A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted 6 robust alpha and theta factors (86.6% variance). Subsequent spatial PCA for each spectral factor revealed seven robust regionally-focused (posterior, central, frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10 and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session x odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by similarity of factor loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Single-trial-based Temporal Principal Component Analysis on Extracting Event-related Potentials of Interest for an Individual Subject

Temporal principal component analysis (t-PCA) has been widely used to extract event-related potentials (ERPs) at the group level of multiple subjects’ ERP data. The key assumption of group t-PCA analysis is that desired ERPs of all subjects share the same waveforms (i.e., temporal components), whereas waveforms of different subjects’ ERPs can be variant in phases, peak latencies and so on, to some extent. Additionally, several PCA-extracted components coming from the same ERP dataset failed to be statistically analysed simultaneously because their polarities and amplitudes were indeterminate. To fill these gaps, a novel technique was proposed and employed to extract desired ERP from single-trial EEG dataset of an individual subject. Firstly, the dataset of all trials and all conditions of one subject were stacked across electrodes to form a matrix. Secondly, the temporal and spatial PCA-components were extracted from single-trial EEG dataset matrix for each subject by t-PCA and Promax rotation. Thirdly, the desired components were selected and projected to the electrode fields simultaneously to correct their variance and polarity indeterminacies. Next, single-trial EEG datasets of the back-projection were averaged to generate the waveforms of desired ERP for each subject and then amplitudes of the desired ERP were quantified. The yields indicated that the proposed approach can efficient exploit the temporal and spatial information of single-trial EEG data and can temporally filter the data to extract the desired ERPs for an individual subject.

Spatially Explicit Models to Investigate Geographic Patterns in the Distribution of Forensic STRs

ABSTRACTHuman forensic STRs are used for individual identification but have been reported to have little power for inter-population analyses. Several methods have been developed which incorporate information on the spatial distribution of individuals to arrive at a description of the arrangement of diversity. We genotyped at 16 forensic STRs a large population sample obtained from many locations in Italy, Greece and Turkey, i.e. three countries seldom represented together in previous studies. Using spatial PCA on the full dataset, we detected patterns of population affinities in the area similar to those of genome-wide SNP and STR studies. Additionally, we devised objective criteria to reduce the overall complexity into reduced datasets. Independent spatially explicit methods applied to these latter datasets converged in showing that the extraction of information on long-to medium-range geographical trends and structuring from the overall diversity is possible. All analyses returned the picture of a background clinal variation, with regional discontinuities captured by each of the reduced datasets. These coincided with the main bodies of water, i.e. the Adriatic/Ionian and the Aegean Seas. High levels of gene flow were inferred within the main continental areas by coalescent simulations. These results are promising in a microevolutionary perspective, in view of the fast pace at which forensic data are being accumulated for many locales. It is foreseeable that this will allow the exploitation of an invaluable genotypic resource, assembled for other (forensic) purposes, to clarify important aspects in the formation of local gene pools.