Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy-study. Supplementary material

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1 Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy-study. Nikolaos Koutsouleris a,ca, MD; Stefan Borgwardt b, MD; Eva M. Meisenzahl, MD; Ronald Bottlender a, MD; Hans-Jürgen Möller a, MD; Anita Riecher-Rössler, MD b a Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany b Department of Psychiatry, University of Basel, Switzerland CA Corresponding Author s adress: Clinic of Psychiatry and Psychotherapy, Luwdig-Maximilian-University, Nussbaumstr. 7, Munich, GERMANY, Phone: , Nikolaos.Koutsouleris@med.uni-muenchen.de Supplementary material Koutsouleris et al. 1

2 Supplementary Tables Table 1: Two-group classification performance in the Munich high-risk population: A diagnostic system for the MRI-based classification of 17 converters and 17 non-converters to psychosis was constructed using the identical machine learning parameters as in the FePsy classification analyses. The observed diagnostic performance of the new machine learning pipeline outperformed the methodology used in Koutsouleris et al. 1 : ARMS-T vs ARMS-NT: Sensitivity 83%, Specificity 80%, Accuracy, 82%. See also Suppl. Figure 2 for an approximation of the decision boundary used by the classifier to distinguish between groups. Abbreviations: Sens Sensitivity, Spec specificity, BAC balanced accuracy, FPR false positive rate, PPV, NPV positive / negative predictive values, LR+ positive Likelihood Ratio, TP true positives, FN false negatives, TN true negatives, FP false positives. Binary classifiers TP TN FP FN Sens Spec BAC FPR PPV NPV LR+ New machine learning method 17 ARMS-T vs 17 ARMS-NT Machine learning method used in Koutsouleris et al ARMS-T vs 18 ARMS-NT Koutsouleris et al. 2

3 Supplementary Figures Supplementary Figure 1: Automated Anatomical Labeling (AAL) of voxel probability maps in the HC vs ARMS-NT, HC vs ARMS-T and ARMS-NT vs ARMS-T comparisons. The figure lists ROIs of the AAL template, where more than 5% of the within-roi voxels had a probability > 50% of reliably contributing to the average neuroanatomical decision boundary (see Figure 1 in the main document for a methodological description of the employed visualization procedure). Each ROI entry lists the (1) percentage of these voxels within the respective region (K ROI ), (2) their average (SD) probability of reliable discriminative involvement (Prob %), as well as (3) the average (SD) volumetric difference (Diff %) in these voxels between the first and second group in the respective binary comparison (with positive difference values indicating more GM volume in the latter vs the former group). Each ROI entry was highlighted by a gray-scaled background, as defined by its average probability (Prob %) at the right top of the figure. Abbreviations: Ant anterior, Inf inferior, Mid middle, Orb orbital, Oper opercularis, Post posterior, Sup superior, Supp supplementary, Tri triangularis. Supplementary Figure 2: Voxel probability map (VPM) of reliable contributions to the ARMS-T vs ARMS-NT decision boundary obtained from the Munich high-risk population (17 converters vs 17 non-converters). See legend of Figure 1, in the main document. Supplementary Figure 3: Overlay of the voxel probability maps (VPMs) obtained from the binary classifications HC vs ARMS-T (red), HC vs ARMS-NT (green) and ARMS-T vs ARMS-NT (blue). The figure was created by (1) thresholding the three VPMs identically at a probability level of 0.5 and (2) smoothing them with a 6 mm full-width at half maximum gaussian kernel in order to facilitate the visualization of neuroanatomical similarities and differences in the average decision boundaries across the three binary classifiers. Discriminative voxels common to the HC vs ARMS-T and HC vs ARMS-NT patterns appear in yellow due to the additive mixture of green and red. Similarly, voxels with a selection probability >0.5 in the HC vs ARMS-T and ARMS-T vs ARMS-NT classifiers are painted in purple. Voxels overlapping between the discriminative patterns of HC vs ARMS-NT and ARMS-T vs ARMS-NT are depicted in cyan. Discriminative voxels used by all three binary classifiers are painted white. Koutsouleris et al. 3

4 Figure 1: Automated Anatomical Labeling (AAL) of voxel probability maps in the HC vs ARMS-NT, HC vs ARMS-T and ARMS-NT vs ARMS-T comparisons. Koutsouleris et al. 4

5 Revised version submitted to Schizophrenia Bulletin September 16, 2011 Figure 2: VPM of the ARMS-T vs ARMS-NT classification analysis in the Munich high-risk population. Koutsouleris et al. 5

6 Revised version submitted to Schizophrenia Bulletin September 16, 2011 Figure 3: VPM overlay of the three binary comparisons HC vs ARMS-T, HC vs ARMS-NT and ARMS-T vs ARMS-NT. Koutsouleris et al. 6

7 References [1] Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Möller HJ, Gaser C. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry 2009; 66: Koutsouleris et al. 7

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