@incollection{GelderVoxel_2024,
author = "G. Marrazzo and F. De Martino and Albert Mukovskiy and Martin A. Giese and de Gelder, Beatrice",
abstract = "In this fMRI study we investigated the role played by biomechanical plausibility in the
representation of bodies in EBA. The extrastriate body area (EBA) (Downing et al. 2001, Peelen
and Downing, 2005) is currently considered to be a ventral cortex object category area, selective
for body stimuli but little yet is understood about its computational functions. In a previous study
we showed that the EBA is sensitive to joints position in still body stimuli (Marrazzo et al.
2023). Here, we used video images to investigate whether disrupting joints configuration affects
the representation of bodies in EBA. Stimuli depicted artificial whole-body movements and were
generated from the MoVI dataset. We selected 60 trials of naturalistic body movement and
created 60 (possible) videos. Additionally, these stimuli underwent further processing where
elbows and knees position/angle were manually modified, to create (from possible stimuli)
biomechanically impossible stimuli. Therefore, the stimuli set included 120 videos (60 possible,
60 impossible). 12 participants were scanned using a 7T (T2*-weighted Multi-Band accelerated
EPI 2D BOLD sequence, MB = 2, voxel size = 0.8 mm3, TR = 2300 ms, TE = 27 ms) in a fast
event-related design over 12 separate runs. Each run consisted of 20 unique stimuli (10 possible,
10 impossible repeated 6 times across the 12 runs) which appeared on the screen for 2-3 s.
Participants were asked to fixate and attention was controlled using catch trials (fixation shape
change). The fMRI response was modeled using several features extracted from the stimuli: 3D
coordinates and rotation matrices of key joints (kp/rot) and a model which represents
within\between distance between joints for each video as a mean to encode biomechanical
information (simdist). The fMRI predicted responses from each model were generated via
banded ridge regression (Nunez-Elizalde et al. 2019, Dupr{\'e} La Tour et al. 2022) using
crossvalidation. Results show a pattern of responses across visual cortex with simdist and kp
model best predicting responses to our stimuli. Specifically, the simdist representation shows
higher prediction accuracy in in high-level temporal areas such as EBA outperforming the kp
model. These findings expand on previous research showing that EBA codes for specific features
of the body, which in the case of kp model, are the joints position (Marrazzo et al. 2023).
Additionally, EBA shows high degree of sensitivity for joints configuration to the point that
biomechanically possible/impossible bodies appear to be differentially encoded.
Acknowledgments: This work was supported by ERC 2019-SyG-RELEVANCE-856495",
booktitle = "Society for Neuroscience",
title = "{V}oxelwise encoding of biomechanics in occipitotemporal cortex using dynamic body stimuli at ultra-high field 7{T}",
year = "2024",
}