Supplementary Material for: Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

<b><i>Aims:</i></b> We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective memory complaints (SMC). We also examined the role of the <i>C9ORF72</i>-related genetic status in the differentiation sensitivity. <b><i>Methods:</i></b> The MRI scans of 50 patients with bvFTD (17 <i>C9ORF72</i> expansion carriers) were analyzed using 6 quantification methods as follows: voxel-based morphometry (VBM), tensor-based morphometry, volumetry (VOL), manifold learning, grading, and white-matter hyperintensities. Each patient was then individually compared to an independent reference group in order to attain diagnostic suggestions. <b><i>Results:</i></b> Only VBM and VOL showed utility in correctly identifying bvFTD from our set of data. The overall classification sensitivity of bvFTD with VOL + VBM achieved a total sensitivity of 60%. Using VOL + VBM, 32% were misclassified as having LBD. There was a trend of higher values for classification sensitivity of the <i>C9ORF72</i> expansion carriers than noncarriers. <b><i>Conclusion:</i></b><i></i> VOL, VBM, and their combination are effective in differential diagnostics between bvFTD and AD or SMC. However, MRI atrophy profiles for bvFTD and LBD are too similar for a reliable differentiation with the quantification methods tested in this study.