Insular volume abnormalities associated with different transition probabilities to psychosis

Background Although individuals vulnerable to psychosis show brain volumetric abnormalities, structural alterations underlying different probabilities for later transition are unknown. The present study addresses this issue by means of voxel-based morphometry (VBM). Method We investigated grey matter volume (GMV) abnormalities by comparing four neuroleptic-free groups: individuals with first episode of psychosis (FEP) and with at-risk mental state (ARMS), with either long-term (ARMS-LT) or short-term ARMS (ARMS-ST), compared to the healthy control (HC) group. Using three-dimensional (3D) magnetic resonance imaging (MRI), we examined 16 FEP, 31 ARMS, clinically followed up for on average 3 months (ARMS-ST, n=18) and 4.5 years (ARMS-LT, n=13), and 19 HC. Results The ARMS-ST group showed less GMV in the right and left insula compared to the ARMS-LT (Cohen's d 1.67) and FEP groups (Cohen's d 1.81) respectively. These GMV differences were correlated positively with global functioning in the whole ARMS group. Insular alterations were associated with negative symptomatology in the whole ARMS group, and also with hallucinations in the ARMS-ST and ARMS-LT subgroups. We found a significant effect of previous antipsychotic medication use on GMV abnormalities in the FEP group. Conclusions GMV abnormalities in subjects at high clinical risk for psychosis are associated with negative and positive psychotic symptoms, and global functioning. Alterations in the right insula are associated with a higher risk for transition to psychosis, and thus may be related to different transition probabilitie

Impact of polygenic schizophrenia-related risk and hippocampal volumes on the onset of psychosis

Alterations in hippocampal volume are a known marker for first-episode psychosis (FEP) as well as for the clinical high-risk state. The Polygenic Schizophrenia-related Risk Score (PSRS), derived from a large case-control study, indicates the polygenic predisposition for schizophrenia in our clinical sample. A total of 65 at-risk mental state (ARMS) and FEP patients underwent structural magnetic resonance imaging. We used automatic segmentation of hippocampal volumes using the FSL-FIRST software and an odds-ratio-weighted PSRS based on the publicly available top single-nucleotide polymorphisms from the Psychiatric Genomics Consortium genome-wide association study (GWAS). We observed a negative association between the PSRS and hippocampal volumes (β=-0.42, P=0.01, 95% confidence interval (CI)=(-0.72 to -0.12)) across FEP and ARMS patients. Moreover, a higher PSRS was significantly associated with a higher probability of an individual being assigned to the FEP group relative to the ARMS group (β=0.64, P=0.03, 95% CI=(0.08-1.29)). These findings provide evidence that a subset of schizophrenia risk variants is negatively associated with hippocampal volumes, and higher values of this PSRS are significantly associated with FEP compared with the ARMS. This implies that FEP patients have a higher genetic risk for schizophrenia than the total cohort of ARMS patients. The identification of associations between genetic risk variants and structural brain alterations will increase our understanding of the neurobiology underlying the transition to psychosis

Vorhersage von Psychosen durch stufenweise Mehrebenenabklärung - Das Basel FePsy (Früherkennung von Psychosen)-Projekt

Hintergrund: In Basel haben wir verschiedene Studien zur Verbesserung der Methodik zur Früherkennung von Psychosen (FePsy) durchgeführt. Methodik: Vom 01.03.2000 bis 29.02.2004 wurden 234 Individuen mithilfe des Basler Screening Instruments für Psychosen (BSIP) gescreent. Bei 10 6 Patienten konnte ein Risikostatus für Psychosen diagnostiziert werden, 53 davon konnten bis zu 7 Jahre (Mittel 5. 4 Jahre) nachuntersucht werden. Die weiteren Untersuchungen erfolgten u.a. mit einem spezifisch entwickelten Anamnese - Instrument, verschiedenen Skalen zur Psychopathologie, Untersuchungen der Neuropsychologie u n d Feinmotorik , klinische m und quantitative m EEG, MRI des Gehirns, Labor. Ergebnisse: Allein auf der Basis des BSIP konnte eine relativ zuverlässige Vorher sage getroffen werden: 21 (39.6 % ) der als „ Risikopatienten “Identifizierten entwickelten innerhalb der Beobachtungszeit tatsächlich eine Psychose. Post hoc konnte durch spezifischere Gewichtung der Psychopathologie und Einbezug neuropsychologischer Untersuchungen die Vorhersagegenauigkeit auf 81 % gesteigert werden. Die anderen oben genannten Verfahren können offensichtlich zur weiteren Verbesserung der Prädiktion beitragen. Schlussfolgerungen: Die Risikoabklärung für Psychose sollte stufenweise und unter Einbezug verschiedener Untersuchungsebenen erfolgen. Background: We have conducted various studies in Basel with the aim of improving the methods for the early detection of psychosis (Fruherkennung von Psychosen, FePsy).Methods: From 1.3.2000 to 29.2.2004 234 individuals were screened using the Basel Screening Instrument for Psychosis (BSIP). 106 patients were identified as at risk for psychosis; out of these 53 remained in follow-up for up to 7 years (mean 5.4 years). The assessments were done with a specifically developed instrument for history taking, various scales for the psychopathology, assessments of neuropsychology and fine motor functioning, clinical and quantitative EEG, MRI of the brain, laboratory etc.Results: Based on the BSIP alone, a relatively reliable prediction was possible: 21 (39.6 %) of the individuals identified as at risk developed psychosis within the follow-up time. Post-hoc prediction could be improved to 81 % by weighting psychopathology and including neuropsychology. Including the other domains obviously allows further improvements of prediction.Conclusions: The risk for psychosis should be assessed in a stepwise procedure. In a first step, a clinically oriented screening should be conducted. If an at-risk status is found, further assessments in various domains should be done in a specialised centre

Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)

MRI Indices of Cortical Development in Young People With Psychotic Experiences: Influence of Genetic Risk and Persistence of Symptoms

Background Psychotic experiences (PEs) are considered part of an extended psychosis phenotype and are associated with an elevated risk of developing a psychotic disorder. Risk of transition increases with persistence of PEs, and this is thought to be modulated by genetic and environmental factors. However, it is unclear if persistence is associated with progressive schizophrenia-like changes in neuroanatomy. Methods We examined cortical morphometry using MRI in 247 young adults, from a population-based cohort, assessed for the presence of PEs at ages 18 and 20. We then incorporated a polygenic risk score for schizophrenia (PRS) to elucidate the effects of high genetic risk. Finally, we used atlas-based tractography data to examine the underlying white matter. Results Individuals with persisting PEs showed reductions in gyrification (local gyrification index: lGI) in the left temporal gyrus as well as atypical associations with brain volume (TBV) in the left occipital and right prefrontal gyri. No main effect was found for the PRS, but interaction effects with PEs were identified in the orbitofrontal, parietal, and temporal regions. Examination of underlying white matter did not provide strong evidence of further disturbances. Conclusions Disturbances in lGI were similar to schizophrenia but findings were mostly limited to those with persistent PEs. These could reflect subtle changes that worsen with impending psychosis or reflect an early vulnerability associated with the persistence of PEs. The lack of clear differences in underlying white matter suggests our findings reflect early disturbances in cortical expansion rather than progressive changes in brain structure

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life

Abnormal reward prediction-error signalling in antipsychotic naive individuals with first-episode psychosis or clinical risk for psychosis.

Ongoing research suggests preliminary, though not entirely consistent, evidence of neural abnormalities in signalling prediction errors in schizophrenia. Supporting theories suggest mechanistic links between the disruption of these processes and the generation of psychotic symptoms. However, it is unknown at what stage in the pathogenesis of psychosis these impairments in prediction-error signalling develop. One major confound in prior studies is the use of medicated patients with strongly varying disease durations. Our study aims to investigate the involvement of the meso-cortico-striatal circuitry during reward prediction-error signalling in earliest stages of psychosis. We studied patients with first-episode psychosis (FEP) and help-seeking individuals at-risk for psychosis due to sub-threshold prodromal psychotic symptoms. Patients with either FEP (n = 14), or at-risk for developing psychosis (n = 30), and healthy volunteers (n = 39) performed a reinforcement learning task during fMRI scanning. ANOVA revealed significant (p < 0.05 family-wise error corrected) prediction-error signalling differences between groups in the dopaminergic midbrain and right middle frontal gyrus (dorsolateral prefrontal cortex, DLPFC). FEP patients showed disrupted reward prediction-error signalling compared to controls in both regions. At-risk patients showed intermediate activation in the midbrain that significantly differed from controls and from FEP patients, but DLPFC activation that did not differ from controls. Our study confirms that FEP patients have abnormal meso-cortical signalling of reward-prediction errors, whereas reward-prediction-error dysfunction in the at-risk patients appears to show a more nuanced pattern of activation with a degree of midbrain impairment but preserved cortical function

Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro Imaging genetics through meta analysis (ENIGMA) Consortium

BACKGROUND: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia

Detecting neuroimaging biomarkers for schizophrenia:a meta-analysis of multivariate pattern recognition studies

Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7–83.5%) and a specificity of 80.3% (95% CI: 76.9–83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9–88.2%) and similar specificity (76.9%, 95% CI: 71.3–81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9–80.4%, specificity of 79.0%, 95% CI: 74.6–82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity