Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis

Möller, Christiane; Pijnenburg, Yolande A.L.; Flier, Wiesje M. van der; Versteeg, Adriaan; Tijms, Betty M.; Munck, Jan C. de; Hafkemeijer, Anne; Rombouts, Serge A.R.B.; Swieten, John van; Dopper, Elise G.P.; Scheltens, Philip; Barkhof, Frederik; Vrenken, Hugo; Wink, Alle Meije

doi:10.1148/radiol.2015150220

Cited by 88 publications

(79 citation statements)

References 47 publications

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“…Especially a comparison with the most frequent dementia type – Alzheimer's disease – would be of interest. Here, previous studies have shown that bvFTD/FTLD can be differentiated with high accuracy from Alzheimer's disease with the same approaches (Dukart et al, 2011, Klöppel et al, 2008, Möller et al, 2016). Note that regions-of-interest and analysis techniques might differ in diagnostic vs. differential diagnostic procedures as shown for imaging approaches recently (Dukart et al, 2010, Dukart et al, 2011, Dukart et al, 2013a, Dukart et al, 2013b).…”

Section: Discussionmentioning

confidence: 79%

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Meyer

Mueller

Stuke

et al. 2017

NeuroImage: Clinical

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PurposeFrontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms.Materials & methodsBrain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, “leave one center out” conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis.ResultsGroup comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach.ConclusionOur study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

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Section: Discussionmentioning

confidence: 79%

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Meyer

Mueller

Stuke

et al. 2017

NeuroImage: Clinical

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“…SVM, nearest neighbor, relevance vector machine, and learning vector quantization were used as second-stage classifiers, however, SVM was better. Recently, Moller et al also used GM density map for SVM-based AD classification (Moller et al, 2016). …”

Section: Classification Framework For Alzheimer’s Disease and Itsmentioning

confidence: 99%

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

et al. 2017

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Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer’s disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD classification and mild cognitive impairment, selected after online database searches in Google Scholar and PubMed (January, 1985 to June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and address some future research directions.

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“…MRI allows detection of bvFTD-specific brain atrophy, which includes frontal and anterior temporal volume loss, and shows a good diagnostic accuracy for differentiating FTD from normal subjects and other dementias in clinical practice [38][39][40]. MRI of bvFTD can show severe frontal and anterior temporal atrophy (Figs.…”

Section: Bvftdmentioning

confidence: 99%

Diagnostic imaging of dementia with Lewy bodies, frontotemporal lobar degeneration, and normal pressure hydrocephalus

Ishii

2019

Jpn J Radiol

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Neuroimaging can provide important biomarkers and is very useful for supporting dementia diagnosis. This review summarizes the neuroimaging findings of dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), and normal pressure hydrocephalus (NPH). In DLB, medial temporal atrophy is milder than that of Alzheimer's disease. 2-fluoro-2-deoxy-d-glucose-positron emission tomography and brain perfusion single-photon emission computed tomography demonstrate hypometabolism and hypoperfusion in the occipital lobe, in addition to decreased metabolism and perfusion in the parietotemporal, posterior cingulate, precuneus, and frontal association cortices. The cingulate island sign, which shows relatively spared middle-to-posterior cingulate cortex metabolism compared with precuneus hypometabolism, is proposed to detect DLB in 2-fluoro-2-deoxy-d-glucose-positron emission tomography imaging. Reduced uptake in dopamine transporter imaging and reduced myocardial uptake in iodine-123 metaiodobenzylguanidine cardiac scintigraphy are indicative biomarkers for DLB diagnosis. Characteristic findings of FTLD include dominant frontotemporal atrophy, hypometabolism, and hypoperfusion. Most idiopathic NPH cases demonstrate disproportionally enlarged subarachnoid space hydrocephalus findings, including dilated ventricular systems, enlarged Sylvian fissures, tight sulci in the midline, and a high convexity.

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Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis

Cited by 88 publications

References 47 publications

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

Diagnostic imaging of dementia with Lewy bodies, frontotemporal lobar degeneration, and normal pressure hydrocephalus

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