Simple Viewing Tests Can Detect Eye Movement Abnormalities That Distinguish Schizophrenia Cases from Controls with Exceptional Accuracy

• Published in: Biological psychiatry (1969) Vol. 72; no. 9; pp. 716 - 724
• Main Authors: Benson, Philip J., Beedie, Sara A., Shephard, Elizabeth, Giegling, Ina, Rujescu, Dan, St. Clair, David
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.11.2012; Elsevier
• Subjects: Adult; Adult and adolescent clinical studies; Biological and medical sciences; Case-Control Studies; Classification; Eye Movements - physiology; eye-movement phenotype; Female; Humans; Male; Medical sciences; Models, Statistical; neural network; Neuropsychological Tests - statistics & numerical data; Ocular Motility Disorders - complications; Ocular Motility Disorders - physiopathology; predictive model; Predictive Value of Tests; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Psychoses; risk marker; Schizophrenia; Schizophrenia - complications; Schizophrenia - diagnosis; Schizophrenia - physiopathology; Smoking - physiopathology; Time Factors; eye-movement phenotype; predictive model; schizophrenia; neural network; risk marker; Classification; Prediction; Schizophrenia; Case control study; Neural network; Abnormal movement; Eye movement; Psychosis; Phenotype; Accuracy; Models
• ISSN: 0006-3223; 1873-2402; 1873-2402
• DOI: 10.1016/j.biopsych.2012.04.019

We have investigated which eye-movement tests alone and combined can best discriminate schizophrenia cases from control subjects and their predictive validity. A training set of 88 schizophrenia cases and 88 controls had a range of eye movements recorded; the predictive validity of the tests was then examined on eye-movement data from 34 9-month retest cases and controls, and from 36 novel schizophrenia cases and 52 control subjects. Eye movements were recorded during smooth pursuit, fixation stability, and free-viewing tasks. Group differences on performance measures were examined by univariate and multivariate analyses. Model fitting was used to compare regression, boosted tree, and probabilistic neural network approaches. As a group, schizophrenia cases differed from control subjects on almost all eye-movement tests, including horizontal and Lissajous pursuit, visual scanpath, and fixation stability; fixation dispersal during free viewing was the best single discriminator. Effects were stable over time, and independent of sex, medication, or cigarette smoking. A boosted tree model achieved perfect separation of the 88 training cases from 88 control subjects; its predictive validity on retest assessments and novel cases and control subjects was 87.8%. However, when we examined the whole data set of 298 assessments, a cross-validated probabilistic neural network model was superior and could discriminate all cases from controls with near perfect accuracy at 98.3%. Simple viewing patterns can detect eye-movement abnormalities that can discriminate schizophrenia cases from control subjects with exceptional accuracy.