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: Biomedical Optics Express

Article Title: Optimization of diffuse Raman spectroscopy for in-vivo quantification of foreign body response in a small animal model

doi: 10.1364/boe.512118

Figure Lengend Snippet: Fig. 3. a) Raman spectra measured for the phantom sample (top) representing the Initial conditions (no collagen) sample (Red) and the sample containing 200 µm collagen (Blue). The black spectra show the difference spectra for phantom samples containing 40, 80 and 200 µm thick collagen layers. The shaded grey area indicates the 930 cm−1 band assigned to collagen. Raman spectra of collagen and skin are included for reference, Spectra are shifted vertically for clarity. b) Prediction results for the collagen thickness from the training set showing a leave-one-out validation. Vertical error bars show the RMSE of the PLS model on the training data set and horizontal error bars indicate the uncertainty on the manufactured collagen thickness (10%). The black line shows the prediction line when the prediction value is equal to the actual thickness. c) Results of the independent testing of the PLS model for collagen thickness. The black line indicates a perfect model when the prediction values are equal to the actual thicknesses.

Article Snippet: The PLS model was created using the “Statistics and Machine learning toolbox” from MATLAB 2022b.

Techniques: Biomarker Discovery

Journal: Biomedical Optics Express

Article Title: Optimization of diffuse Raman spectroscopy for in-vivo quantification of foreign body response in a small animal model

doi: 10.1364/boe.512118

Figure Lengend Snippet: Fig. 4. a) a) A schematic of the linear DRS instrument. The central red shows the optical fibre delivering the laser beam to a Powell lens to create a line-shaped laser beam. on the sample surface. The two fibre heads are shown symmetrical on translation stage to allow them to be focused on the two linear detection areas parallel to the laser line. The yellow area shows the paths of the collected Raman light focused on the two linear fibre bundles. The Blue surface shows the sample surface on which the enclosed blue areas show the collection areas, and the red areas shows the excitation area. The labelled Spectrometer slit shows the configuration of the two fibre bundles arranged vertically on the spectrometer slit and CCD b) Raman spectra measured using the optimised Linear DRS Raman instrument for the phantom sample representing the Initial conditions (no collagen) sample (Red) and the sample containing 200 µm collagen (Blue) shown normalised as they are used withing the PLS training model. The black spectra show the subtracted spectra for phantom samples containing 10, 50 and 180 µm collagen. The shaded grey area shows the 930 cm−1 band assigned to collagen. Raman spectra of collagen and skin. All spectra are shifted vertically for clarity c) PLS prediction results for the collagen thickness from the training set showing a leave-one-out validation. Vertical error bars show the RMSE of the PLS model on the training data set and horizontal error bars indicate the uncertainty on the manufactured collagen thickness (10%). d) Results of the independent testing of the PLS model for collagen thickness.

Article Snippet: The PLS model was created using the “Statistics and Machine learning toolbox” from MATLAB 2022b.

Techniques: Biomarker Discovery