Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

Antti Cajanus; Anette Hall; Juha 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, Juha 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 journal › Article › Scientific › peer-review

18 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
DOIs	https://doi.org/10.1159/000486849
Publication status	Published - 2018
Externally published	Yes
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

ASJC Scopus subject areas

Cognitive Neuroscience
Psychiatry and Mental health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1159/000486849Licence: CC BY-NC-ND

Cite this

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, 51-59. https://doi.org/10.1159/000486849

@article{a383d4028923436e837024a8d986cda2,

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 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.}",

note = "Funding Information: This work received funding from the following bodies: European Union{\textquoteright}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 P{\"a}ivikki and Sakari Sohlberg foundation. Publisher Copyright: {\textcopyright} 2018 The Author(s) Published by S. Karger AG, Basel.",

year = "2018",

doi = "10.1159/000486849",

language = "English",

volume = "8",

pages = "51--59",

}

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, 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, 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.

N1 - Funding Information: 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. Publisher Copyright: © 2018 The Author(s) Published by S. Karger AG, Basel.

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.

KW - Dementia

KW - Frontotemporal dementia

KW - Frontotemporal lobar degeneration

KW - Machine learning

KW - MRI

KW - Neuroimaging

U2 - 10.1159/000486849

DO - 10.1159/000486849

M3 - Article

AN - SCOPUS:85042718386

VL - 8

SP - 51

EP - 59

JO - Dementia and Geriatric Cognitive Disorders Extra

JF - Dementia and Geriatric Cognitive Disorders Extra

ER -

Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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