The Trier Social Stress Test (TSST) is the most widely used laboratory stress protocol in psychoneuroendocrinology. Despite its popularity, surprisingly few attempts have been made to explore the ecological validity of the TSST. In the present study, 31 young healthy subjects (24 females) were exposed to the TSST about 4 weeks before completing an oral exam on a separate day. Salivary cortisol levels increased significantly in response to both stimuli (TSST: F(2.21, 66.33)=5.73, p=0.004; oral exam: F(1.98, 59.28)=4.38, p=0.017) with similar mean response curves and significant correlations between cortisol increases and areas under the response curves (increase: r=0.67; AUC: r=0.56; both p≤0.01). Correspondingly, changes in positive and negative affect did also show significant correlations between conditions (increase: positive affect: r=0.36; negative affect: r=0.50; both: p≤0.05; AUC: positive affect: r=0.81; negative affect: r=0.70; both p≤0.01) while mean time course dynamics were significantly different (positive affect: F(2.55, 76.60)=10.15, p=0.001; negative affect: F(1.56, 46.82)=23.32, p=0.001), indicating that the oral exam had a more pronounced impact on affect than the TSST. Our findings provide new evidence for the view that cortisol as well as subjective stress responses to the TSST are indeed significantly associated with acute stress responses in real life.
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.
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