Effectiveness of group metacognitive training and cognitive-behavioural therapy in a transdiagnostic manner for young patients with psychotic and non-psychotic disorders

The Structure and Diagnostic Accuracy of the QbTest in Pediatric ADHD: A Retrospective Clinical Study

OBJECTIVE

The QbTest that combines a continuous performance task (CPT) with a motion-tracking system may help identify attention deficit hyperactivity disorder (ADHD). This study examined the structure and diagnostic ability of the QbTest in children and adolescents.

METHOD

Retrospective data from 1,274 children and adolescents were analyzed. The study assessed data on a principal component analysis (PCA), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The QbActivity component included the variables micro-events, distance, area, and time active; the QbImpulsivity included normalized commissions and commissions (anticipatory errors were added in a version for 6-12-year-olds only); and the QbInattention included omissions, reaction time, and reaction time variation. Sensitivity ranged between 22% and 50%, specificity 79% and 96%, PPVs 40% and 95%, and NPVs 24% and 66%.

CONCLUSION

The structure of the QbTest with three cardinal parameters and nine/ten CPT and motion analysis variables was supported. The diagnostic accuracy was found to be poor to moderate. Given that this is a retrospective study, the interpretation of diagnostic accuracy should be considered within this context.

Effect Modification of Cortisol on the Associations Between Obsessive-Compulsive Symptoms on Suicidality in Patients With Acute Coronary Syndrome

OBJECTIVE

Obsessive-compulsive symptoms (OCS) and suicidal ideation (SI) are common in patients with acute coronary syndrome (ACS). This study investigated the associations of OCS and serum cortisol levels with SI, and further evaluated the possible modifying effects of cortisol on the associations between OCS and SI in acute and chronic phases of ACS.

METHODS

In total, 969 ACS patients were recruited from a tertiary university hospital in Korea within 2 weeks of disease onset and evaluated in terms of OCS (using the OCS dimension of the Symptom Checklist-90-Revised), serum cortisol levels, and SI (using the "suicidal thoughts" item of the Montgomery-Åsberg Depression Rating Scale). Covariates included sociodemographics, depression, vascular risk factors, and disease severity. After 1 year, 711 patients were re-evaluated in terms of SI. Logistic regression analysis was performed with adjustment for covariates.

RESULTS

Higher OCS was significantly associated with SI both at baseline and follow-up. Serum cortisol showed no such association, but modified the association between OCS and SI. That was the associations were significant only in the higher but not in the lower serum cortisol levels, with significant interaction terms after adjusted for relevant covariates.

CONCLUSION

Evaluating OCS and serum cortisol levels at the acute phase could improve the accuracy of clinical predictions of SI both in the acute and chronic phases of ACS.

Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.