Inferring occluded projectile motion changes connectivity within a visuo-fronto-parietal network

Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes.

Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2024-09-01
DOI
https://doi.org/10.3929/ethz-b-000681570
Primary Topic
Mobile Health and mHealth Applications
Type
article
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article

Inferring occluded projectile motion changes connectivity within a visuo-fronto-parietal network

Repository for Publications and Research Data (ETH Zurich)
Mobile Health and mHealth Applications
article

Inferring occluded projectile motion changes connectivity within a visuo-fronto-parietal network

article en

Abstract

Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes.

Repository for Publications and Research Data (ETH Zurich)
Openalex Percentile: Top 40%
Mobile Health and mHealth Applications
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Inferring occluded projectile motion changes connectivity within a visuo-fronto-parietal network · Repository for Publications and Research Data (ETH Zurich) (2024) | TGRS Research Map | TGRS