Abstract

Tower of London Test: A Comparison between Conventional Statistic Approach and Modelling Based on Artificial Neural Network in Differentiating Fronto-Temporal Dementia from Alzheimer’s Disease

Highlights

  • Alzheimer’s disease (AD) is the prevalent type of dementia in the elderly, followed by frontotemporal dementia (FTD) which is considered the second commonest cause of dementia in persons younger than 65 [18]

  • The goal of the present study was to evaluate the sensitivity of Tower of London (ToL) to differentiate AD from FTD in a large sample of subjects, comparing two different statistical approaches, namely a classical analysis vs non linear analysis consisting on artificial neural networks

  • Ninety-four patients with FTD were recruited during eight consecutive months, at the Dementia Research Centers involved in the study [41 women; mean age 68.4 (SD = 8.4 years); mean education 8.5 years (SD = 4.5); MMSE = 23.6 adjusted score (SD = 2.9)] and 160 AD patients [102 women; mean age 77.7 (SD = 5.2 years); mean education 6.5 years (SD = 3.5); MMSE = 23.1 adjusted score (SD = 2.2)]

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Summary

Introduction

Alzheimer’s disease (AD) is the prevalent type of dementia in the elderly, followed by FTD which is considered the second commonest cause of dementia in persons younger than 65 [18]. There is evidence that late onset FTD is not uncommon, generating some difficulties to the differential diagnostic process from frontal variant of AD, when language or dysexecutive deficits are prevalent. An early differentiation between these two forms may help choosing a therapeutic approach with cholinesterases inhibitors which are restricted to AD, while for FTD there is no mention for symptomatic or disease modifying therapy. Both AD and FTD have significant implications for family members and a correct genetic counselling is largely dependent on a correct diagnosis. Mostly descriptive for FTD [26] and AD [23], several neuropsychological [37], behavioural [16], neuroimaging [29] and functional [12,15] tools have been proposed to achieve an early and reliable differentiation

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