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

doi:10.1159/000486849

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

19 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 language	English
Pages (from-to)	51-59
Number of pages	9
Journal	Dementia and Geriatric Cognitive Disorders Extra
Volume	8
Issue number	1
DOIs	https://doi.org/10.1159/000486849
Publication status	Published - 3 Apr 2018
MoE publication type	A1 Journal article-refereed

Funding

This work received funding from the following bodies: European Union’s Seventh Frame work Programme for research, technological development, and demonstration under grant agreement No. 611005 (PredictND); VTR funding from Kuopio University Hospital; the Finnish Medical Foundation; the Olvi Foundation; the Finnish Alzheimer Research Association; the Finnish Brain Foundation; and the Päivikki and Sakari Sohlberg foundation.

Keywords

Dementia
Frontotemporal dementia
Frontotemporal lobar degeneration
Machine learning
MRI
Neuroimaging

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10.1159/000486849

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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

@article{7b88391538fe4ad18f01b5a5c441877a,

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.",

keywords = "Dementia, Frontotemporal dementia, Frontotemporal lobar degeneration, Machine learning, MRI, Neuroimaging",

author = "Antti Cajanus and Anette Hall and J. 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",

month = apr,

day = "3",

doi = "10.1159/000486849",

language = "English",

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journal = "Dementia and Geriatric Cognitive Disorders Extra",

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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, vol. 8, no. 1, pp. 51-59. https://doi.org/10.1159/000486849

TY - JOUR

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

AU - Cajanus, Antti

AU - Hall, Anette

AU - Koikkalainen, J.

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 - 2018/4/3

Y1 - 2018/4/3

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.

KW - Dementia

KW - Frontotemporal dementia

KW - Frontotemporal lobar degeneration

KW - Machine learning

KW - MRI

KW - Neuroimaging

UR - https://www.scopus.com/pages/publications/85042718386

U2 - 10.1159/000486849

DO - 10.1159/000486849

M3 - Article

C2 - 29606954

AN - SCOPUS:85042718386

SN - 1664-5464

VL - 8

SP - 51

EP - 59

JO - Dementia and Geriatric Cognitive Disorders Extra

JF - Dementia and Geriatric Cognitive Disorders Extra

IS - 1

ER -

Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

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