Evidence for a competitive relationship between executive functions and statistical learning.

The ability of the brain to extract patterns from the environment and predict future events, known as statistical learning, has been proposed to interact in a competitive manner with prefrontal lobe-related networks and their characteristic cognitive or executive functions. However, it remains unclear whether these cognitive functions also possess a competitive relationship with implicit statistical learning across individuals and at the level of latent executive function components. In order to address this currently unknown aspect, we investigated, in two independent experiments (NStudy1 = 186, NStudy2 = 157), the relationship between implicit statistical learning, measured by the Alternating Serial Reaction Time task, and executive functions, measured by multiple neuropsychological tests. In both studies, a modest, but consistent negative correlation between implicit statistical learning and most executive function measures was observed. Factor analysis further revealed that a factor representing verbal fluency and complex working memory seemed to drive these negative correlations. Thus, the antagonistic relationship between implicit statistical learning and executive functions might specifically be mediated by the updating component of executive functions or/and long-term memory access.

Differential associations between insight and quality-of-life dimensions among individuals with schizophrenia.

PURPOSE/OBJECTIVE: In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life. Yet, insight and quality of life are broad constructs that encompass several dimensions. Here, we investigated differential associations between insight and quality-of-life dimensions using a psychological network approach. RESEARCH METHOD/DESIGN: We extracted data from the French network of rehabilitation centers REHABase (January 2016 to December 2022, N = 1,056). Our psychological network analysis modeled insight and quality of life as a network of interacting dimensions: three insight dimensions (awareness of illness, reattribution of symptoms to the disease, and recognition of treatment need) and eight quality-of-life dimensions (autonomy, physical and psychosocial well-being, relationships with family, friends and romantic partners, resilience, and self-esteem). RESULTS: Insight was negatively associated with quality of life. Our psychological network analysis revealed a strong negative association between awareness of disease and self-esteem. Both dimensions were the strongest nodes in the overall network. Our network analysis also revealed a significant but positive connection between recognition of treatment needs and resilience. CONCLUSION/IMPLICATIONS: While insight and quality of life are overall negatively associated, we found both negative and positive connections between insight and quality-of-life dimensions. The negative relationship between insight and quality of life may reflect the deleterious effects of diagnostic labeling on a patient's self-esteem. Yet, acknowledgment of treatment needs may have positive effects on quality of life and may promote recovery, perhaps because it emphasizes the need for support rather than labels and abnormalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Contribution of socio-demographic and clinical characteristics to predict initial referrals to psychosocial interventions in patients with serious mental illness.

AIMS: Psychosocial rehabilitation (PSR) is at the core of psychiatric recovery. There is a paucity of evidence regarding how the needs and characteristics of patients guide clinical decisions to refer to PSR interventions. Here, we used explainable machine learning methods to determine how socio-demographic and clinical characteristics contribute to initial referrals to PSR interventions in patients with serious mental illness. METHODS: Data were extracted from the French network of rehabilitation centres, REHABase, collected between years 2016 and 2022 and analysed between February and September 2022. Participants presented with serious mental illnesses, including schizophrenia spectrum disorders, bipolar disorders, autism spectrum disorders, depressive disorders, anxiety disorders and personality disorders. Information from 37 socio-demographic and clinical variables was extracted at baseline and used as potential predictors. Several machine learning models were tested to predict initial referrals to four PSR interventions: cognitive behavioural therapy (CBT), cognitive remediation (CR), psychoeducation (PE) and vocational training (VT). Explanatory power of predictors was determined using the artificial intelligence-based SHAP (SHapley Additive exPlanations) method from the best performing algorithm. RESULTS: Data from a total of 1146 patients were included (mean age, 33.2 years [range, 16-72 years]; 366 [39.2%] women). A random forest algorithm demonstrated the best predictive performance, with a moderate or average predictive accuracy [micro-averaged area under the receiver operating curve from 'external' cross-validation: 0.672]. SHAP dependence plots demonstrated insightful associations between socio-demographic and clinical predictors and referrals to PSR programmes. For instance, patients with psychotic disorders were more likely to be referred to PE and CR, while those with non-psychotic disorders were more likely to be referred to CBT and VT. Likewise, patients with social dysfunctions and lack of educational attainment were more likely to be referred to CR and VT, while those with better functioning and education were more likely to be referred to CBT and PE. CONCLUSIONS: A combination of socio-demographic and clinical features was not sufficient to accurately predict initial referrals to four PSR programmes among a French network of rehabilitation centres. Referrals to PSR interventions may also involve service- and clinician-level factors. Considering socio-demographic and clinical predictors revealed disparities in referrals with respect to diagnoses, current clinical and psychological issues, functioning and education.

Functioning, clinical severity, education and sex moderate the inverse relationship between insight and quality of life in patients with schizophrenia.

BACKGROUND: In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life, a phenomenon that has been described as the "insight paradox". Here, we aimed to determine whether certain socio-demographic and clinical characteristics strengthen or weaken this negative relationship. METHODS: We used data from the French network of rehabilitation centers REHABase (N = 769). We explored mean differences in quality-of-life scores between patients with good insight vs. poor insight. We also explored modifying effects of socio-demographic and clinical characteristics (sex, education, age, functioning, clinical severity, duration of illness). RESULTS: Patients with good insight had a decreased quality-of-life total score. Similar effects were found for the following sub-dimensions of quality of life: autonomy, physical and psychological well-being, and self-esteem. The negative effect of insight on quality of life was attenuated for people who had >12 years of education and for people with a higher level of functioning. By contrast, the negative effect of insight on quality of life was accentuated in people with greater clinical severity. Functioning and clinical severity showed similar modifying effects for other quality-of-life dimensions: autonomy, physical and psychological well-being, and self-esteem. Finally, males demonstrated an increased negative association between insight and self-esteem. CONCLUSIONS: The relationship between insight and quality of life is moderated by socio-demographic and clinical circumstances. Future inquiries may utilize our findings by integrating socio-demographic and clinical factors in treatment programs designs to conjointly improve insight and quality of life.

Mechanistic account of the left auditory cortex for tone-matching in schizophrenia: A pilot transcranial random noise stimulation (tRNS) sham-controlled study.

OBJECTIVE: Deficits in the ability to match tones following brief delay and their contribution to higher-order cognitive alterations have been repeatedly documented in schizophrenia. The aim was to explore if left fronto-temporal high-frequency transcranial random noise stimulation (hf-tRNS), with electrodes placed over brain regions involved in tone-matching would significantly modulate performances in participants with schizophrenia. METHODS: In a randomized, double-blind sham-controlled study, 10 participants with schizophrenia were allocated to receive ten sessions of either active or sham hf-tRNS. The anode was placed over the left prefrontal cortex and the cathode over the left temporoparietal junction. A tone-matching task was administered before and after the hf-tRNS. RESULTS: We calculated the changes in tone-matching performance before and after hf-tRNS session in each group. A significant between-group difference was observed for the difficult tone-matching conditions (W= 14.500, p = 0.032), with tone-matching improvement in the sham group and no improvement in the active group. DISCUSSION: hf-tRNS could disrupt the test-retest learning effect in the tone-matching task in individuals with schizophrenia. It is likely that this disruption resulted from cathodal-induced inhibition of the functional coupling between auditory cortical areas that correlates with tone-matching performance in patients. CONCLUSION: The findings contribute to our understanding of hf-tRNS effects on early auditory processing in schizophrenia.