Detecting the Psychosis Prodrome Across High-Risk Populations Using Neuroanatomical Biomarkers
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Schizophrenia Bulletin vol. 41 no. 2 pp. 471–482, 2015 doi:10.1093/schbul/sbu078 Advance Access publication June 9, 2014 Detecting the Psychosis Prodrome Across High-Risk Populations Using Neuroanatomical Biomarkers Nikolaos Koutsouleris*,1, Anita Riecher-Rössler2,4, Eva M. Meisenzahl1, Renata Smieskova2, Erich Studerus2, Lana Kambeitz-Ilankovic1, Sebastian von Saldern1, Carlos Cabral1, Maximilian Reiser3, Peter Falkai1, and Stefan Borgwardt2 1Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; 2Department of Psychiatry, University of Basel, Basel, Switzerland; 3Department of Radiology, Ludwig-Maximilian-University, Munich, Germany 4This author contributed equally to this article. *To whom correspondence should be addressed; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstr. 7, 80336 Munich, Germany; tel: +49-89-4400-55885, fax: +49-89-4400-55776, e-mail: [email protected] To date, the MRI-based individualized prediction of psy- Key words: neuroanatomical biomarker/individualized chosis has only been demonstrated in single-site studies. It risk stratification/cross-center/machine learning remains unclear if MRI biomarkers generalize across dif- ferent centers and MR scanners and represent accurate sur- Introduction rogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction Two decades of early recognition research revealed system identified patients with a later disease transition that patterns of subtle prodromal symptoms allow per- among 73 clinically defined high-risk persons recruited at sons with at-risk mental states (ARMS) for psychoses two different early recognition centers. Prognostic per- to be reliably identified using clinical high-risk criteria. formance was measured using cross-validation, indepen- Compared to the general population, these persons have dent test validation, and Kaplan-Meier survival analysis. a 100-fold higher risk for developing these devastating Transition outcomes were correctly predicted in 80% of mental illnesses. However, relying solely on these symp- test cases (sensitivity: 76%, specificity: 85%, positive likeli- toms leads to a correct 24-month disease prediction in hood ratio: 5.1). Thus, given a 54-month transition risk of only 29%1 of cases and a 10-year transition likelihood 45% across both centers, MRI-based predictors provided between 35%2 and 49%.3 Therefore, tailoring preven- a 36%-increase of prognostic certainty. After stratifying tive treatment to each individual’s risk while minimizing individuals into low-, intermediate-, and high-risk groups harmful medication effects requires these rates to be con- using the predictor’s decision score, the high- vs low-risk siderably improved using valid and accessible prognostic groups had median psychosis-free survival times of 5 vs markers. 51 months and transition rates of 88% vs 8%. The predic- An array of candidate biomarkers has been recently tor’s decision function involved gray matter volume altera- identified, suggesting that the ARMS is associated with tions in prefrontal, perisylvian, and subcortical structures. alterations of neurocognitive performance4 and brain Our results support the existence of a cross-center neu- abnormalities at the neuroanatomical,5 functional,6–8 and roanatomical signature of emerging psychosis enabling chemical9,10 levels. Overall, these alterations seem to be individualized risk staging across different high-risk pop- similar to, but less severe than those in the established dis- ulations. Supplementary results revealed that (1) poten- ease.11 More specifically, structural imaging studies com- tially confounding between-site differences were effectively paring ARMS individuals with a subsequent full-blown mitigated using statistical correction methods, and (2) the illness (ARMS-T) to those without a later disease tran- detection of the prodromal signature considerably depended sition (ARMS-NT) showed reduced gray matter (GM) on the available sample sizes. These observations pave the in prefrontal, temporal, cingulate, insular, and subcorti- way for future multicenter studies, which may ultimately cal brain structures in the former vs the latter group.12 facilitate the neurobiological refinement of risk criteria However, most of these structural differences—albeit sig- and personalized preventive therapies based on individual- nificant at the group level—have so far failed to produce ized risk profiling tools. clinically viable markers of the psychosis prodrome. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] 471 N. Koutsouleris et al The gap between this research and its desired clinical prospective, naturalistic studies conducted independently application may originate from current study designs and at the Early Recognition Services of the Department univariate analysis methods: (1) vulnerability for reporting of Psychiatry and Psychotherapy (“Früherkennungs- bias due to the small-sample problem inherent in mono- und Therapiezentrum,” FETZ), Ludwig-Maximilian- centric high-risk research,13 (2) decomposition of com- University, Munich, Germany, and the Department of plex psychosis-related brain patterns into single voxels or Psychiatry (“Früherkennung von Psychosen,” FePsy), clusters,14 and hence (3) severe limitations in tracing these University of Basel, Switzerland. Detailed study descrip- patterns in individuals as these univariate projections are tions were given in earlier work.21,23 In summary, both quantified in terms ofgroup-level statistical significance. sites employed clinical ultra-high-risk criteria closely cor- Unfortunately, significance is largely useless for guiding responding to the well-established Personal Assessment the diagnostic process because a biomarker’s applicability and Crisis Evaluation (PACE) definitions.15–17 At the rather depends on its sensitivity and specificity, which mea- FETZ, cognitive-perceptual basic symptoms additionally sure its predictive performance at the single-subject level.15 defined psychosis risk:3 Hence, ARMS inclusion required In contrast, multivariate pattern recognition methods, (a) basic symptoms (FETZ), and/or (b) Attenuated such as support vector machines (SVM),16 have recently Positive Symptoms (FETZ and FePsy), and/or (c) Brief emerged as promising tools for the individualized diag- Limited Intermittent Psychotic Symptoms (FETZ and nosis of various neuropsychiatric conditions17,18 based on FePsy) fulfilling specific time criteria, ord ( ) Decline in their capacity to “learn” the innate interregional depen- global functioning combined with risk-conferring traits dencies of distributed brain pathologies from training as defined by each center’s intake criteria (Supplementary data and generalize the learned discriminative rules to Methods). Additionally, prodromal symptoms were new, unseen patients.19 Thus, these methods may promote assessed with the Brief Psychiatric Rating Scale (BPRS) an objective way to increase prognostic certainty to levels and Scale for the Assessment of Negative Symptoms required for individualized prevention, as shown by our (SANS; Basel) and with the Positive and Negative previous work in two independent ARMS samples from Symptoms Scale (PANSS; Munich). Munich and Basel.20,21 Exclusion criteria were age < 18 years, insufficient Although these initial results support the utility of knowledge of German, IQ < 70, previously diagnosed neuroimaging in predicting psychosis, tough replica- psychotic disorders or transient psychotic episodes meet- tion and verification of these initial candidate markers ing transition criteria (see below), a diagnosed brain is needed according to current biomarker development disease or substance dependency (except for cannabis regulations.22 A crucial validation step within this trans- in FePsy), previous head traumas, neurological, serious lational process is the demonstration of individualized medical or surgical illnesses, or lifetime diagnoses of psy- disease prediction based on a single neuroanatomical chotic or borderline personality disorders as assessed by signature evidenced across independent ARMS cohorts. an experienced psychiatrist in detailed clinical interviews. The identification of such a cross-center signature is chal- Prior to study inclusion, only 4 out of 73 ARMS individ- lenged by the clinical, neurobiological, and MRI-related uals had received low doses of atypical antipsychotics for heterogeneity of current small-sample ARMS cohorts. behavioral control by the referring psychiatrist or general Hence, our previous findings potentially resulted from an practitioner (3 participants olanzapine, 1 risperidone), all accidentally good separability of our single-center study for less than 3 weeks. populations.20,21 All subjects were regularly followed over 4 years and This study is to our knowledge the first and to date were offered supportive counseling and clinical manage- largest study on the MRI-based cross-center predic- ment. At both centers, transition criteria were monitored tion of psychosis. We hypothesized that the underly- monthly in the first year and quarterly thereafter using ing biomarker would consist of a pattern of prefrontal, BPRS/PANSS severity thresholds closely correspond- temporal, and subcortical brain alterations enabling ing to refs.24,25: hallucinations