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The profile of executive function (EF) in adults with Schizophrenia (SCZ) and autism spectrum disorder (ASD) remains unclear. This study aims to ascertain if distinct EF patterns can be identified between each clinical condition by comparing the neuropsychological profile of adults with SCZ and ASD, for whom the differential diagnosis is still highly challenging. Forty-five individuals (15 SCZ, 15 ASD, 15 controls) matched for age, sex, education level, and handedness underwent intelligence evaluation and neuropsychological testing for working memory, inhibition, planning and set-shifting, and verbal fluency subdomains. Principal component analysis (2D-PCA) using variables representing 4 domains was employed to identify patterns in neuropsychological profiles. The ASD group had lower scores on the Digits Forward subtest compared to the SCZ group (7.2 ± 2.1 vs. 9.3 ± 1.9, p = 0.003; Cohen's d: 1.05). ASD also performed significantly worse on the Stroop Word Test compared to the control group (77.7± 17.9 vs. 98.0 ± 12.7, p = 0.009; Cohen's d: 1.31). No significant differences were observed between ASD and SCZ on other EF measures. The larger contributors for the dimensions in 2D-PCA were the Digits Forward subtest and Stroop Word Test. Still, there was substantial overlap between the clinical groups. This study suggests a high degree of similarity of EF between SCZ and ASD. Through four EF measures, the discrimination of low and high-functioning EF groups spanning both diagnostic categories may help to identify the individuals who could better benefit from cognitive rehabilitation strategies. [ABSTRACT FROM AUTHOR]

The Ruminative Response Scale for Eating Disorders (RRS-ED) measures ruminative thought content specifically related to eating disordered themes, assessing two domains of rumination, brooding and reflection. This study aims to examine the factor structure of the RRS-ED in a Portuguese community sample, using correlated two-factor models, unifactorial and bifactor models and test for invariance across sex. A sample of 535 adults (179 male; 356 female) filled out the RRS-ED. A subsample (n=347) answered additional measures of repetitive negative thinking and eating psychopathology. The bifactor model of the RRS-ED provided the best fit, demonstrating a reliable general rumination factor. Also, the bifactor model of the RRS-ED was invariant across sex. RRS-ED showed moderate to strong correlations with negative perseverative thinking and eating psychopathology. Both domain-specific factors of RRS-ED were associated with higher levels of eating psychopathology. Findings indicate that RRS-ED is a reliable and valid measure to assess the ruminative response from the general population in Portugal, showing initial evidence that supports the use of a total score of RRS-ED as an overall measure of rumination, while specific factor scores should be reported with caution. Future studies are needed to replicate the findings and further corroborate the unidimensionality of the RRS-ED. [ABSTRACT FROM AUTHOR]

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n -back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2023 Massachusetts Institute of Technology.)