and showing that these landscapes are spatially similar between the two contrasts in their fine-grained activation patterns).

Extended Data Fig. 2 Activation maps for the Alice language localizer contrast (Native-language > Degraded-language) in the right hemisphere of a sample participant for each language (see Fig. 1 for the maps from the left hemisphere).

A significance map was generated for each participant by FreeSurfer44; each map was smoothed using a Gaussian kernel of 4 mm full-width half-max and thresholded at the 70th percentile of the positive contrast for each participant (this was done separately for each hemisphere; note that the same participants are used here as those used in Fig. 1). The surface overlays were rendered on the 80% inflated white-gray matter boundary of the fsaverage template using FreeView/FreeSurfer. Opaque red and yellow correspond to the 80th and 99th percentile of positive-contrast activation for each subject, respectively. Further, here and in Fig. 1, small and/or idiosyncratic bits of activation (relatively common in individual-level language mapsfor example, 9, 10) were removed. In particular, clusters were excluded if a) their surface area was below 100 mm^2, or b) they did not overlap (by > 10%) with a mask created for a large number (n = 80456) participants by overlaying the individual maps and excluding vertices that did not show language responses in at least 5% of the cohort. (We ensured that the idiosyncrasies were individual- and not language-specific: for each cluster removed, we checked that a similar cluster was not present for the second native speaker of that language.) These maps were used solely for visualization; all the statistical analyses were performed on the data analyzed in the volume.

Extended Data Fig. 3 Volume-based activation maps for the Native-language > Degraded-language contrast in the left hemisphere of a sample participant for each language (the same participants are used as those used in Fig. 1 and Extended Fig. 2).

a) Binarized maps that were generated for each participant by selecting the top 10% most responsive (to this contrast) voxels within each language parcel. These sets of voxels correspond to the fROIs used in the analyses reported in Extended Data Fig. 4 (except for the estimation of the responses to the conditions of the Alice localizer, where a subset of the runs was used to ensure independence; the fROIs in those cases will be similar but not identical to those displayed). b) Whole-brain maps that are thresholded at the p < 0.001 uncorrected level.

Extended Data Fig. 4 Percent BOLD signal change across (panel a) and within each of (panel b) the LH language functional ROIs (defined by the Native-language > Degraded-language contrast from the Alice localizer, cf. the Sentences > Nonwords contrast from the English localizer as in the main text and analyses; Fig. 3a and Supplementary Fig. 3) for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the spatial working memory (WM) task and the math task.

The dots correspond to languages (n = 45), and the labels (panel a only) mark the averages for each language family. In all panels, box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. Across the six fROIs, the Native-language condition elicits a reliably greater response than both the Degraded-language condition (2.32 vs. 0.91 % BOLD signal change relative to the fixation baseline; t(44)=18.57, p < 0.001) and the Unfamiliar-language condition (2.32 vs. 0.99; t(44)=18.02, p < 0.001). Responses to the Native-language condition are also significantly higher than those to the spatial working memory task (2.32 vs. 0.06; t(44)=11.16, p < 0.001) and the math task (2.32 vs. −0.02; t(40)=20.8, p < 0.001). These results also hold for each fROI separately, correcting for the number of fROIs (Native-language > Degraded-language: ps<0.05; Native-language > Unfamiliar-language: ps<0.05; Native-language > Spatial WM: ps<0.05; and Native-language > Math: ps<0.05). All t-tests were two-tailed and corrected for the number of fROIs in the per-fROI analyses.

Extended Data Fig. 5 Percent BOLD signal change across the LH language functional ROIs (defined by the Sentences > Nonwords contrast) for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the spatial working memory (WM) task, and the math task shown for each language separately.

The dots correspond to participants for each language (n = 2 in all languages except Slovene, Swahili, Tagalog, Telugu, where n = 1). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. (Note that the scale of the y-axis differs across languages in order to allow for easier between-condition comparisons in each language).

Extended Data Fig. 6 A comparison of individual LH topographies between speakers of the same language vs. between speakers of different languages.

The goal of this analysis was to test whether inter-language / inter-language-family similarities might be reflected in the similarity structure of the activation patterns. To perform this analysis, we computed a Dice coefficient57 for each pair of individual activation maps for the Intact-language > Degraded-language contrast (a total of n = 3,655 pairs across the 86 participants). To do so, we used the binarized maps like those shown in Extended Data Fig. 3a, where in each LH language parcel the top 10% of most responsive voxels were selected. Then, for each pair of images, we divided the number of overlapping voxels multiplied by 2 by the sum of the voxels across the two images (this value was always the same and equaling 1,358 given that each map had the same number of selected voxels). The resulting values can vary from 0 (no overlapping voxels) to 1 (all voxels overlap). a) A comparison of Dice coefficients for pairs of maps between languages (left, n = 3,655 pairs) vs. within languages (right; this could be done for 41/45 languages for which two speakers were tested). If the activation landscapes are more similar within than between languages, then the Dice coefficients for the within-language comparisons should be higher. Instead, no reliable difference was observed by an independent-samples t-test (average within-language: 0.17 (SD = 0.07), average between-language: 0.16 (SD = 0.06); t(40.7)=−0.52, p = 0.61; see also Extended Data Fig. 8 for evidence that the range of overlap values in probabilistic atlases created from speakers of diverse languages vs. speakers of the same language are comparable). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. b) Dice coefficient values for all pairs of within- and between-language comparisons (the squares in black on the diagonal correspond to languages with only one speaker tested). As can be seen in the figure and in line with the results in panel a, no structure is discernible that would suggest greater within-language / within-language-family topographic similarity. Similar to the results from the within- vs. between-language comparison in a, the within-language-family vs. between-language-family comparison did not reveal a difference (t(19.8)=0.71, p = 0.49). In summary, in the current dataset (collected with the shallow sampling approach, that is, a small number of speakers from a larger number of languages), no clear similarity structure is apparent that would suggest more similar topographies among speakers of the same language, or among speakers of languages that belong to the same language family.

Extended Data Fig. 7 Inter-region functional correlations in the language and the Multiple Demand networks during story comprehension for each of the 45 languages.

Inter-region functional correlations for the LH and RH of the language and the Multiple Demand (MD) networks during a naturalistic cognition paradigm (story comprehension in the participant’s native language) shown for each language separately.

Extended Data Fig. 8 Comparison of three probabilistic overlap maps (atlases).

Comparison of three probabilistic overlap maps (atlases): a) the Alice atlas (n = 86 native speakers of 45 languages) created from the Native-language > Degraded-language maps; b) the English atlas (n = 629 native English speakers; this is a subset of the Fedorenko lab’s Language Atlas (LanA56) created from the Sentences > Nonwords maps; and) the Russian Atlas (n = 19 native Russian speakers) created from the Native-language > Degraded-language maps for the Russian version of the Alice localizer. All three atlases were created by selecting for each participant the top 10% of voxels (across the brain) based on the t-values for the relevant contrast in each participant, binarizing these maps, and then overlaying them in the common space. In each atlas, the value in each voxel corresponds to the proportion of participants (between 0 and 1) for whom that voxel belongs to the 10% of most language-responsive voxels. The probabilistic landscapes are similar across the atlases: within the union of the language parcels (see Extended Data Fig. 1 caption for an explanation of why this approach is more conservative than performing the comparison across the brain), the Alice atlas is voxel-wise spatially correlated with both the English atlas (r = 0.83) and the Russian atlas (r = 0.85). Furthermore, the range of non-zero overlap values is comparable between the Alice atlas (0.1–0.87; average within the language parcels=0.08, median=0.05) and each of the other atlases (the English atlas: 0.002–0.79; average within the language parcels=0.07, median=0.03; the Russian atlas: 0.05–0.84; average within the language parcels=0.13, median=0.11). The latter result suggests that the inter-individual variability in the topographies of activation landscapes elicited in 86 participants of 45 diverse languages is comparable to the inter-individual variability observed among native speakers of the same language.

Extended Data Fig. 9 Responses in the domain-general Multiple Demand network to the conditions of the Alice localizer task, the spatial working memory task, and the math task.

Percent BOLD signal change across the domain-general Multiple Demand (MD) network15,52 functional ROIs for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the hard and easy conditions of the spatial working memory (WM) task, and the hard and easy conditions of the math task. The dots correspond to languages (n = 45 except for the Math Task, where n = 41). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. As in the main analyses (Fig. 3c), the individual MD fROIs were defined by the Hard > Easy contrast in the spatial WM task (see54 for evidence that other Hard > Easy contrasts activate similar areas). As expected given past worke.g., 54, the MD fROIs show strong responses to both the spatial WM task and the math task, with stronger responses to the harder condition in each (3.05 vs. 1.93 for the spatial WM task, t(44)=23.1, p < 0.001; and 1.68 vs. 0.62 for the math task, t(40)=8.87, p < 0.001). These robust responses in the MD network suggest that the lack of responses to the spatial WM and math tasks in the language areas can be meaningfully interpreted. Furthermore, in line with past work e.g.58,59,60, MD fROIs show a stronger response to the acoustically degraded condition than the native language condition (0.26 vs. -0.10, t(44)=4.92, p < 0.01), and to the unfamiliar language condition than the native language condition (0.15 vs. -0.10, t(44)=4.96, p < 0.01). All t-tests were two-tailed with no adjustment for multiple comparisons.

Extended Data Fig. 10 Comparison of the individual activation maps for the Native-language > Degraded-language contrast and the Native-language > Unfamiliar-language contrast in four sample participants.

The activation landscapes are broadly similar: across the full set of 86 participants, the average Fisher-transformed voxel-wise spatial correlation within the union of the language parcels between the maps for the two contrasts is r = 0.66 (SD = 0.40). (Note that this correlation is lower than the correlation between the Native-language > Degraded-language contrast and the Sentences > Nonwords contrast in English (see Extended Data Fig. 1). This difference may be due to the greater variability in the participants’ responses to an unfamiliar language.) Furthermore, across the language fROIs, the magnitudes of the Native-language > Degraded-language and the Native-language > Unfamiliar-language effects are similar (mean = 1.02, SD(across languages)=0.41 vs. mean=1.07, SD = 0.37, respectively; t(44)=1.15, p = 0.26).

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Malik-Moraleda, S., Ayyash, D., Gallée, J. et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nat Neurosci 25, 1014–1019 (2022). https://doi.org/10.1038/s41593-022-01114-5

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