Dataset Information

An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.

ABSTRACT:

Background

The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer's disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD.

Methods

In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in "FTD dominant" structures over that obtained from "AD dominant" structures. The weights and the identification of "FTD/AD dominant" structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases.

Results

AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p?ConclusionsBrain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.

SUBMITTER: Yu Q

PROVIDER: S-EPMC7805212 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.

Yu Qun Q Mai Yingren Y Ruan Yuting Y Luo Yishan Y Zhao Lei L Fang Wenli W Cao Zhiyu Z Li Yi Y Liao Wang W Xiao Songhua S Mok Vincent C T VCT Shi Lin L Liu Jun J

Alzheimer's research & therapy 20210112 1

<h4>Background</h4>The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer's disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differ ...[more]

PMID: 33436059

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Project description:BackgroundIn a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method.MethodsWe recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability.ResultsThe classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1-4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA.ConclusionsIn the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

Project description:Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

Dataset Information

An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.

Background

Methods

Results

Publications

An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.

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