Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer's dementia

Trzepacz, Paula T.; Yu, Peng; Sun, Jia; Schuh, Kory; Case, Michael; Witte, Michael M.; Hochstetler, Helen; Hake, Ann Marie

doi:10.1016/j.neurobiolaging.2013.06.018

Cited by 116 publications

(85 citation statements)

References 39 publications

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“…Vice versa, if all three classical AD CSF biomarkers are normal, this can be considered neurochemically incompatible with a diagnosis of AD. In all other cases, no diagnostic label can be given and further diagnostic workup, for example, determining Ab 1-40 , in the case of normal values for Ab 1-42 with increased levels of T-tau and P-tau 181P , will be useful or the use of other biomarkers such as the measurement of hippocampal volume through MRI, FDG, and/or amyloid PET [15][16][17]. In addition, follow-up should be considered, as well as a careful clinical and neuropsychological study of the specific clinical phenotype.…”

Section: Interpretation and Reporting Of Ad Csf Biomarker Resultsmentioning

confidence: 99%

“…This therefore advocates the routine analysis of CSF biomarkers as part of the clinical workup of patients with cognitive impairment possibly due to AD, to increase diagnostic accuracy [14]. Other biomarkers are also available for the diagnosis of AD, including amyloid brain imaging, magnetic resonance imaging (MRI), and 18F-fluorodeoxyglucose (FDG) and/or amyloid positron emission tomography (PET) [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

Molinuevo

Blennow

Dubois³

et al. 2014

Alzheimer's & Dementia

172

136

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Section: Interpretation and Reporting Of Ad Csf Biomarker Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

Molinuevo

Blennow

Dubois³

et al. 2014

Alzheimer's & Dementia

172

136

View full text Add to dashboard Cite

show abstract

“…For example, Trzepacz et al [104] compared the power of three neuroimaging modalities (sMRI, [ 11 C]PiB PET, and [ 18 F]FDG PET), coupled with APOE genotype, for predicting MCI conversion to AD. Zhang et al [105] compared several feature selection and machine learning algorithms, where ADNI multi-dimensional data sets, including genetic data along with sMRI, [ 18 F]FDG PET, and CSF biomarkers, were used to classify CN, MCI, and AD participants.…”

Section: Resultsmentioning

confidence: 99%

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

Saykin

Shen

Yao

et al. 2015

Alzheimer's & Dementia

250

214

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INTRODUCTION Genetic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been crucial in advancing the understanding of AD pathophysiology. Here we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing (WES, WGS) data have been obtained and disseminated. RESULTS ADNI genetic data have been downloaded thousands of times and over 300 publications have resulted, including reports of large scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies employed ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first WES and WGS data sets and reports in healthy controls, MCI, and AD. DISCUSSION Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data, and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multi-omics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

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“…The experiments were conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database [7]. It consists of 16 subjects with paired MRI and CT scans.…”

Section: Resultsmentioning

confidence: 99%

Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning

Zhao

Wang

Chen

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Automatic segmentation of medical images finds abundant applications in clinical studies. Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning of craniomaxillofacial (CMF) surgeries as it shows clear bony structures. However, CT imaging poses radiation risks for the subjects being scanned. Alternatively, Magnetic Resonance Imaging (MRI) is considered to be safe and provides good visualization of the soft tissues, but the bony structures appear invisible from MRI. Therefore, the segmentation of bony structures from MRI is quite challenging. In this paper, we propose a cascaded generative adversarial network with deep-supervision discriminator (Deep-supGAN) for automatic bony structures segmentation. The first block in this architecture is used to generate a high-quality CT image from an MRI, and the second block is used to segment bony structures from MRI and the generated CT image. Different from traditional discriminators, the deep-supervision discriminator distinguishes the generated CT from the ground-truth at different levels of feature maps. For segmentation, the loss is not only concentrated on the voxel level but also on the higher abstract perceptual levels. Experimental results show that the proposed method generates CT images with clearer structural details and also segments the bony structures more accurately compared with the state-of-the-art methods.

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Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer's dementia

Cited by 116 publications

References 39 publications

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning

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