Detecting frontotemporal dementia syndromes using MRI biomarkers

Background: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal de...

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Main Authors: Marie Bruun, Juha Koikkalainen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Mark van Gils, Hilkka Soininen, Anne M. Remes, Päivi Hartikainen, Gunhild Waldemar, Patrizia Mecocci, Frederik Barkhof, Yolande Pijnenburg, Wiesje M. van der Flier, Steen G. Hasselbalch, Jyrki Lötjönen, Kristian S. Frederiksen
Format: Article
Language:English
Published: Elsevier 2019-01-01
Series:NeuroImage: Clinical
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158219300610
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author Marie Bruun
Juha Koikkalainen
Hanneke F.M. Rhodius-Meester
Marta Baroni
Le Gjerum
Mark van Gils
Hilkka Soininen
Anne M. Remes
Päivi Hartikainen
Gunhild Waldemar
Patrizia Mecocci
Frederik Barkhof
Yolande Pijnenburg
Wiesje M. van der Flier
Steen G. Hasselbalch
Jyrki Lötjönen
Kristian S. Frederiksen
spellingShingle Marie Bruun
Juha Koikkalainen
Hanneke F.M. Rhodius-Meester
Marta Baroni
Le Gjerum
Mark van Gils
Hilkka Soininen
Anne M. Remes
Päivi Hartikainen
Gunhild Waldemar
Patrizia Mecocci
Frederik Barkhof
Yolande Pijnenburg
Wiesje M. van der Flier
Steen G. Hasselbalch
Jyrki Lötjönen
Kristian S. Frederiksen
Detecting frontotemporal dementia syndromes using MRI biomarkers
NeuroImage: Clinical
author_facet Marie Bruun
Juha Koikkalainen
Hanneke F.M. Rhodius-Meester
Marta Baroni
Le Gjerum
Mark van Gils
Hilkka Soininen
Anne M. Remes
Päivi Hartikainen
Gunhild Waldemar
Patrizia Mecocci
Frederik Barkhof
Yolande Pijnenburg
Wiesje M. van der Flier
Steen G. Hasselbalch
Jyrki Lötjönen
Kristian S. Frederiksen
author_sort Marie Bruun
title Detecting frontotemporal dementia syndromes using MRI biomarkers
title_short Detecting frontotemporal dementia syndromes using MRI biomarkers
title_full Detecting frontotemporal dementia syndromes using MRI biomarkers
title_fullStr Detecting frontotemporal dementia syndromes using MRI biomarkers
title_full_unstemmed Detecting frontotemporal dementia syndromes using MRI biomarkers
title_sort detecting frontotemporal dementia syndromes using mri biomarkers
publisher Elsevier
series NeuroImage: Clinical
issn 2213-1582
publishDate 2019-01-01
description Background: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Keywords: Dementia, Frontotemporal lobar degeneration, Differential diagnosis, Behavioral variant frontotemporal dementia, Primary progressive aphasia, MRI
url http://www.sciencedirect.com/science/article/pii/S2213158219300610
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spelling doaj-adb78c564df34938908e63a4cb7c434b2020-11-24T23:49:10ZengElsevierNeuroImage: Clinical2213-15822019-01-0122Detecting frontotemporal dementia syndromes using MRI biomarkersMarie Bruun0Juha Koikkalainen1Hanneke F.M. Rhodius-Meester2Marta Baroni3Le Gjerum4Mark van Gils5Hilkka Soininen6Anne M. Remes7Päivi Hartikainen8Gunhild Waldemar9Patrizia Mecocci10Frederik Barkhof11Yolande Pijnenburg12Wiesje M. van der Flier13Steen G. Hasselbalch14Jyrki Lötjönen15Kristian S. Frederiksen16Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark; Corresponding author at: Danish Dementia Research Centre, Neuro Science Centre, Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark.Combinostics Ltd., Tampere, FinlandAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the NetherlandsInstitute of Gerontology and Geriatrics, University of Perugia, Perugia, ItalyDanish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, DenmarkVTT Technical Research Center of Finland Ltd, Tampere, FinlandInstitute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Neurocenter, neurology, Kuopio University Hospital, Kuopio, FinlandUnit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital, Oulu, FinlandNeurocenter, neurology, Kuopio University Hospital, Kuopio, FinlandDanish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, DenmarkInstitute of Gerontology and Geriatrics, University of Perugia, Perugia, ItalyDepartment of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; UCL institutes of Neurology and Healthcare Engineering, London, UKAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the NetherlandsAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the NetherlandsDanish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, DenmarkCombinostics Ltd., Tampere, FinlandDanish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, DenmarkBackground: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Keywords: Dementia, Frontotemporal lobar degeneration, Differential diagnosis, Behavioral variant frontotemporal dementia, Primary progressive aphasia, MRIhttp://www.sciencedirect.com/science/article/pii/S2213158219300610