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.
Key Words
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Article Info
Publication History
Published online: May 24, 2012
Accepted:
April 3,
2012
Received in revised form:
April 2,
2012
Received:
August 27,
2011
Identification
Copyright
© 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.