期刊
BIOLOGICAL PSYCHIATRY
卷 72, 期 9, 页码 716-724出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.biopsych.2012.04.019
关键词
Classification; eye-movement phenotype; neural network; predictive model; risk marker; schizophrenia
资金
- Royal Society of London
- Millar-Mackenzie Trust
- National Institute of Mental Health Genome-Wide Association Study
- University of Aberdeen
- European Framework 6 (SGENE)
- Scottish Chief Scientist Office [CZB 4/734]
- Chief Scientist Office [CZB/4/734] Funding Source: researchfish
Background: We have investigated which eye-movement tests alone and combined can best discriminate schizophrenia cases from control subjects and their predictive validity. Methods: 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. Results: 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%. Conclusions: Simple viewing patterns can detect eye-movement abnormalities that can discriminate schizophrenia cases from control subjects with exceptional accuracy.
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