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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)

  • 2014-2023
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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: Bron, Esther E.;

    This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0

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    ZENODO
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    ZENODO
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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: Van Loenhoud, Anna Catharina; Van Der Flier, Wiesje Maria; Wink, Alle Meije; Dicks, Ellen; +5 Authors

    Objective: To investigate the relationship between cognitive reserve (CR) and clinical progression across the Alzheimer’s disease (AD) spectrum. Methods: We selected 839 Aβ-positive subjects with normal cognition (NC, n=175), mild cognitive impairment (MCI, n=437) or AD dementia (n=227) from the Alzheimer’s Disease Neuroimaging Initiative. CR was quantified using standardized residuals (W-scores) from a (covariate-adjusted) linear regression with global cognition (ADAS-Cog 13) as an independent variable-of-interest, and either gray matter volumes or white matter hyperintensity volume as dependent variables. These W-scores, reflecting whether an individual’s degree of cerebral damage is lower or higher than clinically expected, were tested as predictors of diagnostic conversion (i.e. NC to MCI/AD dementia, or MCI to AD dementia) and longitudinal changes in memory (ADNI-MEM) and executive functions (ADNI-EF). Results: The median follow-up period was 24 months (interquartile range: 6-42). Corrected for age, sex, APOE4-status and baseline cerebral damage, higher gray matter volume-based W-scores (i.e. greater CR) were associated with a lower diagnostic conversion risk (hazard ratio [HR]= .22, p<.001) and slower decline in memory (β=.48, p<.001) and executive functions (β=.67, p<.001). Stratified by disease stage, we found similar results for NC (diagnostic conversion: HR=.30, p=.038; ADNI-MEM: β=.52, p=.028; ADNI-EF: β=.42, p=.077) and MCI (diagnostic conversion: HR=.21, p<.001; ADNI-MEM: β=.43, p=.003; ADNI-EF: β=.59, p<.001), but opposite findings (i.e. more rapid decline) for AD dementia (ADNI-MEM: β=-.91, p=.002; ADNI-EF: β=-.77, p=.081). Conclusions: Among Aβ-positive individuals, greater CR related to attenuated clinical progression in pre-dementia stages of AD, but accelerated cognitive decline after the onset of dementia. Tables 5-12Supplemental Data.docxFigure 3Table e-1-8Supplemental Data.docxFigure e-1Appendix e-1Co-investigator appendix

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  • Authors: Kharabian Masouleh, Shahrzad; Eickhoff, Simon; Hoffstaedter, Felix; Genon, Sarah;

    Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported “structural brain behavior” (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

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  • Authors: Ledig, Christian; Schuh, Andreas; Guerrero, Ricardo; Heckemann, Rolf A.; +1 Authors

    Data accompanying the article: C. Ledig, A. Schuh, R. Guerrero, R. Heckemann, D. Rueckert, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, Scientific Reports, 2018. Data derived from 5074 images from the ADNI cohort: - structural segmentations (138 regions, MALPEM); - binary brain masks (pincram); - features (volumes, asymmetry, atrophy rates) and disease labels; - lists of processed images IsSupplementTo: Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D (2018) Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Reports, 2018. https://doi.org/10.1038/s41598-018-29295-9

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    Authors: Bhagwat, Nikhil; Pipitone, Jon; Winterburn, Julie L.; Guo, Ting; +6 Authors

    Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.

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    Frontiers in Neuroscience
    2016 . Peer-reviewed
    Data sources: Frontiers
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      Frontiers in Neuroscience
      2016 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A.; +7 Authors

    In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab® and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

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    Frontiers in Neuroinformatics
    2016 . Peer-reviewed
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      Frontiers in Neuroinformatics
      2016 . Peer-reviewed
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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
  • 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: Bron, Esther E.;

    This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0

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    Software . 2021
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    Authors: Van Loenhoud, Anna Catharina; Van Der Flier, Wiesje Maria; Wink, Alle Meije; Dicks, Ellen; +5 Authors

    Objective: To investigate the relationship between cognitive reserve (CR) and clinical progression across the Alzheimer’s disease (AD) spectrum. Methods: We selected 839 Aβ-positive subjects with normal cognition (NC, n=175), mild cognitive impairment (MCI, n=437) or AD dementia (n=227) from the Alzheimer’s Disease Neuroimaging Initiative. CR was quantified using standardized residuals (W-scores) from a (covariate-adjusted) linear regression with global cognition (ADAS-Cog 13) as an independent variable-of-interest, and either gray matter volumes or white matter hyperintensity volume as dependent variables. These W-scores, reflecting whether an individual’s degree of cerebral damage is lower or higher than clinically expected, were tested as predictors of diagnostic conversion (i.e. NC to MCI/AD dementia, or MCI to AD dementia) and longitudinal changes in memory (ADNI-MEM) and executive functions (ADNI-EF). Results: The median follow-up period was 24 months (interquartile range: 6-42). Corrected for age, sex, APOE4-status and baseline cerebral damage, higher gray matter volume-based W-scores (i.e. greater CR) were associated with a lower diagnostic conversion risk (hazard ratio [HR]= .22, p<.001) and slower decline in memory (β=.48, p<.001) and executive functions (β=.67, p<.001). Stratified by disease stage, we found similar results for NC (diagnostic conversion: HR=.30, p=.038; ADNI-MEM: β=.52, p=.028; ADNI-EF: β=.42, p=.077) and MCI (diagnostic conversion: HR=.21, p<.001; ADNI-MEM: β=.43, p=.003; ADNI-EF: β=.59, p<.001), but opposite findings (i.e. more rapid decline) for AD dementia (ADNI-MEM: β=-.91, p=.002; ADNI-EF: β=-.77, p=.081). Conclusions: Among Aβ-positive individuals, greater CR related to attenuated clinical progression in pre-dementia stages of AD, but accelerated cognitive decline after the onset of dementia. Tables 5-12Supplemental Data.docxFigure 3Table e-1-8Supplemental Data.docxFigure e-1Appendix e-1Co-investigator appendix

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  • Authors: Kharabian Masouleh, Shahrzad; Eickhoff, Simon; Hoffstaedter, Felix; Genon, Sarah;

    Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported “structural brain behavior” (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

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  • Authors: Ledig, Christian; Schuh, Andreas; Guerrero, Ricardo; Heckemann, Rolf A.; +1 Authors

    Data accompanying the article: C. Ledig, A. Schuh, R. Guerrero, R. Heckemann, D. Rueckert, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, Scientific Reports, 2018. Data derived from 5074 images from the ADNI cohort: - structural segmentations (138 regions, MALPEM); - binary brain masks (pincram); - features (volumes, asymmetry, atrophy rates) and disease labels; - lists of processed images IsSupplementTo: Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D (2018) Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Reports, 2018. https://doi.org/10.1038/s41598-018-29295-9

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    Authors: Bhagwat, Nikhil; Pipitone, Jon; Winterburn, Julie L.; Guo, Ting; +6 Authors

    Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.

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    Frontiers in Neuroscience
    2016 . Peer-reviewed
    Data sources: Frontiers
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      Frontiers in Neuroscience
      2016 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A.; +7 Authors

    In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab® and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

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    Frontiers in Neuroinformatics
    2016 . Peer-reviewed
    Data sources: Frontiers
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      Frontiers in Neuroinformatics
      2016 . Peer-reviewed
      Data sources: Frontiers
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