Assessment of regional glucose metabolism by [18F]fluorodeoxyglucose position emission tomography ([18F]FDG PET) serves as a biomarker for differential diagnosis of dementia. Conversely, depressive cognitive impairment shows no abnormalities on cerebral [18F]FDG PET.

This study validates the diagnostic value of [18F]FDG PET in addition to clinical diagnosis in a real-life gerontopsychiatric clinical population.

Ninety-eight consecutive patients with depression and cognitive impairment were included. Baseline clinical diagnoses were independently established before and after disclosure of [18F]FDG PET, and dichotomised into neurodegenerative or non-neurodegenerative diseases (level 1). Subsequently, neurodegenerative cases were allocated to diagnostic subgroups (Alzheimer’s disease, Lewy body diseases, frontotemporal lobar degeneration, neurodegenerative other; level 2). An interdisciplinary, biomarker-supported consensus diagnosis after a median follow-up of 6.6 month after [18F]FDG PET served as reference. Changes of clinical diagnoses and diagnostic accuracy were assessed.

After disclosure of [18F]FDG PET, level-1 clinical diagnoses changed in 23% (95% CI 16–33%) of cases, improving the diagnostic accuracy from 72% (95% CI 62–81%) to 92% (95% CI 84–96%) (P < 0.001). [18F]FDG PET was of particular value for exclusion of neurodegenerative disease. Concerning level-2 decisions, the clinical diagnoses changed in 30% (95% CI 21–40%) of cases, increasing its accuracy from 64% (95% CI 54–74%) to 85% (95% CI 76–91%) (P < 0.001). A major fraction of incorrect level-2 diagnoses comprised Alzheimer’s disease misdiagnosed as Lewy body diseases.

[18F]FDG PET provides a significant incremental diagnostic value beyond the clinical diagnosis in depressive cognitive impairment. Thus, [18F]FDG PET should be considered in the diagnostic work-up of patients with mental disorders and cognitive impairment.

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

Mental illness is known to come along with a large mortality gap compared to thegeneral population and it is a risk for COVID-19 related morbidity andmortality. Achieving high vaccination rates in people with mental illness is therefore important. Reports are conflicting on whether vaccination rates comparable to those of the general population can be achieved and which variables represent risk factors for nonvaccination in people with mental illness.

The COVID Ψ Vac study collected routine data on vaccination status, diagnostic groups, sociodemographics, and setting characteristics from in- and day-clinic patients of 10 psychiatric hospitals in Germany in August 2021. Logistic regression modeling was used to determine risk factors for nonvaccination.

Complete vaccination rates were 59% (n = 776) for the hospitalized patients with mental illness versus 64% for the regionally and age-matched general population. Partial vaccination rates were 68% (n = 893) for the hospitalised patients with mental illness versus 67% for the respective general population and six percentage (n = 74) of this hospitalized population were vaccinated during the hospital stay. Rates showed a large variation between hospital sites. An ICD-10 group F1, F2, or F4 main diagnosis, younger age, and coercive accommodation were further risk factors for nonvaccination in the model.

Vaccination rates were lower in hospitalized people with mental illness than in the general population. By targeting at-risk groups with low-threshold vaccination programs in all health institutions they get in contact with, vaccination rates comparable to those in the general population can be achieved.

Panic disorder (PD) is a prevalent and impairing anxiety disorder with previous reports suggesting that the longer the condition remains untreated, the greater the likelihood of nonresponse. However, patients with PD may wait for years before receiving a guideline-recommended pharmacological treatment. The widespread prescription of benzodiazepines (BDZ) for managing anxiety symptoms and disorders might delay the administration of pharmacotherapy according to guidelines (eg, selective serotonin reuptake inhibitors, SSRIs). The present study aimed to determine the mean duration of untreated illness (DUI) in a sample of PD patients, to quantify and compare DUI-SSRI to DUI-BDZ, and to compare findings with those from previous investigations.

Three hundred and fourteen patients with a Diagnostic and Statistical Manual of Mental Disorders, fifth edition diagnosis of PD were recruited from an Italian outpatient psychotherapy unit, and epidemiological and clinical variables were retrieved from medical records. Descriptive statistical analyses were undertaken for sociodemographic and clinical variables, Wilcoxon matched-pair signed rank test was applied to compare the distribution of DUI-SSRI vs DUI-BDZ, and Welch’s t test was performed to compare findings with those from previous studies.

The mean DUI-SSRI of the total sample was 64.25 ± 112.74 months, while the mean DUI-BDZ was significantly shorter (35.09 ± 78.62 months; P < 0.0001). A significantly longer DUI-SSRI, compared to findings from previous studies, was also observed.

The present results confirm a substantial delay in implementing adequate pharmacological treatments in patients with PD, and highlight the discrepancy between recommendations from international treatment guidelines and common clinical practice in relation to BDZ prescription.

Autoimmune mechanisms are related to disease development in a subgroup of patients with psychosis. The contribution of immunoglobulin G (IgG) antibodies against myelin oligodendrocyte glycoprotein (MOG) is mainly unclear in this context.

Therefore, two patients with psychosis and anti-MOG antibodies – detected in fixed cell-based and live cell-based assays – are presented.

Patient 1 suffered from late-onset psychosis with singular white matter lesions in magnetic resonance imaging (MRI) and intermittent electroencephalography (EEG) slowing. Patient 2 suffered from a chronic paranoid–hallucinatory disorder with intermittent confusional states, non-specific white matter alterations on MRI, a disorganised alpha rhythm on EEG, and elevated cerebrospinal fluid protein. Both patients had anti-MOG antibody titres of 1 : 320 in serum (reference < 1 : 20).

The arguments for and against a causal role for anti-MOG antibodies are discussed. The antibodies could be relevant, but due to moderate titres, they may have caused a rather ‘subtle clinical picture’ consisting of psychosis instead of ‘classical’ MOG encephalomyelitis.

Autoimmune encephalitis (AE) is an important consideration during the diagnostic work-up of secondary mental disorders. Indeed, isolated psychiatric syndromes have been described in case reports of patients with underlying AE. Therefore, the authors performed a systematic literature review of published cases with AE that have predominant psychiatric/neurocognitive manifestations. The aim of this paper is to present the clinical characteristics of these patients.

The authors conducted a systematic Medline search via Ovid, looking for case reports/series of AEs with antineuronal autoantibodies (Abs) against cell surface/intracellular antigens combined with predominant psychiatric/neurocognitive syndromes. The same was done for patients with Hashimoto encephalopathy/SREAT. Only patients with signs of immunological brain involvement or tumors in their diagnostic investigations or improvement under immunomodulatory drugs were included.

We identified 145 patients with AE mimicking predominant psychiatric/neurocognitive syndromes. Of these cases, 64% were female, and the mean age among all patients was 43.9 (±22.1) years. Most of the patients had Abs against neuronal cell surface antigens (55%), most frequently against the NMDA-receptor (N = 46). Amnestic/dementia-like (39%) and schizophreniform (34%) syndromes were the most frequently reported. Cerebrospinal fluid changes were found in 78%, electroencephalography abnormalities in 61%, and magnetic resonance imaging pathologies in 51% of the patients. Immunomodulatory treatment was performed in 87% of the cases, and 94% of the patients responded to treatment.

Our findings indicate that AEs can mimic predominant psychiatric and neurocognitive disorders, such as schizophreniform psychoses or neurodegenerative dementia, and that affected patients can be treated successfully with immunomodulatory drugs.

The general understanding of the ‘vulnerability–stress model’ of mental disorders neglects the modifying impact of resilience-increasing factors such as coping ability.

Probing a conceptual framework integrating both adverse events and coping factors in an extended ‘vulnerability–stress–coping model’ of mental disorders, the effects of functional neuropeptide S receptor gene (NPSR1) variation (G), early adversity (E) and coping factors (C) on anxiety were addressed in a three-dimensional G × E × C model.

In two independent samples of healthy probands (discovery: n = 1403; replication: n = 630), the interaction of NPSR1 rs324981, childhood trauma (Childhood Trauma Questionnaire, CTQ) and general self-efficacy as a measure of coping ability (General Self-Efficacy Scale, GSE) on trait anxiety (State-Trait Anxiety Inventory) was investigated via hierarchical multiple regression analyses.

In both samples, trait anxiety differed as a function of NPSR1 genotype, CTQ and GSE score (discovery: β = 0.129, P = 3.938 × 10−8; replication: β = 0.102, P = 0.020). In A allele carriers, the relationship between childhood trauma and anxiety was moderated by general self-efficacy: higher self-efficacy and childhood trauma resulted in low anxiety scores, and lower self-efficacy and childhood trauma in higher anxiety levels. In turn, TT homozygotes displayed increased anxiety as a function of childhood adversity unaffected by general self-efficacy.

Functional NPSR1 variation and childhood trauma are suggested as prime moderators in the vulnerability–stress model of anxiety, further modified by the protective effect of self-efficacy. This G × E × C approach – introducing coping as an additional dimension further shaping a G × E risk constellation, thus suggesting a three-dimensional ‘vulnerability–stress–coping model’ of mental disorders – might inform targeted preventive or therapeutic interventions strengthening coping ability to promote resilient functioning.