September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Multivariate analysis of structure-function-behavior relations supporting face recognition behavior in autistic and non-autistic adolescents
Author Affiliations & Notes
  • Yiming Qian
    Department of Psychology, the Pennsylvania State University
  • K. Suzanne Scherf
  • Footnotes
    Acknowledgements  The research was supported by Pennsylvania Department of Health SAP grant 4100047862 (M.B., K.S.S., N.M.), NICHD/NIDCD P01/U19 HD35469-07 (M.B., PI Nancy Minshew).
Journal of Vision September 2024, Vol.24, 927. doi:https://doi.org/10.1167/jov.24.10.927
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Yiming Qian, K. Suzanne Scherf; Multivariate analysis of structure-function-behavior relations supporting face recognition behavior in autistic and non-autistic adolescents. Journal of Vision 2024;24(10):927. https://doi.org/10.1167/jov.24.10.927.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Most neuroimaging studies assessing brain-behavior relations tend to evaluate pairwise associations between a single neural metric (e.g., fMRI activation or microstructural properties) and a behavioral metric (e.g., accuracy). However, this approach does not characterize how brain structure and function interact holistically to subserve behavior. To address this gap, we integrated fMRI, sMRI, DTI, and functional connectivity measures to investigate how relational patterns among these metrics predict face recognition (FR) behavior over time. The data presented here represent the baseline comparison between autistic (N = 29) and non-autistic (N = 22) adolescents (11-17 years). We chose to study the FR system due to the prominent difficulty in face recognition behavior that exists in autism and the whole-brain nature of the FR neural system. For the fMRI and sMRI metrics, we individually defined six ROIs—the bilateral fusiform face area (FFA) and amygdala as the core regions in the FR system, and bilateral early visual cortex (EVC). Structural connectivity was defined as the radial diffusivity of the white matter tracts seeding from each ROI. Functional connectivity was defined across all ROIs using GIMME and quantified using overall node strength. Partial Least Squares, a multivariate method, was then utilized to understand how the relation among these metrics predicted FR behavior. The results showed robust brain-behavior associations. Specifically, for both groups, higher functional activation within regions (right FFA and left amygdala), functional connectivity (left EVC), and radial diffusivity in most ROIs within the network together positively predicted higher FR scores (i.e., CFMT). The relation between the brain metrics and FR behavior was stronger in the autistic than the non-autistic group. This work may inspire new ways of thinking about how neural networks dynamically organize through development to support behavioral change and, more specifically, why face recognition is a challenging social behavior for autistic individuals.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×