Publications by authors named "Alessandro Bertolino"

165 Publications

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

Neuroimage 2021 Oct 9;245:118636. Epub 2021 Oct 9.

Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Piazza Giulio Cesare, 11, Bari, IT 70124, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore MD 21205, United States. Electronic address:

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.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118636DOI Listing
October 2021

ITAlian partnership for psychosis prevention (ITAPP): Improving the mental health of young people.

Eur Psychiatry 2021 09 21;64(1):e62. Epub 2021 Sep 21.

Dipartimento di Salute Mentale e Fisica e Medicina Preventiva, Università degli Studi della Campania Luigi Vanvitelli, Naples, Italy.

Background: The European impact of the clinical high risk for psychosis (CHR-P) paradigm is constrained by the lack of critical mass (detection) to power prognostic and preventive interventions.

Methods: An ITAlian partnership for psychosis prevention (ITAPP) was created across CHR-P centers, which were surveyed to describe: (a) service, catchment area, and outreach; (b) service users; and (c) interventions and outcomes. Descriptive statistics and Kaplan-Meier failure function complemented the analyses.

Results: The ITAPP included five CHR-P clinical academic centers established from 2007 to 2018, serving about 13 million inhabitants, with a recruitment capacity of 277 CHR-P individuals (mean age: 18.7 years, SD: 4.8, range: 12-39 years; 53.1% females; 85.7% meeting attenuated psychotic symptoms; 85.8% without any substance abuse). All centers were multidisciplinary and included adolescents and young adults (transitional) primarily recruited through healthcare services. The comprehensive assessment of at-risk mental state was the most widely used instrument, while the duration of follow-up, type of outreach, and preventive interventions were heterogeneous. Across 205 CHR-P individuals with follow up (663.7 days ± 551.7), the cumulative risk of psychosis increased from 8.7% (95% CI 5.3-14.1) at 1 year to 15.9% (95% CI 10.6-23.3) at 2 years, 21.8% (95% CI 14.9-31.3) at 3 years, 34.8% (95% CI 24.5-47.9) at 4 years, and 51.9% (95% CI 36.3-69.6) at 5 years.

Conclusions: The ITAPP is one of the few CHR-P clinical research partnerships in Europe for fostering detection, prognosis, and preventive care, as well as for translating research innovations into practice.
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http://dx.doi.org/10.1192/j.eurpsy.2021.2232DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8581702PMC
September 2021

Novel Gyrification Networks Reveal Links with Psychiatric Risk Factors in Early Illness.

Cereb Cortex 2021 Sep 14. Epub 2021 Sep 14.

Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany.

Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR < 0.01) and was not moderated by illness group. Higher gyrification was associated with better cognitive performance and lifetime role functioning, but not with symptoms. The findings demonstrated that gyrification can be parsed into novel brain networks that highlight generalized illness effects linked to developmental vulnerability. When combined, our study widens the window into the etiology of psychiatric risk and its expression in adulthood.
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http://dx.doi.org/10.1093/cercor/bhab288DOI Listing
September 2021

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort.

Biol Psychiatry 2021 11 6;90(9):632-642. Epub 2021 Jul 6.

Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom; School of Psychology, University of Birmingham, Birmingham, United Kingdom.

Background: Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.

Methods: We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.

Results: After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.

Conclusions: Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.
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http://dx.doi.org/10.1016/j.biopsych.2021.06.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500930PMC
November 2021

Genome-wide association study detected novel susceptibility genes for social cognition impairment in people with schizophrenia.

World J Biol Psychiatry 2021 Jun 16:1-9. Epub 2021 Jun 16.

Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy.

Objectives: People with schizophrenia (SCZ) present serious and generalised deficits in social cognition (SC), which affect negatively patients' functioning and treatment outcomes. The genetic background of SC has been investigated in disorders other than SCZ providing weak and sparse results. Thus, our aim was to explore possible genetic correlates of SC dysfunctions in SCZ patients with a genome-wide study (GWAS) approach.

Methods: We performed a GWAS meta-analysis of data coming from two cohorts made of 242 and 160 SCZ patients, respectively. SC was assessed with different tools in order to cover its different domains.

Results: We found GWAS significant association between the gene and the patients' ability in social inference as assessed by The Awareness of Social Inference Test; this association was confirmed by both SNP-based analysis (lead SNP rs3019332 -value = 5.24 × 10) and gene-based analysis (-value = 1.09 × 10). Moreover, suggestive associations of other genes with different dimensions of SC were also found.

Conclusions: Our study shows for the first time GWAS significant or suggestive associations of some gene variants with SC domains in people with SCZ. These findings should stimulate further studies to characterise the genetic underpinning of SC dysfunctions in SCZ.
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http://dx.doi.org/10.1080/15622975.2021.1907722DOI Listing
June 2021

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space.

Neuroimage 2021 09 10;238:118200. Epub 2021 Jun 10.

Department of Electrical and Computer Engineering, Johns Hopkins University, USA.

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118200DOI Listing
September 2021

Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia.

Nat Commun 2021 06 9;12(1):3478. Epub 2021 Jun 9.

Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.
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http://dx.doi.org/10.1038/s41467-021-23694-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190281PMC
June 2021

Evidence of an interaction between and polymorphisms on levels of Negative Symptoms of Schizophrenia and their response to antipsychotics.

Eur Psychiatry 2021 04 19;64(1):e39. Epub 2021 Apr 19.

Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.

Background: Genome-Wide Association Studies (GWASs) have identified several genes associated with Schizophrenia (SCZ) and exponentially increased knowledge on the genetic basis of the disease. In addition, products of GWAS genes interact with neuronal factors coded by genes lacking association, such that this interaction may confer risk for specific phenotypes of this brain disorder. In this regard, fragile X mental retardation syndrome-related 1 (FXR1) gene has been GWAS associated with SCZ. FXR1 protein is regulated by glycogen synthase kinase-3β (GSK3β), which has been implicated in pathophysiology of SCZ and response to antipsychotics (APs). rs496250 and rs12630592, two eQTLs (Expression Quantitative Trait Loci) of FXR1 and GSK3β, respectively, interact on emotion stability and amygdala/prefrontal cortex activity during emotion processing. These two phenotypes are associated with Negative Symptoms (NSs) of SCZ suggesting that the interaction between these SNPs may also affect NS severity and responsiveness to medication.

Methods: To test this hypothesis, in two independent samples of patients with SCZ, we investigated rs496250 by rs12630592 interaction on NS severity and response to APs. We also tested a putative link between APs administration and FXR1 expression, as already reported for GSK3β expression.

Results: We found that rs496250 and rs12630592 interact on NS severity. We also found evidence suggesting interaction of these polymorphisms also on response to APs. This interaction was not present when looking at positive and general psychopathology scores. Furthermore, chronic olanzapine administration led to a reduction of FXR1 expression in mouse frontal cortex.

Discussion: Our findings suggest that, like GSK3β, FXR1 is affected by APs while shedding new light on the role of the FXR1/GSK3β pathway for NSs of SCZ.
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http://dx.doi.org/10.1192/j.eurpsy.2021.26DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260562PMC
April 2021

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

BMC Psychol 2021 Mar 23;9(1):47. Epub 2021 Mar 23.

Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.

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.
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http://dx.doi.org/10.1186/s40359-021-00552-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989088PMC
March 2021

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

Neuropsychopharmacology 2021 07 3;46(8):1484-1493. Epub 2021 Mar 3.

Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.

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.
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http://dx.doi.org/10.1038/s41386-021-00977-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209059PMC
July 2021

Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample.

Neurosci Biobehav Rev 2021 06 23;125:478-492. Epub 2021 Feb 23.

Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany. Electronic address:

A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40-0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.
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http://dx.doi.org/10.1016/j.neubiorev.2021.02.032DOI Listing
June 2021

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 17. Epub 2021 Feb 17.

Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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http://dx.doi.org/10.1002/hbm.25364DOI Listing
February 2021

Accuracy of self-assessment of real-life functioning in schizophrenia.

NPJ Schizophr 2021 Feb 15;7(1):11. Epub 2021 Feb 15.

Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy.

A consensus has not yet been reached regarding the accuracy of people with schizophrenia in self-reporting their real-life functioning. In a large (n = 618) cohort of stable, community-dwelling schizophrenia patients we sought to: (1) examine the concordance of patients' reports of their real-life functioning with the reports of their key caregiver; (2) identify which patient characteristics are associated to the differences between patients and informants. Patient-caregiver concordance of the ratings in three Specific Level of Functioning Scale (SLOF) domains (interpersonal relationships, everyday life skills, work skills) was evaluated with matched-pair t tests, the Lin's concordance correlation, Somers' D, and Bland-Altman plots with limits of agreement (LOA). Predictors of the patient-caregiver differences in SLOF ratings were assessed with a linear regression with multivariable fractional polynomials. Patients' self-evaluation of functioning was higher than caregivers' in all the evaluated domains of the SLOF and 17.6% of the patients exceeded the LOA, thus providing a self-evaluation discordant from their key caregivers. The strongest predictors of patient-caregiver discrepancies were caregivers' ratings in each SLOF domain. In clinically stable outpatients with a moderate degree of functional impairment, self-evaluation with the SLOF scale can become a useful, informative and reliable clinical tool to design a tailored rehabilitation program.
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http://dx.doi.org/10.1038/s41537-021-00140-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884703PMC
February 2021

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 11. Epub 2021 Feb 11.

Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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http://dx.doi.org/10.1002/hbm.25320DOI Listing
February 2021

Factors Associated With Real-Life Functioning in Persons With Schizophrenia in a 4-Year Follow-up Study of the Italian Network for Research on Psychoses.

JAMA Psychiatry 2021 May;78(5):550-559

Department of Psychiatry, University of Campania "Luigi Vanvitelli," Naples, Italy.

Importance: The goal of schizophrenia treatment has shifted from symptom reduction and relapse prevention to functional recovery; however, recovery rates remain low. Prospective identification of variables associated with real-life functioning domains is essential for personalized and integrated treatment programs.

Objective: To assess whether baseline illness-related variables, personal resources, and context-related factors are associated with work skills, interpersonal relationships, and everyday life skills at 4-year follow-up.

Design, Setting, And Participants: This multicenter prospective cohort study was conducted across 24 Italian university psychiatric clinics or mental health departments in which 921 patients enrolled in a cross-sectional study were contacted after 4 years for reassessment. Recruitment of community-dwelling, clinically stable persons with schizophrenia was conducted from March 2016 to December 2017, and data were analyzed from January to May 2020.

Main Outcomes And Measures: Psychopathology, social and nonsocial cognition, functional capacity, personal resources, and context-related factors were assessed, with real-life functioning as the main outcome. Structural equation modeling, multiple regression analyses, and latent change score modeling were used to identify variables that were associated with real-life functioning domains at follow-up and with changes from baseline in these domains.

Results: In total, 618 participants (427 male [69.1%]; mean [SD] age, 45.1 [10.5] years) were included. Five baseline variables were directly associated with real-life functioning at follow-up: neurocognition with everyday life (β, 0.274; 95% CI, 0.207-0.341; P < .001) and work (β, 0.101; 95% CI, 0.005-0.196; P = .04) skills; avolition with interpersonal relationships (β, -0.126; 95% CI, -0.190 to -0.062; P < .001); positive symptoms with work skills (β, -0.059; 95% CI, -0.112 to -0.006; P = .03); and social cognition with work skills (β, 0.185; 95% CI, 0.088-0.283; P < .001) and interpersonal functioning (β, 0.194; 95% CI, 0.121-0.268; P < .001). Multiple regression analyses indicated that these variables accounted for the variability of functioning at follow-up after controlling for baseline functioning. In the latent change score model, higher neurocognitive abilities were associated with improvement of everyday life (β, 0.370; 95% CI, 0.253-0.486; P < .001) and work (β, 0.102; 95% CI, 0.016-0.188; P = .02) skills, social cognition (β, 0.133; 95% CI, 0.015-0.250; P = .03), and functional capacity (β, 1.138; 95% CI, 0.807-1.469; P < .001); better baseline social cognition with improvement of work skills (β, 0.168; 95% CI, 0.075-0.261; P < .001) and interpersonal functioning (β, 0.140; 95% CI, 0.069-0.212; P < .001); and better baseline everyday life skills with improvement of work skills (β, 0.121; 95% CI, 0.077-0.166; P < .001).

Conclusions And Relevance: Findings of this large prospective study suggested that baseline variables associated with functional outcome at follow-up included domains not routinely assessed and targeted by intervention programs in community mental health services. The key roles of social and nonsocial cognition and of baseline everyday life skills support the adoption in routine mental health care of cognitive training programs combined with personalized psychosocial interventions aimed to promote independent living.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.4614DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876615PMC
May 2021

Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach.

Schizophr Bull 2021 07;47(4):1130-1140

Institute for Mental Health, University of Birmingham, Birmingham, UK.

Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.
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http://dx.doi.org/10.1093/schbul/sbaa185DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266654PMC
July 2021

Prevalence of antipsychotic-induced extrapyramidal symptoms and their association with neurocognition and social cognition in outpatients with schizophrenia in the "real-life".

Prog Neuropsychopharmacol Biol Psychiatry 2021 07 20;109:110250. Epub 2021 Jan 20.

Department of Psychiatry, University of Campania "Luigi Vanvitelli" Naples, Italy.

First generation antipsychotics (FGAs) are more likely to induce extrapyramidal side-effects (EPS) than second generation antipsychotics (SGAs), and EPS have been shown associated to cognitive deficits in schizophrenia. So far, no study has explored the relationships between EPS and social cognition (SC) in people with schizophrenia. Therefore, we assessed the prevalence of EPS in a large sample of drug-treated community-dwelling persons with schizophrenia and explored their relationships with patients' neurocognitive and SC abilities. 875 patients underwent EPS, psychopathological, neurocognitive and SC assessments by means of standardized measures. Relationships between EPS, psychopathology and neurocognitive and SC measures were investigated by correlation tests. Moreover, a partial correlation network was computed by means of a network analysis. 256 patients were treated with FGAs alone or in combination with SGA and 619 with SGAs. EPS were significantly more frequent in FGA-treated group than in the SGA-treated one. Patients with EPS disclosed a more severe psychopathology and were more impaired in neurocognitive and SC measures compared to those without EPS. Disorganization, expressive deficit, and duration of illness were significantly associated to both neurocognitive and SC measures while EPS were associated to neurocognitive measures only. The network analysis showed that parkinsonism was the sole EPS directly connected to both psychopathological and neurocognitive indices whereas no direct connection emerged between EPS and SC measures. Present findings confirm that EPS are still present in the era of SGAs and contribute, together with other clinical variables, to the neurocognitive but not to the SC impairment of patients with schizophrenia.
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http://dx.doi.org/10.1016/j.pnpbp.2021.110250DOI Listing
July 2021

General psychopathology links burden of recent life events and psychotic symptoms in a network approach.

NPJ Schizophr 2020 Dec 15;6(1):40. Epub 2020 Dec 15.

Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.

Recent life events have been implicated in the onset and progression of psychosis. However, psychological processes that account for the association are yet to be fully understood. Using a network approach, we aimed to identify pathways linking recent life events and symptoms observed in psychosis. Based on previous literature, we hypothesized that general symptoms would mediate between recent life events and psychotic symptoms. We analyzed baseline data of patients at clinical high risk for psychosis and with recent-onset psychosis (n = 547) from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. In a network analysis, we modeled links between the burden of recent life events and all individual symptoms of the Positive and Negative Syndrome Scale before and after controlling for childhood trauma. To investigate the longitudinal associations between burden of recent life events and symptoms, we analyzed multiwave panel data from seven timepoints up to month 18. Corroborating our hypothesis, burden of recent life events was connected to positive and negative symptoms through general psychopathology, specifically depression, guilt feelings, anxiety and tension, even after controlling for childhood trauma. Longitudinal modeling indicated that on average, burden of recent life events preceded general psychopathology in the individual. In line with the theory of an affective pathway to psychosis, recent life events may lead to psychotic symptoms via heightened emotional distress. Life events may be one driving force of unspecific, general psychopathology described as characteristic of early phases of the psychosis spectrum, offering promising avenues for interventions.
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http://dx.doi.org/10.1038/s41537-020-00129-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738498PMC
December 2020

Basic Symptoms Are Associated With Age in Patients With a Clinical High-Risk State for Psychosis: Results From the PRONIA Study.

Front Psychiatry 2020 17;11:552175. Epub 2020 Nov 17.

Department of Education, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy.

In community studies, both attenuated psychotic symptoms (APS) and basic symptoms (BS) were more frequent but less clinically relevant in children and adolescents compared to adults. In doing so, they displayed differential age thresholds that were around age 16 for APS, around age 18 for perceptive BS, and within the early twenties for cognitive BS. Only the age effect has previously been studied and replicated in clinical samples for APS. Thus, we examined the reported age effect on and age thresholds of 14 criteria-relevant BS in a patient sample at clinical-high risk of psychosis ( = 261, age 15-40 yrs.), recruited within the European multicenter PRONIA-study. BS and the BS criteria, "Cognitive Disturbances" (COGDIS) and "Cognitive-perceptive BS" (COPER), were assessed with the "Schizophrenia Proneness Instrument, Adult version" (SPI-A). Using logistic regressions, prevalence rates of perceptive and cognitive BS, and of COGDIS and COPER, as well as the impact of social and role functioning on the association between age and BS were studied in three age groups (15-18 years, 19-23 years, 24-40 years). Most patients (91.2%) reported any BS, 55.9% any perceptive and 87.4% any cognitive BS. Furthermore, 56.3% met COGDIS and 80.5% COPER. Not exhibiting the reported differential age thresholds, both perceptive and cognitive BS, and, at trend level only, COPER were less prevalent in the oldest age group (24-40 years); COGDIS was most frequent in the youngest group (15-18 years). Functional deficits did not better explain the association with age, particularly in perceptive BS and cognitive BS meeting the frequency requirement of BS criteria. Our findings broadly confirmed an age threshold in BS and, thus, the earlier assumed link between presence of BS and brain maturation processes. Yet, age thresholds of perceptive and cognitive BS did not differ. This lack of differential age thresholds might be due to more pronounced the brain abnormalities in this clinical sample compared to earlier community samples. These might have also shown in more frequently occurring and persistent BS that, however, also resulted from a sampling toward these, i.e., toward COGDIS. Future studies should address the neurobiological basis of CHR criteria in relation to age.
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http://dx.doi.org/10.3389/fpsyt.2020.552175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7707000PMC
November 2020

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

JAMA Psychiatry 2021 Feb;78(2):195-209

Department of Child and Adolescent Psychiatry and Psychotherapy, University of Zürich, Zürich, Switzerland.

Importance: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.

Objectives: To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system.

Design, Setting, And Participants: This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.

Main Outcomes And Measures: Accuracy and generalizability of prognostic systems.

Results: A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.

Conclusions And Relevance: These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.3604DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711566PMC
February 2021

The influence of autistic symptoms on social and non-social cognition and on real-life functioning in people with schizophrenia: Evidence from the Italian Network for Research on Psychoses multicenter study.

Eur Psychiatry 2020 11 10;63(1):e98. Epub 2020 Nov 10.

Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy.

Background: Autism spectrum disorders (ASDs) and schizophrenia spectrum disorders (SSDs), although conceptualized as separate entities, may share some clinical and neurobiological features. ASD symptoms may have a relevant role in determining a more severe clinical presentation of schizophrenic disorder but their relationships with cognitive aspects and functional outcomes of the disease remain to be addressed in large samples of individuals.

Aims: To investigate the clinical, cognitive, and functional correlates of ASD symptoms in a large sample of people diagnosed with schizophrenia.

Methods: The severity of ASD symptoms was measured with the PANSS Autism Severity Scale (PAUSS) in 921 individuals recruited for the Italian Network for Research on Psychoses multicenter study. Based on the PAUSS scores, three groups of subjects were compared on a wide array of cognitive and functional measures.

Results: Subjects with more severe ASD symptoms showed a poorer performance in the processing speed (p = 0.010), attention (p = 0.011), verbal memory (p = 0.035), and social cognition (p = 0.001) domains, and an overall lower global cognitive composite score (p = 0.010). Subjects with more severe ASD symptoms also showed poorer functional capacity (p = 0.004), real-world interpersonal relationships (p < 0.001), and participation in community-living activities (p < 0.001).

Conclusions: These findings strengthen the notion that ASD symptoms may have a relevant impact on different aspects of the disease, crucial to the life of people with schizophrenia. Prominent ASD symptoms may characterize a specific subpopulation of individuals with SSD.
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http://dx.doi.org/10.1192/j.eurpsy.2020.99DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737172PMC
November 2020

Which factors delay treatment in bipolar disorder? A nationwide study focussed on duration of untreated illness.

Early Interv Psychiatry 2021 10 15;15(5):1136-1145. Epub 2020 Oct 15.

Department of Biomedical and Clinical Sciences "Luigi Sacco", Psychiatry Unit 2, ASST-Fatebenefratelli-Sacco, Milan, Italy.

Aim: The aim of the present study was to detect factors associated with duration of untreated illness (DUI) in bipolar disorder (BD).

Method: A total of 1575 patients were selected for the purposes of the study. Correlation analyses were performed to analyse the relation between DUI and quantitative variables. The length of DUI was compared between groups defined by qualitative variables through one-way analyses of variance or Kruskal-Wallis's tests according to the distribution of the variable. Linear multivariable regressions were used to find the most parsimonious set of variables independently associated with DUI: to this aim, qualitative variables were inserted with the numeric code of their classes by assuming a proportional effect moving from one class to another.

Results: An inverse significant correlation between length of DUI and time between visits in euthymic patients was observed (r = -.52, P < .001). DUI resulted to be longer in patients with: at least one lifetime marriage/partnership (P = .009), a first psychiatric diagnosis of major depressive disorder or substance abuse (P < .001), a depressive polarity of first episode (P < .001), no lifetime psychotic symptoms (P < .001), BD type 2 (P < .001), more lifetime depressive/hypomanic episodes (P < .001), less lifetime manic episodes (P < .001), presence of suicide attempts (P = .004), depressive episodes (P < .001), hypomanic episodes (P = .004), hospitalizations (P = .011) in the last year.

Conclusions: Different factors resulted to increase the length of DUI in a nationwide sample of bipolar patients. In addition, the DUI was found to show a negative long-term effect in terms of more suicidal behaviour, more probability of hospitalization and depressive/hypomanic episodes.
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http://dx.doi.org/10.1111/eip.13051DOI Listing
October 2021

Greater male than female variability in regional brain structure across the lifespan.

Hum Brain Mapp 2020 Oct 12. Epub 2020 Oct 12.

FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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http://dx.doi.org/10.1002/hbm.25204DOI Listing
October 2020

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

Hum Brain Mapp 2020 Oct 7. Epub 2020 Oct 7.

Neuroscience Research Australia, Sydney, Australia.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.
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http://dx.doi.org/10.1002/hbm.25206DOI Listing
October 2020

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

JAMA Psychiatry 2021 Jan;78(1):47-63

Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, the Netherlands.

Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.

Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.

Design, Setting, And Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244.

Main Outcomes And Measures: Interregional profiles of group difference in cortical thickness between cases and controls.

Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders.

Conclusions And Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.2694DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450410PMC
January 2021

The genetic architecture of human brainstem structures and their involvement in common brain disorders.

Nat Commun 2020 08 11;11(1):4016. Epub 2020 Aug 11.

Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson's disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
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http://dx.doi.org/10.1038/s41467-020-17376-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7421944PMC
August 2020

Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes.

Biol Psychiatry 2020 12 26;88(11):829-842. Epub 2020 May 26.

Neuropsychiatry and Brain Imaging Group, Department of Psychiatry, University of Basel, Basel, Switzerland.

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context.

Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels.

Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample.

Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.
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http://dx.doi.org/10.1016/j.biopsych.2020.05.020DOI Listing
December 2020

The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis.

Schizophr Bull 2021 01;47(1):249-258

Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany.

Depression frequently occurs in first-episode psychosis (FEP) and predicts longer-term negative outcomes. It is possible that this depression is seen primarily in a distinct subgroup, which if identified could allow targeted treatments. We hypothesize that patients with recent-onset psychosis (ROP) and comorbid depression would be identifiable by symptoms and neuroanatomical features similar to those seen in recent-onset depression (ROD). Data were extracted from the multisite PRONIA study: 154 ROP patients (FEP within 3 months of treatment onset), of whom 83 were depressed (ROP+D) and 71 who were not depressed (ROP-D), 146 ROD patients, and 265 healthy controls (HC). Analyses included a (1) principal component analysis that established the similar symptom structure of depression in ROD and ROP+D, (2) supervised machine learning (ML) classification with repeated nested cross-validation based on depressive symptoms separating ROD vs ROP+D, which achieved a balanced accuracy (BAC) of 51%, and (3) neuroanatomical ML-based classification, using regions of interest generated from ROD subjects, which identified BAC of 50% (no better than chance) for separation of ROP+D vs ROP-D. We conclude that depression at a symptom level is broadly similar with or without psychosis status in recent-onset disorders; however, this is not driven by a separable depressed subgroup in FEP. Depression may be intrinsic to early stages of psychotic disorder, and thus treating depression could produce widespread benefit.
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http://dx.doi.org/10.1093/schbul/sbaa094DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825071PMC
January 2021

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA.

Neuroimage 2020 09 26;218:116956. Epub 2020 May 26.

CIBERSAM, Madrid, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).
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http://dx.doi.org/10.1016/j.neuroimage.2020.116956DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7524039PMC
September 2020
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