Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification

Abstract

Background: Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition.

Methods: First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation.

Results: The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments.

Conclusions: Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.

Fig. 1.

Out-of-training prediction probabilities in the three classification analyses. A: HC vs at-risk mental states (ARMS) analysis, B: Binary prediction probabilities in the healthy controls (HC) vs ARMS-E vs ARMS-L analysis (left) and HC vs ARMS-NT vs ARMS-T analysis (right), C: Multigroup prediction probabilities in the HC vs ARMS-E vs ARMS-L analysis (left) and HC vs ARMS-NT vs ARMS-T analysis (right).

Fig. 2.

Neurocognitive feature selection probabilities. In each inner cross-validation (CV1) training sample, the set of neurocognitive features used by an support-vector machine (SVM) ensemble to categorize between the study groups was optimized by means of recursive classifier elimination (ensemble thinning, see Methods in online supplementary material). This procedure removed SVM classifiers containing irrelevant/redundant neurocognitive variables from the respective SVM ensembles. Thus, we were able to compute the feature selection probability of each neurocognitive variable as the ratio between the number of SVM models that used the respective variable as discriminative feature and the total number of SVM models (see table 6 in online supplementary material) across all CV1 training data partitions in each of the following classification analyses: A: healthy controls (HC) vs at-risk mental states (ARMS), B: HC vs ARMS-E vs ARMS-L, C: HC vs ARMS-NT vs ARMS-T.

Fig. 3.

Discriminative neurocognitive profiles of the three classification experiments. The support-vector machine (SVM) results of our study were obtained based on complex and subtle patterns of neurocognitive between-group differences that are difficult to visualize due to the nonlinearity of the classification method. Therefore, these nonlinear discriminative neurocognitive patterns were approximated (1) by computing the difference vector between the scaled (0,1) and adjusted (age, gender) neurocognitive features of all nearest-neighbor support-vector pairs that constituted the optimal separating decision boundary of a SVM model, trained on a given inner cross-validation (CV1) training sample and (2) by calculating the arithmetic mean and SE of the mean for these difference vectors obtained across all CV1 training samples in each of three classification experiments: A: healthy controls (HC) vs at-risk mental states (ARMS), B: HC vs ARMS-E vs ARMS-L, C: HC vs ARMS-NT vs ARMS-T. Zero-crossings of SEs indicate unreliable between-group differences.