Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis

Abstract

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.

Keywords: Bipolar; Depression; Individual prediction; Machine learning; Psychosis; Smooth pursuit eye movements.

Figure 1

Overview of study samples that were included into the machine training and validation procedures.

Figure 2

Examples of pursuit stimuli with pursuit recordings (eye position and eye velocity) in a control subject and a psychosis proband. Foveopetal step-ramp tasks (A) are used to measure saccade free pursuit initiation. Variables of interest are pursuit latency (time between target step and green dot), initial eye acceleration (blue line) and early maintenance gain (blue line in grey shaded intervals). Triangular wave tasks (B) are used to measure sustained predictive maintenance gain in predefined intervals (blue line in grey shaded intervals) excluding artifacts induced by target reversals. The figure has been adapted from one of our prior publications by Brakemeier and colleagues.