IN VIVO MEASUREMENT OF CHOLINERGIC BASAL FOREBRAIN ATROPHY IN DEMENTIA WITH LEWY BODIES

Abstract

mean and unit variance over subjects. The dataset was then divided into training (n1⁄4153) and testing sets (n1⁄4152), preserving pathology proportions. Univariate feature selection was applied such that only the top 10% of regions most important for prediction were used in the models. Group separation was quantified using a linear support vector machine with cross-validation. Class weighting was applied to adjust for unbalanced groups. Three classifiers were trained to separate each of the three main dementia groups from the pooled group of all other dementias. A further three classifiers were trained to discriminate each pair of the main dementia groups.Results: Feature selection demonstrated higher accuracies for group separation than using all features. Central structures including the putamen, thalamus and substantia nigra, and temporal lobe regions helped to distinguish FTLD from DLB. AD and FTLD were best distinguished using frontal/occipital lobe regions, and AD and DLB using temporal/parietal regions (Table1). Classification accuracy was greatest for AD pathology in all models (Figure1).Conclusions:Classification of dementia pathology based on brain substructure volumes may help to maximise the diagnostic information available from clinical T1-weighted MRI. Performing feature selection using these volumes in autopsy-confirmed subjects could identify brain regions with diagnostic specificity for particular pathologies.