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Abstract:

Previous functional imaging studies demonstrated body-selective patches in the primate visual temporal cortex, comparing activations to static bodies and static images of other categories. However, the use of static instead of dynamic displays of moving bodies may have underestimated the extent of the body patch network. Indeed, body dynamics provide information about action and emotion and may be processed in patches not activated by static images. Thus, to map with fMRI the full extent of the macaque body patch system in the visual temporal cortex, we employed dynamic displays of natural-acting monkey bodies, dynamic monkey faces, objects, and scrambled versions of these videos, all presented during fixation. We found nine body patches in the visual temporal cortex, starting posteriorly in the superior temporal sulcus (STS) and ending anteriorly in the temporal pole. Unlike for static images, body patches were present consistently in both the lower and upper banks of the STS. Overall, body patches showed a higher activation by dynamic displays than by matched static images, which, for identical stimulus displays, was less the case for the neighboring face patches. These data provide the groundwork for future single-unit recording studies to reveal the spatiotemporal features the neurons of these body patches encode. These fMRI findings suggest that dynamics have a stronger contribution to population responses in body than face patches.

Authors: Bognár, A. Raman, R. Taubert, Nick; Li, B Zafirova, Y Giese, Martin A.; Gelder, B. De Vogels, R.

Research Areas: Biomedical and Biologically Motivated Technical Applications

Type of Publication: Article

Abstract:

This ultrahigh field 7 T fMRI study addressed the question of whether there exists a core network of brain areas at the service of different aspects of body perception. Participants viewed naturalistic videos of monkey and human faces, bodies, and objects along with mosaic-scrambled videos for control of low-level features. Independent component analysis (ICA) based network analysis was conducted to find body and species modulations at both the voxel and the network levels. Among the body areas, the highest species selectivity was found in the middle frontal gyrus and amygdala. Two large-scale networks were highly selective to bodies, dominated by the lateral occipital cortex and right superior temporal sulcus (STS) respectively. The right STS network showed high species selectivity, and its significant human body-induced node connectivity was focused around the extrastriate body area (EBA), STS, temporoparietal junction (TPJ), premotor cortex, and inferior frontal gyrus (IFG). The human body-specific network discovered here may serve as a brain-wide internal model of the human body serving as an entry point for a variety of processes relying on body descriptions as part of their more specific categorization, action, or expression recognition functions.

Authors: Li, Baichen Solanas, Marta Poyo Marrazzo, Giuseppe Raman, Rajani Taubert, Nick; Giese, Martin A.; Vogels, Rufin de Gelder, Beatrice

Research Areas: Uncategorized

Type of Publication: Article

Abstract:

For social species, including primates, the recognition of dynamic body actions is crucial for survival. However, the detailed neural circuitry underlying this process is currently not well understood. In monkeys, body-selective patches in the visual temporal cortex may contribute to this processing. We propose a physiologically-inspired neural model of the visual recognition of body movements, which combines an existing image-computable model (`ShapeComp') that produces high-dimensional shape vectors of object silhouettes, with a neurodynamical model that encodes dynamic image sequences exploiting sequence-selective neural fields. The model successfully classifies videos of body silhouettes performing different actions. At the population level, the model reproduces characteristics of macaque single-unit responses from the rostral dorsal bank of the Superior Temporal Sulcus (Anterior Medial Upper Body (AMUB) patch). In the presence of time gaps in the stimulus videos, the predictions made by the model match the data from real neurons. The underlying neurodynamics can be analyzed by exploiting the framework of neural field dynamics.

Authors: Kumar, Prerana; Taubert, Nick; Raman, Rajani Bognár, Anna Nejad, Ghazaleh Ghamkhari Vogels, Rufin Giese, Martin A.

Research Areas: Neural and Computational Principles of Action and Social Processing

Type of Publication: In Collection

JRESEARCH_BOOK_TITLE: Artificial Neural Networks and Machine Learning -- ICANN 2023

Publisher: Springer Nature Switzerland

Editor: Iliadis, Lazaros and Papaleonidas, Antonios and Angelov, Plamen and Jayne, Chrisina

Address: Cham

Pages: 533-544

ISBN: 978-3-031-44210-0

Authors: Bognár, A. Mukovskiy, Albert; Nejad, G. Ghamkhari Taubert, Nick; Stettler, Michael; Martini, Lucas M.; Raman, R. Giese, Martin A.; Vogels, R.

Research Areas: Biomedical and Biologically Motivated Technical Applications

Type of Publication: In Collection