Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases.
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ABSTRACT:
SUBMITTER: Wu J
PROVIDER: S-EPMC8377280 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectiveIndividuals with biomarker evidence of β-amyloid (Aβ) deposition are increasingly being enrolled in clinical treatment trials but there is a need to identify markers to predict which of these individuals will also develop tau deposition. We aimed to determine whether Aβ-positive individuals can remain tau-negative for at least 5 years and identify characteristics that could distinguish between these individuals and those who develop high tau within this period.MethodsTau PET positivity was defined using a Gaussian mixture model with log-transformed standard uptake value ratio values from 7 temporal and medial parietal regions using all participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) with flortaucipir PET. Tau PET scans were classified as normal if the posterior probability of elevated tau was less than 1%. Aβ PET positivity was defined based on ADNI cutpoints. We identified all Aβ-positive individuals from ADNI who had normal tau PET more than 5 years after their first abnormal Aβ PET (amyloid with low tau [ALT] group) and all Aβ-positive individuals with abnormal tau PET within 5 years (biomarker AD). In a case-control design, logistic regression was used to model the odds of biomarker AD vs ALT accounting for sex, age, APOE ε4 carriership, Aβ Centiloid, and hippocampal volume.ResultsWe identified 45 individuals meeting criteria for ALT and 157 meeting criteria for biomarker AD. The ALT group had a lower proportion of APOE ε4 carriers, lower Aβ Centiloid, larger hippocampal volumes, and more preserved cognition, and were less likely to develop dementia, than the biomarker AD group. APOE ε4, higher Aβ Centiloid, and hippocampal atrophy were independently associated with increased odds of abnormal tau within 5 years. A Centiloid value of 50 effectively discriminated biomarker AD and ALT with 80% sensitivity and specificity. The majority of the ALT participants did not develop dementia throughout the 5-year interval.DiscussionAβ-positive individuals can remain tau-negative for at least 5 years. Baseline characteristics can help identify these ALT individuals who are less likely to develop dementia. Conservative Aβ cutpoints should be utilized for clinical trials to better capture individuals with high risk of developing biomarker AD.
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Project description:The analysis of ratios of body measurements is deeply ingrained in the taxonomic literature. Whether for plants or animals, certain ratios are commonly indicated in identification keys, diagnoses, and descriptions. They often provide the only means for separation of cryptic species that mostly lack distinguishing qualitative characters. Additionally, they provide an obvious way to study differences in body proportions, as ratios reflect geometric shape differences. However, when it comes to multivariate analysis of body measurements, for instance, with linear discriminant analysis (LDA) or principal component analysis (PCA), interpretation using body ratios is difficult. Both techniques are commonly applied for separating similar taxa or for exploring the structure of variation, respectively, and require standardized raw or log-transformed variables as input. Here, we develop statistical procedures for the analysis of body ratios in a consistent multivariate statistical framework. In particular, we present algorithms adapted to LDA and PCA that allow the interpretation of numerical results in terms of body proportions. We first introduce a method called the "LDA ratio extractor," which reveals the best ratios for separation of two or more groups with the help of discriminant analysis. We also provide measures for deciding how much of the total differences between individuals or groups of individuals is due to size and how much is due to shape. The second method, a graphical tool called the "PCA ratio spectrum," aims at the interpretation of principal components in terms of body ratios. Based on a similar idea, the "allometry ratio spectrum" is developed which can be used for studying the allometric behavior of ratios. Because size can be defined in different ways, we discuss several concepts of size. Central to this discussion is Jolicoeur's multivariate generalization of the allometry equation, a concept that was derived only with a heuristic argument. Here we present a statistical derivation of the allometric size vector using the method of least squares. The application of the above methods is extensively demonstrated using published data sets from parasitic wasps and rock crabs.
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Project description:Background: Voxel-based morphometry studies have repeatedly highlighted the presence of distributed gray matter changes in schizophrenia, but to date, it is not clear if clinically useful prognostic information can be gleaned from structural imaging. The suspected association between gray matter volume (GMV) and duration of psychotic illness, antipsychotic exposure, and symptom severity also limits the prognostic utility of morphometry. We address the question of whether morphometric information from patients with drug-naive first-episode psychosis can predict the linear trajectory of symptoms following early antipsychotic intervention using a longitudinal design. Method: Sixty-two first-episode, drug-naive patients with schizophrenia underwent brain magnetic resonance imaging scans at baseline, treated with antipsychotics, and rescanned after 1-year follow-up. Positive and Negative Syndrome Scale (PANSS) was used to assess their clinical manifestations. A multivariate approach to detect covariance-based network-like spatial components [Source Based Morphometry (SBM)] was performed to analyze the GMV. Paired t tests were used to study changes in the loading coefficients of GMV in the spatial components between two time points. The reduction in PANSS scores between the baseline (T0) and 1-year follow-up (T1) expressed as a ratio of the baseline scores (reduction ratio) was computed for positive, negative, and disorganization symptoms. Separate multiple regression analyses were conducted to predict the longitudinal change in symptoms (treatment response) using the loading coefficients of spatial components that differed between T0 and T1 with age, gender, duration of illness, and antipsychotic dose as covariates. We also tested the putative "toxicity" effects of baseline symptom severity on the GMV at 1 year using multiple regression analysis. Results: Of the 30 spatial components of gray matter extracted using SBM, loading coefficients of anterior cingulate cortex (ACC), insula and inferior frontal gyrus (IFG), superior temporal gyrus (STG), middle temporal gyrus (MTG), precuenus, and dorsolateral prefrontal cortex (DLPFC) reduced with time in patients. Specifically, the lower volume of insula and IFG at baseline predicted a lack of improvement in positive and disorganization symptoms. None of the symptom severity scores (positive, negative, or disorganization) at baseline independently predicted the reduced GMV at 1 year. Conclusions: The baseline deficit in a covariance-based network-like spatial component comprising of insula and IFG is predictive of the clinical course of schizophrenia. We do not find any evidence to support the notion of symptoms per se being neurotoxic to gray matter tissue. If judiciously combined with other available predictors of clinical outcome, multivariate morphometric information can improve our ability to predict prognosis in schizophrenia.
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Project description:Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is to make statistical inference for a possibly sparse and low-rank coefficient matrix. The low-rank structure in the coefficient matrix is of intrinsic multivariate nature, which, when combined with sparsity, can further lift dimension reduction, conduct variable selection, and facilitate model interpretation. Using a Bayesian approach, we develop a unified sparse and low-rank multivariate regression method to both estimate the coefficient matrix and obtain its credible region for making inference. The newly developed sparse and low-rank prior for the coefficient matrix enables rank reduction, predictor selection and response selection simultaneously. We utilize the marginal likelihood to determine the regularization hyperparameter, so our method maximizes its posterior probability given the data. For theoretical aspect, the posterior consistency is established to discuss an asymptotic behavior of the proposed method. The efficacy of the proposed approach is demonstrated via simulation studies and a real application on yeast cell cycle data.
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Project description:Sensorimotor transformation is indispensable to the accurate motion of the human body in daily life. For instance, when we grasp an object, the distance from our hands to an object needs to be calculated by integrating multisensory inputs, and our motor system needs to appropriately activate the arm and hand muscles to minimize the distance. The sensorimotor transformation is implemented in our neural systems, and recent advances in measurement techniques have revealed an important property of neural systems: a small percentage of neurons exhibits extensive activity while a large percentage shows little activity, i.e., sparse coding. However, we do not yet know the functional role of sparse coding in sensorimotor transformation. In this paper, I show that sparse coding enables complete and robust learning in sensorimotor transformation. In general, if a neural network is trained to maximize the performance on training data, the network shows poor performance on test data. Nevertheless, sparse coding renders compatible the performance of the network on both training and test data. Furthermore, sparse coding can reproduce reported neural activities. Thus, I conclude that sparse coding is necessary and a biologically plausible factor in sensorimotor transformation.
| S-EPMC4413851 | biostudies-other
Project description:Covariance estimation is essential yet underdeveloped for analyzing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional data using bivariate penalized splines. The tensor-product B-spline formulation of the proposed method enables a simple spectral decomposition of the associated covariance operator and explicit expressions of the resulting eigenfunctions as linear combinations of B-spline bases, thereby dramatically facilitating subsequent principal component analysis. We derive a fast algorithm for selecting the smoothing parameters in covariance smoothing using leave-one-subject-out cross-validation. The method is evaluated with extensive numerical studies and applied to an Alzheimer's disease study with multiple longitudinal outcomes.
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Project description:We propose a generic method for the statistical analysis of collections of anatomical shape complexes, namely sets of surfaces that were previously segmented and labeled in a group of subjects. The method estimates an anatomical model, the template complex, that is representative of the population under study. Its shape reflects anatomical invariants within the dataset. In addition, the method automatically places control points near the most variable parts of the template complex. Vectors attached to these points are parameters of deformations of the ambient 3D space. These deformations warp the template to each subject's complex in a way that preserves the organization of the anatomical structures. Multivariate statistical analysis is applied to these deformation parameters to test for group differences. Results of the statistical analysis are then expressed in terms of deformation patterns of the template complex, and can be visualized and interpreted. The user needs only to specify the topology of the template complex and the number of control points. The method then automatically estimates the shape of the template complex, the optimal position of control points and deformation parameters. The proposed approach is completely generic with respect to any type of application and well adapted to efficient use in clinical studies, in that it does not require point correspondence across surfaces and is robust to mesh imperfections such as holes, spikes, inconsistent orientation or irregular meshing. The approach is illustrated with a neuroimaging study of Down syndrome (DS). The results demonstrate that the complex of deep brain structures shows a statistically significant shape difference between control and DS subjects. The deformation-based modelingis able to classify subjects with very high specificity and sensitivity, thus showing important generalization capability even given a low sample size. We show that the results remain significant even if the number of control points, and hence the dimension of variables in the statistical model, are drastically reduced. The analysis may even suggest that parsimonious models have an increased statistical performance. The method has been implemented in the software Deformetrica, which is publicly available at www.deformetrica.org.
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Project description:BackgroundPatients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD.MethodsWe have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection.ResultsWe have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587.ConclusionsOur results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.
| S-EPMC3477025 | biostudies-literature
Project description:BackgroundPrevalence and risk factors for focal hemosiderin deposits are important considerations when planning amyloid-modifying trials for treatment and prevention of Alzheimer's disease (AD).MethodsSubjects were cognitively normal (n = 171), early-mild cognitive impairment (MCI) (n = 240), late-MCI (n = 111), and AD (n = 40) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Microhemorrhages and superficial siderosis were assessed at baseline and on all available MRIs at 3, 6, and 12 months. β-amyloid load was assessed with (18)F-florbetapir positron emission tomography.ResultsPrevalence of superficial siderosis was 1% and prevalence of microhemorrhages was 25% increasing with age (P < .001) and β-amyloid load (P < .001). Topographic densities of microhemorrhages were highest in the occipital lobes and lowest in the deep/infratentorial regions. A greater number of microhemorrhages at baseline was associated with a greater annualized rate of additional microhemorrhages by last follow-up (rank correlation = 0.49; P < .001).ConclusionsFocal hemosiderin deposits are relatively common in the ADNI cohort and are associated with β-amyloid load.
| S-EPMC3770782 | biostudies-literature