Voxelwise encoding models of body stimuli reveal a representational gradient from low-level visual features to postural features in occipitotemporal cortex.

Previous research on body representation in the brain has focused on category-specific representation, using fMRI to investigate the response pattern to body stimuli in occipitotemporal cortex. But the central question of the specific computations involved in body selective regions has not been addressed so far. This study used ultra-high field fMRI and banded ridge regression to investigate the computational mechanisms of coding body images, by comparing the performance of three encoding models in predicting brain activity in occipitotemporal cortex and specifically in the extrastriate body area (EBA). Our results indicate that bodies are encoded in occipitotemporal cortex and in the EBA according to a combination of low-level visual features and postural features.

A large-scale brain network of species-specific dynamic human body perception.

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.

Effect of Pre-Existent Sarcopenia on Oncological Outcome of Advanced Thyroid Cancer Patients Treated with Tyrosine Kinase Inhibitors.

(1) Background: Sarcopenia is associated with poor survival and treatment outcomes in several human cancers. The aim of the study was to investigate the prevalence of sarcopenia in a cohort of 58 Caucasian patients with advanced thyroid cancer before and during TKI treatment. The impact of this condition on the outcome of patients was also evaluated. (2) Methods: Sarcopenia was evaluated using the Skeletal Muscle Index (SMI). (3) Results: Pre-treatment sarcopenia was found in 20.7% of patients and this condition significantly affected treatment outcome, emerging as the parameter that has the greatest impact on Progression Free Survival (PFS) (HR 4.29; 95% CI, 1.21−15.11, p = 0.02). A significant reduction in SMI values was observed 3 (p = 0.002) and 12 months (p < 0.0001) after TKI treatment. At a 12-month follow-up, sarcopenia prevalence increased up to 38.5%. Here, 12-month sarcopenia was predicted by a lower SMI (p = 0.029), BMI (p = 0.02) and weight (p = 0.04) and by the presence of bone metastases (p = 0.02). (4) Conclusions: This is the first study that evaluated sarcopenia prevalence and its change over time in Caucasian patients with advanced thyroid cancer under TKI therapy. Sarcopenia seems to be a prognostic factor of TKI treatment outcome, suggesting the importance of the assessment of the nutritional status and body composition in advanced thyroid cancer patients.

Decoding the difference between explicit and implicit body expression representation in high level visual, prefrontal and inferior parietal cortex.

Recent studies provide an increasing understanding of how visual objects categories like faces or bodies are represented in the brain and also raised the question whether a category based or more dynamic network inspired models are more powerful. Two important and so far sidestepped issues in this debate are, first, how major category attributes like the emotional expression directly influence category representation and second, whether category and attribute representation are sensitive to task demands. This study investigated the impact of a crucial category attribute like emotional expression on category area activity and whether this varies with the participants' task. Using (fMRI) we measured BOLD responses while participants viewed whole body expressions and performed either an explicit (emotion) or an implicit (shape) recognition task. Our results based on multivariate methods show that the type of task is the strongest determinant of brain activity and can be decoded in EBA, VLPFC and IPL. Brain activity was higher for the explicit task condition in VLPFC and was not emotion specific. This pattern suggests that during explicit recognition of the body expression, body category representation may be strengthened, and emotion and action related activity suppressed. Taken together these results stress the importance of the task and of the role of category attributes for understanding the functional organization of high level visual cortex.