Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care

Altered structural network architecture is predictive of the presence of psychotic symptoms in patients with 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) represents a homogeneous model of schizophrenia particularly suitable for the search of neural biomarkers of psychosis. Impairments in structural connectivity related to the presence of psychotic symptoms have been reported in patients with 22q11DS. However, the relationships between connectivity changes in patients with different symptomatic profiles are still largely unknown and warrant further investigations. In this study, we used structural connectivity to discriminate patients with 22q11DS with (N = 31) and without (N = 31) attenuated positive psychotic symptoms. Different structural connectivity measures were used, including the number of streamlines connecting pairs of brain regions, graph theoretical measures, and diffusion measures. We used univariate group comparisons as well as predictive multivariate approaches. The univariate comparison of connectivity measures between patients with or without attenuated positive psychotic symptoms did not give significant results. However, the multivariate prediction revealed that altered structural network architecture discriminates patient subtypes (accuracy = 67.7%). Among the regions contributing to the classification we found the anterior cingulate cortex, which is known to be associated to the presence of psychotic symptoms in patients with 22q11DS. Furthermore, a significant discrimination (accuracy = 64%) was obtained with fractional anisotropy and radial diffusivity in the left inferior longitudinal fasciculus and the right cingulate gyrus. Our results point to alterations in structural network architecture and white matter microstructure in patients with 22q11DS with attenuated positive symptoms, mainly involving connections of the limbic system. These alterations may therefore represent a potential biomarker for an increased risk of psychosis that should be further tested in longitudinal studies

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care

Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n = 35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (padj = 6.73 × 10−6). Novel reciprocal case–control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to the deletion conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present

A network approach to understanding neurodevelopmental and clinical pathways of vulnerability to psychopathology in 22q11.2 Deletion Syndrome

Psychotic disorders remain among the most severe and debilitating forms of psychopathology, exerting a dramatic cost on patients, families and society as a whole. Recently there has been considerable hope that understanding the clinical and neurodevelopmental mechanisms preceding the emergence of psychosis, will allow earlier and more effective treatment strategies. However, progress has been hindered by difficulties in investigating stages preceding onset of psychosis. Moreover, researchers have struggled with the massively multifactorial nature of the disease mechanisms at stake. In order to better understand the pathophysiology of psychosis longitudinal designs and dedicated computational approaches and will be required. The present dissertation will focus on the study of 22q11.2 Deletion Syndrome (22q11DS), a genetic disorder that contributes a dramatically increased risk of developing psychosis. Longitudinal follow-up of youth with 22q11DS offers a unique opportunity to characterize early stages of vulnerability to psychosis. The overall objective of this dissertation will be the development and implementation of computational tools of dynamic network analysis to characterize clinical pathways and neurodevelopmental mechanisms of vulnerability to psychopathology in 22q11DS. The first study employs structural covariance (SC) to describe alterations of large-scale cortical networks in 22q11DS. We show that alterations of SC networks related to the presence of psychotic symptoms in 22q11DS, with a particularly prominent role anterior cingulate dysconnectivity. In the second study we implement a novel dynamic network analysis technique to describe trajectories of SC maturation in 22q11DS. We show that late adolescence is a critical period for the disruption of SC architecture in 22q11DS, that might contribute vulnerability to psychosis. In the third study, we employ t dynamic network analysis and graph signal processing to analyse longitudinal clinical data. We show that this approach can provide an intuitive and quantitative characterization of relevant clinical pathways of casual interaction between symptoms. Specifically, we demonstrate the central role of reduced tolerance to stress in 22q11DS. Moreover, we show that a network approach to psychopathology carries potential for predicting clinical evolution of individual subjects. In study 4 we further explored the role exposure to environmental stress in contributing to psychopathology in 22q11DS. We propose that the syndrome could represent an ideal model to characterize mechanisms of gene-environment interaction. In study 5 we explored neurobiological mechanisms contributing to increased vulnerability to stress in 22q11DS, focusing on the candidate role of dysregulation of the Hypothalamus-Pituitary-Adrenal axis. We show that an atypical longitudinal trajectory of pituitary volume decline, suggestive of extinction of HPA-axis functionality, is associate with reduced tolerance to environmental stress and contributes pleiotropic vulnerability to psychopathology. HPA-axis dys-maturation also affected trajectories of hippocampal and cortical maturation in 22q11DS. In conclusion, our results suggest that a dynamic network approach to developmental psychopathology has the potential of improving our understanding of both neurodevelopmental mechanisms and clinical pathways of vulnerability to mental health disturbances.</p

Association Between Parental Anxiety and Depression Level and Psychopathological Symptoms in Offspring With 22q11.2 Deletion Syndrome

22q11.2 deletion syndrome (22q11DS) is recognized as one of the strongest genetic risk factors for the development of psychopathology, including dramatically increased prevalence of schizophrenia anxiety disorders, mood disorders, and Attention Deficit Hyperactivity Disorder (ADHD). Despite sharing a homogenous genetic deletion, the psychiatric phenotype in 22q11DS still present significant variability across subjects. The origins of such variability remain largely unclear. Levels of parental psychopathology could significantly contribute to phenotypic variability of offspring psychopathology, through mechanisms of gene x gene (GxG) and gene x environment (GxE) interactions. However, this hypothesis has not been explicitly tested to date in 22q11DS. In the present manuscript, we employed a longitudinal design to investigate bi-directional interactions of parental anxiety and depressive symptoms, estimated with Beck Depression Inventory and Beck Anxiety Inventory, and offspring level of psychopathology assessed with a combination of parentally reported Child Behavioral Checklist, Youth Self Report Questionnaire, and Structured Clinical Interviews for Prodromal Syndromes (SIPS). We tested associations in both typically developing healthy controls (HCs) (N = 88 participants; N = 131 time points) and in individuals with 22q11DS (N = 103 participants; N = 198 time points). We observed that 22q11DS individuals with higher levels of parental anxiety and depression presented significant increases in multiple forms of psychopathology, including higher internalizing and externalizing symptoms, as estimated both by parental and self-report questionnaires, along with higher negative and generalized symptoms as measured with the SIPS. Associations for positive and disorganized dimensions of the SIPS were not statistically significant. Purely longitudinal analysis pointed to bi-directional interactions of parental and child psychopathology, with marginally stronger longitudinal associations between early parental anxiety-depression and subsequent child psychopathology. Interestingly, associations between psychopathology across generations were significantly stronger in 22q11DS individuals compared to HCs. Our results show that parental levels of anxiety and depression are associated with levels of offspring psychopathology, particularly in individuals with 22q11DS. These findings point to the existence of GxG or GxE mechanisms, that should be investigated in future work. From a clinical perspective, they highlight a strong rational for the management of parental psychological well-being in 22q11DS.status: publishe