Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

Antti Cajanus, Anette Hall, J. Koikkalainen, Eino Solje, Antti Tolonen, Timo Urhemaa, Yawu Liu, Ramona M. Haanpää, Päivi Hartikainen, Seppo Helisalmi, Ville Korhonen, Daniel Rueckert, Steen Hasselbalch, Gunhild Waldemar, Patrizia Mecocci, Ritva Vanninen, Mark van Gils, Hilkka Soininen, Jyrki Lötjönen, Anne M. Remes

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)51-59
JournalDementia and Geriatric Cognitive Disorders Extra
Volume8
Issue number1
DOIs
Publication statusPublished - 3 Apr 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Dementia
  • Frontotemporal dementia
  • Frontotemporal lobar degeneration
  • Machine learning
  • MRI
  • Neuroimaging

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    Cajanus, A., Hall, A., Koikkalainen, J., Solje, E., Tolonen, A., Urhemaa, T., Liu, Y., Haanpää, R. M., Hartikainen, P., Helisalmi, S., Korhonen, V., Rueckert, D., Hasselbalch, S., Waldemar, G., Mecocci, P., Vanninen, R., van Gils, M., Soininen, H., Lötjönen, J., & 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