Psychiatry - Research Publications

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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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    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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    O8.5. SIGNS OF ADVERSITY - A NOVEL MACHINE LEARNING APPROACH TO CHILDHOOD TRAUMA, BRAIN STRUCTURE AND CLINICAL PROFILES
    Popovic, D ; Ruef, A ; Dwyer, DB ; Hedderich, D ; Antonucci, LA ; Kambeitz-Ilankovic, L ; Öztürk, ÖF ; Dong, MS ; Paul, R ; Kambeitz, J ; Ruhrmann, S ; Chisholm, K ; Schultze-Lutter, F ; Falkai, P ; Bertolino, A ; Lencer, R ; Dannlowski, U ; Upthegrove, R ; Salokangas, RKR ; Pantelis, C ; Meisenzahl, E ; Wood, S ; Brambilla, P ; Borgwardt, S ; Koutsouleris, N (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Childhood maltreatment (CM) is a major psychiatric risk factor and leads to long-lasting physical and mental health implications throughout the affected individual’s lifespan. Nonetheless, the neuroanatomical correlates of CM and their specific clinical impact remain elusive. This might be attributed to the complex, multidimensional nature of CM as well as to the restrictions of traditional analysis pipelines using nosological grouping, univariate analysis and region-of-interest approaches. To overcome these issues, we present a novel transdiagnostic and naturalistic machine learning approach towards a better and more comprehensive understanding of the clinical and neuroanatomical complexity of CM. Methods We acquired our dataset from the multi-center European PRONIA cohort (www.pronia.eu). Specifically, we selected 649 male and female individuals, comprising young, minimally medicated patients with clinical high-risk states for psychosis as well as recent-onset of depression or psychosis and healthy volunteers. As part of our analysis approach, we created a new Matlab Toolbox, which performs multivariate Sparse Partial Least Squares Analysis in a robust machine learning framework. We employed this algorithm to detect multi-layered associations between combinations of items from the Childhood Trauma Questionnaire (CTQ) and grey matter volume (GMV) and assessed their generalizability via nested cross-validation. The clinical relevance of these CM signatures was assessed by correlating them to a wide range of clinical measurements, including current functioning (GAF, GF), depressivity (BDI), quality of life (WHOQOL-BREF) and personality traits (NEO-FFI). Results Overall, we detected three distinct signatures of sexual, physical and emotional maltreatment. The first signature consisted of an age-dependent sexual abuse pattern and a corresponding GMV pattern along the prefronto-thalamo-cerebellar axis. The second signature yielded a sex-dependent physical and sexual abuse pattern with a corresponding GMV pattern in parietal, occipital and subcortical regions. The third signature was a global emotional trauma signature, independent of age or sex, and projected to a brain structural pattern in sensory and limbic brain regions. Regarding the clinical impact of these signatures, the emotional trauma signature was most strongly associated with massively impaired state- and trait-level characteristics. Both on a phenomenological and on a brain structural level, the emotional trauma pattern was significantly correlated with lower levels of functioning, higher depression scores, decreased quality of life and maladaptive personality traits. Discussion Our findings deliver multimodal, data-driven evidence for a differential impact of sexual, physical and emotional trauma on brain structure and clinical state- and trait-level phenotypes. They also highlight the multidimensional nature of CM, which consists of multiple layers of highly complex trauma-brain patterns. In broader terms, our study emphasizes the potential of machine learning approaches in generating novel insights into long-standing psychiatric topics.
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    O6.6. MULTIMODAL PROGNOSIS OF NEGATIVE SYMPTOM SEVERITY IN INDIVIDUALS WITH INCREASED RISK OF DEVELOPING PSYCHOSIS
    Hauke, D ; Schmidt, A ; Studerus, E ; Andreou, C ; Riecher-Rössler, A ; Radua, J ; Kambeitz, J ; Ruef, A ; Dwyer, D ; Sanfelici, R ; Penzel, N ; Haas, S ; Antonucci, L ; Schultze-Lutter, F ; Ruhrmann, S ; Hietala, J ; Brambilla, P ; Koutsouleris, N ; Meisenzahl, E ; Pantelis, C ; Rosen, M ; Salokangas, RKR ; Upthegrove, R ; Wood, S ; Borgwardt, S (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Precise prognosis of clinical outcomes in individuals at clinical high-risk (CHR) of developing psychosis is imperative to guide treatment selection. While much effort has been put into the prediction of transition to psychosis in CHR individuals, prognostic models focusing on negative symptom progression in this population are widely missing. This is a major oversight bearing in mind that 82% of CHR individuals exhibit at least one negative symptom in the moderate to severe range at first clinical presentation, whereas 54% still meet this criteria after 12 months. Negative symptoms are strong predictors of poor functional outcome irrespective of other symptoms such as depression or anxiety. Prognostic tools are therefore urgently required to track negative symptom progression in CHR individuals in order to guide early personalized interventions. Here, we applied machine-learning to multi-site data from five European countries with the aim of predicting negative symptoms of at least moderate severity 9-month after study inclusion. Methods We analyzed data from the ‘Personalized Prognostic Tools for Early Psychosis Management’ (PRONIA; www.pronia.eu) study, which consisted of 94 individuals at clinical high-risk of developing psychosis (CHR). Predictive models either included baseline level of negative symptoms, measured with the Structured Interview for Prodromal Syndromes, whole-brain gyrification pattern, or both to forecast negative symptoms of moderate severity or above in CHR individuals. Using data from the clinical and gyrification model, further sequential testing simulations were conducted to stratify CHR individuals into different risk groups. Lastly, we assessed the models’ ability to predict functional outcomes in CHR individuals. Results Baseline negative symptom severity alone predicted moderate to severe negative symptoms with a balanced accuracy (BAC) of 68%, whereas predictive models trained on gyrification measures achieved a BAC of 64%. Stacking the two modalities allowed for an increased BAC of 72%. Additional sequential testing simulations suggested, that CHR patients could be stratified into a high risk group with 83% probability of experiencing at least moderate negative symptoms at follow-up and a medium/low risk group with a risk ranging from 25 to 38%, when using the two models sequentially. Furthermore, the models trained to predict negative symptom severity from baseline symptoms were less predictive of role (60% BAC) and social (62% BAC) functioning at follow-up. However, the model trained on gyrification data also predicted role (74% BAC) and social (73% BAC) functioning later on. The stacking model predicted role, and social functioning with 64% BAC and 66% BAC respectively. Discussion To the best of our knowledge this is the first study using state-of-the-art predictive modelling to prospectively identify CHR subjects with negative symptoms in the moderate to above moderate severity range who potentially require further therapeutic consideration. While the predictive performance will need to be validated in other samples and may be improved by expanding the models with additional predictors, we believe that this pragmatic approach will help to stratify individual risk profiles and optimize personal interventions in the future.
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    O6.4. ASSOCIATION BETWEEN CLUSTERS OF FORMAL THOUGHT DISORDERS SEVERITY AND NEUROCOGNITIVE AND FUNCTIONAL OUTCOME INDICES IN THE EARLY STAGES OF PSYCHOSIS – RESULTS FROM THE PRONIA COHORT
    Öztürk, ÖF ; Pigoni, A ; Wenzel, J ; Haas, S ; Popovic, D ; Ruef, A ; Dwyer, DB ; Kambeitz-Ilankovic, L ; Haidl, T ; Rosen, M ; Kambeitz, J ; Ruhrmann, S ; Chisholm, K ; Schultze-Lutter, F ; Liddle, PF ; Upthegrove, R ; Salokangas, RKR ; Pantelis, C ; Meisenzahl, E ; Wood, SJ ; Brambilla, P ; Borgwardt, S ; Falkai, P ; Antonucci, LA ; Koutsouleris, N (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Formal thought disorder (FThD) has been associated with more severe illness courses and functional deficits in psychosis patients. Given these associations, it remains unclear whether the presence of FThD accounts for the heterogeneous presentation of psychoses, and whether it characterises a specific subgroup of patients showing prominent differential illness severity, neurocognitive and functional impairments already in the early stages of psychosis. Thus, our aim is 1) to evaluate whether there are stable subtypes of patients with Recent-Onset Psychosis (ROP) that are characterized by distinct FThD patterns, 2) to investigate whether this FThD-related stratification is associated with clinical, and neurocognitive phenotypes at an early stage of the disease, and 3) to explore correlation patterns among the FThD-related symptoms, functioning and neurocognition through network analysis. Methods 279 individuals experiencing ROP were recruited for this project as part of multi-site European PRONIA study. In the present study, FThD was assessed with conceptual disorganization, difficulty in abstract thinking, poverty of content of speech, increased latency of response and poverty of speech items from the Positive and Negative Symptom Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS). We first applied a multi-step clustering protocol comparing three clustering algorithms: (i) k-means, (ii) hierarchical clustering, and (iii) partitioning around medoids with the number of clusters ranging from 2 to 10. Our protocol runs following four checkpoints; (i) validity [ClValid package], (ii) re-evaluation of validity results and unbiased determination of the winning algorithm [NbClust package], (iii) stability test [ClusterStability package] and (iv) generalizability [predict.strength package] testing for the most optimal clustering solution. Thereafter, we investigated whether the identified FThD subgrouping solution was associated with neurocognitive performance, social and occupational functioning by using Welch’s two-sample t-test or Mann-Whitney-U test based on the distribution of data, and explored the interrelation of these domains with network analysis by using qgraph package with the spearman correlation matrix among variables. All analyses and univariate statistical comparisons were conducted with R version 3.5.2. We used the False Discovery Rate (FDR)37 to correct all P-values for the multiple comparisons. Results The k-means algorithm-based on two-cluster solution (FThD high vs. low) surviving these validity, stability and generalizability tests was chosen for further association tests and network analysis with core disease phenotypes. Patients in FThD high subgroup had lower scores in global (pfdr = 0.0001), social (pfdr < 0.0001) and role (pfdr < 0.0001) functioning, in semantic (pfdr < 0.0001) and phonological verbal fluency (pfdr = 0.0004), verbal short-term memory (pfdr = 0.0018) and abstract thinking (pfdr = 0.0099). Cluster assignment was not informed by the global disease severity (pfdr = 0.7786) but was associated with more pronounced negative symptoms (pfdr = 0.0001) in the FThD high subgroup. Discussion Our findings highlight how the combination of unsupervised machine learning algorithms with network analysis techniques may provide novel insight about the mappings between psychopathology, neurocognition and functioning. Furthermore, they point how FThD may represent a target variable for individualized psycho-, socio-, logotherapeutic interventions aimed at improving neurocognition abilities and functioning. Prospective studies should further test this promising perspective.
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    Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes
    Popovic, D ; Ruef, A ; Dwyer, DB ; Antonucci, LA ; Eder, J ; Sanfelici, R ; Kambeitz-Ilankovic, L ; Oztuerk, OF ; Dong, MS ; Paul, R ; Paolini, M ; Hedderich, D ; Haidl, T ; Kambeitz, J ; Ruhrmann, S ; Chisholm, K ; Schultze-Lutter, F ; Falkai, P ; Pergola, G ; Blasi, G ; Bertolino, A ; Lencer, R ; Dannlowski, U ; Upthegrove, R ; Salokangas, RKR ; Pantelis, C ; Meisenzahl, E ; Wood, SJ ; Brambilla, P ; Borgwardt, S ; Koutsouleris, N (ELSEVIER SCIENCE INC, 2020-12-01)
    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.