Whereas we know a fair amount on the role of the amygdala in the acute stress response, virtually nothing is known about its role during the recovery period after the stress has waned. Functional... Show moreWhereas we know a fair amount on the role of the amygdala in the acute stress response, virtually nothing is known about its role during the recovery period after the stress has waned. Functional connectivity analysis of the amygdala during this period might be useful in revealing brain circuits promoting adaptive recovery from a stressful event, as well as consolidation of emotionally relevant information in preparing for future challenges. Healthy participants were randomly assigned to either a psychosocial stress task (n = 18; stress group) or a comparable non-stressful control procedure (n = 20; controls). To study the prolonged effects of stress on amygdala functional connectivity, resting-state fMRI scans were acquired an hour after the stress task. Amygdala functional connectivity with other brain regions was assessed using seed-based correlations. The stress group exhibited a strong physiological and behavioral reaction to psychosocial stress exposure. Compared with controls the stress group showed increased amygdala functional connectivity with three cortical midline structures: the posterior cingulate cortex and precuneus (p<.05, corrected), and the medial prefrontal cortex (p<.05, small volume corrected). An hour after psychosocial stress, changes in amygdala functional connectivity were detected with cortical midline structures involved in the processing and regulation of emotions, as well as autobiographical memory. It is hypothesized that these effects could relate to top-down control of the amygdala and consolidation of self-relevant information after a stressful event. These results on functional connectivity in the recovery phase after stress might provide an important new vantage point in studying both sensitivity and resilience to stress. (C) 2011 Elsevier Inc. All rights reserved. Show less
Ferrarini, L.; Veer, I.M.; Lew, B. van; Oei, N.Y.L.; Buchem, M.A. van; Reiber, J.H.C.; ... ; Milles, J. 2011
In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological... Show moreIn recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover. functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however. was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r2 statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches. based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity. (C) 2011 Elsevier Inc. All rights reserved. Show less
