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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
37 Research products (1 rule applied)

  • 2014-2023
  • Open Access
  • Research data
  • Other research products

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Duan, Leo L.; Roy, Arkaprava;

    Spectral clustering views the similarity matrix as a weighted graph, and partitions the data by minimizing a graph-cut loss. Since it minimizes the across-cluster similarity, there is no need to model the distribution within each cluster. As a result, one reduces the chance of model misspecification, which is often a risk in mixture model-based clustering. Nevertheless, compared to the latter, spectral clustering has no direct ways of quantifying the clustering uncertainty (such as the assignment probability), or allowing easy model extensions for complicated data applications. To fill this gap, we propose the Bayesian forest model as a generative graphical model for spectral clustering. This is motivated by our discovery that the posterior connecting matrix in a forest model has almost the same leading eigenvectors, as the ones used by normalized spectral clustering. To induce a distribution for the forest, we develop a “forest process” as a graph extension to the urn process, while we carefully characterize the differences in the partition probability. We derive a simple Markov chain Monte Carlo algorithm for posterior estimation, and demonstrate superior performance compared to existing algorithms. We illustrate several model-based extensions useful for data applications, including high-dimensional and multi-view clustering for images. Supplementary materials for this article are available online.

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    Dataset . 2023
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    Authors: Giorgio, Joseph; Jagust, William; Baker, Suzanne; Landau, Susan; +2 Authors

    Multimodal predictor and outcome data used to train machine learning models to predict future tau accumulation. Predictor data includes measures of Aß (PET), medial temporal grey matter density (MRI) and APOE genotype. Outcome data include longitudinal categories of clinical decline and tau burden/rates of decline (PET). The data for each of the samples presented in the manuscript is found in the ‘master_data_file.xlsx. Also included are all source data used to generate the figures and tables presented in the manuscript ‘Source data.xlsx’. A detailed description of these data is given in the ‘description of uploaded files.doc’. The custom code developed to implement the machine learning approach is provided as a MATLAB package ‘Giorgio et al 2022 Custom code’. Within this folder are top level wrapper functions, the machine learning implementation, post hoc analysis functions and sample data to run the code. A full description of the attached code and data can be found in ‘Giorgio et al 2020 Custom code read me.doc’.

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    Apollo
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    License: CC BY
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    Apollo
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Apollo
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      Apollo
      Dataset . 2022
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    Authors: Visser, Pieter Jelle; Reus, Lianne M.; Gobom, Johan; Jansen, Iris; +25 Authors

    Additional file 1: Data S1. Participant characteristics. S1a: Characteristics of individuals with CSF Aβ1-42 and tau measurements available; S1b: Characteristics of individuals with CSF proteomic data. Data S2. Protein annotation and statistics of group comparisons of protein levels. Data S3a. Full list of GO biological processes associated with proteins that differ according to group and clinical stage. Data S3b. SynGO enriched synaptic cellular components and biological processes that differ according to group. Data S4a. Estimated marginal means of AD GWAS-based polygenic risk scores in controls, AD individuals with increased t-tau and AD individuals with normal t-tau. Data S4b. Top 1000 SNPS from GWAS on AD individuals with increased t-tau and normal t-tau in pooled ADNI and EMIF-AD MBD cohorts. Data S4c. Difference in MAGMA gene score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S4d. Difference in GO biological process MAGMA geneset score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S5a. Correlation between genetic risk score and CSF protein level in individuals with abnormal Aβ1-42. Data S5b. Association of the number of GMNC rs9877502-A risk alleles and number of APOE-e4 alleles with CSF protein concentrations in a linear model in individuals with AD. Data S5c. GO-BP processes enriched for proteins that have a positive or negative association with the number of rs9877502-A risk alleles in an additive model. Data S6. Annual change in imaging measures.

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    Authors: Kim, Hyung-Ji; Oh, Jungsu S.; Lim, Jae-Sung; Lee, Sunju; +7 Authors

    Additional file 1: Table S1. The result of stepwise backward elimination. Figure S1. Flow chart for this study of ADNI dataset. The solid outline squares represent subjects that remained. The dash line squares represent excluded subjects. Abbreviations: MCI, Mild cognitive impairment. Figure S2. Correlation between regional SUVR and cortical thickness in the converter group. (A) SUVR of the right middle frontal cortex and medial aspect of the cerebrum; (B) SUVR of the left hippocampus and medial aspect of the cerebrum; (C) SUVR of the right striatum lateral aspect of the cerebrum; (D) SUVR of the left occipital cortex lateral aspect of the cerebrum; (E) Right FBB composite and medial aspect of the cerebrum; (F) Left FBB composite and medial aspect of the cerebrum; (G) Right FBB composite and lateral aspect of the brain; and (H) Left FBB composite and lateral aspect of the brain. FDR correction with p < 0.05, and p value < 0.001. There was no statistical correlation in the non-converter group; thus, only the converter group’s results are shown from (A) to (G). SUVR, standard uptake value ratio; FBB, florbetaben; FDR, false discovery rate.

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    Authors: Bhattacharyya, Arinjita; Pal, Subhadip; Mitra, Riten; Rai, Shesh;

    Additional file 1 The additional tables and figures are presented in the Supplementary file.

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    Authors: Kim, Hyung-Ji; Oh, Jungsu S.; Lim, Jae-Sung; Lee, Sunju; +7 Authors

    Additional file 2: Table S2. The results of Z-test (comparison of correlation coefficient) between the two group.

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    Authors: Kim, Hyung-Ji; Oh, Jungsu S.; Lim, Jae-Sung; Lee, Sunju; +7 Authors

    Additional file 3.

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    Authors: Liu, Mingming; Yang, Jing; Liu, Yushi; Jia, Bochao; +3 Authors

    Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which is an important goal of precision medicine. However, in practice, one top methodological challenge hindering the heterogeneity investigation is that the true subgroup membership of each individual is often unknown. In this article, we aim to identify latent subgroups of individuals who share a common disorder progress over time, to predict latent subgroup memberships, and to estimate and infer the heterogeneous trajectories among the subgroups. To achieve these goals, we apply a concave fusion learning method to conduct subgroup analysis for longitudinal trajectories of the Alzheimer's disease data. The heterogeneous trajectories are represented by subject-specific unknown functions which are approximated by B-splines. The concave fusion method can simultaneously estimate the spline coefficients and merge them together for the subjects belonging to the same subgroup to automatically identify subgroups and recover the heterogeneous trajectories. The resulting estimator of the disease trajectory of each subgroup is supported by an asymptotic distribution. It provides a sound theoretical basis for further conducting statistical inference in subgroup analysis.

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    Authors: Baik, Jae Young; Kim, Mansu; Bao, Jingxuan; Long, Qi; +1 Authors

    Additional file 1. Table S1: Significance level of brain regions using imaging-diagnosis analysis. Table S2(a): Significance level of identified genes significantly associated with diagnostic outcome using correlation analysis. Table S2(b): Significance level of identified genes related with AD based on DisGeNET. Table S2(c): Significance level of identified genes unrelated with AD based on DisGeNET.

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    Authors: C. Silva, Tiago; Zhang, Wei; Young, Juan I.; Gomez, Lissette; +5 Authors

    Additional file 2: Supplementary Table 1. Quality control (QC) information on DNA methylation samples and probes for each dataset contributing to the sex-specific meta-analyses. Under Probes QC, shown are the number of probes remaining after each QC procedure. Under Samples QC, shown are the number of samples remaining after each QC procedure. Supplementary Table 2. At P < 10-5, sex-specific meta-analyses identified a total of 23 CpGs and 4 CpGs signicantly associated with AD diagnosis in female samples and male samples, respectively . For each CpG, annotations include the location of the CpG based on hg19/GRCh37 genomic annotation (chr, position), nearby genes based on GREAT (GREAT_annotation), the type of associated genomic feature (RefGene_Group), Illumina gene annotations, location with respect to CpG islands (Relation_to_Island), and overlap with enhancers identified in Nasser et al. [53] study (PMID: 33828297). Inverse-variance weighted fixed-effects meta-analysis models were used to combine cohort-specific results from logistic regression models that included covariate variables age, batch, and immune cell-type proportions. A total of 9 CpGs had the same direction of change in males and females (highlighted in gray). Odds ratios (OR) describe changes in odds of AD (on the multiplicative scale) associated with a one percent increase in methylation beta values (i.e., increase in methylation beta values by 0.01) after adjusting for covariate variables. Highlighted in red are CpGs that mapped to promoter regions. 95% CI = 95% confidence interval for odds ratio. Supplementary Table 3. In female samples, a total of 41 DMRs were significantly associated with AD diagnosis at 5% Sidak corrected P-value. Among them, 6 DMRs overlapped with enhancer regions from Nasser et al. study (Nature 2021; PMID: 33828297) (Enhancer = TRUE). Highlighted in red are DMRs that mapped to promoter regions. Direction indicates hypermethylation (+) or hypomethylation (-) in AD subjects, which was determined based on hyper- or hypo- methylation of the majority of the CpGs (located within the DMR) in meta-analysis. Supplementary Table 4. CpGs within top 10 most significant DMRs in females. Direction indicates hypermethylation (+) or hypomethylation (-) in AD samples in the ADNI and AIBL datasets. Supplementary Table 5. In male samples, a total of 24 DMRs were significantly associated with AD diagnosis at 5% Sidak corrected P-value. Among them, 7 DMRs overlapped with enhancer regions from Nasser et al. study (Nature 2021; PMID: 33828297) (Enhancer = TRUE). Highlighted in red are DMRs that mapped to promoter regions. Direction indicates hypermethylation (+) or hypomethylation (-) in AD subjects, which was determined based on hyper- or hypo- methylation of the majority of the CpGs (located within the DMR) in meta-analysis. Supplementary Table 6. CpGs within the top 10 most significant DMRs in males. Direction indicates hypermethylation (+) or hypomethylation (-) in AD samples in the ADNI and AIBL datasets. Supplementary Table 7. Information on brain samples used in cross-tissue meta-analysis. Supplementary Table 8. Results of analysis of female samples. In (a) and (b), we analyzed matched DNAm-RNA from the ADNI dataset (adni.loni.usc.edu), and tested association of DNA methylation at significant CpGs with expression levels of genes located nearby. At 5% FDR, for CpGs in the promoter regions (i.e., within +/- 2k bp from TSS), DNAm at 23 CpGs (mapped to 5 DMRs) were significantly associated with expressions of their target genes. For CpGs in distal regions (>2k bp from TSS), we tested association between the CpGs with 10 genes upstream and 10 genes downstream from the CpG location. Only 1 CpG was significantly associated with expression of its target gene at 5% FDR. In (c), we performed a meta-analysis for gene expressions of the target genes using two prefrontal cortex brain samples datasets in AD (GEO accessions: GSE33000, GSE44772), to test association between gene expression and AD, adjusting for age, sex and surrogate variables for cell types. Supplementary Table 9. Results of analysis of male samples with matched DNAm-RNA data in the ADNI dataset. In (a) and (b), we tested association of DNA methylation at significant CpGs with expression levels of genes located nearby. At 5% FDR, for CpGs in the promoter regions (i.e., within +/- 2k bp from TSS), DNAm at 12 CpGs (mapped to 2 DMRs) were significantly associated with expressions of their target genes. For CpGs in distal regions (>2k bp from TSS), we tested association between the CpGs with 10 genes upstream and 10 genes downstream from the CpG location. A total of 13 distal CpGs (mapped to 5 DMRs) were significantly associated with expressions of their target genes at 5% FDR. In (c), we performed a meta-analysis for gene expressions of the target genes using two prefrontal cortex brain samples datasets in AD (GEO accessions: GSE33000, GSE44772), to test association between gene expression and AD, adjusting for age, sex and surrogate variables for cell types. Supplementary 10. In femlaes, a total of 64 CpG - mQTL pairs were significant in both brain and blood samples analyses. The blood mQTLs and brain mQTLs were obtained from the GoDMC database and xQTL server, respectively. Supplementary 11. In males, a total of 19 CpG - mQTL pairs were significant in both brain and blood samples analyses. The blood mQTLs and brain mQTLs were obtained from the GoDMC database and xQTL server, respectively. Supplementary Table 12. In females, a total of 155 mQTLs in the blood overlapped with the 24 GWAS nominated LD blocks in Kunkle et al. [60] (PMID: 30820047). The mQTLs in blood were obtained from the GoDMC database. Annotations for CpGs include location of the CpG based on hg19/GRCh37 genomic annotation (Chr, Position), Illumina gene annotation (UCSC_RefGene_Name), the type of associated genomic feature (UCSC_RefGene_Group), and location with respect to CpG islands (Relation_to_Island). Supplementary Table 13. In males, a total of 864 mQTLs in the blood overlapped with the 24 GWAS nominated LD blocks in Kunkle et al. [60] (PMID: 30820047). The mQTLs in blood were obtained from the GoDMC database. Annotations for CpGs include location of the CpG based on hg19/GRCh37 genomic annotation (Chr, Position), Illumina gene annotation (UCSC_RefGene_Name), the type of associated genomic feature (UCSC_RefGene_Group), and location with respect to CpG islands (Relation_to_Island). Supplementary Table 14. Overlap of AD-associated DMRs with AD GWAS loci reported in Kunkle et al. [60]. Supplementary Table 15. Sensitivity analysis for model that additionally adjust for smoking scores, which was computed using the SSc method as implemented in R package EpiSmokEr (PMID: 31466478). All 27 sex-specific CpGs from Supplementary Table 2 remained highly significant, with meta-analysis P-values ranging from 5.83 x 10-8 to 2.59 x 10-5. Supplementary Table 16. Sensitivity analysis comparing logistic regression model that additionally adjusts years of education vs. model not adjust education in the analysis of ADNI dataset. Supplementary Table 17. Results of internal validation that compared logsitic regression models with or without education effect. A 10-fold cross-validation using the ADNI dataset showed the estimated average AUCs for the best performing logistic regression models with and without education were 0.707 and 0.710 in females, and 0.650 and 0.604 in males. The MRS was computed as the sum of methylation beta values for significant CpGs weighted by their estimated effect sizes obtained in the meta-analysis. In males, significant CpGs used for the MRS included 2 out of the 5 significant CpGs in the meta-analysis of methylation-by-sex interaction effect which were also available in AddNeuroMed dataset. In females, significant CpGs used for MRS included 9 out of 23 CpGs in meta-analysis that compared AD vs. CN samples which were also available in AddNeuroMed dataset.

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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
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    Authors: Duan, Leo L.; Roy, Arkaprava;

    Spectral clustering views the similarity matrix as a weighted graph, and partitions the data by minimizing a graph-cut loss. Since it minimizes the across-cluster similarity, there is no need to model the distribution within each cluster. As a result, one reduces the chance of model misspecification, which is often a risk in mixture model-based clustering. Nevertheless, compared to the latter, spectral clustering has no direct ways of quantifying the clustering uncertainty (such as the assignment probability), or allowing easy model extensions for complicated data applications. To fill this gap, we propose the Bayesian forest model as a generative graphical model for spectral clustering. This is motivated by our discovery that the posterior connecting matrix in a forest model has almost the same leading eigenvectors, as the ones used by normalized spectral clustering. To induce a distribution for the forest, we develop a “forest process” as a graph extension to the urn process, while we carefully characterize the differences in the partition probability. We derive a simple Markov chain Monte Carlo algorithm for posterior estimation, and demonstrate superior performance compared to existing algorithms. We illustrate several model-based extensions useful for data applications, including high-dimensional and multi-view clustering for images. Supplementary materials for this article are available online.

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    Authors: Giorgio, Joseph; Jagust, William; Baker, Suzanne; Landau, Susan; +2 Authors

    Multimodal predictor and outcome data used to train machine learning models to predict future tau accumulation. Predictor data includes measures of Aß (PET), medial temporal grey matter density (MRI) and APOE genotype. Outcome data include longitudinal categories of clinical decline and tau burden/rates of decline (PET). The data for each of the samples presented in the manuscript is found in the ‘master_data_file.xlsx. Also included are all source data used to generate the figures and tables presented in the manuscript ‘Source data.xlsx’. A detailed description of these data is given in the ‘description of uploaded files.doc’. The custom code developed to implement the machine learning approach is provided as a MATLAB package ‘Giorgio et al 2022 Custom code’. Within this folder are top level wrapper functions, the machine learning implementation, post hoc analysis functions and sample data to run the code. A full description of the attached code and data can be found in ‘Giorgio et al 2020 Custom code read me.doc’.

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      Apollo
      Dataset
      License: CC BY
      Data sources: Apollo
      Apollo
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Visser, Pieter Jelle; Reus, Lianne M.; Gobom, Johan; Jansen, Iris; +25 Authors

    Additional file 1: Data S1. Participant characteristics. S1a: Characteristics of individuals with CSF Aβ1-42 and tau measurements available; S1b: Characteristics of individuals with CSF proteomic data. Data S2. Protein annotation and statistics of group comparisons of protein levels. Data S3a. Full list of GO biological processes associated with proteins that differ according to group and clinical stage. Data S3b. SynGO enriched synaptic cellular components and biological processes that differ according to group. Data S4a. Estimated marginal means of AD GWAS-based polygenic risk scores in controls, AD individuals with increased t-tau and AD individuals with normal t-tau. Data S4b. Top 1000 SNPS from GWAS on AD individuals with increased t-tau and normal t-tau in pooled ADNI and EMIF-AD MBD cohorts. Data S4c. Difference in MAGMA gene score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S4d. Difference in GO biological process MAGMA geneset score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S5a. Correlation between genetic risk score and CSF protein level in individuals with abnormal Aβ1-42. Data S5b. Association of the number of GMNC rs9877502-A risk alleles and number of APOE-e4 alleles with CSF protein concentrations in a linear model in individuals with AD. Data S5c. GO-BP processes enriched for proteins that have a positive or negative association with the number of rs9877502-A risk alleles in an additive model. Data S6. Annual change in imaging measures.

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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Dataset . 2022
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      Data sources: Datacite
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    Authors: Kim, Hyung-Ji; Oh, Jungsu S.; Lim, Jae-Sung; Lee, Sunju; +7 Authors

    Additional file 1: Table S1. The result of stepwise backward elimination. Figure S1. Flow chart for this study of ADNI dataset. The solid outline squares represent subjects that remained. The dash line squares represent excluded subjects. Abbreviations: MCI, Mild cognitive impairment. Figure S2. Correlation between regional SUVR and cortical thickness in the converter group. (A) SUVR of the right middle frontal cortex and medial aspect of the cerebrum; (B) SUVR of the left hippocampus and medial aspect of the cerebrum; (C) SUVR of the right striatum lateral aspect of the cerebrum; (D) SUVR of the left occipital cortex lateral aspect of the cerebrum; (E) Right FBB composite and medial aspect of the cerebrum; (F) Left FBB composite and medial aspect of the cerebrum; (G) Right FBB composite and lateral aspect of the brain; and (H) Left FBB composite and lateral aspect of the brain. FDR correction with p < 0.05, and p value < 0.001. There was no statistical correlation in the non-converter group; thus, only the converter group’s results are shown from (A) to (G). SUVR, standard uptake value ratio; FBB, florbetaben; FDR, false discovery rate.

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    Dataset . 2022
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    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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