Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Anatomical connectivity patterns predict face selectivity in the fusiform gyrus

Abstract

A fundamental assumption in neuroscience is that brain structure determines function. Accordingly, functionally distinct regions of cortex should be structurally distinct in their connections to other areas. We tested this hypothesis in relation to face selectivity in the fusiform gyrus. By using only structural connectivity, as measured through diffusion-weighted imaging, we were able to predict functional activation to faces in the fusiform gyrus. These predictions outperformed two control models and a standard group-average benchmark. The structure–function relationship discovered from the initial participants was highly robust in predicting activation in a second group of participants, despite differences in acquisition parameters and stimuli. This approach can thus reliably estimate activation in participants who cannot perform functional imaging tasks and is an alternative to group-activation maps. Additionally, we identified cortical regions whose connectivity was highly influential in predicting face selectivity within the fusiform, suggesting a possible mechanistic architecture underlying face processing in humans.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Schematic model design.
Figure 2: Benchmark comparisons per participant.
Figure 3: Actual and predicted fMRI activation to faces > scenes in the fusiform gyrus of five example participants.
Figure 4: Model coefficients for each target region from the final connectivity model.
Figure 5: Spatial relationship of function with connection strength to the highest predictors.

References

  1. Johansen-Berg, H. et al. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc. Natl. Acad. Sci. USA 101, 13335–13340 (2004).

    Article  CAS  Google Scholar 

  2. Passingham, R.E., Stephan, K.E. & Kotter, R. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606–616 (2002).

    Article  CAS  Google Scholar 

  3. Haxby, J.V. et al. The effect of face inversion on activity in human neural systems for face and object perception. Neuron 22, 189–199 (1999).

    Article  CAS  Google Scholar 

  4. Tsao, D.Y., Schweers, N., Moeller, S. & Freiwald, W.A. Patches of face-selective cortex in the macaque frontal lobe. Nat. Neurosci. 11, 877–879 (2008).

    Article  CAS  Google Scholar 

  5. Perrett, D.I., Hietanen, J.K., Oram, M.W. & Benson, P.J. Organization and functions of cells responsive to faces in the temporal cortex. Phil. Trans. R. Soc. Lond. B 335, 23–30 (1992).

    Article  CAS  Google Scholar 

  6. Tsao, D.Y., Freiwald, W.A., Tootell, R.B. & Livingstone, M.S. A cortical region consisting entirely of face-selective cells. Science 311, 670–674 (2006).

    Article  CAS  Google Scholar 

  7. Moeller, S., Freiwald, W.A. & Tsao, D.Y. Patches with links: a unified system for processing faces in the macaque temporal lobe. Science 320, 1355–1359 (2008).

    Article  CAS  Google Scholar 

  8. Kanwisher, N., McDermott, J. & Chun, M.M. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17, 4302–4311 (1997).

    Article  CAS  Google Scholar 

  9. Kanwisher, N., Stanley, D. & Harris, A. The fusiform face area is selective for faces not animals. Neuroreport 10, 183–187 (1999).

    Article  CAS  Google Scholar 

  10. Epstein, R. & Kanwisher, N. A cortical representation of the local visual environment. Nature 392, 598–601 (1998).

    Article  CAS  Google Scholar 

  11. Barton, J.J., Press, D.Z., Keenan, J.P. & O'Connor, M. Lesions of the fusiform face area impair perception of facial configuration in prosopagnosia. Neurology 58, 71–78 (2002).

    Article  Google Scholar 

  12. Pitcher, D., Walsh, V., Yovel, G. & Duchaine, B. TMS evidence for the involvement of the right occipital face area in early face processing. Curr. Biol. 17, 1568–1573 (2007).

    Article  CAS  Google Scholar 

  13. McNeil, J.E. & Warrington, E.K. Prosopagnosia: a face-specific disorder. Q. J. Exp. Psychol. A 46, 1–10 (1993).

    Article  CAS  Google Scholar 

  14. Landis, T., Cummings, J.L., Christen, L., Bogen, J.E. & Imhof, H.G. Are unilateral right posterior cerebral lesions sufficient to cause prosopagnosia? Clinical and radiological findings in six additional patients. Cortex 22, 243–252 (1986).

    Article  CAS  Google Scholar 

  15. McCarthy, G., Puce, A., Gore, J.C. & Allison, T. Face-specific processing in the human fusiform gyrus. J. Cogn. Neurosci. 9, 605–610 (1997).

    Article  CAS  Google Scholar 

  16. Behrens, T.E. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757 (2003).

    Article  CAS  Google Scholar 

  17. Behrens, T.E. et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003).

    Article  CAS  Google Scholar 

  18. Catani, M., Jones, D.K., Donato, R. & Ffytche, D.H. Occipito-temporal connections in the human brain. Brain 126, 2093–2107 (2003).

    Article  Google Scholar 

  19. Seltzer, B. & Pandya, D.N. Parietal, temporal, and occipital projections to cortex of the superior temporal sulcus in the rhesus monkey: a retrograde tracer study. J. Comp. Neurol. 343, 445–463 (1994).

    Article  CAS  Google Scholar 

  20. Gloor, P. The Temporal Lobe and Limbic System (Oxford Univ. Press, New York, 1997).

  21. Gholipour, A., Kehtarnavaz, N., Briggs, R., Devous, M. & Gopinath, K. Brain functional localization: a survey of image registration techniques. IEEE Trans. Med. Imaging 26, 427–451 (2007).

    Article  Google Scholar 

  22. Hastie, T., Tibshirani, R. & Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York, 2009).

  23. Golland, P. & Fischl, B. Permutation tests for classification: towards statistical significance in image-based studies. Inf. Process. Med. Imaging 18, 330–341 (2003).

    Article  Google Scholar 

  24. Hilgetag, C.C. & Kaiser, M. Clustered organization of cortical connectivity. Neuroinformatics 2, 353–360 (2004).

    Article  Google Scholar 

  25. Sporns, O. & Zwi, J.D. The small world of the cerebral cortex. Neuroinformatics 2, 145–162 (2004).

    Article  Google Scholar 

  26. Saxe, R., Moran, J.M., Scholz, J. & Gabrieli, J. Overlapping and non-overlapping brain regions for theory of mind and self reflection in individual subjects. Soc. Cogn. Affect. Neurosci. 1, 229–234 (2006).

    Article  Google Scholar 

  27. Hinds, O.P. et al. Accurate prediction of V1 location from cortical folds in a surface coordinate system. Neuroimage 39, 1585–1599 (2008).

    Article  Google Scholar 

  28. Annese, J., Gazzaniga, M. & Toga, A. Localization of the human cortical visual area MT based on computer aided histological analysis. Cereb. Cortex 15, 1044–1053 (2005).

    Article  CAS  Google Scholar 

  29. Dickerson, B.C. et al. Detection of cortical thickness correlates of cognitive performance: reliability across MRI scan sessions, scanners, and field strengths. Neuroimage 39, 10–18 (2008).

    Article  CAS  Google Scholar 

  30. Ishai, A. Let's face it: it's a cortical network. Neuroimage 40, 415–419 (2008).

    Article  Google Scholar 

  31. Kanwisher, N. & Yovel, G. The fusiform face area: a cortical region specialized for the perception of faces. Phil. Trans. R. Soc. Lond. B 361, 2109–2128 (2006).

    Article  Google Scholar 

  32. Epstein, R.A. Parahippocampal and retrosplenial contributions to human spatial navigation. Trends Cogn. Sci. 12, 388–396 (2008).

    Article  Google Scholar 

  33. Sewards, T.V. Neural structures and mechanisms involved in scene recognition: a review and interpretation. Neuropsychologia 49, 277–298 (2011).

    Article  Google Scholar 

  34. Haxby, J.V., Hoffman, E.A. & Gobbini, M.I. The distributed human neural system for face perception. Trends Cogn. Sci. 4, 223–233 (2000).

    Article  CAS  Google Scholar 

  35. Schmahmann, J.D. & Pandya, D.N. Course of the fiber pathways to pons from parasensory association areas in the rhesus monkey. J. Comp. Neurol. 326, 159–179 (1992).

    Article  CAS  Google Scholar 

  36. Schmahmann, J.D. & Pandya, D.N. Prelunate, occipitotemporal, and parahippocampal projections to the basis pontis in rhesus monkey. J. Comp. Neurol. 337, 94–112 (1993).

    Article  CAS  Google Scholar 

  37. Glickstein, M. et al. Visual pontocerebellar projections in the macaque. J. Comp. Neurol. 349, 51–72 (1994).

    Article  CAS  Google Scholar 

  38. O'Reilly, J.X., Beckmann, C.F., Tomassini, V., Ramnani, N. & Johansen-Berg, H. Distinct and overlapping functional zones in the cerebellum defined by resting state functional connectivity. Cereb. Cortex 20, 953–965 (2010).

    Article  Google Scholar 

  39. Dauguet, J. et al. Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain. Neuroimage 37, 530–538 (2007).

    Article  Google Scholar 

  40. Peled, S., Berezovskii, V., Hendrickson, P., Born, R. & Westin, C. Histological validation of DTI using WGA-HRP in a macaque. Proc. Intl. Soc. Mag. Reson. Med. 13, 1323 (2005).

    Google Scholar 

  41. Thomas, C. et al. Reduced structural connectivity in ventral visual cortex in congenital prosopagnosia. Nat. Neurosci. 12, 29–31 (2009).

    Article  CAS  Google Scholar 

  42. Reese, T.G., Heid, O., Weisskoff, R.M. & Wedeen, V.J. Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magn. Reson. Med. 49, 177–182 (2003).

    Article  CAS  Google Scholar 

  43. Oliva, A. & Torralba, A. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42, 145–175 (2001).

    Article  Google Scholar 

  44. Dale, A.M. Optimal experimental design for event-related fMRI. Hum. Brain Mapp. 8, 109–114 (1999).

    Article  CAS  Google Scholar 

  45. Pitcher, D., Dilks, D.D., Saxe, R.R., Triantafyllou, C. & Kanwisher, N. Differential selectivity for dynamic versus static information in face-selective cortical regions. Neuroimage 56, 2356–2363 (2011).

    Article  Google Scholar 

  46. Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004).

    Article  Google Scholar 

  47. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    Article  CAS  Google Scholar 

  48. Desikan, R.S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Article  Google Scholar 

  49. Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M.F. & Woolrich, M.W. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34, 144–155 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank N. Kanwisher, S. Ghosh, F. Polli and the Athinoula A. Martinos Imaging Center at McGovern Institute for Brain Research, Massachusetts Institute of Technology. This work was supported by US Public Health Service DA023427, US National Institute of Mental Health F32 MH084488, US National Eye Institute T32 EY013935, the Poitras Center for Affective Disorders Research, the Simons Foundation and the Ellison Medical Foundation.

Author information

Authors and Affiliations

Authors

Contributions

Z.M.S. and D.E.O. designed and performed experiments, analyzed data and wrote the manuscript. K.K. designed and performed experiments. G.R. performed experiments and analyzed data. J.D.E.G. and R.R.S. designed experiments and helped write the manuscript.

Corresponding authors

Correspondence to Zeynep M Saygin or David E Osher.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Table 1, Supplementary Results and Supplementary Discussion (PDF 803 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saygin, Z., Osher, D., Koldewyn, K. et al. Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat Neurosci 15, 321–327 (2012). https://doi.org/10.1038/nn.3001

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3001

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing