Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

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Standard

Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis. / Cajanus, Antti; Hall, Anette; Koikkalainen, Juha; Solje, Eino; Tolonen, Antti; Urhemaa, Timo; Liu, Yawu; Haanpää, Ramona M; Hartikainen, Päivi; Helisalmi, Seppo; Korhonen, Ville; Rueckert, Daniel; Hasselbalch, Steen; Waldemar, Gunhild; Mecocci, Patrizia; Vanninen, Ritva; van Gils, Mark; Soininen, Hilkka; Lötjönen, Jyrki; Remes, Anne M.

I: Dementia and Geriatric Cognitive Disorders Extra, Bind 8, Nr. 1, 2018, s. 51-59.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Cajanus, A, Hall, A, Koikkalainen, J, Solje, E, Tolonen, A, Urhemaa, T, Liu, Y, Haanpää, RM, Hartikainen, P, Helisalmi, S, Korhonen, V, Rueckert, D, Hasselbalch, S, Waldemar, G, Mecocci, P, Vanninen, R, van Gils, M, Soininen, H, Lötjönen, J & Remes, AM 2018, 'Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis', Dementia and Geriatric Cognitive Disorders Extra, bind 8, nr. 1, s. 51-59. https://doi.org/10.1159/000486849

APA

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

Vancouver

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

Author

Cajanus, Antti ; Hall, Anette ; Koikkalainen, Juha ; Solje, Eino ; Tolonen, Antti ; Urhemaa, Timo ; Liu, Yawu ; Haanpää, Ramona M ; Hartikainen, Päivi ; Helisalmi, Seppo ; Korhonen, Ville ; Rueckert, Daniel ; Hasselbalch, Steen ; Waldemar, Gunhild ; Mecocci, Patrizia ; Vanninen, Ritva ; van Gils, Mark ; Soininen, Hilkka ; Lötjönen, Jyrki ; Remes, Anne M. / Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis. I: Dementia and Geriatric Cognitive Disorders Extra. 2018 ; Bind 8, Nr. 1. s. 51-59.

Bibtex

@article{753c0e2a95bf4817a677aae37bfd9aec,
title = "Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis",
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.",
author = "Antti Cajanus and Anette Hall and Juha Koikkalainen and Eino Solje and Antti Tolonen and Timo Urhemaa and Yawu Liu and Haanp{\"a}{\"a}, {Ramona M} and P{\"a}ivi Hartikainen and Seppo Helisalmi and Ville Korhonen and Daniel Rueckert and Steen Hasselbalch and Gunhild Waldemar and Patrizia Mecocci and Ritva Vanninen and {van Gils}, Mark and Hilkka Soininen and Jyrki L{\"o}tj{\"o}nen and Remes, {Anne M}",
year = "2018",
doi = "10.1159/000486849",
language = "English",
volume = "8",
pages = "51--59",
journal = "Dementia and Geriatric Cognitive Disorders",
issn = "1420-8008",
publisher = "S Karger AG",
number = "1",

}

RIS

TY - JOUR

T1 - Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

AU - Cajanus, Antti

AU - Hall, Anette

AU - Koikkalainen, Juha

AU - Solje, Eino

AU - Tolonen, Antti

AU - Urhemaa, Timo

AU - Liu, Yawu

AU - Haanpää, Ramona M

AU - Hartikainen, Päivi

AU - Helisalmi, Seppo

AU - Korhonen, Ville

AU - Rueckert, Daniel

AU - Hasselbalch, Steen

AU - Waldemar, Gunhild

AU - Mecocci, Patrizia

AU - Vanninen, Ritva

AU - van Gils, Mark

AU - Soininen, Hilkka

AU - Lötjönen, Jyrki

AU - Remes, Anne M

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1159/000486849

DO - 10.1159/000486849

M3 - Journal article

C2 - 29606954

VL - 8

SP - 51

EP - 59

JO - Dementia and Geriatric Cognitive Disorders

JF - Dementia and Geriatric Cognitive Disorders

SN - 1420-8008

IS - 1

ER -

ID: 216511881