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.

The structure of negative symptoms of schizophrenia is still a matter of controversy. Although a two-dimensional model (comprising the expressive deficit dimension and the motivation and pleasure dimension) has gained a large consensus, it has been questioned by recent investigations.

To investigate the latent structure of negative symptoms and its stability over time in people with schizophrenia using network analysis.

Negative symptoms were assessed in 612 people with schizophrenia using the Brief Negative Symptom Scale (BNSS) at baseline and at 4-year follow-up. A network invariance analysis was conducted to investigate changes in the network structure and strength of connections between the two time points.

The network analysis carried out at baseline and follow-up, supported by community detection analysis, indicated that the BNSS's items aggregate to form four or five distinct domains (avolition/asociality, anhedonia, blunted affect and alogia). The network invariance test indicated that the network structure remained unchanged over time (network invariance test score 0.13; P = 0.169), although its overall strength decreased (6.28 at baseline, 5.79 at follow-up; global strength invariance test score 0.48; P = 0.016).

The results lend support to a four- or five-factor model of negative symptoms and indicate overall stability over time. These data have implications for the study of pathophysiological mechanisms and the development of targeted treatments for negative symptoms.

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.