Linden, D. E. | bioRxiv Science

bioRxiv2018

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-beamformed Connectivity Analysis

The resting activity of the brain can be described by so-called intrinsic connectivity networks (ICNs), which consist of spatially and temporally distributed, but functionally connected, nodes. The coordinated activity of the resting state can be explored via magnetoencephalography (MEG) by studying frequency-dependent functional brain networks at the source level. Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown. This is a particular problem for studies of associations between ICN metrics and personality variables or other traits, and for studies of differences between patient and control groups, which both depend critically on the reliability of the metrics used. A detailed investigation of the reliability of metrics derived from resting-state MEG repeat scans is therefore a prerequisite for the development of connectomic biomarkers.\n\nHere, we first estimated both static (SFC) and dynamic functional connectivity (DFC) after beamforming source reconstruction using the imaginary part of the phase locking index (iPLV) and the correlation of the amplitude envelope (CorEnv). Using our approach, functional network microstates (FCstates) were derived from the DFC and chronnectomics were computed from the evolution of FCstates across experimental time. In both temporal scales, the reliability of network metrics (SFC), the FCstates and the related chronnectomics were evaluated for every frequency band.\n\nChronnectomic parameters and FCstates were generally more reliable than node-wise static network metrics. CorEnv-based network metrics were more reproducible at the static approach. This analysis encourages the analysis of MEG resting-state via DFC.

neuroscience↗

bioRxiv2017

A Novel, Fast and Efficient Single-Sensor Automatic Sleep-Stage Classification Based on Complementary Cross-Frequency Coupling Estimates

ObjectiveLimitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels, have long been recognised. Manual staging is resource-intensive and time-consuming and considerable efforts have to be spent to ensure inter-rater reliability. There is thus great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC).\n\nMethodsIn this paper, we present a single EEG-sensor ASSC technique based on dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database using 20 healthy young adults with repeat recordings.\n\nResultsWe achieved very high classification sensitivity, specificity and accuracy of 96.2 {+/-} 2.2%, 94.2 {+/-} 2.3%, and 94.4 {+/-} 2.2% across 20 folds, respectively and high mean F1-score (92%, range 90-94%) when multi-class Bayes Naive classifier was applied.\n\nConclusionsOur method outperformed the accuracy of previous studies on different datasets but also on the same database.\n\nSignificanceSingle-sensor ASSC makes the whole methodology appropriate for longitudinal monitoring using wearable EEG in real world and lab-oriented environments.

neuroscience↗