In recent years, numerous studies have highlighted the overlap between autism spectrum disorder (ASD) and catatonia, both from a clinical and pathophysiological perspective. This study aimed to investigate the relationship between the autism spectrum (autistic traits and ASD signs, symptoms, and behavioral manifestation) and Catatonia Spectrum (CS).

A total sample of 376 subjects was distributed in four diagnostic groups. Subjects were assessed with the Structured Clinical Interview for DSM-5, Research Version, the Adult Autism Subthreshold Spectrum (AdAS Spectrum), and CS. In the statistical analyses, the total sample was also divided into three groups according to the degree of autism severity, based on the AdAS Spectrum total score.

A statistically significant positive correlation was found between AdAS Spectrum and CS total score within the total sample, the gender subgroups, and the diagnostic categories. The AdAS Spectrum domains found to be significantly and strongly correlated with the total CS score were hyper–hypo reactivity to sensory input, verbal communication, nonverbal communication, restricted interests and rumination, and inflexibility and adherence to routine. The three groups of different autistic severity were found to be distributed across all diagnostic groups and the CS score increased significantly from the group without autistic traits to the group with ASD.

Our study reports a strong correlation between autism spectrum and CS.

Honoraria, advisory board, or consulting fees from Angelini, Astra Zeneca, Bristol-Myers Squibb, Gedeon Richter Bulgaria, Innova-Pharma, Janssen Pharmaceuticals, Lundbeck, Otsuka, Pfizer, and Pierre Fabre, for services not related to this abstract

An extensive literature regarding gender differences relevant to several aspects of schizophrenia is nowadays available. It includes some robust findings as well as some inconsistencies. The identification of gender differences and the understanding of their explanations may help to clarify the underlying etiopathogenetic mechanisms of specific aspects of the disorder.

The present study aimed at investigating gender differences on premorbid, clinical, cognitive and outcome indices, as well as their impact on recovery, in a large sample of patients with schizophrenia recruited within the multicenter study of the Italian Network for Research on Psychoses.

State-of-the-art instruments were used to assess the investigated domains. Group comparisons between male and female patients were performed on all considered indices. The associations of premorbid, clinical and cognitive indices with recovery in the two patient groups were investigated by means of multiple regressions.

Males with respect to females had a worse premorbid adjustment – limited to the academic dimension – an earlier age of onset, a higher frequency of history of substance and alcohol abuse, more severe negative symptoms (both avolition and expressive deficit), positive symptoms and impairment of social cognition. No gender difference was observed in neurocognition nor in the rates of recovery.

Although males showed some disadvantages in the clinical picture, this was not translated into a worse outcome. This finding may be related to the complex interplay of several factors acting as predictors or mediators of outcome.

No significant relationships.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Despite innovative treatments, the impairment in real-life functioning in subjects with schizophrenia (SCZ) remains an unmet need in the care of these patients. Recently, real-life functioning in SCZ was associated with abnormalities in different electrophysiological indices. It is still not clear whether this relationship is mediated by other variables, and how the combination of different EEG abnormalities influences the complex outcome of schizophrenia.

The purpose of the study was to find EEG patterns which can predict the outcome of schizophrenia and identify recovered patients.

Illness-related and functioning-related variables were measured in 61 SCZ at baseline and after four-years follow-up. EEGs were recorded at the baseline in resting-state condition and during two auditory tasks. We performed Sparse Partial Least Square (SPLS) Regression, using EEG features, age and illness duration to predict clinical and functional features at baseline and follow up. Through a Linear Support Vector Machine (Linear SVM) we used electrophysiological and clinical scores derived from SPLS regression, in order to classify recovered patients at follow-up.

We found one significant latent variable (p<0.01) capturing correlations between independent and dependent variables at follow-up (RHO=0.56). Among individual predictors, age and illness-duration showed the highest scores; however, the score for the combination of the EEG features was higher than all other predictors. Within dependent variables, negative symptoms showed the strongest correlation with predictors. Scores resulting from SPLS Regression classified recovered patients with 90.1% of accuracy.

A combination of electrophysiological markers, age and illness-duration might predict clinical and functional outcome of schizophrenia after 4 years of follow-up.

No significant relationships.

Central to recovery-oriented approaches in schizophrenia are treatment integration and personalization, targeting key variables beyond symptom reduction. The Italian network for research on psychoses conducted a study demonstrating, using network analysis, the central role of community activities in bridging the effects of symptoms, cognition, functional capacity and service engagement on real-word functioning. A 4-year follow-up study was recently completed and the presentation will illustrate the findings. Network analysis was used to test whether relationships among all variables at baseline were similar at follow-up. In addition, the network structure was compared between subjects classified as recovered or non-recovered at follow-up. Six hundred eighteen subjects were assessed at both baseline and 4-year follow-up. Results showed that the network structure was stable from baseline to follow-up, and the overall strength of the connections among variables did not significantly change. Functional capacity and everyday life skills were the most central variables in the network at both baseline and follow-up, while psychopathological variables were more peripheral. The network structure of non-recovered patients was similar to the one observed in the whole sample, but very different from that of recovered subjects, showing few connections among the different nodes. These data strongly suggest that connections among symptoms/dysfunctions tend to maintain over time, contributing to poor outcome in schizophrenia. Early treatment plans, targeting variables with high centrality, might prevent the emergence of self-reinforcing networks of symptoms and dysfunctions in people with schizophrenia.

Armida Mucci has been a consultant and/or advisor to or has received honoraria from Gedeon Richter Bulgaria, Janssen Pharmaceuticals, Lundbeck, Otsuka, Pfizer and Pierre Fabre. None of these has any impact on this abstract and on the presented study.

No significant relationships.

Different electrophysiological indices have been investigated to identify diagnostic and prognostic markers of schizophrenia (SCZ). However, these indices have limited use in clinical practice, since both specificity and association with illness outcome remain unclear. In recent years, machine learning techniques, through the combination of multidimensional data, have been used to better characterize SCZ and to predict illness course.

The aim of the present study is to identify multimodal electrophysiological biomarkers that could be used in clinical practice in order to improve precision in diagnosis and prognosis of SCZ.

Illness-related and functioning-related variables were measured at baseline in 113 subjects with SCZ and 57 healthy controls (HC), and after four-year follow-up in 61 SCZ. EEGs were recorded at baseline in resting-state condition and during two auditory tasks (MMN-P3a and N100-P3b). Through a Linear Support Vector Machine, using EEG data as predictors, four models were generated in order to classify SCZ and HC. Then, we combined unimodal classifiers’ scores through a stacking procedure. Pearson’s correlations between classifiers score with illness-related and functioning-related variables, at baseline and follow-up, were performed.

Each EEG model produced significant classification (p < 0.05). Global classifier discriminated SCZ from HC with accuracy of 75.4% (p < 0.01). A significant correlation (r=0.40, p=0.002) between the global classifier scores with negative symptoms at follow-up was found. Within negative symptoms, blunted affect showed the strongest correlation.

Abnormalities in electrophysiological indices might be considered trait markers of schizophrenia. Our results suggest that multimodal electrophysiological markers might have prognostic value for negative symptoms.

Eating disorders (EDs) exist on a continuum ranging from innocuous but persisting dieting, through subthreshold states of uncertain significance, to full-blown cases of anorexia nervosa (AN) and bulimia nervosa (BN). Subthreshold EDs resemble AN or BN, but do not meet all their diagnostic criteria. The terms “partial syndromes” and “atypical EDs” are also used to identify these conditions.Objectives. To verify whether atypical syndromes are less severe forms of EDs or represent starting phases of EDs, we designed a two-step prospective study assessing the natural outcome of those syndromes over a 4-year time period.

In the first step, we screened 495 female students attending the first year of their high school by means of ad hoc rating questionnaires. Those students identified to being at risk for an ED underwent a psychiatric diagnostic interview. In the second step, 4 years later, we reassessed 140 of those schoolgirls who had participated in the first step.

In the first step, we identified 1 subject with full-blown BN, 1 with partial-syndrome BN, 2 with subclinical BN and 8 with subclinical AN. At the reassessment 4 years later, none of the girls had received any form of help. The full-blown BN case, the partial-syndrome BN case and 1 of the subclinical AN cases persisted unchanged; 1 of the subclinical BN cases shifted to subclinical AN; the remaining subclinical cases remitted spontaneously.

Present findings provide the evidence that subclinical AN and subclinical BN represent unstable states that can spontaneously remit over time.

Abnormalities of emotional processing have been reported in both anxiety and depressive disorders. Anxiety might be related to a hyperactive preattentive fear response or to a reduced efficiency of late regulatory processes, whereas depression might be related to a reduced response to positive emotions. In the present study event-related potentials (ERPs) were used to investigate involuntary processing of stimuli with different emotional valence in 33 healthy controls [HC] and 55 clinically stable patients (16 with panic disorder [PD], 15 with obsessive-compulsive disorder [OCD], 13 with unipolar depression [UD] and 11 with bipolar depression [BD]).

The ERPs were recorded during a target detection task, in which erotic, threatening, disgusting and neutral stimuli were used as distracters.

Patients showed abnormalities of the sequence, duration and topography of the ERP components related to emotional processing. Patients with anxiety disorders showed abnormalities of the early components in response to threatening stimuli. Patients with BD showed abnormalities of both early and late ERPs components. OCD and UD patients demonstrated similar abnormalities of the late ERP components in response to erotic stimuli.

According to our results, abnormalities of emotional processing are observed in both anxiety and affective disorders and might have both distinct and shared characteristics in these disorders.

Cognitive impairment in patients with eating disorders was reported by the majority of studies addressing this issue. However, heterogeneous patterns of cognitive dysfunctions were observed and, in a minority of studies, no impairment was found. The present study was aimed to define the pattern of neurocognitive impairment in a large sample of bulimia nervosa (BN) patients and to demonstrate that neuroendocrine, personality and clinical characteristics influence neurocognitive performance in BN.

Attention, executive control, conditional and incidental learning were evaluated in 83 untreated female patients with BN and 77 healthy controls. Cortisol and 17β-estradiol plasma levels were assessed. Cloninger's Temperament and Character Inventory-Revised (TCI-R), the Eating Disorder Inventory-2 and the Montgomery-Asberg Depression Rating Scale were administered.

No impairment of cognitive performance was found in subjects with BN vs. healthy controls. The higher the cortisol level and “Self-directedness” scores the better the performance on conditional learning, while 17β-estradiol levels showed an opposite pattern of association; “Reward dependence” scores were associated to a worse performance on incidental learning; depressive symptomatology negatively influenced the performance on the WCST.

No cognitive impairment was found in untreated patients with BN in the explored cognitive domains. An influence of neuroendocrine, personality and clinical variables on neurocognitive functioning was found, which might explain discrepancies in literature findings.