NEURAL-NETWORK SUBTYPING OF DEPRESSION

Objective: To examine the applicability of a neural network classifica tion strategy to examine the independent contribution of psychomotor d isturbance (PMD) and endogeneity symptoms to the DSM-III-H definition of melancholia. Method: We studied 407 depressed patients with the cli nical dataset comprising 17 endogeneity symptoms and the 18-item CORE measure of behaviourally rated PMD. A multilayer perceptron neural net work was used to fit non-linear models of varying complexity. A linear discriminant function analysis was also used to generate a model for comparison with the non-linear models. Results: Models (linear and non -linear) using PMD items only and endogeneity symptoms only had simila r rates of successful classification, while non-linear models combinin g both PMD and symptom scores achieved the best classifications. Concl usions: Our current non-linear model was superior to a linear analysis , a finding which may have wider application to psychiatric classifica tion. Our non-linear analysis of depressive subtypes supports the bina ry view that melancholic and nonmelancholic depression are separate cl inical disorders rather than different forms of the same entity. This study illustrates how non-linear modelling with neural networks is a p otentially fruitful approach to the study of the diagnostic taxonomy o f psychiatric disorders and to clinical decision-making.