DODavid E Osher

David E Osher

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


David E Osher has a total of 5 co-authors. Here are the top co-authors by number of shared publications:

  • Gretchen Reynolds

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

    1 shared publication
  • John D E Gabrieli

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

    1 shared publication
  • Kami Koldewyn

    Department of Psychology, School of Psychology and Sport Science, Bangor University, Bangor, UK

    1 shared publication
  • Rebecca R Saxe

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

    1 shared publication
  • Zeynep M. Saygin

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

    1 shared publication

Latest Publications

  • Open Access

    Anatomical connectivity patterns predict face selectivity in the fusiform gyrus

    by Zeynep M Saygin, David E Osher, Kami Koldewyn, Gretchen Reynolds, John D E Gabrieli, Rebecca R Saxe

    The authors show that structural connectivity, as measured by diffusion-weighted imaging, can predict functional activation to faces in the fusiform gyrus. The structure–function correspondence developed in one group of subjects outperformed two other predictive models and was able to predict activation in a second group of subjects. 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.