An Artificial Neural Network That Uses Eye-Tracking Performance to Identify Patients With Schizophrenia

Several researchers have underscored the importance of precise characterization of eye-tracking dysfunction (ETD) in patients with schizophrenia. This biological trait appears to be useful in estimating the probability of genetic recombination in an individual, so it may be helpful in linkage studie...

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Bibliographic Details
Published in:Schizophrenia bulletin 1999-01, Vol.25 (4), p.789-799
Main Authors: Campana, Arturo, Duci, Alessandro, Gambini, Orsola, Scarone, Silvio
Format: Article
Language:English
Subjects:
Adult
Adult and adolescent clinical studies
Antipsychotic Agents - therapeutic use
Biological and medical sciences
Female
Human
Humans
Male
Medical sciences
Neural Networks
Neural Networks (Computer)
Psychology. Psychoanalysis. Psychiatry
Psychopathology. Psychiatry
Psychoses
Random Allocation
Saccades - physiology
Schizophrenia
Schizophrenia - diagnosis
Schizophrenia - drug therapy
Visual Tracking
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Summary:Several researchers have underscored the importance of precise characterization of eye-tracking dysfunction (ETD) in patients with schizophrenia. This biological trait appears to be useful in estimating the probability of genetic recombination in an individual, so it may be helpful in linkage studies. This article describes a non-linear computational model for using ETD to identify schizophrenia. A back-propagation neural network (BPNN) was used to classify schizophrenia patients and normal control subjects on the basis of their eye-tracking performance. Better classification results were obtained with BPNN than with a linear computational model (discriminant analysis): a priori predictions were approximately 80 percent correct. These results suggest, first, that eye-tracking patterns can be useful in distinguishing patients with schizophrenia from a normal comparison group with an accuracy of approximately 80 percent. Second, parallel distributed processing networks are able to detect higher order nonlinear relationships among predictor quantitative measurements of eye-tracking performance.
ISSN:0586-7614
1745-1701
DOI:10.1093/oxfordjournals.schbul.a033419