The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these studies are mainly based on the statistical correlation or Bayesian causality inference, lacking interpretability at the physical and neuroscience level. Here, we propose a physics-based framework of the Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN. Testing on fMRI data of 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the FPN and lower synchronization in the DMN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns at group level. These results together indicate that oxytocin may increase the ability to overcome the corresponding internal oscillation dispersion and support the flexibility in neural synchrony in various social contexts, providing new evidence for explaining the oxytocin modulated social behaviors. Our proposed Kuramoto model-based framework can be a potential tool in network neuroscience and offers physical and neural insights into phase dynamics of the brain.
Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI‐based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal‐pyramid Graph Convolutional Network (STpGCN), to capture the spatial–temporal graph representation of functional brain activities. By designing multi‐scale spatial–temporal pathways and bottom‐up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi‐scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called BrainNetX to better explain the decoding results by automatically annotating task‐related brain regions from the brain‐network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain‐decoding performance compared to competing baseline models; BrainNetX successfully annotates task‐relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI‐based brain decoding.
The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, those studies are mainly based on the statistical correlation or bayesian causality inference, lacking physical and neuroscience level interpretability. Here, we propose a Kuramoto model physical-based framework to investigate oxytocin effects on the phase dynamical neural coupling in DMN and FPN. Tested on fMRI data from 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the DMN and lower synchronization in the FPN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns across time. These results together imply that oxytocin may increase the capability to overcome the dispersion of corresponding intrinsic oscillations and yield flexibility in neural synchrony under various social contexts, providing new evidence to account for oxytocin modulated social behaviors. Our proposed Kuramoto model-based framework can be a potential tool in network neuroscience and offers physical and neural insights into phase dynamics in the brain.
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