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.

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Keywords Alzheimer’s disease, behavioral variant frontotemporal dementia, classification, differential diagnosis, diffusion tensor imaging, functional MRI, machine learning
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Journal Journal of Alzheimer's Disease
Bouts, M., Moller, C., Hafkemeijer, A, van Swieten, J.C, Dopper, E.G.P, van der Flier, W.M, … Rombouts, S. (2018). Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging. Journal of Alzheimer's Disease, 62(4), 1827–1839. doi:10.3233/jad-170893