title: A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia.
abstract: The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration.
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales Sydney, NSW, Australia.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Wang
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales Sydney, NSW, Australia.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.affiliation: Norwich Medical School, University of East Anglia Norwich, UK.
authors.forename: Maxime
authors.initials: M
authors.lastname: Bertoux
authors.affiliation: School of Medical Sciences, University of New South Wales Sydney, NSW, Australia.
authors.forename: John R
authors.initials: JR
authors.lastname: Hodges
authors.affiliation: Norwich Medical School, University of East Anglia Norwich, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Hornberger
keywords: AD; Bayesian; MRI; bvFTD; classification; machine learning