Functional connectivity analysis
aCompCor
REGRESSIONS
The functional connectivity analysis was an exploratory step based on the observations in the cluster analysis. Hence, we employed a ROI-to-ROI approach and focused on the hippocampus and middle frontal gyri. The ROIs were defined and segmented based on the FSL Harvard–Oxford atlas (including 132 ROIs).We performed a functional connectivity analysis using the CONN toolbox. Before the classical first level analysis, a denoising step using the anatomical component-based noise correction procedure (aCompCor) was implemented. We used linear regressions to remove the potential confounding effects in the BOLD signal, including the noise signals derived from WM and CSF, the estimated motions, the identified outlier scans (i.e. scrubbing), and the constant and first-order linear session effects. Further, we applied a temporal band-pass filter of 0.008–0.09 Hz to reduce the low-frequency drift derived from physiological sources or head-motions.The first level analysis was performed with a weighted general linear model with bivariate correlations. We used a Hemodynamic Response Function (HRF) to weigh down the beginning for a delay of the BOLD response. For the group analysis, we used two workloads and three conditions as regressors of interest and age as regressor of no interest. The thresholds of statistical significance were set at a voxel-level uncorrected 