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    Impaired olfactory ability associated with larger left hippocampus and rectus volumes at earliest stages of schizophrenia: A sign of neuroinflammation?
    Masaoka, Y ; Velakoulis, D ; Brewer, WJ ; Cropley, VL ; Bartholomeusz, CF ; Yung, AR ; Nelson, B ; Dwyer, D ; Wannan, CMJ ; Izumizaki, M ; McGorry, PD ; Wood, SJ ; Pantelis, C (ELSEVIER IRELAND LTD, 2020-07)
    Impaired olfactory identification has been reported as a first sign of schizophrenia during the earliest stages of illness, including before illness onset. The aim of this study was to examine the relationship between volumes of these regions (amygdala, hippocampus, gyrus rectus and orbitofrontal cortex) and olfactory ability in three groups of participants: healthy control participants (Ctls), patients with first-episode schizophrenia (FE-Scz) and chronic schizophrenia patients (Scz). Exploratory analyses were performed in a sample of individuals at ultra-high risk (UHR) for psychosis in a co-submission paper (Masaoka et al., 2020). The relationship to brain structural measures was not apparent prior to psychosis onset, but was only evident following illness onset, with a different pattern of relationships apparent across illness stages (FE-Scz vs Scz). Path analysis found that lower olfactory ability was related to larger volumes of the left hippocampus and gyrus rectus in the FE-Scz group. We speculate that larger hippocampus and rectus in early schizophrenia are indicative of swelling, potentially caused by an active neurochemical or immunological process, such as inflammation or neurotoxicity, which is associated with impaired olfactory ability. The volumetric decreases in the chronic stage of Scz may be due to degeneration resulting from an active immune process and its resolution.
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    Common mechanisms of executive attention underlie executive function and effortful control in children
    Tiego, J ; Bellgrove, MA ; Whittle, S ; Pantelis, C ; Testa, R (WILEY, 2020-05)
    Executive Function (EF) and Effortful Control (EC) have traditionally been viewed as distinct constructs related to cognition and temperament during development. More recently, EF and EC have been implicated in top-down self-regulation - the goal-directed control of cognition, emotion, and behavior. We propose that executive attention, a limited-capacity attentional resource subserving goal-directed cognition and behavior, is the common cognitive mechanism underlying the self-regulatory capacities captured by EF and EC. We addressed three related questions: (a) Do behavioral ratings of EF and EC represent the same self-regulation construct? (b) Is this self-regulation construct explained by a common executive attention factor as measured by performance on cognitive tasks? and (c) Does the executive attention factor explain additional variance in attention deficit hyperactivity disorder (ADHD) problems to behavioral ratings of self-regulation? Measures of performance on complex span, general intelligence, and response inhibition tasks were obtained from 136 preadolescent children (M = 11 years, 10 months, SD = 8 months), along with self- and parent-reported EC, and parent-reported EF, and ADHD problems. Results from structural equation modeling demonstrated that behavioral ratings of EF and EC measured the same self-regulation construct. Cognitive tasks measured a common executive attention factor that significantly explained 30% of the variance in behavioral ratings of self-regulation. Executive attention failed to significantly explain additional variance in ADHD problems beyond that explained by behavioral ratings of self-regulation. These findings raise questions about the utility of task-based cognitive measures in research and clinical assessment of self-regulation and psychopathology in developmental samples.
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    S166. EFFECTIVE CONNECTIVITY OF FRONTOSTRIATAL SYSTEMS IN FIRST-EPISODE PSYCHOSIS
    Sabaroedin, K ; Razi, A ; Aquino, K ; Chopra, S ; Finlay, A ; Nelson, B ; Allott, K ; Alvarez-Jimenez, M ; Graham, J ; Baldwin, L ; Tahtalian, S ; Yuen, HP ; Harrigan, S ; Cropley, V ; Pantelis, C ; Wood, S ; O’Donoghue, B ; Francey, S ; McGorry, P ; Fornito, A (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Neuroimaging studies have found dysconnectivity of frontostriatal circuits across a broad spectrum of psychotic symptoms. However, it is unknown whether dysconnectivity within frontostriatal circuits originates from disrupted bottom-up or top-down control signaling within these systems. Here, we used dynamic causal modelling (DCM) to examine the effective connectivity of frontostriatal systems in first-episode psychosis (FEP). Methods A total of 55 FEP patients (26 males; mean [SD] age = 19.24 [2.89]) and 24 healthy controls (15 males; mean [SD] age = 21.83 [1.93]) underwent a resting-state functional magnetic resonance imaging protocol. Biologically plausible connections between eight left hemisphere regions encompassing the dorsal and ventral frontostriatal systems were modelled using spectral DCM. The regions comprise dorsolateral prefrontal cortex, ventromedial prefrontal cortex, anterior hippocampus, amygdala, dorsal caudate, nucleus accumbens, thalamus, and the midbrain. Effective connectivity between groups were assessed using a parametric Bayesian model. Associations between effective connectivity parameters and positive symptoms, measured by the Brief Psychiatric Rating Scale positive subscale, was assessed in the patient group in a separate Bayesian general linear model. Results DCM shows evidence for differences in effective connectivity between patients and healthy controls, namely in the bottom-down connections distributed in the frontostriatal system encompassing the hippocampus, amygdala, striatum, and midbrain. Compared to healthy controls, patients also demonstrated increased disinhibition of the midbrain. In patients, positive symptoms are associated with increased top-down connections to the midbrain. Outgoing connection from the midbrain to the nucleus accumbens is also increased in association with positive symptoms. Discussion Aberrant top-down connectivity in the frontostriatal system in patients is consistent with top-down dysregulation of dopamine function in FEP, as dopaminergic activity in the midbrain is proposed to be under the control of higher brain areas. In patients, increased self-inhibition of the midbrain, as well as symptom associations in both ingoing and outgoing connections of this region, are congruous with hyperactivity of the midbrain as proposed by the dopamine dysregulation hypothesis. Here, we demonstrate that mathematical models of brain imaging signals can be used to identify the key disruptions driving brain circuit dysfunction, identifying new targets for treatment.
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    S94. PREDICTION OF CANNABIS RELAPSE IN CLINICAL HIGH-RISK INDIVIDUALS AND RECENT ONSET PSYCHOSIS - PRELIMINARY RESULTS FROM THE PRONIA STUDY
    Penzel, N ; Sanfelici, R ; Betz, L ; Antonucci, L ; Falkai, P ; Upthegrove, R ; Bertolino, A ; Borgwardt, S ; Brambilla, P ; Lencer, R ; Meisenzahl, E ; Ruhrmann, S ; Salokangas, RKR ; Pantelis, C ; Schultze-Lutter, F ; Wood, S ; Koutsouleris, N ; Kambeitz, J (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Evidence exists that cannabis consumption is associated with the development of psychosis. Further, continued cannabis use in individuals with recent onset psychosis (ROP) increases the risk for rehospitalization, high symptom severity and low general functioning. Clear inter-individual differences in the vulnerability to the harmful effects of the drug have been pointed out. These findings emphasize the importance of investigating the inter-individual variability in the role of cannabis use in ROP and to understand how cannabis use relates to subclinical conditions that predate the full-blown disease in clinical high-risk (CHR). Specific symptoms have been linked with continued cannabis consume, still research is lacking on how different factors contribute together to an elevated risk of cannabis relapse. Multivariate techniques have the capacity to extract complex patterns from high dimensional data and apply generalized rules to unseen cases. The aim of the study is therefore to assess the predictability of cannabis relapse in ROP and CHR by applying machine learning to clinical and environmental data. Methods All participants were recruited within the multi-site, longitudinal PRONIA study (www.pronia.eu). 112 individuals (58 ROP and 54 CHR) from 8 different European research centres reported lifetime cannabis consume at baseline and were abstinent for at least 4 weeks. We defined cannabis relapse as any cannabis consume between baseline and 9 months follow-up reported by the individual. To predict cannabis relapse, we trained a random forest algorithm implemented in the mlr package, R version 3.5.2. on 183 baseline variables including clinical symptoms, general functioning, demographics and consume patterns within a repeated-nested cross-validation framework. The data underwent pre-processing through pruning of non-informative variables and median-imputation for missing values. The number of trees was set to 500, while the number of nodes, sample fraction and mtry were optimized. All hyperparameters were tuned with the model-based optimization implemented in the mlrMBO R package. Results After 9 months 50 individuals (48 % ROP, 52 % CHR) have relapsed on cannabis use. Relapse was over all timepoints associated with more severe psychotic symptoms measured by PANSS positive and PANSS general (p<0.05) and a significant interaction between positive symptoms and time of measurement (p<0.05). Our random forest classifier could predict cannabis relapse with a balanced accuracy, sensitivity, and specificity of, respectively, 66.5 %, 66.0 % and 67.0 %. The most predictive variables were a higher cumulative frequency of cannabis consumption in the last 3 months, worse general functioning in the last month, higher density of place of living, younger age and a shorter interval time since the last consumption. Discussion Our results using a state-of-the-art machine learning approach suggest that the multivariate signature of baseline demographic and clinical data could predict follow up cannabis relapse above chance level in CHR and ROP. Our findings revealing that cannabis relapse is associated with more severe symptoms is in line with previous literature and emphasizes the need for targeted treatment towards abstinence from cannabis. The information of demographic and clinical patterns might be useful in order to specifically address therapeutic strategies in individuals at higher risk for relapse. This might include special programs for younger patients and taking into account the place of living, like urban areas. Further research is needed in order to validate our model in an independent sample.
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    O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS
    Cropley, V ; Tian, Y ; Fernando, K ; Mansour, S ; Pantelis, C ; Cocchi, L ; Zalesky, A (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychopathology. Distinct from chronological age, brain age refers to the age of an individual that is inferred from a normative model of brain structure for individuals of the same age and sex. We predicted brain age from a common set of grey matter features and examined whether the difference between an individual’s chronological and brain age was associated with the severity of psychopathology in children and adolescents. Methods Participants included 1313 youths (49.8% male) aged 8–21 who underwent structural imaging as part of the Philadelphia Neurodevelopmental Cohort. Independent Component Analysis was used to obtain 7 psychopathology dimensions representing Conduct, Anxiety, Obsessive-Compulsive, Attention, Depression, Bipolar, and Psychosis symptoms and an overall measure of severity (General Psychopathology). Using 10-fold cross-validation, support vector machine regression was trained in 402 typically developing youth to predict individual age based on a feature space comprising 111 grey matter regions. This yielded a brain age prediction for each individual. Brain age gap was calculated for each individual by subtracting chronological age from predicted brain age. The general linear model was used to test for an association between brain age gap and each of the 8 dimensions of psychopathology in a test sample of 911 youth. The regional specificity and spatial pattern of brain age gap was also investigated. Error control across the 8 models was achieved with a false discovery rate of 5%. Results Brain age gap was significantly associated with dimensions characterizing obsessive-compulsive (t=2.5, p=0.01), psychosis (t=3.16, p=0.0016) and general psychopathology (t=4.08, p<0.0001). For all three dimensions, brain age gap was positively associated with symptom severity, indicating that individuals with a brain that was predicted to be ‘older’ than expectations set by youth of the same chronological age and sex tended to have higher symptom scores. Findings were confirmed with a categorical approach, whereby higher brain age gap was observed in youth with a lifetime endorsement of psychosis (t=2.35, p=0.02) and obsessive-compulsive (t=2.35, p=0.021) symptoms, in comparison to typically developing individuals. Supplementary analyses revealed that frontal grey matter was the most important feature mediating the association between brain age gap and psychosis symptoms, whereas subcortical volumes were most important for the association between brain age gap and obsessive-compulsive and general symptoms. Discussion We found that the brain was ‘older’ in youth experiencing higher subclinical symptoms of psychosis, obsession-compulsion, and general psychopathology, compared to normally developing youth of the same chronological age. Our results suggest that deviations in normative brain age patterns in youth may contribute to the manifestation of specific psychiatric symptoms of subclinical severity that cut across psychopathology dimensions.
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    S12. A MACHINE LEARNING FRAMEWORK FOR ROBUST AND RELIABLE PREDICTION OF SHORT- AND LONG-TERM CLINICAL RESPONSE IN INITIALLY ANTIPSYCHOTIC-NAïVE SCHIZOPHRENIA PATIENTS BASED ON MULTIMODAL NEUROPSYCHIATRIC DATA
    Ambrosen, KS ; Skjerbæk, MW ; Foldager, J ; Axelsen, MC ; Bak, N ; Arvastson, L ; Christensen, SR ; Johansen, LB ; Raghava, JM ; Oranje, B ; Rostrup, E ; Nielsen, MØ ; Osler, M ; Fagerlund, B ; Pantelis, C ; Kinon, BJ ; Glenthøj, BY ; Hansen, LK ; Ebdrup, BH (Oxford University Press (OUP), 2020-05-18)
    Abstract Background The treatment response of patients with schizophrenia is heterogeneous, and markers of clinical response are missing. Studies using machine learning approaches have provided encouraging results regarding prediction of outcomes, but replicability has been challenging. In the present study, we present a novel methodological framework for applying machine learning to clinical data. Herein, algorithm selection and other methodological choices were based on model performance on a simulated dataset, to minimize bias and avoid overfitting. We subsequently applied the best performing machine learning algorithm to a rich, multimodal neuropsychiatric dataset. We aimed to 1) classify patients from controls, 2) predict short- and long-term clinical response in a sample of initially antipsychotic-naïve first-episode schizophrenia patients, and 3) validate our methodological framework. Methods We included data from 138 antipsychotic-naïve, first-episode schizophrenia patients, who had undergone assessments of psychopathology, cognition, electrophysiology, structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Baseline diagnostic classification algorithms also included data from 151 matched healthy controls. Short-term treatment response was defined as change in psychopathology after the initial antipsychotic treatment period. Long-term treatment response (4–16 years) was based on data from Danish registers. The simulated dataset was generated to resemble the real data with respect to dimensionality, multimodality, and pattern of missing data. Noise levels were tunable to enable approximation to the signal-to-noise ratio in the real data. Robustness of the results was ensured by running two parallel, fundamentally different machine learning pipelines, a ‘single algorithm approach’ and an ‘ensemble approach’. Both pipelines included nested cross-validation, missing data imputation, and late integration. Results We significantly classified patients from controls with a balanced accuracy of 64.2% (95% CI = [51.7, 76.7]) for the single algorithm approach and 63.1% (95% CI = [50.4, 75.8]) for the ensemble approach. Post hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- and long-term clinical response. To validate our methodological framework based on simulated data, we selected the best, a medium, and the most poorly performing algorithm on the simulated data and applied them to the real data. We found that the ranking of the algorithms was kept in the real data. Discussion Our rigorous modelling framework incorporating simulated data and parallel pipelines discriminated patients from controls, but our extensive, multimodal neuropsychiatric data from antipsychotic-naïve schizophrenia patients were not predictive of the clinical outcome. Nevertheless, our novel approach holds promise as an important step to obtain reliable, unbiased results with modest sample sizes when independent replication samples are not available.
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    T223. MULTIVARIATE PREDICTION OF FOLLOW UP SOCIAL AND OCCUPATIONAL OUTCOME IN CLINICAL HIGH-RISK INDIVIDUALS BASED ON GRAY MATTER VOLUMES AND HISTORY OF ENVIRONMENTAL ADVERSE EVENTS
    Antonucci, L ; Pigoni, A ; Sanfelici, R ; Kambeitz-Ilankovic, L ; Dwyer, D ; Ruef, A ; Chisholm, K ; Haidl, T ; Rosen, M ; Kambeitz, J ; Ruhrmann, S ; Schultze-Lutter, F ; Falkai, P ; Lencer, R ; Dannlowski, U ; Upthegrove, R ; Salokangas, R ; Pantelis, C ; Meisenzahl, E ; Wood, S ; Brambilla, P ; Borgwardt, S ; Bertolino, A ; Koutsouleris, N (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Functional deficits associated with the Clinical High Risk (CHR) status very often lead to inability to attend school, unemployment, as well as social isolation, thus calling for predictors of individual functional outcomes which may facilitate the identification of people requiring care irrespective of transition to psychosis. Studies have revealed that a pattern of cortical and subcortical gray matter volumes (GMV) anomalies measured at baseline in CHR individuals could predict their functional abilities at follow up. Furthermore, literature is consistent in revealing the crucial role of several environmental adverse events in increasing the risk of developing either transition to psychosis, or a worse overall personal functioning. Therefore, the aim of this study is to employ machine learning to test the individual and combined ability of baseline GMV data and of history of environmental adverse events in predicting good vs. poor social and occupational outcome in CHR individuals at follow up. Methods 92 CHR individuals recruited from the 7 discovery PRONIA sites were included in this project. Social and occupational impairment at follow up (9–12 months) were respectively measured through the Global Functioning: Social (GF:S) and Role (GF:R) scale, and CHR with a follow up rating of 7 or below were labeled as having a poor functional outcome. This way, we could separate our cohort in 52 poor outcome CHR and 40 good outcome CHR. GMV data were preprocessed following published procedures which allowed also to correct for site effects. The environmental classifier was built based on Childhood Trauma Questionnaire, Bullying Scale, and Premorbid Adjustment Scale (childhood, early adolescence, late adolescence and adulthood) scores. Raw scores have been normalized according to the psychometric properties of the healthy samples used for validating these questionnaires and scale, in order to obtain individual scores of deviation from the normative occurrence of adverse environmental events. GMV and environmental-based predictive models were independently trained and tested within a leave-site-out cross validation framework using a Support Vector Machine algorithm (LIBSVM) through the NeuroMiner software, and their predictions were subsequently combined through stacked generalization procedures. Results Our GMV-based model could predict follow up social outcome with 67.4% Balanced Accuracy (BAC) and significance (p=0.01), while it could not predict occupational outcome (46.6% BAC). On the other hand, our environmental-based model could discriminate both poor vs. good social and occupational outcomes at follow up with, respectively, 71% and 66.4% BACs, and significance (both p=0.0001). Specifically, the most reliable features in the environmental classifier were scores reflecting deviations from the normative values in childhood trauma and adult premorbid adjustment, for social outcome prediction, and in bullying experiences and late adolescence premorbid adjustment, for occupational outcome prediction. Only for social outcome prediction, stacked models outperformed individual classifiers’ predictions (74.3% BAC, p=0.0001). Discussion Environmental features seem to be more accurate than GMV in predicting both social and occupational outcomes in CHR. Interestingly, the predictions of follow up social and occupational outcomes rely on different patterns of occurrence of specific environmental adverse events, thus providing novel insights about how environmental adjustment disabilities, bullying and traumatic premorbid experiences may impact on different bad outcomes associated with the CHR status.
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    T162. THICKER PREFRONTAL CORTEX IS ASSOCIATED WITH SUBCLINICAL NEGATIVE SYMPTOMS IN SCHIZOTYPY - AN ENIGMA CONSORTIUM META-ANALYSIS
    Kirschner, M ; Hodzic-Santor, B ; Kircher, T ; Nenadic, I ; Fornito, A ; Green, M ; Quide, Y ; Pantelis, C ; Dannlowski, U ; DeRosse, P ; Chan, R ; Debbané, M ; Rössler, W ; Lebedeva, I ; Park, H ; Marsman, J-B ; Gilleen, J ; Fett, A-K ; van Erp, T ; Turner, J ; Thompson, P ; Aleman, A ; Modinos, G ; Kaiser, S (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Negative symptoms can be seen to represent a continuum from subclinical manifestations in the general population to severe symptoms in schizophrenia. Neuroanatomical studies show evidence of fronto-striatal structural abnormalities linked to negative symptoms in patients with schizophrenia (Walton et al. 2018). However, it remains an open question whether these structural associations are also observed in ostensibly healthy individuals reporting subclinical negative symptoms. The present study used structural T1-weighted brain imaging data from the ENIGMA Schizotypy Working Group to investigate the relationship between subclinical negative symptoms and fronto-striatal structural measures. Methods We included 2,235 healthy unmedicated individuals with varying levels of schizotypy from 17 centers around the world. The complete sample had a weighted mean (range) age of 29.2 (15.9–39.6) and 59.4% (51–100) were male. Subclinical negative symptoms were assessed at each site separately using factor scores from self-report schizotypy questionnaires (i.e., the Community Assessment of Psychic Experiences, the Oxford-Liverpool Inventory of Feelings and Experiences, or the Schizotypal Personality Questionnaire). Based on prior studies in schizophrenia, we obtained cortical thickness from 22 frontal regions-of-interest (ROIs) and subcortical volumes from 6 striatal ROIs using FreeSurfer. We performed meta-analyses of effect sizes (standardized regression coefficients) from a model predicting mean cortical thickness by subclinical negative symptom scores, adjusting for age, sex, and site. The same analysis was repeated for subcortical volumes including intracranial volume as additional covariate. Results Meta-analyses revealed significant positive associations between subclinical negative symptoms and cortical thickness of the left frontal pole (βstd=0.091; pFDR=0.009), right medial orbitofrontal cortex (βstd=0.083; pFDR=0.009) and right anterior cingulate cortex (βstd=0.07; pFDR=0.011). Discussion Using a large sample of healthy unmedicated individuals with varying levels of schizotypal personality traits, this ENIGMA meta-analysis showed that subclinical negative symptoms are associated with thicker prefrontal cortex. The present data are contrary to previous findings in schizophrenia, which demonstrates a relationship between negative symptoms and lower prefrontal cortical thickness (Walton et al. 2018). These divergent neural correlates suggest that thicker cortex could be a potential compensatory mechanism preventing individuals with schizotypy from the clinical manifestation of severe negative symptoms. Alternatively, greater prefrontal cortical thickness could also be associated with pathological processes along the negative symptom continuum prior to clinical manifestation.
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    T60. GENETIC INFLUENCES ON MEMORY FUNCTIONS AND RELATED BRAIN STRUCTURES AND ASSOCIATIONS WITH SCHIZOPHRENIA SPECTRUM DISORDERS: A NATION-WIDE TWIN STUDY
    Lemvigh, C ; Brouwer, R ; Baruel Johansen, L ; Hilker, R ; Pantelis, C ; Glenthoj, B ; Fagerlund, B (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Impaired memory is among the most profound cognitive deficits observed in patients with schizophrenia. Evidence from twin studies suggests that memory is mainly influenced by genetics. Moreover, a few twin studies have demonstrated genetic overlap between specific memory functions and schizophrenia. Memory deficits in schizophrenia seem to involve abnormalities in frontal cortical areas and the middle temporal lobe, particularly the hippocampus. In the general population, twin studies have consistently demonstrated genetic influences on brain volumes, however, evidence from twin pairs discordant for schizophrenia suggests that hippocampus volumes may be more susceptible to environmental effects in patients. Methods Twin pairs concordant or discordant for a diagnosis in the schizophrenia spectrum were recruited nation-wide by linking The Danish Twin Register and The Danish Psychiatric Central Research Register. Both monozygotic (MZ) and dizygotic (DZ) proband pairs as well as healthy control (HC) pairs were identified. A total of 216 twins participated in this study consisting of 32 complete MZ and 24 complete DZ proband pairs, 29 complete MZ and 20 complete DZ HC pairs, and six twins from proband pairs were included without their sibling. Verbal memory was assessed using the list learning task from the Brief Assessment of Cognition in Schizophrenia (BACS), visual memory using the Rey Complex Figure Test (RCFT) and associative memory using 15 word pairs. Structural brain scans were acquired with T1-weighted sequence on a Philips 3.0 T Achieva MRI scanner with a 32-channel SENSE head coil. Images were processed using FreeSurfer (version 5.3) and the Desikan-Killiany atlas was used to extract the volumes of bilateral hippocampi, superior frontal, rostral and caudal middle frontal cortices as well as the whole brain volume. Structural equation modelling was applied to examine the genetic and environmental contributions to the variability in memory and brain measures and to quantify associations with schizophrenia spectrum liability. Results Significant heritability estimates were observed for verbal memory (h2=0.53), visual memory (h2=0.58) and associative memory (immediate h2=0.33, delayed h2=0.54), whereas the copy and recognition task from RCFT were only explained by unique environmental factors. Except for verbal memory, all memory measures were significantly associated with schizophrenia spectrum liability, and these were mainly due to overlapping genetic factors. Genetic factors also significantly contributed to whole brain (h2=0.36), right superior frontal (h2=0.48), left rostral middle frontal (h2=0.40) and hippocampus volumes (right h2=0.29, left h2=0.50). Common environmental factors significantly influenced whole brain (c2=0.51), right hippocampus (c2=0.51) and right rostral middle frontal (c2=0.47) volumes. Hippocampus volumes were significantly associated with schizophrenia spectrum liability, and for the left hippocampus this association was due to overlapping genetic factors. Discussion Specific memory measures and related brain areas were heritable, providing further evidence of the importance of genetics in memory functioning. Furthermore, the majority of the applied memory measures and left hippocampal volume were (genetically) associated with schizophrenia spectrum liability, suggesting a partially shared etiology. The heritable memory measures and related brain areas showing associations with disease may represent endophenotypes for schizophrenia spectrum disorders. In future analyses, we plan to examine the covariance between memory, brain volumes and schizophrenia.
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    T21. DEVELOPMENT OF PROTEOMIC PREDICTION MODELS FOR OUTCOMES IN THE CLINICAL HIGH RISK STATE AND PSYCHOTIC EXPERIENCES IN ADOLESCENCE: MACHINE LEARNING ANALYSES IN TWO NESTED CASE-CONTROL STUDIES
    Mongan, D ; Föcking, M ; Healy, C ; Raj Susai, S ; Cagney, G ; Cannon, M ; Zammit, S ; Nelson, B ; McGorry, P ; Nordentoft, M ; Krebs, M-O ; Riecher-Rössler, A ; Bressan, R ; Barrantes-Vidal, N ; Borgwardt, S ; Ruhrmann, S ; Sachs, G ; Van der Gaag, M ; Rutten, B ; Pantelis, C ; De Haan, L ; Valmaggia, L ; Kempton, M ; McGuire, P ; Cotter, D (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Individuals at clinical high risk (CHR) of psychosis have an approximately 20% probability of developing psychosis within 2 years, as well as an associated risk of non-psychotic disorders and functional impairment. People with subclinical psychotic experiences (PEs) are also at risk of future psychotic and non-psychotic disorders and decreased functioning. It is difficult to accurately predict outcomes in individuals at risk of psychosis on the basis of symptoms alone. Biomarkers for accurate prediction of outcomes could inform the clinical management of this group. Methods We conducted two nested case-control studies. We employed discovery-based proteomic methods to analyse protein expression in baseline plasma samples in EU-GEI and age 12 plasma samples in ALSPAC using liquid chromatography mass spectrometry. Differential expression of quantified proteomic markers was determined by analyses of covariance (with false discovery rate of 5%) comparing expression levels for each marker between those who did not and did not develop psychosis in Study 1 (adjusting for age, gender, body mass index and years in education), and between those who did and did not develop PEs in Study 2 (adjusting for gender, body mass index and maternal social class). Support vector machine algorithms were used to develop models for prediction of transition vs. non-transition (as determined by the Comprehensive Assessment of At Risk Mental States) and poor vs. good functional outcome at 2 years in Study 1 (General Assessment of Functioning: Disability subscale score </=60 vs. >60). Similar algorithms were used to develop a model for prediction of PEs vs. no PEs at age 18 in Study 2 (as determined by the Psychosis Like Symptoms Interview). Results In Study 1, 35 of 166 quantified proteins were significantly differentially expressed between CHR participants who did and did not develop psychosis. Functional enrichment analysis provided evidence for particular implication of the complement and coagulation cascade (false discovery rate-adjusted Fisher’s exact test p=2.23E-21). Using 65 clinical and 166 proteomic features a model demonstrated excellent performance for prediction of transition status (area under the receiver-operating curve [AUC] 0.96, positive predictive value [PPV] 83.0%, negative predictive value [NPV] 93.8%). A model based on the ten most predictive proteins accurately predicted transition status in training (AUC 0.96, PPV 87.5%, NPV 95.8%) and withheld data (AUC 0.92, PPV 88.9%, NPV 91.4%). A model using the same 65 clinical and 166 proteomic features predicted 2-year functional outcome with AUC 0.72 (PPV 67.6%, NPV 47.6%). In Study 2, 5 of 265 quantified proteins were significantly differentially expressed between participants who did and did not report PEs at age 18. A model using 265 proteomic features predicted PEs at age 18 with AUC 0.76 (PPV 69.1%, NPV 74.2%). Discussion With external validation, models incorporating proteomic data may contribute to improved prediction of clinical outcomes in individuals at risk of psychosis.