WHAT CAN ARTIFICIAL NEURAL NETWORKS TEACH US ABOUT NEURODEGENERATIVE DISORDERS WITH EXTRAPYRAMIDAL FEATURES

Artificial neural networks (ANNs), computer paradigms that can learn, excel in pattern recognition tasks such as disease diagnosis. Artifici al neural networks operate in two different learning modes: supervised , in which a known diagnostic outcome is presented to the ANN, and uns upervised, in which the diagnostic outcome is not presented. A supervi sed learning ANN could emulate human expert diagnostic performance and identify relevant predictive markers in the diagnostic task, while an unsupervised learning ANN could suggest reasonable alternative diagno stic classification criteria. In the present study, we used ANN method ology to try to overcome the neuropathological difficulties in differe ntiating the subtypes of progressive supranuclear palsy (PSP), and in differentiating PSP from postencephalitic parkinsonism (PEP) and corti cobasal degeneration, or Pick's disease from corticobasal degeneration . First, we applied supervised learning ANN to classify 62 cases of th ese disorders and to identify diagnostic markers that distinguish them . In a second experiment, we used unsupervised learning ANN to investi gate possible alternative nosological classifications. Artificial neur al networks input data for each case consisted of values representing histological features, including neurofibrillary tangles, neuronal los s and gliosis found in multiple brain sampling areas. The supervised l earning ANN achieved excellent accuracy in classifying PSP but had dif ficulty classifying the other disorders. This method identified a few features that might help to differentiate PEP, supported currently pro posed criteria for Pick's disease, corticobasal degeneration and typic al PSP, but detected no features to characterize the atypical subtype of PSP. In general, unsupervised learning ANN supported the present no sological classification for PSP, PEP, Pick's disease and corticobasal degeneration, although it overlapped some groups. Artificial neural n etworks methodology appears promising for studying neurodegenerative d isorders.