Daniël Roelfs

Genome-wide association studies (GWAS) and family-based studies have revealed partly overlapping genetic architectures between various psychiatric disorders. Given clinical overlap between disorders, our knowledge of the genetic architectures underlying specific symptom profiles and risk factors is limited. Here, we aimed to derive distinct profiles relevant to mental health in healthy individuals and to study how these genetically relate to each other and to common psychiatric disorders. Using independent component analysis, we decomposed self-report mental health questionnaires from 136,678 healthy individuals of the UK Biobank, excluding data from individuals with a diagnosed neurological or psychiatric disorder, into 13 distinct profiles relevant to mental health, capturing different symptoms as well as social and risk factors underlying reduced mental health. Utilizing genotypes from 117,611 of those individuals with White British ancestry, we performed GWAS for each mental health profile and assessed genetic correlations between these profiles, and between the profiles and common psychiatric disorders and cognitive traits. We found that mental health profiles were genetically correlated with a wide range of psychiatric disorders and cognitive traits, with strongest effects typically observed between a given mental health profile and a disorder for which the profile is common (e.g. depression symptoms and major depressive disorder, or psychosis and schizophrenia). Strikingly, although the profiles were phenotypically uncorrelated, many of them were genetically correlated with each other. This study provides evidence that statistically independent mental health profiles partly share genetic underpinnings and show genetic overlap with psychiatric disorders, suggesting that shared genetics across psychiatric disorders cannot be exclusively attributed to the known overlapping symptomatology between the disorders.

Sleep deprivation influences several critical functions, yet how it affects human brain white matter (WM) is not well understood. The aim of the present work was to investigate the effect of 32 hours of sleep deprivation on WM microstructure compared to changes observed in a normal sleep-wake cycle (SWC). To this end, we utilised diffusion weighted imaging (DWI) including the diffusion tensor model, diffusion kurtosis imaging and the spherical mean technique, a novel biophysical diffusion model. 46 healthy adults (23 sleep deprived vs 23 with normal SWC) underwent DWI across four time points (morning, evening, next day morning and next day afternoon, after a total of 32 hours). Linear mixed models revealed significant group × time interaction effects, indicating that sleep deprivation and normal SWC differentially affect WM microstructure. Voxel-wise comparisons showed that these effects spanned large, bilateral WM regions. These findings provide important insight into how sleep deprivation affects the human brain

Rationale: For visual perspective taking (VPT) using the avatar task, examinations of neural processes using event related potentials (ERP) indicate a distinction between an early posterior perspective calculation process (P3) and a later frontal process (LFSW) managing perspective conflict. While it is unknown if these neural processes are affected in clinical populations, it is unclear if the avatar task can be applied to this group, due to the long duration and sensitivity to data loss. Thus, we performed a methodological study of the avatar task, testing the feasibility of a shortened experimental paradigm. Objective: To investigate whether previously reported behavioural and ERP effects in the avatar task can also be seen if analysing all trials (matching/non-matching) jointly, and whether they remain robust if only a subset of the data is analysed. Method: Healthy individuals (n = 20) completed the avatar task with ERP measurement. ERP components (P3, LFSW) and behavioural data were investigated by A) comparing use of only matching trials (n = 384) versus all trials (n = 768), and B) examining if reduced duration of assessment, by analysing only a subset of the data, impacts ERP findings. Results: We observed minimal differences when analysing data from only matching trial types compared to all trial types. Further, ERP amplitudes and latency findings were replicated when analysing only a subset of the data. Conclusions: The duration of the avatar task can be reduced to avoid long testing times, thus making it better suited for use in clinical populations.