IntroductionNeuroanatomical abnormalities are reported in psychotic disorders compared to healthy controls; nevertheless, less is known about the role of familial liability to psychosis in ...morphological brain changes.ObjectivesUsing an exploratory voxel-based morphometry (VBM) analyses of the whole brain, we evaluated differences on GMVs across the whole brain among first-episode psychosis (FEP) patients, community-controls, and healthy siblings of patients to interrogate the role of familial liability.MethodsData were retrieved from a study (STREAM) conducted in Ribeirão Preto/SP Brazil. We included 71 first-episode psychosis patients (67.6% males, mean age±SD: 18.7±10.8), 24 unaffected siblings of patients (37.5% males, mean age±SD 30.8±10), and 36 controls (71.9% males, mean age±SD: 10±10.5). All magnetic resonance imaging (MRI) scans were acquired on a 3T Philips scanner. VBM data were processed using Statistical Parametric Mapping (SPM) software in MATLAB the MNI coordinate system. We performed exploratory voxel-wise comparisons of GMVs among the three groups using an analysis of covariance (ANCOVA) model in SPM. Results were considered significant if they retained significance after family-wise error (FWE) correction for multiple comparisons (p<0.05). All the analyses were adjusted for age, sex, education in years, and total brain GMV.ResultsThe whole-brain exploratory analyses revealed no significant findings at the p<0.05 level (FWE-corrected). However, pairwise comparisons revealed significant changes betweeen FEP patients and their unaffected siblings. In particular, FEP patients had decreased volumes in the right side of the following regions (FEW = 0.047): superior temporal cortex, Rolandic operculum, insula, Heschel’s gyrus, supramarginal gyrus, superior temporal pole, hippocampus, parahippocampal gyrus, fusiform gyrus, amydgala, olfactory, inferior frontal operculum, cerebellum, posterior and medial orbital frontal cortex, rectus, medial temporal, medial frontal, and putamen. FEP patients also showed decreased volumes on the left side of the following regions (FWE 0.049): frontal superior medial gyrus, superior frontal gyrus, frontal middle part, caudate, anterior cingulate cortex, thalamus, and pallidum. Patients also showed widespread reduced GMV in various GMVs regions compared to controls at FWE<0.05. However, no difference was found between siblings and controls (FWE: >0.05).ConclusionsThe study of healthy siblings of patients with heritable illnesses could help in the understanding of the contribution of genetic background and environmental factors to illness state and predisposition. Differences between patients and their siblings could be attributed to the disease state, considering that the unaffected sibling group and unrelated healthy control group did not differ. We will next evaluate biological and environmental contributors to the reported differences.Disclosure of InterestNone Declared
Abstract
Introduction
Repeated exposure to total sleep deprivation (TSD) within individuals has demonstrated task-specific, trait-like individual differences in cognitive impairment and subjective ...sleepiness. Research has suggested that introversion/extraversion may predict individual vulnerability to TSD. While previous analyses have found that extraversion does not reliably predict objective performance impairment on the psychomotor vigilance test (PVT) during TSD, it is not known whether extraversion may predict individuals’ subjective responses to TSD, including subjective ratings of sleepiness, fatigue, mood, performance, and effort.
Methods
N=21 healthy adults (aged 21-38; 9 women) completed three 4-day/3-night laboratory sessions – each including a baseline night, 36h TSD period, and recovery night –separated by at least 2 weeks each. Two of the sessions were preceded by a week of sleep extension (12h nightly sleep opportunities), while one session was preceded by a week of sleep restriction (6h nightly sleep opportunities), in randomized, counterbalanced order; only the sleep extension sessions are used here. Prior to the experiment, subjects filled out the Eysenck Personality Questionnaire (EPQ), which yielded an extraversion score; one subject did not complete the questionnaire and was excluded from analyses. Every 2h during TSD, subjects completed a 60min neurobehavioral test battery. At the beginning of the test battery, subjects completed the Karolinksa Sleepiness Scale (KSS) and visual analog scales of mood and fatigue (VAS-M and VAS-F). At the end of the test battery, subjects completed self-ratings of their performance (1–7 scale) and effort (1–4 scale). The relationship between extraversion and subjective scores after sleep deprivation (average over last 24h of 36h TSD period) relative to baseline (average over first 12h of TSD period) was analyzed using mixed-effects analysis of covariance, controlling for order, with a random effect over subjects on the intercept.
Results
No significant relationships were observed between extraversion and subjective estimates of sleepiness (KSS, p=0.45), fatigue (VAS-F, p=0.80), mood (VAS-M, p=0.14), performance (p=0.89), and effort (p=0.93).
Conclusion
These results indicate that extraversion is not a reliable predictor of trait-like individual differences in subjective vulnerability to 36h TSD.
Support (if any)
NASA grant NAG9-1161, CDMRP grant W81XWH-20-1-0442
Abstract
Introduction
Although delayed sleep-wake phase disorder (DSWPD) shares phenomenological experiences with chronic insomnia disorder, previous research on the conceptual understanding of DSWPD ...has been limited, with a predominant focus on its chronobiological basis. The present study examined several insomnia-related cognitive and behavioural factors in adolescents with DSWPD.
Methods
Twenty-five adolescents with DSWPD (age = 19.9 ± 1.6; female = 28%), 28 with chronic insomnia disorder (age = 20.4 ± 2.0; female = 61%), diagnosed according to the ICSD-3 criteria, and 25 healthy control (age = 20.4 ± 1.5; female = 68%) were included in the present study. Participants completed 7-day prospective sleep diary and actigraphy monitoring, and a battery of questionnaires on sleep and chronotype measures. Participants were also measured on hyperarousal (Pre-Sleep Arousal Scale, PSAS), sleep reactivity (Ford Insomnia Response to Stress Test, FIRST), sleep-related beliefs (Dysfunctional Beliefs and Attitudes about Sleep, DBAS-16), and sleep hygiene practices (Sleep Hygiene Practices Scale, SHPS). Analysis of covariance (ANCOVA) with gender as the covariate was used for between-group comparisons.
Results
Relative to healthy control group, insomnia and DSWPD group showed significantly more insomnia symptoms as measured by Insomnia Severity Index (p<.001). As compared to healthy control group, DSWPD group showed significantly more delay in circadian phase based on sleep diary and actigraphy derived mid-point of sleep, as well as greater preference towards eveningness as measured by the Morningness-Eveningness Questionnaire. DSWPD group also showed significantly more cognitive and somatic hyperarousal (p<.001, d = 1.36–2.31), sleep reactivity (p<.001, d = 1.73), dysfunctional belief about sleep (p<.001, d = 1.59), as well as poorer sleep hygiene practices (p<.001, d = 2.62), compared to healthy control. There were no significant differences in the circadian parameters, PSAS, FIRST, DBAS-16, and SHPS between DSWPD and insomnia groups.
Conclusion
Several insomnia-related cognitive and behavioural factors, namely hyperarousal, sleep reactivity, maladaptive beliefs about sleep, and poor sleep hygiene, are also evident in youths with DSWPD. The findings had important implications for the conceptual understanding and clinical management of DSWPD.
Support (if any)
This work was supported by the Seed Fund for Basic Research (University Research Committee, The University of Hong Kong) awarded to Dr. Shirley Li.
En este artículo se presentan los resultados de una primera aproximación al análisis del efecto moderador del contexto social, cultural y geográfico en indicadores subjetivos del envejecimiento ...saludable en personas mayores de 46 años residentes en territorios con características diferenciadas en Costa Rica. Se trabajó con una muestra de 305 personas residentes en tres áreas geográficas: una urbana, una semiurbana y una tercera principalmente rural. La diferenciación de las tres zonas se basó en criterios de densidad poblacional, infraestructura y acceso a bienes y servicios. Los indicadores subjetivos del envejecimiento saludable analizados fueron: participación social, apoyo social, salud percibida, espiritualidad, autoeficacia, comportamientos de autocuidado, bienestar subjetivo (satisfacción con la vida y bienestar psicológico) y estado de ánimo; todas las variables fueron condicionadas por zona de residencia, edad y sexo. Para analizar los indicadores subjetivos se estimó un análisis de covarianza (ANCOVA) o un análisis multivariado de covarianza (MANCOVA), dependiendo del número de variables dependientes analizadas. En general, se identificaron indicadores subjetivos de envejecimiento saludable altos en las personas participantes del estudio, quienes reportaron altos niveles de participación social, satisfacción con la vida y estados de salud y ánimo positivos. Se encontraron diferencias por edad entre los grupos. Sin embargo, no se evidenciaron diferencias estadísticamente significativas en los indicadores subjetivos analizados según la zona de residencia o el sexo. En síntesis, este estudio encontró que los indicadores subjetivos de envejecimiento saludable analizados eran muy similares en residentes de tres zonas geográficas con características distintas. Estos hallazgos iniciales se discuten desde una perspectiva cultural y geográfica y en relación con los modelos de envejecimiento saludable.
This paper introduces the R package WRS2 that implements various robust statistical methods. It elaborates on the basics of robust statistics by introducing robust location, dispersion, and ...correlation measures. The location and dispersion measures are then used in robust variants of independent and dependent samples
t
tests and ANOVA, including between-within subject designs and quantile ANOVA. Further, robust ANCOVA as well as robust mediation models are introduced. The paper targets applied researchers; it is therefore kept rather non-technical and written in a tutorial style. Special emphasis is placed on applications in the social and behavioral sciences and illustrations of how to perform corresponding robust analyses in R. The R code for reproducing the results in the paper is given in the
Supplementary Materials
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•Both methods are equally effective for developing and analyzing the structural relationship.•CB-SEM demands a lot from the data, whereas PLS-SEM is quite lenient.•For a factor-based model, CB-SEM ...should be used.•For a composite-based model, PLS-SEM should be considered.•CB-SEM and PLSc-SEM methods provide almost similar results.
This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is based on covariance, and the second one is based on variance (partial least squares). It further assesses the difference between PLS and Consistent PLS algorithms. To assess the same, empirical data is used. Four hundred sixty-six respondents from India, Saudi Arabia, South Africa, the USA, and few other countries are considered. The structural model is tested with the help of both approaches. Findings indicate that the item loadings are usually higher in PLS-SEM than CB-SEM. The structural relationship is closer to CB-SEM if a consistent PLS algorithm is undertaken in PLS-SEM. It is also found that average variance extracted (AVE) and composite reliability (CR) values are higher in the PLS-SEM method, indicating better construct reliability and validity. CB-SEM is better in providing model fit indices, whereas PLS-SEM fit indices are still evolving. CB-SEM models are better for factor-based models like ours, whereas composite-based models provide excellent outcomes in PLS-SEM. This study contributes to the existing literature significantly by providing an empirical comparison of all the three methods for predictive research domains. The multi-national context makes the study relevant and replicable universally. We call for researchers to revisit the widely used SEM approaches, especially using appropriate SEM methods for factor-based and composite-based models.
•Green transformational leadership influences green human resource management (GHRM) practices.•Green innovation predicts environmental performance.•Green innovation mediates the influence of GHRM ...practices on environmental performance.
Drawing upon the resource-based view and the ability-motivation-opportunity theory, we examined how green human resource management interplays on to the linkages amongst green transformational leadership, green innovation and environmental performance. Using a survey questionnaire, we collected triadic data from 309 manufacturing sector small and medium-sized enterprises (SMEs). We used covariance-based structural equation modeling (SEM) to examine hypotheses in this study. Results of the study suggest that green HRM practices mediates the influence of green transformational leadership on green innovation. We also found that green HRM indirectly through green innovation influences firm's environmental performance. Overall, the findings of our study support all hypotheses of direct and indirect effects and have several theoretical and practical implications. Finally, our study significantly advances theory and suggests that HRM-performance relationship neither depends upon the additive effect of green transformational leadership and green innovation as antecedent and mediator, respectively, nor on their interactive effect but a mix of both combinational forms (ie., additive and interactive) to affect firm environmental performance. Overall, our study contributes and advances the previous studies wherein in leadership plays critical role to influence the HRM practices and that in turn to predict green innovation in the organization.
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