Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition

Abstract

Context: Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging-based diagnostic classification of neuropsychiatric patient populations.

Objective: To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level.

Design: Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs.

Setting: Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany.

Participants: The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs.

Main outcome measures: Specificity, sensitivity, and accuracy of classification.

Results: The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases.

Conclusions: Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis.

Figure 1

Schematic representation of nonlinear support vector machine (SVM) classification (A) and large-margin classification (B). A, At left, 2 groups of individuals (red and green shapes) cannot be separated in the input space by a linear classifier because the relationship between the data instances and their class labels is nonlinear (black circle). At right, With the use of radial basis functions, the data can be mapped into a high-dimensional space where the groups can be separated by means of linear classification. The shaded shapes represent the support vectors that define the optimal separating hyperplane (OSH) (yellow). B, At left, infinite separating boundaries (dotted lines) may exist between 2 classes (red and green circles). At right, the SVM algorithm determines the OSH by maximizing the margin between the nearest data instances of opposite classes.

Figure 2

Discriminative patterns of the healthy control group 1 (HC₁) vs at-risk mental state, early (ARMS-E) vs at-risk mental state, late (ARMS-L) classification analysis. See the “Methods” section for an explanation of the visualization technique. Warm and cool colors represent volumetric reductions and increments, respectively, in the second vs the first group of the binary classifier. The units are gray matter volume residuals (after removing the effects of age and sex by means of partial correlation analysis and after scaling to a range of [−1, 1]). The gray matter volume reduction scales differed between HC₁ vs ARMS-E (A), HC₁ vs ARMS-L (B), and ARMS-E vs ARMS-L (C), with the largest effects being observed in the HC₁ vs ARMS-L classifier and the most subtle differences being present in the discriminative pattern of ARMS-E vs ARMS-L.

Figure 3

Discriminative patterns of the healthy control group 2 (HC₂) vs at-risk mental state (ARMS) with disease transition (ARMS-T) vs ARMS without disease transition (ARMS-NT) classification analysis. A, HC₂ vs ARMS-T. B, HC₂ vs ARMS-NT. C, ARMS-T vs ARMS-NT. See the “Methods” section for an explanation of the visualization technique. Warm and cool colors represent volumetric reductions and increments, respectively, in the second vs the first group of the binary classifier.