Cognitive impairment (CI) is one of the most prevalent and burdensome consequences of COVID-19 infection, which can persist up to months or even years after remission of the infection. Current guidelines on post-COVID CI are based on available knowledge on treatments used for improving CI in other conditions. The current review aims to provide an updated overview of the existing evidence on the efficacy of treatments for post-COVID CI.

A systematic literature search was conducted for studies published up to December 2023 using three databases (PubMed–Scopus–ProQuest). Controlled and noncontrolled trials, cohort studies, case series, and reports testing interventions on subjects with CI following COVID-19 infection were included.

After screening 7790 articles, 29 studies were included. Multidisciplinary approaches, particularly those combining cognitive remediation interventions, physical exercise, and dietary and sleep support, may improve CI and address the different needs of individuals with post-COVID-19 condition. Cognitive remediation interventions can provide a safe, cost-effective option and may be tailored to deficits in specific cognitive domains. Noninvasive brain stimulation techniques and hyperbaric oxygen therapy showed mixed and preliminary results. Evidence for other interventions, including pharmacological ones, remains sparse. Challenges in interpreting existing evidence include heterogeneity in study designs, assessment tools, and recruitment criteria; lack of long-term follow-up; and under-characterization of samples in relation to confounding factors.

Further research, grounded on shared definitions of the post-COVID condition and on the accurate assessment of COVID-related CI, in well-defined study samples and with longer follow-ups, is crucial to address this significant unmet need.

Different electrophysiological (EEG) indices have been investigated as possible biomarkers of schizophrenia. However, these indices have a very limited use in clinical practice, as their associations with clinical and functional outcomes remain unclear. This study aimed to investigate the associations of multiple EEG markers with clinical variables and functional outcomes in subjects with schizophrenia (SCZs).

Resting-state EEGs (frequency bands and microstates) and auditory event-related potentials (MMN-P3a and N100-P3b) were recorded in 113 SCZs and 57 healthy controls (HCs) at baseline. Illness- and functioning-related variables were assessed both at baseline and at 4-year follow-up in 61 SCZs. We generated a machine-learning classifier for each EEG parameter (frequency bands, microstates, N100-P300 task, and MMN-P3a task) to identify potential markers discriminating SCZs from HCs, and a global classifier. Associations of the classifiers’ decision scores with illness- and functioning-related variables at baseline and follow-up were then investigated.

The global classifier discriminated SCZs from HCs with an accuracy of 75.4% and its decision scores significantly correlated with negative symptoms, depression, neurocognition, and real-life functioning at 4-year follow-up.

These results suggest that a combination of multiple EEG alterations is associated with poor functional outcomes and its clinical and cognitive determinants in SCZs. These findings need replication, possibly looking at different illness stages in order to implement EEG as a possible tool for the prediction of poor functional outcome.

Deficits in social cognition (SC) are significantly related to community functioning in schizophrenia (SZ). Few studies investigated longitudinal changes in SC and its impact on recovery. In the present study, we aimed: (a) to estimate the magnitude and clinical significance of SC change in outpatients with stable SZ who were assessed at baseline and after 4 years, (b) to identify predictors of reliable and clinically significant change (RCSC), and (c) to determine whether changes in SC over 4 years predicted patient recovery at follow-up.

The reliable change index was used to estimate the proportion of true change in SC, not attributable to measurement error. Stepwise multiple logistic regression models were used to identify the predictors of RCSC in a SC domain (The Awareness of Social Inference Test [TASIT]) and the effect of change in TASIT on recovery at follow-up.

In 548 participants, statistically significant improvements were found for the simple and paradoxical sarcasm of TASIT scale, and for the total score of section 2. The reliable change index was 9.8. A cut-off of 45 identified patients showing clinically significant change. Reliable change was achieved by 12.6% and RCSC by 8% of participants. Lower baseline TASIT sect. 2 score predicted reliable improvement on TASIT sect. 2. Improvement in TASIT sect. 2 scores predicted functional recovery, with a 10-point change predicting 40% increase in the probability of recovery.

The RCSC index provides a conservative way to assess the improvement in the ability to grasp sarcasm in SZ, and is associated with recovery.

Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).

SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.

The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).

We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.

Impairment in a wide range of cognitive abilities has been consistently reported in individuals with schizophrenia. Both neurocognitive and social cognitive deficits are thought to underlie severe functional disabilities associated with schizophrenia. Despite the key role in schizophrenia outcome, cognition is still poorly assessed in both research and clinical settings.

In this guidance paper, we provide a systematic review of the scientific literature and elaborate several recommendations for the assessment of cognitive functions in schizophrenia both in research settings and in real-world clinical practice.

Expert consensus and systematic reviews provided guidance for the optimal assessment of cognitive functions in schizophrenia. Based on the reviewed evidence, we recommend a comprehensive and systematic assessment of neurocognitive and social cognitive domains in schizophrenia, in all phases of the disorder, as well as in subjects at risk to develop psychosis. This European Psychiatric Association guidance recommends not only the use of observer reports but also self-reports and interview-based cognitive assessment tools. The guidance also provides a systematic review of the state of the art of assessment in the first episode of psychosis patients and in individuals at risk for psychosis.

The comprehensive review of the evidence and the recommendations might contribute to advance the field, allowing a better cognitive assessment, and avoiding overlaps with other psychopathological dimensions. The dissemination of this guidance paper may promote the development of shared guidelines concerning the assessment of cognitive functions in schizophrenia, with the purpose to improve the quality of care and to obtain recovery.

Although cognitive impairment is a core symptom of schizophrenia related to poorer outcomes in different functional domains, it still remains a major therapeutic challenge. To date, no comprehensive treatment guidelines for cognitive impairment in schizophrenia are implemented.

The aim of the present guidance paper is to provide a comprehensive meta-review of the current available evidence-based treatments for cognitive impairment in schizophrenia. The guidance is structured into three sections: pharmacological treatment, psychosocial interventions, and somatic treatments.

Based on the reviewed evidence, this European Psychiatric Association guidance recommends an appropriate pharmacological management as a fundamental starting point in the treatment of cognitive impairment in schizophrenia. In particular, second-generation antipsychotics are recommended for their favorable cognitive profile compared to first-generation antipsychotics, although no clear superiority of a single second-generation antipsychotic has currently been found. Anticholinergic and benzodiazepine burdens should be kept to a minimum, considering the negative impact on cognitive functioning. Among psychosocial interventions, cognitive remediation and physical exercise are recommended for the treatment of cognitive impairment in schizophrenia. Noninvasive brain stimulation techniques could be taken into account as add-on therapy.

Overall, there is definitive progress in the field, but further research is needed to develop specific treatments for cognitive impairment in schizophrenia. The dissemination of this guidance paper may promote the development of shared guidelines concerning the treatment of cognitive functions in schizophrenia, with the purpose to improve the quality of care and to achieve recovery in this population.

Network analysis has been used to explore the interplay between psychopathology and functioning in psychosis, but no study has used dedicated statistical techniques to focus on the bridge symptoms connecting these domains. The current study aims to estimate the network of depressive, negative, and positive symptoms, general psychopathology, and real-world functioning in people with first-episode schizophrenia or schizophreniform disorder, focusing on bridge nodes.

Baseline data from the OPTiMiSE trial were analyzed. The sample included 446 participants (age 40.0 ± 10.9 years, 70% males). The network was estimated with a Gaussian graphical model, using scores on individual items of the positive and negative syndrome scale (PANSS), the Calgary depression scale for schizophrenia, and the personal and social performance scale. Stability, strength centrality, expected influence (EI), predictability, and bridge centrality statistics were computed. The top 20% scoring nodes on bridge strength were selected as bridge nodes.

Nodes from different rating scales assessing similar psychopathological and functioning constructs tended to cluster together in the estimated network. The most central nodes (EI) were Delusions, Emotional Withdrawal, Depression, and Depressed Mood. Bridge nodes included Depression, Conceptual Disorganization, Active Social Avoidance, Delusions, Stereotyped Thinking, Poor Impulse Control, Guilty Feelings, Unusual Thought Content, and Hostility. Most of the bridge nodes belonged to the general psychopathology subscale of the PANSS. Depression (G6) was the bridge node with the highest value.

The current study provides novel insights for understanding the complex phenotype of psychotic disorders and the mechanisms underlying the development and maintenance of comorbidity and functional impairment after psychosis onset.

Mental disorders in comorbidity with chronic skin diseases may worsen disease outcome and patients’ quality of life. We hypothesized the comorbidity of depression, anxiety syndromes, or symptoms as attributable to biological mechanisms that the combined diseases share.

We conducted a systematic review based on the Preferred Reporting Items for Systematic Review and Meta-Analysis statement searching into PubMed, PsycInfo, and Scopus databases. We examined the literature regarding the comorbidity of psoriasis (Ps), atopic dermatitis (AD), or hidradenitis suppurativa with depression and/or anxiety in adults ≥18 years and the hypothetical shared underlying biological mechanisms.

Sixteen studies were analyzed, mostly regarding Ps and AD. Brain-derived neurotrophic factor/tropomyosin receptor kinase B signaling and nuclear factor kappa-light-chain-enhancer of activated B cells/p38 mitogen-activated protein kinase pathways arose as shared mechanisms in Ps animal models with depression- and/or anxiety-like behaviors. Activated microglia and neuroinflammatory responses emerged in AD depressive models. As to genetic studies, atopic-dermatitis patients with comorbid anxiety traits carried the short variant of serotonin transporter and a polymorphism of the human translocator protein gene. A GA genotype of catechol-O-methyltransferase gene was instead associated with Ps. Reduced natural killer cell activity, IL-4, serotonin serum levels, and increased plasma cortisol and IgE levels were hypothesized in comorbid depressive AD patients. In Ps patients with comorbid depression, high serum concentrations of IL-6 and IL-18, as well as IL-17A, were presumed to act as shared inflammatory mechanisms.

Further studies should investigate mental disorders and chronic skin diseases concurrently across patients’ life course and identify their temporal relation and biological correlates. Future research should also identify biological characteristics of individuals at high risk of the comorbid disorders and associated complications.

Autism spectrum disorders (ASDs) and schizophrenia spectrum disorders (SSDs), although conceptualized as separate entities, may share some clinical and neurobiological features. ASD symptoms may have a relevant role in determining a more severe clinical presentation of schizophrenic disorder but their relationships with cognitive aspects and functional outcomes of the disease remain to be addressed in large samples of individuals.

To investigate the clinical, cognitive, and functional correlates of ASD symptoms in a large sample of people diagnosed with schizophrenia.

The severity of ASD symptoms was measured with the PANSS Autism Severity Scale (PAUSS) in 921 individuals recruited for the Italian Network for Research on Psychoses multicenter study. Based on the PAUSS scores, three groups of subjects were compared on a wide array of cognitive and functional measures.

Subjects with more severe ASD symptoms showed a poorer performance in the processing speed (p = 0.010), attention (p = 0.011), verbal memory (p = 0.035), and social cognition (p = 0.001) domains, and an overall lower global cognitive composite score (p = 0.010). Subjects with more severe ASD symptoms also showed poorer functional capacity (p = 0.004), real-world interpersonal relationships (p < 0.001), and participation in community-living activities (p < 0.001).

These findings strengthen the notion that ASD symptoms may have a relevant impact on different aspects of the disease, crucial to the life of people with schizophrenia. Prominent ASD symptoms may characterize a specific subpopulation of individuals with SSD.

Patients with schizophrenia display experiential anomalies in their feelings and cognitions arising in the domain of their lived body. These abnormal bodily phenomena (ABP) are not part of diagnostic criteria for schizophrenia. One of the reasons is the difficulty to assess specific ABP for schizophrenia spectrum disorders. The present study aimed to explore the presence in patients with schizophrenia of specific ABP.

We used a semistructured interview—the Abnormal Bodily Phenomena questionnaire (ABPq), an instrument devised to detect and measure ABP specific to patients with schizophrenia. Fifty-one outpatients affected by schizophrenia and 28 euthymic outpatients affected by bipolar disorder type I with psychotic features (BD-pf-e) were recruited. Before assessing the specificity for schizophrenia of the observed ABP, we tested the internal consistency and the convergent validity of the ABPq in patients with schizophrenia. Specificity was assessed by examining potential differences in ABPq among the patients with schizophrenia in remission (SCZ-r) and BD-pf-e.

The ABPq shows strong internal consistency and convergent validity. As to the specificity, ABP measured by ABPq were more frequent and severe in SCZ-r than in BD-pf-e. In particular, all ABPq dimensions, except “Coherence,” had at least mild severity in over 50% of SCZ-r, while dimensions with at least mild severity were observed in 5–10% of the BD-pf-e.

These findings can contribute to establish more precise phenomenal boundaries between schizophrenia and bipolar disorder, to explore the borders between nonpsychotic and psychotic forms of ABP, between ABP and negative and disorganized symptoms, and to enlighten core aspects of schizophrenia.

Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions.

Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression.

After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms.

In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem.