The role of adult attachment in the risk pathways for psychosis: modulation on personality traits, emotional availability and neurobiology of emotion processing

Non applicabil

Investigating defensive functioning and alexithymia in substance use disorder patients

Background: Substance Use Disorder (SUD) causes a great deal of personal suffering for patients. Recent evidence highlights how defenses and emotion regulation may play a crucial part in the onset and development of this disorder. The aim of this study was to investigate potential differences in the defensive functioning between SUD patients and non-clinical controls. Secondly, we aimed at investigating the relationships between alexithymia and maladaptive/assimilation defenses. Methods: The authors assessed defensive functioning (Response Evaluation Measure-71, REM-71), personality (MMPI-II), and alexithymia (TAS-20) of 171 SUD patients (17% female; mean age = 36.5), compared to 155 controls. Authors performed a series of ANOVAs to investigate the defensive array in SUD patients compared to that of nonclinical controls. Student t test for indipendent samples was used to compare clinical characteristics between the SUD group and the controls. To investigate the role of single defenses in explaining alexithimia’s subscores, stepwise multiple regression analysis were carried out on socio-demographic characteristics of participants (gender, age, and years of education), with REM-71 defenses as predictors. Results: SUD patients presented a more maladaptive/assimilation (Factor 1) defensive array (p < .001). Among SUD sub-groups, Alcohol Use Disorder patients showed more disfuncional defenses. Factor 1 defenses were related to a worse psychological functioning. In addition, alexyhimia (particularly DIF) was strongly related to Factor 1 defenses, expecially Projection (38% of variance explained, β = .270, p < .001). Conclusion: The REM-71 and the TAS-20 might be useful screening instruments among SUD patients

Association between formal thought disorders, neurocognition and functioning in the early stages of psychosis: a systematic review of the last half-century studies

Recent review articles provided an extensive collection of studies covering many aspects of format thought disorders (FTD) among their epidemiology and phenomenology, their neurobiological underpinnings, genetics as well as their transdiagnostic prevalence. However, less attention has been paid to the association of FTD with neurocognitive and functioning deficits in the early stages of evolving psychosis. Therefore, this systematic review aims to investigate the state of the art regarding the association between FTD, neurocognition and functioning in the early stages of evolving psychotic disorders in adolescents and young adults, by following the PRISMA flowchart. A total of 106 studies were screened. We included 8 studies due to their reports of associations between FTD measures and functioning outcomes measured with different scales and 7 studies due to their reports of associations between FTD measures and neurocognition. In summary, the main findings of the included studies for functioning outcomes showed that FTD severity predicted poor social functioning, unemployment, relapses, re-hospitalisations, whereas the main findings of the included studies for neurocognition showed correlations between attentional deficits, executive functions and FTD, and highlighted the predictive potential of executive dysfunctions for sustained FTD. Further studies in upcoming years taking advantage of the acceleration in computational psychiatry would allow researchers to re-investigate the clinical importance of FTD and their role in the transition from at-risk to full-blown psychosis conditions. Employing automated computer-assisted diagnostic tools in the early stages of psychosis might open new avenues to develop targeted neuropsychotherapeutics specific to FTD

Insecure attachment as a transdiagnostic risk factor for major psychiatric conditions: A meta-analysis in bipolar disorder, depression and schizophrenia spectrum disorder

Insecure attachment has been suggested as a major risk factor for mental health problems as well as a key element for the development and trajectory of psychiatric disorders. The aim of this meta-analysis was to assess whether insecure attachment constitutes a global transdiagnostic risk factor in bipolar disorder, depression, and schizophrenia spectrum disorders. We conducted a PRISMA-based systematic quantitative review to explore the prevalence of insecure attachment among patients of three representative psychiatric disorders - major depression, schizophrenia spectrum disorders and bipolar disorder - in comparison with healthy controls (HC) from a transdiagnostic point of view. Effect sizes on differences of anxious, avoidant and insecure prevalence were calculated based on 40 samples including a total of n = 2927 individuals. Overall, results indicated a large effect on prevalence of insecure attachment across all disorders compared to HC (k = 30, g = 0.88, I2 = 71.0%, p < 0.001). In a transdiagnostic comparison, the only difference was found in avoidant attachment, which was significantly lower (p = 0.04) compared to HC in the schizophrenia spectrum disorder subgroup (k = 10, g = 0.31, I2 = 76.60%, p < 0.0001) than the depression subgroup subgroup (k = 12, g = 0.83, I2 = 46.65%, p < 0.0001). The lack of further transdiagnostic differences between three distinct psychiatric disorders corroborates insecure attachment as a general vulnerability factor to psychopathology. Our findings warrant further investigations, which should explore the pathways from attachment insecurity towards psychopathology. Insecure attachment likely has implications on assessment, prediction and treatment of psychiatric patients

individualized diagnostic and prognostic models for patients with psychosis risk syndromes a meta analytic view on the state of the art

Abstract Background The Clinical High Risk(CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods like machine learning(ML) and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic, i.e., distinguishing CHR from healthy individuals, and prognostic models, i.e., predicting a future outcome, based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity both of CHR populations and methodologies applied. Methods We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and ML. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. Results Forty-four articles were included covering 3707 individuals for prognostic and 1052 for diagnostic studies(572 CHR and 480 healthy controls). CHR patients could be classified against healthy controls with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity 78%. ML models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies, yet no other moderator effects. Conclusions Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice

T179. DO INDIVIDUALS IN A CLINICAL HIGH-RISK STATE FOR PSYCHOSIS DIFFER FROM HEALTHY CONTROLS IN THEIR CORTICAL FOLDING PATTERNS?

Background Volumetric brain differences between persons meeting criteria for a clinical high-risk state for psychosis (CHR) and healthy controls (HC) have been previously reported, yet little is known about potential abnormalities in surface-based morphological measures. Gyrification (i.e., the amount of cortical convolution) remains relatively stable across the lifespan and is minimally influenced by ubiquitous confounding factors (e.g., drug use, medication, or stress). Recently, a multi-site analysis conducted in 104 CHR persons found global increases in cortical gyrification compared to HC (Sasabayashi et al. 2017). If replicated, gyrification abnormalities in CHR could potentially serve as early neuromarkers of elevated risk, and thus could eventually be used to identify objectively and efficiently the CHR state. Methods A total of 124 CHR and 264 HC subjects were recruited as part of the PRONIA consortium (www.pronia.eu), a large-scale international longitudinal study currently consisting of 10 European sites. Cortical surfaces were reconstructed from structural MRI images using a volume-based, newly introduced technique called the Projection-Based-Thickness (PBT) as available in the SPM-based-toolbox CAT12. Local gyrification was quantified automatically across the whole brain as absolute mean curvature for each vertex of the brain surface mesh consisting of thousands of individual measurement points. Vertex-wise differences of curvature values were calculated applying a General Linear Model, corrected for age, gender and site effects. Results were investigated at corrected and uncorrected levels. Results We found no significant differences in vertex-wise gyrification between CHR and HC at either corrected or uncorrected levels (p>0.05). Further investigations of potential confounding site effects also did not reveal differences. Discussion Our preliminary findings suggest that CHR subjects do not show whole-brain gyrification abnormalities when compared with healthy subjects. These negative results agree with literature suggesting that cortical convolution might be more affected by neurodevelopmental or genetic factors, and thus deviations from normal patterns might not be detectable in heterogeneous samples of at-risk subjects wherein the etiology and ultimate prognosis is unknown. In order to better investigate differences in cortical folding and address the role of gyrification as neuroanatomical biomarker for psychosis, future investigations should focus on subgroups within CHR populations (e.g. patients groups defined by basic symptoms, ultra-high risk, or familial risk) in addition to specific analyses of individuals with higher neurodevelopmental (e.g., obstetric complications) or genetic (e.g., polygenic risk) loadings

Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis

Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p  0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice

Strategies for Psychiatric Rehabilitation and their Cognitive Outcomes in Schizophrenia: Review of Last Five-year Studies

Background: Cognitive deficits are core features of Schizophrenia, showing poor response to antipsychotic treatment, therefore non-pharmacological rehabilitative approaches to such a symptom domain need to be identified. However, since not all patients with Schizophrenia exhibit the same cognitive impairment profile, individualized rehabilitative approaches should be set up. Objectives: We explored the last five-year literature addressing the issue of cognitive dysfunction response to rehabilitative methodologies in Schizophrenia to identify possible predictors of response and individualized strategies to treat such a dysfunction. Conclusion: A total of 76 studies were reviewed. Possible predictors of cognitive rehabilitation outcome were identified among patient-specific and approachspecific variables and a general overview of rehabilitative strategies used in the last five years has been depicted. Studies suggest the existence of multifaced and multi-domain variables that could significantly predict pro-cognitive effects of cognitive rehabilitation, which could also be useful for identifying individual-specific rehabilitation trajectories over time. An individualized rehabilitative approach to cognitive impairment in Schizophrenia is possible if taking into account both patient and approach specific predictors of outcomes

How recent learning shapes the brain: Memory-dependent functional reconfiguration of brain circuits

The process of storing recently encoded episodic mnestic traces so that they are available for subsequent retrieval is accompanied by specific brain functional connectivity (FC) changes. In this fMRI study, we examined the early processing of memories in twenty-eight healthy participants performing an episodic memory task interposed between two resting state sessions. Memory performance was assessed through a forced-choice recognition test after the scanning sessions. We investigated resting state system configuration changes via Independent Component Analysis by cross-modeling baseline resting state spatial maps onto the post-encoding resting state, and post-encoding resting state spatial maps onto baseline. We identified both persistent and plastic components of the overall brain functional configuration between baseline and post-encoding. While FC patterns within executive, default mode, and cerebellar circuits persisted from baseline to post-encoding, FC within the visual circuit changed. A significant session × performance interaction characterized medial temporal lobe and prefrontal cortex FC with the visual circuit, as well as thalamic FC within the executive control system. Findings reveal early-stage FC changes at the system-level subsequent to a learning experience and associated with inter-individual variation in memory performance

Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

Background: Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods: Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results: The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion: Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability