Single Subject Classification of Alzheimer’s Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging

Article type: Research Article

Authors: Bouts, Mark J.R.J.^{a; b; c; *} | Möller, Christiane^{a; b; c} | Hafkemeijer, Anne^{a; b; c} | van Swieten, John C.^{d; e} | Dopper, Elise^{e; f} | van der Flier, Wiesje M.^{f; g} | Vrenken, Hugo^{h; i} | Wink, Alle Meije^h | Pijnenburg, Yolande A.L.^f | Scheltens, Philip^f | Barkhof, Frederik^{h; j} | Schouten, Tijn. M.^{a; b; c} | de Vos, Frank^{a; b; c} | Feis, Rogier A.^{b; c} | van der Grond, Jeroen^b | de Rooij, Mark^{a; c} | Rombouts, Serge A.R.B.^{a; b; c}

Affiliations: [a] Institute of Psychology, Leiden University, Leiden, The Netherlands | [b] Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands | [c] Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands | [d] Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands | [e] Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands | [f] Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands | [g] Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands | [h] Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands | [i] Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands | [j] Institute of Neurology and Healthcare Engineering, University College London, London, UK

Correspondence: [*] Correspondence to: Mark J.R.J. Bouts, PhD, Leiden University, Institute of Psychology, PO Box 9555, 2300 RB Leiden, The Netherlands. Tel.: +31 71 5276911; E-mail: [email protected].

Abstract: Background/Objective:Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to—on a subject-basis—differentiate these dementia-types is not yet known. Methods:Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier’s ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). Results:Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). Conclusion:Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

Keywords: Alzheimer’s disease, behavioral variant frontotemporal dementia, classification, differential diagnosis, diffusion tensor imaging, functional MRI, machine learning

DOI: 10.3233/JAD-170893

Journal: Journal of Alzheimer's Disease, vol. 62, no. 4, pp. 1827-1839, 2018