The Social Aptitudes Scale: looking at both "ends" of the social functioning dimension

Examining the psychometric properties of the Norwegian version of the Social Aptitudes Scale in two clinical samples

BACKGROUND

Few studies have examined the psychometric properties of the Social Aptitudes Scale (SAS). The study aims of the current paper were to examine the internal consistency and the validity of the Norwegian SAS.

METHODS

Parents of children from a clinical neuropediatric sample (N = 257) and from a clinical sample from child and adolescent's mental health services (N = 804) filled in the SAS.

RESULTS

Internal consistency for the SAS were good in both samples and correlations between the SAS and different scales were in the expected directions. The results from the Confirmatory Factor Analyses indicated poor model fit.

CONCLUSIONS

Future validity studies should investigate whether SAS is suitable as a screening instrument for detecting autism spectrum disorder.

Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Maternal depression trajectories and offspring positive attributes and social aptitudes at early adolescence: 2004 Pelotas birth cohort

There is growing evidence that adolescent positive attributes and social aptitudes are associated with beneficial outcomes, including higher educational attainment and lower risk of later psychiatric disorder. Although maternal depression is a well-known risk factor for a variety of offspring adverse outcomes, less is known on its repercussion on children's positive behavioral traits. This study aimed to evaluate the impact of maternal depression trajectories on offspring positive attributes and social aptitudes, testing sex-moderated models for the studied association. The 2004 Pelotas Birth Cohort is an ongoing cohort originally comprised by 4231 live births from Brazil. We included 3465 11-year-old adolescents (48.6% female; maternal self-reported skin color: 27.0% non-white). Maternal depressive symptoms were assessed by the Edinburgh Postnatal Depression Scale (EPDS) at all follow-ups. Adolescent positive attributes and social aptitudes were ascertained by specific subscales of Development and Well-Being Assessment (DAWBA). Multivariate linear regression was used to examine the effect of maternal depression trajectories on offspring's outcomes, adjusting for potential confounding variables. Moderation was assessed with interaction terms. Adolescents from mothers who presented high-chronic levels of depressive symptoms during offspring's life have lower scores of positive attributes and social aptitudes. Boys exposed to maternal depression during their lifetime are more affected than girls regarding positive attributes, but this sex difference was not observed for social aptitudes. Interventions targeting the promotion of adaptive behavioral traits may represent an effective way to buffer the adverse impact of maternal depression on offspring development, especially for vulnerable groups such as male adolescents.

Measuring strengths and weaknesses in dimensional psychiatry

BACKGROUND

The Extended Strengths and Weaknesses Assessment of Normal Behavior (E-SWAN) reconceptualizes each diagnostic criterion for selected DSM-5 disorders as a behavior, which can range from high (strengths) to low (weaknesses). Initial development focused on Panic Disorder, Social Anxiety, Major Depression, and Disruptive Mood Dysregulation Disorder.

METHODS

Data were collected from 523 participants (ages 6-17). Parents completed each of the four E-SWAN scales and traditional unidirectional scales addressing the same disorders. Distributional properties, Item Response Theory Analysis (IRT), and Receiver Operating Characteristic (ROC) curves were used to assess and compare the performance of E-SWAN and traditional scales.

RESULTS

In contrast to the traditional scales, which exhibited truncated distributions, all four E-SWAN scales had symmetric distributions. IRT analyses indicate the E-SWAN subscales provided reliable information about respondents throughout the population distribution; traditional scales only provided reliable information about respondents at the high end of the distribution. Predictive value for DSM-5 diagnoses was comparable to prior scales.

CONCLUSIONS

E-SWAN bidirectional scales can capture the full spectrum of the population distribution of behavior underlying DSM disorders. The additional information provided can better inform examination of inter-individual variation in population studies, as well as facilitate the identification of factors related to resiliency in clinical samples.