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Multimodal mapping of the face connectome

Nature Human Behaviour volume 4, pages 397–411 (2020)Cite this article

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Abstract

Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition.

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Fig. 1: The anatomical connectome of the face network.
Fig. 2: The relative contribution of long-range and short-range fibres to the face connectome.
Fig. 3: All of the brain connectivity maps of the face network and their correspondence.
Fig. 4: The brain dynamics within the face network during the face localizer task.
Fig. 5: The spatial specificity of face connectome organizations in the right hemisphere.
Fig. 6: Schematic overview of the methods and results used in the brain–behaviour association.
Fig. 7: The association between the degree of lateralization and the ratio of intra- and interhemispheric connection across all face areas.

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Data availability

All data used in the present study were obtained from the WU-Minn HCP Consortium S900 Release. They are publicly available at https://www.humanconnectome.org.

Code availability

Most analyses were conducted using common software (FSL, SPM) or an open source toolbox that can be downloaded from GitHub (Louvain community detection algorithm, NetworkX, or AFQ toolbox). Custom codes can be accessed at https://github.com/mirrorneuronwang/HCP_face_connectome and are available from the corresponding authors on request.

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Acknowledgements

We thank K. Zhang, N. Asadi, S. Zhang, H. Zhang and R. H. Hyon for their advisory help in data analysis; A. Kohlmeyer for assistance with high-performance cluster computing; 30 undergraduates from Temple University for their work in generating and inspecting all coordinates of face ROIs, especially R. Ho, I. Hanik, P. Coleman and L. J. Hoffman. The superficial white matter atlas (LNAO-SWM79) was provided by P. Guevara. This work was supported by a National Institute of Health grant to I.R.O. (RO1 MH091113). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The work also used Temple University High Performance Cluster Service (Owlsnest), which was supported by a National Science Foundation grant (no. 1625061). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. HCP data were provided by the HCP, WU-Minn Consortium (principal investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

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  1. These authors contributed equally: Yin Wang, Athanasia Metoki.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Yin Wang

  2. Department of Psychology, Temple University, Philadelphia, PA, USA

    Athanasia Metoki, David V. Smith, Susan Benear, Haroon Popal, Ying Lin & Ingrid R. Olson

  3. Department of Psychology, Drexel University, Philadelphia, PA, USA

    John D. Medaglia

  4. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    John D. Medaglia

  5. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

    Yinyin Zang

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  1. Yin Wang
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Contributions

Y.W. conceived and designed this research. Y.W., A.M., D.V.S., J.D.M., Y.Z., S.B., H.P. and Y.L. performed research and analysed data. Y.W., A.M. and I.R.O. wrote the paper. I.R.O. was principal investigator of the laboratory.

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Correspondence to Yin Wang or Ingrid R. Olson.

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Extended data

Extended Data Fig. 1 The Interhemispheric Fibers Connecting Bilateral Face ROIs.

Probabilistic tractography indicated that bilateral posterior ROIs (EVC, OFA, FFA, STS and PCC) are connected through the splenium of the corpus callosum whereas bilateral frontal ROIs are connected through the genu (IFG) or rostrum (OFC) of the corpus callosum. Bilateral ATLs have two separate interhemispheric connections, either via the splenium of the corpus callosum (by climbing up posteriorly along the temploral lobe) or the anterior commissure. Each amygdala is connected to the other by the anterior commissure. These findings accord well with previous work on the callosal fiber organization 118, AMG/ATL interhemispheric connections 119, and EVC interhemispheric connections which begin at the boundary of V1 and V2 120–122. Upper row: medial views; Lower row: axial views. Abbreviations: EVC: early visual cortex; OFA: occipital face area; FFA: fusiform face area; ATL: anterior temporal lobe; STS: superior temporal sulcus; IFG: inferior frontal gyrus; AMG: amygdala; OFC: orbitofrontal cortex; PCC: posterior cingulate cortex.

Extended Data Fig. 2 Additional DCM Analysis With Larger Model Space.

For simplicity, we only compared two DCM models in Fig. 4. These two models, however, have limitations, given that they were merely built from preceding PPI results. For instance, the EVC→OFA connectivity is theoretically important but was not significant in the left hemisphere (LH) of PPI results (that’s why we did not included it in the original feedforward model). In addition, one might also be interested in exploring the relative contribution of each recurrent connection to the right hemisphere (RH) face processing. To address these questions, we built larger model space with seven competing models (feedforward models in red colour and recurrent ones in blue colour). (a) The first two models were the ones we used in Fig. 4. Model 1 was feedforward (based on of PPI results in LH) and Model 2 was recurrent (based on of PPI results in RH). Since Model 1 had no direct feedforward connectivity from EVC to OFA, we next built Model 3 with additional EVC→OFA. Model 4 to 7 were recurrent models modified from Model 2. As there were four recurrent connections in Model 2, we removed one feedback connection each time to examine their respective importance to the recurrent model (that is how much the model performance would suffer in the absence of a particular recurrent connection). Model 4 removed EVC←OFA; Model 5 removed EVC←FFA; Model 6 removed EVC←STS; and Model 7 removed OFA←STS. (b) Bayesian model selection (both FFX and RFX) indicates the EVC→OFA connection is important for the feedforward model, as Model 3 performs better than Model 1 in LH. This is consistent with the Haxby model suggesting the critical role of the OFA receiving information from EVC to initiate the face processing. In addition, both feedforward models perform better than any recurrent models in LH, whereas the PPI-derived recurrent model (Model 2) performed the best in RH. These results accord well with our results in Fig. 4. Moreover, among all four recurrent connections, two feedback connections (EVC←OFA, OFA←STS) seem to be particularly important for RH recurrent processing, since removal of either one can lead to enormous drop of model performance (that is Model 4 and 7). In sum, this additional DCM analysis supports our claims in Fig. 4: the LH is dominant with feedforward face processing whereas the RH is dominant with recurrent processing.

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Supplementary Tables 1 and 2 and Supplementary Figs. 1–6.

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Wang, Y., Metoki, A., Smith, D.V. et al. Multimodal mapping of the face connectome. Nat Hum Behav 4, 397–411 (2020). https://doi.org/10.1038/s41562-019-0811-3

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