Dataset Information

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.

SUBMITTER: Bruun M

PROVIDER: S-EPMC6369219 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Detecting frontotemporal dementia syndromes using MRI biomarkers.

Bruun Marie M Koikkalainen Juha J Rhodius-Meester Hanneke F M HFM Baroni Marta M Gjerum Le L van Gils Mark M Soininen Hilkka H Remes Anne M AM Hartikainen Päivi P Waldemar Gunhild G Mecocci Patrizia P Barkhof Frederik F Pijnenburg Yolande Y van der Flier Wiesje M WM Hasselbalch Steen G SG Lötjönen Jyrki J Frederiksen Kristian S KS

NeuroImage. Clinical 20190204

<h4>Background</h4>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.<h4>Methods</h4>In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two m ...[more]

PMID: 30743135

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Project description:ObjectiveThe prognostic value of cerebrospinal fluid neurofilament light chain, total tau, phosphorylated tau181, and amyloid beta1-42 was examined in frontotemporal dementia subtypes.MethodsWe compared baseline biomarkers between 49 controls, 40 patients with behavioral variant frontotemporal dementia, 24 with semantic variant primary progressive aphasia, and 26 with nonfluent variant primary progressive aphasia. Linear mixed effect models were used to assess the value of baseline biomarkers in predicting clinical and radiographic change in patient cohorts over multiple yearly follow up visits.ResultsNeurofilament light chain concentrations were lowest in controls. Elevated baseline neurofilament light chain predicted faster worsening in clinical severity, frontotemporal volume and frontotemporal fractional anisotropy in patients with behavioral variant frontotemporal dementia and nonfluent variant primary progressive aphasia. High total tau similarly predicted faster progression in nonfluent variant primary progressive aphasia. In behavioral variant frontotemporal dementia, higher phosphorylated tau181 predicted faster clinical progression whereas lower amyloid beta1-42 predicted faster volumetric and fractional anisotropy reduction. Neurofilament light chain and phosphorylated tau181 were of greater predictive value in patients with tau pathology as compared to TDP-43 pathology. Baseline neurofilament light chain correlated with baseline clinical severity and frontotemporal volume in behavioral variant frontotemporal dementia. Baseline total tau correlated with baseline clinical severity in semantic variant primary progressive aphasia.InterpretationHigh cerebrospinal fluid neurofilament light chain predicts more aggressive disease in behavioral variant frontotemporal dementia and nonfluent variant primary progressive aphasia. Total tau, phosphorylated tau181, and amyloid beta1-42 also predict some measures of disease aggressiveness in frontotemporal dementia.

Project description:Abstract Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. Three hundred eighty-seven mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). The w-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as ‘normal’ or ‘abnormal’ based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the ‘normal’ and ‘abnormal’ groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials. Bocchetta et al. quantified brain anomalies on MRI in a large cohort of C9orf72, MAPT, and GRN mutation carriers. They defined the imaging markers associated with the largest clinical and behavioral changes over one year in presymptomatic carriers, providing important data to inform participants’ stratification in trials. Graphical Abstract Graphical abstract

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Detecting frontotemporal dementia syndromes using MRI biomarkers.

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Detecting frontotemporal dementia syndromes using MRI biomarkers.

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