Structural imaging in the clinical diagnosis of Alzheimer's disease: problems and tools.
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ABSTRACT:
SUBMITTER: Frisoni GB
PROVIDER: S-EPMC1737393 | biostudies-literature | 2001 Jun
REPOSITORIES: biostudies-literature
Publications
Structural imaging in the clinical diagnosis of Alzheimer's disease: problems and tools.
Journal of neurology, neurosurgery, and psychiatry 20010601 6
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Project description:In vivo imaging of the tau protein has the potential to aid in quantitative diagnosis of Alzheimer's disease, corroborate or dispute the amyloid hypothesis, and demonstrate biomarker engagement in clinical drug trials. A host of tau positron emission tomography agents have been designed, validated, and tested in humans. Several agents have characteristics approaching the ideal imaging tracer with some limitations, primarily regarding off-target binding. Dozens of clinical trials evaluating imaging techniques and several pharmaceutical trials have begun to integrate tau imaging into their protocols.
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Project description:Alzheimer's Disease (AD) and Non-Demented Control (NDC) human sera were probed onto human protein microarrays in order to identify differentially expressed autoantibody biomarkers that could be used as diagnostic indicators. In the study presented here, 50 AD and 40 NDC human serum samples were probed onto human protein microarrays in order to identify differentially expressed autoantibodies. Microarray data was analyzed using several statistical significance algorithms, and autoantibodies that demonstrated significant group prevelance were selected as biomarkers of disease. Prediction classification analysis tested the diagnostic efficacy of the identified biomarkers; and differentiation of AD samples from other neurodegeneratively-diseased and non-neurodegeneratively-diseased controls (Parkinson's disease and breast cancer, respectively) confirmed their specificity.
2011-07-12 | E-GEOD-29676 | biostudies-arrayexpress
Project description:BackgroundStructural magnetic resonance imaging (sMRI) can reflect structural abnormalities of the brain. Due to its high tissue contrast and spatial resolution, it is considered as an MRI sequence in diagnostic tasks related to Alzheimer's disease (AD). Thus far, most studies based on sMRI have only focused on pathological changes in disease-related brain regions in Euclidean space, ignoring the association and interaction between brain regions represented in non-Euclidean space. This non-Euclidean spatial information can provide valuable information for brain disease research. However, few studies have combined Euclidean spatial information in images and graph spatial information in brain networks for the early diagnosis of AD. The purpose of this study is to explore how to effectively combine multispatial information for enhancing AD diagnostic performance.MethodsA multispatial information representation model (MSRNet) was constructed for the diagnosis of AD using sMRI. Specifically, the MSRNet included a Euclidean representation channel integrating a multiscale module and a feature enhancement module, in addition to a graph (non-Euclidean) representation channel integrating a node feature aggregation mechanism. This was accomplished through the adoption of a multilayer graph convolutional neural network and a node connectivity aggregation mechanism with fully connected layers. Each participants' gray-matter volume map and preconstructed radiomics-based morphology brain network (radMBN) were used as MSRNet inputs for the learning of multispatial information. Other than the multispatial information representation in MSRNet, an interactive mechanism was proposed to connect the Euclidean and graph representation channels by five disease-related brain regions which were identified based on a classifier operated on with two feature strategies of voxel intensities and radiomics features. MSRNet focused on disease-related brain regions while integrating multispatial information to effectively enhance disease discrimination.ResultsThe MSRNet was validated on four publicly available datasets, achieving accuracies 92.8% and 90.6% for AD in intra-database and inter-database cross-validation, respectively. The accuracy of MSRNet in distinguishing between late mild cognitive impairment (MCI) and early MCI, and between progressive MCI and stable MCI, reached 79.8% and 73.4%, respectively. The experiments demonstrated that the model's decision scores exhibited good detection capability for MCI progression. Furthermore, the potential of decision scores for improving diagnostic performance was exhibited by combining decision scores with other clinical indicators for AD identification.ConclusionsThe MSRNet model could conduct an effective multispatial information representation in the sMRI-based diagnosis of AD. The proposed interaction mechanism in the MSRNet could help the model focus on AD-related brain regions, thus further improving the diagnostic ability.
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Project description:Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.
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2011-07-13 | GSE29676 | GEO
Project description:BackgroundMachine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with "ill-posed" problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the "curse of dimensionality" very often dimension reduction is applied to the data.MethodologyBaseline structural MRI data from cognitively normal and Alzheimer's disease (AD) patients from the AD Neuroimaging Initiative database were used in this study. We evaluated here the ill-posedness of this classification problem across different dimensions and sample sizes and its relationship to the performance of regularized logistic regression (RLR), linear support vector machine (SVM) and linear regression classifier (LRC). In addition, these methods were compared with their principal components space counterparts.Principal findingsIn voxel space the prediction performance of all methods increased as sample sizes increased. They were not only relatively robust to the increase of dimension, but they often showed improvements in accuracy. We linked this behavior to improvements in conditioning of the linear kernels matrices. In general the RLR and SVM performed similarly. Surprisingly, the LRC was often very competitive when the linear kernel matrices were best conditioned. Finally, when comparing these methods in voxel and principal component spaces, we did not find large differences in prediction performance.Conclusions and significanceWe analyzed the problem of classifying AD MRI images from the perspective of linear ill-posed problems. We demonstrate empirically the impact of the linear kernel matrix conditioning on different classifiers' performance. This dependence is characterized across sample sizes and dimensions. In this context we also show that increased dimensionality does not necessarily degrade performance of machine learning methods. In general, this depends on the nature of the problem and the type of machine learning method.
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Project description:ObjectiveTo develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM)-based classification of structural MR (sMR) images.BackgroundLibraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects.MethodsOne hundred ninety patients with probable AD were age- and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training were done by four-fold cross validation (CV). The remaining independent sample of 50 AD and 50 CN was used to obtain a minimally biased estimate of the generalization error of the algorithm.ResultsThe CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3%, respectively, and the developed models generalized well on the independent test data sets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology.ConclusionsThis paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy.
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Project description:Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, first, the discriminative landmarks are automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; and second, high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD versus HC and 79.02% for MCI versus HC, respectively.
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Project description:Recently approved anti-amyloid immunotherapies for Alzheimer's disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing.
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Project description:In 2018, the US National Institute on Aging and the Alzheimer's Association proposed a purely biological definition of Alzheimer's disease that relies on biomarkers. Although the intended use of this framework was for research purposes, it has engendered debate and challenges regarding its use in everyday clinical practice. For instance, cognitively unimpaired individuals can have biomarker evidence of both amyloid β and tau pathology but will often not develop clinical manifestations in their lifetime. Furthermore, a positive Alzheimer's disease pattern of biomarkers can be observed in other brain diseases in which Alzheimer's disease pathology is present as a comorbidity. In this Personal View, the International Working Group presents what we consider to be the current limitations of biomarkers in the diagnosis of Alzheimer's disease and, on the basis of this evidence, we propose recommendations for how biomarkers should and should not be used for diagnosing Alzheimer's disease in a clinical setting. We recommend that Alzheimer's disease diagnosis be restricted to people who have positive biomarkers together with specific Alzheimer's disease phenotypes, whereas biomarker-positive cognitively unimpaired individuals should be considered only at-risk for progression to Alzheimer's disease.
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