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Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics.

ABSTRACT: Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance-wakefulness (W), NREM-N2, NREM-N3, and REM sleep-with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5-4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

SUBMITTER: Imperatori LS

PROVIDER: S-EPMC8120343 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundClinical trial and epidemiological studies need high quality biospecimens from a representative sample of participants to investigate genetic influences on treatment response and disease. Obtaining blood biospecimens presents logistical and financial challenges. As a result, saliva biospecimen collection is becoming more frequent because of the ease of collection and lower cost. This article describes an assessment of saliva biospecimen samples collected through the mail, trial participant demographic and behavioral characteristics, and their association with saliva and DNA quantity and quality.MethodsSaliva biospecimens were collected using the Oragene(R) DNA Self-Collection Kits from participants in a National Cancer Institute funded smoking cessation trial. Saliva biospecimens from 565 individuals were visually inspected for clarity prior to and after DNA extraction. DNA samples were then quantified by UV absorbance, PicoGreen(R), and qPCR. Genotyping was performed on 11 SNPs using TaqMan(R) SNP assays and two VNTR assays. Univariate, correlation, and analysis of variance analyses were conducted to observe the relationship between saliva sample and participant characteristics.ResultsThe biospecimen kit return rate was 58.5% among those invited to participate (n = 967) and 47.1% among all possible COMPASS participants (n = 1202). Significant gender differences were observed with males providing larger saliva volume (4.7 vs. 4.5 ml, p = 0.019), samples that were more likely to be judged as cloudy (39.5% vs. 24.9%, p < 0.001), and samples with greater DNA yield as measured by UV (190.0 vs. 138.5, p = 0.002), but reduced % human DNA content (73.2 vs. 77.6 p = 0.005) than females. Other participant characteristics (age, self-identified ethnicity, baseline cigarettes per day) were associated with saliva clarity. Saliva volume and saliva and DNA clarity were positively correlated with total DNA yield by all three quantification measurements (all r > 0.21, P < 0.001), but negatively correlated with % human DNA content (saliva volume r = -0.148 and all P < 0.010). Genotyping completion rate was not influenced by saliva or DNA clarity.ConclusionFindings from this study show that demographic and behavioral characteristics of smoking cessation trial participants have significant associations with saliva and DNA metrics, but not with the performance of TaqMan(R) SNP or VNTR genotyping assays.Trial registrationCOMPASS; registered as NCT00301145 at clinicaltrials.gov.

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Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics.

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