Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience

The Morphology of Somatosensory Evoked Potentials During Middle Cerebral Artery Aneurysm Clipping (MoSAC): A Pilot Study

Somatosensory evoked potential (SEP) monitoring is a standard tool during clipping of aneurysms of the middle cerebral artery (MCA), and the parameter used to detect a state of cortical ischemia is amplitude. We think that the sensitivity of SEP can however be improved by using other parameters. Our study moves in this direction via SEP morphology. In this pilot preliminary study, involving a small sample without postoperative neurological deficit, we aimed at investigating the value of SEP morphology (in the 15- to 35-ms time frame), in comparison with SEP amplitude (N20 peak-to-peak), as a measure of sensitivity to blood flow reduction. The changes in the SEP morphology of 16 patients undergoing clipping of an unruptured MCA aneurysm was studied. We applied the Morph-Fréchet index for each recorded SEP (at 30-second intervals), quantifying the pattern shape change with regard to the average SEP recorded after dura opening (baseline). We also compared 3 measurements of the SEP morphology, without and with GARCH-derived filter. Filtered Morph-Fréchet never exceeded the individual's "normality" range in baseline but did so in 81% of the risk phase on average across the 16 subjects, which is more than that for amplitude (36%, P = .002). This pilot study indicates that a measurement derived from the networking nature of the brain was sensitive to blood flow reduction. The SEP morphology approach promises to improve SEP monitoring sensitivity during clipping of unruptured MCA aneurysms. New and Noteworthy. The higher sensitivity to blood flow reduction of SEP morphology than amplitude promises to improve the effectiveness of intraoperative monitoring during MCA aneurysm clipping procedures.

Functional connectome of the five-factor model of personality

A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analysing big data-sets with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and nonlinear statistical dependencies across the graph “nodes”, which were defined as distinct large-scale brain circuits identified via independent component analysis. Multivariate regression models and “train/test” approaches were used to examine the associations between FFM traits and connectomic indices as well as to assess the generalizability of the main findings, while accounting for age and sex variability. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This offers a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.