Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

Cajanus A, Hall A, Koikkalainen J, Solje E, Tolonen A, Urhemaa T, Liu Y, Haanpaa RM, Hartikainen P, Helisalmi S, Korhonen V, Rueckert D, Hasselbalch S, Waldemar G, Mecocci P, Vanninen R, Van Gils M, Soininen H, Lotjonen J, Remes AM (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Dementia and Geriatric Cognitive Disorders Extra Karger Publishers

Book Volume: 8

Pages Range: 51-59

Journal Issue: 1

DOI: 10.1159/000486849

Abstract

Aims: 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 C9ORF72-related genetic status in the differentiation sensitivity. Methods: The MRI scans of 50 patients with bvFTD (17 C9ORF72 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. Results: 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 C9ORF72 expansion carriers than noncarriers. Conclusion: 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.

Involved external institutions

University of Eastern Finland FI Finland (FI) Combinostics Oy FI Finland (FI) VTT Technical Research Centre of Finland FI Finland (FI) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Copenhagen DK Denmark (DK) Università degli Studi di Perugia IT Italy (IT) Kuopio University Hospital / Pohjois-Savon sairaanhoitopiiri FI Finland (FI)

How to cite

APA:

Cajanus, A., Hall, A., Koikkalainen, J., Solje, E., Tolonen, A., Urhemaa, T.,... Remes, A.M. (2018). Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis. Dementia and Geriatric Cognitive Disorders Extra, 8(1), 51-59. https://doi.org/10.1159/000486849

MLA:

Cajanus, Antti, et al. "Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis." Dementia and Geriatric Cognitive Disorders Extra 8.1 (2018): 51-59.

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