A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

Nazem, Amir; Tang, Chris C.; Spetsieris, Phoebe; Dresel, Christian; Gordon, M.S.; Diehl‐Schmid, Janine; Grimmer, Timo; Yakushev, Igor; Mattis, Paul J.; Ma, Yilong; Dhawan, Vijay; Eidelberg, David

doi:10.1016/j.dadm.2018.07.009

Cited by 20 publications

(20 citation statements)

References 46 publications

(104 reference statements)

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“…Studies that have utilized pattern classification techniques have found that structural MRI can differentiate bvFTD from controls with an accuracy of 85% 85,86 , and differentiate bvFTD from Alzheimer's dementia with an accuracy of 82% 86 . Diagnostic accuracy has also been shown to be excellent with pattern analysis of FDG-PET, with discriminatory power of 92.2% to differentiate bvFTD from Alzheimer's dementia and 87.6% to differentiate bvFTD from the other language variants of FTD 87 . In fact, simple visual assessments of FDG-PET looking for frontal, anterior cingulate and anterior temporal hypometabolism have excellent specificity (97.6%) and sensitivity (86%) in differentiating bvFTD from Alzheimer's dementia, and can improve differentiation based on clinical features alone 88 .…”

Section: Imaging In the Clinical Syndromes Of Ftd Behavioral Variant Ftdmentioning

confidence: 99%

FTD spectrum: Neuroimaging across the FTD spectrum

Whitwell

2019

Progress in Molecular Biology and Translational Science

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Frontotemporal dementia is a complex and heterogeneous neurodegenerative disease that encompasses many clinical syndromes, pathological diseases, and genetic mutations. Neuroimaging has played a critical role in our understanding of the underlying pathophysiology of frontotemporal dementia and provided biomarkers to aid diagnosis. Early studies defined patterns of neurodegeneration and hypometabolism associated with the clinical, pathological and genetic aspects of frontotemporal dementia, with more recent studies highlighting how the breakdown of structural and functional brain networks define frontotemporal dementia. Molecular positron emission tomography ligands allowing the in vivo imaging of tau proteins have also provided important insights, although more work is needed to understand the biology of the currently available ligands.

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Section: Imaging In the Clinical Syndromes Of Ftd Behavioral Variant Ftdmentioning

confidence: 99%

FTD spectrum: Neuroimaging across the FTD spectrum

Whitwell

2019

Progress in Molecular Biology and Translational Science

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“…These patterns with numerical expressions help the differential diagnosis, the evaluation of disease severity, and tracking therapy response [21]. The bv‐FTD‐related pattern scores successfully distinguished bv‐FTD from AD, FTD language variants (sv‐PPA and nfv‐PPA), and controls, although the authors did not identify the differential diagnostic efficacy respectively between bv‐FTD and sv‐PPA or nfv‐PPA [22]. This study aimed to establish an sv‐PPA‐related pattern (sv‐PPARP) using PCA and evaluate its potential in differential diagnosis and disease severity assessment.…”

Section: Introductionmentioning

confidence: 99%

A disease‐specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia

Huang

et al. 2021

Euro J of Neurology

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Background and purpose:The diagnosis and monitoring of semantic variant primary progressive aphasia (sv-PPA) are clinically challenging. We aimed to establish a distinctive metabolic pattern in sv-PPA for diagnosis and severity evaluation.Methods: Fifteen sv-PPA patients and 15 controls were enrolled to identify sv-PPArelated pattern (sv-PPARP) by principal component analysis of 18 F-fluorodeoxyglucose positron emission tomography. Eighteen Alzheimer disease dementia (AD) and 14 behavioral variant frontotemporal dementia (bv-FTD) patients were enrolled to test the discriminatory power. Correspondingly, regional metabolic activities extracted from the voxelwise analysis were evaluated for the discriminatory power. Results:The sv-PPARP was characterized as decreased metabolic activity mainly in the bilateral temporal lobe (left predominance), middle orbitofrontal gyrus, left hippocampus/ parahippocampus gyrus, fusiform gyrus, insula, inferior orbitofrontal gyrus, and striatum, with increased activity in the bilateral lingual gyrus, cuneus, calcarine gyrus, and right precentral and postcentral gyrus. The pattern expression had significant discriminatory power (area under the curve [AUC] = 0.98, sensitivity = 100%, specificity = 94.4%) in distinguishing sv-PPA from AD, and the asymmetry index offered complementary discriminatory power (AUC = 0.91, sensitivity = 86.7%, specificity = 92.9%) in distinguishing sv-PPA from bv-FTD. In sv-PPA patients, the pattern expression correlated with Boston Naming Test scores at baseline and showed significant increase in the subset of patients with follow-up. The voxelwise analysis showed similar topography, and the regional

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“…PET with 18 F-FDG is an established clinical tool for early and differential diagnosis of dementing and movement disorders (9,10). Although multivariate decomposition of PET data has been successfully applied in both neurodegenerative dementia (11) and Parkinsonian syndromes (12), RSNs could be identified in 18 F-FDG PET data only recently (13)(14)(15)(16). In particular, our group has found spatially similar RSNs in fMRI and 18 F-FDG PET data in the same group of healthy subjects (15).…”

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confidence: 82%

Integrity of Neurocognitive Networks in Dementing Disorders as Measured with Simultaneous PET/Functional MRI

et al. 2020

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Functional MRI (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on the specificity of these alterations in patients with Alzheimer disease (AD) and behavioral-variant frontotemporal dementia (bvFTD) are still limited. Recently, NCNs have been successfully captured using PET with 18 F-FDG. Methods: Network integrity was measured in 72 individuals (38 male) with mild AD or bvFTD, and in healthy controls, using a simultaneous restingstate fMRI and 18 F-FDG PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent-component analysis revealed 4 major NCNs: anterior default-mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, the integrity of the posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group the anterior DMN and CEN appeared to be additionally affected. In PET data, only the integrity of the posterior DMN in patients with AD was reduced, whereas 3 remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, the integrity of the anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of 2 imaging modalities was low overall. Conclusion: FMRI and 18 F-FDG PET capture partly different aspects of network integrity. A higher disease specificity for NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

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A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

Cited by 20 publications

References 46 publications

FTD spectrum: Neuroimaging across the FTD spectrum

FTD spectrum: Neuroimaging across the FTD spectrum

A disease‐specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia

Integrity of Neurocognitive Networks in Dementing Disorders as Measured with Simultaneous PET/Functional MRI

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