The effects of anxiety, depressive, and obsessive-compulsive subclinical symptoms on performance monitoring

Validation of the Russianversion of the Maudsley Obsessive-Compulsive Inventory (MOCI) in Population and Clinical Samples

Relevance. Obsessive-compulsive disorder (OCD) is the third most common psychiatric disorder, potentially disabling with significant social and economic consequences. In Russia, little attention is paid to the study of OCD, which leads to the problem of differential diagnosis and effective treatment of OCD. One of the reasons for the insufficient attention to OCD is the lack of validated Russian questionnaires for diagnosing OCD. The purpose of this work is the initial validation of a translated version of the Maudsley Obsessive-Compulsive Inventory (MOCI). Characteristics of the surveyed groups. A population-based sample of 300 students (212 women and 88 men) and a clinical sample of 13 patients with anxiety and depressive disorders (8 women and 5 men) and 13 patients with OCD (11 women and 2 men) participated in the study. Severity of OCD symptoms were assessed with the translated version of Maudsley questionnaire. Results. According to our data, the MOCI questionnaire allows to differentiate reliably (p = 0,027) patients with OCD from patients with anxiety-depressive disorders and can be suitable as a primary diagnostic test for identifying OCD patients (p < 0,05) and the risk group. The reliability and convergent validity of the questionnaire were shown.

A systematic review and meta-analysis of altered electrophysiological markers of performance monitoring in Obsessive-Compulsive Disorder (OCD), Gilles de la Tourette Syndrome (GTS), Attention-Deficit/Hyperactivity disorder (ADHD) and Autism

Altered performance monitoring is implicated in obsessive-compulsive disorder (OCD), Gilles de la Tourette syndrome (GTS), attention-deficit/hyperactivity disorder (ADHD) and autism. We conducted a systematic review and meta-analysis of electrophysiological correlates of performance monitoring (error-related negativity, ERN; error positivity, Pe; feedback-related negativity, FRN; feedback-P3) in individuals with OCD, GTS, ADHD or autism compared to control participants, or associations between correlates and symptoms/traits of these conditions. Meta-analyses on 97 studies (5890 participants) showed increased ERN in OCD (Hedge's g = 0.54[CIs:0.44,0.65]) and GTS (g = 0.99[CIs:0.05,1.93]). OCD also showed increased Pe (g = 0.51[CIs:0.21,0.81]) and FRN (g = 0.50[CIs:0.26,0.73]). ADHD and autism showed reduced ERN (ADHD: g=-0.47[CIs:-0.67,-0.26]; autism: g=-0.61[CIs:-1.10,-0.13]). ADHD also showed reduced Pe (g=-0.50[CIs:-0.69,-0.32]). These findings suggest overlap in electrophysiological markers of performance monitoring alterations in four common neurodevelopmental conditions, with increased amplitudes of the markers in OCD and GTS and decreased amplitudes in ADHD and autism. Implications of these findings in terms of shared and distinct performance monitoring alterations across these neurodevelopmental conditions are discussed. PROSPERO pre-registration code: CRD42019134612.

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.