Dataset Information

Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption.

ABSTRACT: A?, tau, and P-tau have been widely accepted as reliable markers for Alzheimer's disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated by network disruption probability. Presented results show that network disruption probability has a good linear relationship with Mini Mental State Examination (MMSE). The proposed model combined with Support vector machine (SVM) achieves a relative high 10-fold cross-validated performance in classification of AD vs. normal and mild cognitive impairment (MCI) vs. normal (95% accuracy, 95% sensitivity, 95% specificity for AD vs. normal; 90% accuracy, 94% sensitivity, 83% specificity for MCI vs. normal). This research evaluates the progression of AD and facilitates AD early diagnosis.

SUBMITTER: Liu H

PROVIDER: S-EPMC4718081 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption.

Liu Haochen H Wei Chunxiang C He Hua H Liu Xiaoquan X

Frontiers in neuroscience 20160119

Aβ, tau, and P-tau have been widely accepted as reliable markers for Alzheimer's disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated b ...[more]

PMID: 26834548

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Project description:Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

Project description:Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of toxic misfolded proteins, which are believed to have propagated from disease-specific epicenters through their corresponding large-scale structural networks in the brain. Although previous cross-sectional studies have identified potential AD-associated epicenters and corresponding brain networks, it is unclear whether these networks are associated with disease progression. Hence, this study aims to identify the most vulnerable epicenters and corresponding large-scale structural networks involved in the early stages of AD and to evaluate its associations with multiple cognitive domains using longitudinal study design. Annual neuropsychological and MRI assessments were obtained from 23 patients with AD, 37 patients with amnestic mild cognitive impairment (MCI), and 33 healthy controls (HC) for 3 years. Candidate epicenters were identified as regions with faster decline rate in the gray matter volume (GMV) in patients with MCI who progressed to AD as compared to those regions in patients without progression. These epicenters were then further used as pre-defined regions of interest to map the synchronized degeneration network (SDN) in HCs. Spatial similarity, network preference and clinical association analyses were used to evaluate the specific roles of the identified SDNs. Our results demonstrated that the hippocampus and posterior cingulate cortex (PCC) were the most vulnerable AD-associated epicenters. The corresponding PCC-SDN showed significant spatial association with the patterns of GMV atrophy rate in each patient group and the overlap of these patterns was more evident in the advanced stages of the disease. Furthermore, individuals with a higher GMV atrophy rate of the PCC-SDN also showed faster decline in multiple cognitive domains. In conclusion, our findings suggest the PCC and hippocampus are two vulnerable regions involved early in AD pathophysiology. However, the PCC-SDN, but not hippocampus-SDN, was more closely associated with AD progression. These results may provide insight into the pathophysiology of AD from large-scale network perspective.

Project description:Background and objectivesDuchenne muscular dystrophy (DMD) is a progressive muscle degenerative disorder with a well-characterized disease phenotype but considerable interindividual heterogeneity that is not well understood. The aim of this study was to evaluate the effects of dystrophin variations and genetic modifiers of DMD on rate and age of muscle replacement by fat.MethodsOne hundred seventy-five corticosteroid treated participants from the ImagingDMD natural history study underwent repeated magnetic resonance spectroscopy (MRS) of the vastus lateralis (VL) and soleus (SOL) to determine muscle fat fraction (FF). MRS was performed annually in most instances; however, some individuals had additional visits at 3 or 6 monthss intervals. FF changes over time were modeled using nonlinear mixed effects to estimate disease trajectories based on the age that the VL or SOL reached half-maximum change in FF (mu) and the time required for FF change (sigma). Computed mu and sigma values were evaluated for dystrophin variations that have demonstrated the ability to lead to a mild phenotype as well as compared between different genetic polymorphism groups.ResultsParticipants with dystrophin gene deletions amenable to exon 8 skipping (n = 4) had minimal increases in SOL FF and had an increase in VL mu value by 4.4 years compared with a reference cohort (p = 0.039). Participants with nonsense variations within exons that may produce milder phenotypes (n = 11) also had minimal increases in SOL and VL FFs. No differences in estimated FF trajectories were seen for individuals amenable to exon 44 skipping (n = 10). Modeling of the SPP1, LTBP4, and thrombospondin-1 (THBS1) genetic modifiers did not result in significant differences in muscle FF trajectories between genotype groups (p > 0.05); however, trends were noted for the polymorphisms associated with long-range regulation of LTBP4 and THBS1 that deserve further follow-up.DiscussionThe results of this study link the historically mild phenotypes seen in individuals amenable to exon 8 skipping and with certain nonsense variations with alterations in trajectories of lower extremity muscle replacement by fat.

Dataset Information

Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption.

Publications

Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption.

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