Detecting frontotemporal dementia syndromes using MRI biomarkers

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

doi:10.1016/j.nicl.2019.101711

Detecting frontotemporal dementia syndromes using MRI biomarkers

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

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

43 Citations (Scopus)

Abstract

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.

Original language	English
Article number	101711
Journal	NeuroImage: Clinical
Volume	22
DOIs	https://doi.org/10.1016/j.nicl.2019.101711
Publication status	Published - 1 Jan 2019
Externally published	Yes
Publication type	Not Eligible

Funding

Marie Bruun, Hanneke FM Rhodius-Meester, Marta Baroni, Le Gjerum, Yolande Pijnenburg, Anne M. Remes, Mark van Gils, Kristian S. Frederiksen, Gunhild Waldemar, Patrizia Mecocci and Steen Gregers Hasselbalch report no disclosures. Hilkka Soininen has served in advisory boards for ACImmune, MSD, and Orion Pharma. Jyrki Lötjönen and Juha Koikkalainen are shareholders in Combinostics Oy. Wiesje M van der Flier performs contract research for Biogen. Research programs of Wiesje van der Flier have been funded by ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, stichting Dioraphte, Gieskes-Strijbis fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, Combinostics. All funding is paid to Wiesje M van der Flier's institution. Frederik Barkhof is supported by the NIHR UCLH Biomedical Research Centre. This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd., GE Healthcare Ltd., Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, ‘S. Maria della Misericordia’ Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre. This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd., GE Healthcare Ltd., Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, ‘S. Maria della Misericordia’ Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre.

Keywords

Behavioral variant frontotemporal dementia
Dementia
Differential diagnosis
Frontotemporal lobar degeneration
MRI
Primary progressive aphasia

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Neurology
Clinical Neurology
Cognitive Neuroscience

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Bruun, M., Koikkalainen, J., Rhodius-Meester, H. F. M., Baroni, M., Gjerum, L., van Gils, M., Soininen, H., Remes, A. M., Hartikainen, P., Waldemar, G., Mecocci, P., Barkhof, F., Pijnenburg, Y., van der Flier, W. M., Hasselbalch, S. G., Lötjönen, J., & Frederiksen, K. S. (2019). Detecting frontotemporal dementia syndromes using MRI biomarkers. NeuroImage: Clinical, 22, Article 101711. https://doi.org/10.1016/j.nicl.2019.101711

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title = "Detecting frontotemporal dementia syndromes using MRI biomarkers",

abstract = "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 = "Behavioral variant frontotemporal dementia, Dementia, Differential diagnosis, Frontotemporal lobar degeneration, MRI, Primary progressive aphasia",

author = "Marie Bruun and Juha Koikkalainen and Rhodius-Meester, {Hanneke F.M.} and Marta Baroni and Le Gjerum and {van Gils}, Mark and Hilkka Soininen and Remes, {Anne M.} and P{\"a}ivi Hartikainen and Gunhild Waldemar and Patrizia Mecocci and Frederik Barkhof and Yolande Pijnenburg and {van der Flier}, {Wiesje M.} and Hasselbalch, {Steen G.} and Jyrki L{\"o}tj{\"o}nen and Frederiksen, {Kristian S.}",

note = "Funding Information: Marie Bruun, Hanneke FM Rhodius-Meester, Marta Baroni, Le Gjerum, Yolande Pijnenburg, Anne M. Remes, Mark van Gils, Kristian S. Frederiksen, Gunhild Waldemar, Patrizia Mecocci and Steen Gregers Hasselbalch report no disclosures. Hilkka Soininen has served in advisory boards for ACImmune, MSD, and Orion Pharma. Jyrki L{\"o}tj{\"o}nen and Juha Koikkalainen are shareholders in Combinostics Oy. Wiesje M van der Flier performs contract research for Biogen. Research programs of Wiesje van der Flier have been funded by ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, stichting Dioraphte, Gieskes-Strijbis fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, Combinostics. All funding is paid to Wiesje M van der Flier's institution. Frederik Barkhof is supported by the NIHR UCLH Biomedical Research Centre. Funding Information: This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd., GE Healthcare Ltd., Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, {\textquoteleft}S. Maria della Misericordia{\textquoteright} Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre. Funding Information: This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd., GE Healthcare Ltd., Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, {\textquoteleft}S. Maria della Misericordia{\textquoteright} Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre. Publisher Copyright: {\textcopyright} 2019",

year = "2019",

month = jan,

day = "1",

doi = "10.1016/j.nicl.2019.101711",

language = "English",

volume = "22",

}

Bruun, M, Koikkalainen, J, Rhodius-Meester, HFM, Baroni, M, Gjerum, L, van Gils, M, Soininen, H, Remes, AM, Hartikainen, P, Waldemar, G, Mecocci, P, Barkhof, F, Pijnenburg, Y, van der Flier, WM, Hasselbalch, SG, Lötjönen, J & Frederiksen, KS 2019, 'Detecting frontotemporal dementia syndromes using MRI biomarkers', NeuroImage: Clinical, vol. 22, 101711. https://doi.org/10.1016/j.nicl.2019.101711

TY - JOUR

T1 - Detecting frontotemporal dementia syndromes using MRI biomarkers

AU - Bruun, Marie

AU - Koikkalainen, Juha

AU - Rhodius-Meester, Hanneke F.M.

AU - Baroni, Marta

AU - Gjerum, Le

AU - van Gils, Mark

AU - Soininen, Hilkka

AU - Remes, Anne M.

AU - Hartikainen, Päivi

AU - Waldemar, Gunhild

AU - Mecocci, Patrizia

AU - Barkhof, Frederik

AU - Pijnenburg, Yolande

AU - van der Flier, Wiesje M.

AU - Hasselbalch, Steen G.

AU - Lötjönen, Jyrki

AU - Frederiksen, Kristian S.

N1 - Funding Information: Marie Bruun, Hanneke FM Rhodius-Meester, Marta Baroni, Le Gjerum, Yolande Pijnenburg, Anne M. Remes, Mark van Gils, Kristian S. Frederiksen, Gunhild Waldemar, Patrizia Mecocci and Steen Gregers Hasselbalch report no disclosures. Hilkka Soininen has served in advisory boards for ACImmune, MSD, and Orion Pharma. Jyrki Lötjönen and Juha Koikkalainen are shareholders in Combinostics Oy. Wiesje M van der Flier performs contract research for Biogen. Research programs of Wiesje van der Flier have been funded by ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, stichting Dioraphte, Gieskes-Strijbis fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, Combinostics. All funding is paid to Wiesje M van der Flier's institution. Frederik Barkhof is supported by the NIHR UCLH Biomedical Research Centre. Funding Information: This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd., GE Healthcare Ltd., Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, ‘S. Maria della Misericordia’ Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre. Funding Information: This work was co-funded by the European Commission under grant agreement 611005 (PredictND). The PredictND consortium consisted of collaborators from VTT Technical Research Centre of Finland Ltd., GE Healthcare Ltd., Imperial College London, Alzheimer Europe, Alzheimer Center Amsterdam, Amsterdam UMC, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, ‘S. Maria della Misericordia’ Hospital of Perugia, Italy, the department of Neurology from the University of Eastern Finland and Combinostics Ltd., Finland. FB is supported by the NIHR UCLH biomedical research centre. Publisher Copyright: © 2019

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Behavioral variant frontotemporal dementia

KW - Dementia

KW - Differential diagnosis

KW - Frontotemporal lobar degeneration

KW - MRI

KW - Primary progressive aphasia

UR - http://www.scopus.com/inward/record.url?scp=85061193776&partnerID=8YFLogxK

U2 - 10.1016/j.nicl.2019.101711

DO - 10.1016/j.nicl.2019.101711

M3 - Article

C2 - 30743135

AN - SCOPUS:85061193776

SN - 2213-1582

VL - 22

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

M1 - 101711

ER -

Detecting frontotemporal dementia syndromes using MRI biomarkers

Abstract

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Keywords

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