Journal: Neurobiology of Stress

Article Title: Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

doi: 10.1016/j.ynstr.2023.100578

Figure Lengend Snippet: Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Article Snippet: To evaluate multivariate patterns of correlation between the nodal-level topological property (degree centrality) and SA alterations across subjects, we used PLSC via the publicly available PLS toolbox ( https://www.rotman-baycrest.on.ca/index.php?section=84 ) in MATLAB R2018b (MathWorks, Natick, USA).

Techniques: Functional Assay, Magnetic Resonance Imaging

Journal: British journal of anaesthesia

Article Title: Association of EEG Trajectories during Emergence from Anaesthesia with Delirium in the Post-Anaesthesia Care Unit, an Early Sign of Postoperative Complications

doi: 10.1016/j.bja.2018.09.016

Figure Lengend Snippet: Relevant results from univariable logistic regression of preoperative patient factors, interventions, and intra-/postoperative patient characteristics. CAM-ICU, Confusion Assessment Method for the Intensive Care Unit; CI, confidence interval, ddSWA, delta-dominant slow-wave anaesthesia; MAC, minimum alveolar concentration; nSWA, non-slow-wave anaesthesia; OR, odds ratio; sdSWA, spindle-dominant slow-wave anaesthesia. ∗ Data are represented as median (25th percentile, 75th percentile) or number (percentage of non-delirious and delirious patients, respectively). † P -values are based on the Wald statistic from univariate logistic regression. ORs are shown when P -values are <0.05. ORs for discrete and continuous variables represent a one-unit increase except where indicated otherwise by the OR scaling factor. ‡ Neurodegenerative disease includes dementia, mild cognitive impairment, and Parkinson's. Parkinson's results are not displayed because n =1. ¶ Variables considered a priori . § Additional variables included in multivariable model before pruning

Article Snippet: Statistical analyses and model construction All statistics were conducted using native toolboxes and custom scripts in MATLAB R2015b (MathWorks, Natick, MA, USA) with the exception of the final multivariable logistic model, which was constructed in Stata 14 (StataCorp, College Station, TX, USA).

Techniques: Concentration Assay, Medications, Blocking Assay

Journal: British journal of anaesthesia

Article Title: Association of EEG Trajectories during Emergence from Anaesthesia with Delirium in the Post-Anaesthesia Care Unit, an Early Sign of Postoperative Complications

doi: 10.1016/j.bja.2018.09.016

Figure Lengend Snippet: Adjusted odds ratios from multivariable logistic regression. Odds ratios and [95% confidence intervals (CIs)] calculated from the model described in Methods and Supplementary information of the relevant EEG and non-EEG parameters. *Odds ratios adjusted for interaction with another co-variate. Legend quantities are odds ratios. See text for an explanation of confounding regarding spine surgery.

Article Snippet: Statistical analyses and model construction All statistics were conducted using native toolboxes and custom scripts in MATLAB R2015b (MathWorks, Natick, MA, USA) with the exception of the final multivariable logistic model, which was constructed in Stata 14 (StataCorp, College Station, TX, USA).

Techniques: