Detecting frontotemporal dementia syndromes using MRI biomarkers

Marie Bruun (Corresponding Author), Juha Koikkalainen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Mark van Gils, Hilkka Soininen, Anne 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 journalArticleScientificpeer-review

3 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 languageEnglish
Article number101711
Pages (from-to)101711
JournalNeuroImage: Clinical
Volume22
DOIs
Publication statusPublished - 4 Feb 2019
MoE publication typeA1 Journal article-refereed

Fingerprint

Frontotemporal Dementia
Biomarkers
Magnetic Resonance Imaging
Area Under Curve
Primary Progressive Aphasia
Sensitivity and Specificity
Atrophy
Lewy Body Disease
Vascular Dementia
Semantics
ROC Curve
Multicenter Studies
Dementia
Alzheimer Disease
Cohort Studies
Brain

Keywords

  • dementia
  • frontotemporal lobar degeneration
  • differential diagnosis
  • behavioral variant frontotemporal dementia
  • primary progressive aphasia
  • MRI

Cite this

Bruun, M., Koikkalainen, J., Rhodius-Meester, H. F. M., Baroni, M., Gjerum, L., van Gils, M., ... Frederiksen, K. S. (2019). Detecting frontotemporal dementia syndromes using MRI biomarkers. NeuroImage: Clinical, 22, 101711. [101711]. https://doi.org/10.1016/j.nicl.2019.101711
Bruun, Marie ; Koikkalainen, Juha ; Rhodius-Meester, Hanneke F.M. ; Baroni, Marta ; Gjerum, Le ; van Gils, Mark ; Soininen, Hilkka ; Remes, Anne ; Hartikainen, Päivi ; Waldemar, Gunhild ; Mecocci, Patrizia ; Barkhof, Frederik ; Pijnenburg, Yolande ; van der Flier, Wiesje M. ; Hasselbalch, Steen G. ; Lötjönen, Jyrki ; Frederiksen, Kristian S. / Detecting frontotemporal dementia syndromes using MRI biomarkers. In: NeuroImage: Clinical. 2019 ; Vol. 22. pp. 101711.
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title = "Detecting frontotemporal dementia syndromes using MRI biomarkers",
abstract = "BackgroundDiagnosing 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.MethodsIn 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).ResultsThe 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.ConclusionThis 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",
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 Anne Remes 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.}",
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language = "English",
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Bruun, M, Koikkalainen, J, Rhodius-Meester, HFM, Baroni, M, Gjerum, L, van Gils, M, Soininen, H, Remes, A, 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, pp. 101711. https://doi.org/10.1016/j.nicl.2019.101711

Detecting frontotemporal dementia syndromes using MRI biomarkers. / Bruun, Marie (Corresponding Author); Koikkalainen, Juha; Rhodius-Meester, Hanneke F.M.; Baroni, Marta; Gjerum, Le; van Gils, Mark; Soininen, Hilkka; Remes, Anne; Hartikainen, Päivi; Waldemar, Gunhild; Mecocci, Patrizia; Barkhof, Frederik; Pijnenburg, Yolande; van der Flier, Wiesje M.; Hasselbalch, Steen G.; Lötjönen, Jyrki; Frederiksen, Kristian S.

In: NeuroImage: Clinical, Vol. 22, 101711, 04.02.2019, p. 101711.

Research output: Contribution to journalArticleScientificpeer-review

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

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.

PY - 2019/2/4

Y1 - 2019/2/4

N2 - BackgroundDiagnosing 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.MethodsIn 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).ResultsThe 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.ConclusionThis study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

AB - BackgroundDiagnosing 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.MethodsIn 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).ResultsThe 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.ConclusionThis study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

KW - dementia

KW - frontotemporal lobar degeneration

KW - differential diagnosis

KW - behavioral variant frontotemporal dementia

KW - primary progressive aphasia

KW - MRI

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DO - 10.1016/j.nicl.2019.101711

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Bruun M, Koikkalainen J, Rhodius-Meester HFM, Baroni M, Gjerum L, van Gils M et al. Detecting frontotemporal dementia syndromes using MRI biomarkers. NeuroImage: Clinical. 2019 Feb 4;22:101711. 101711. https://doi.org/10.1016/j.nicl.2019.101711