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Research Data Supporting "Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease"


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Type

Dataset

Change log

Authors

Giorgio, Joseph 
Landau, Susan 
Jagust, William 
Tino, Peter 

Description

Multimodal biological and cognitive data used as predictors and outcomes for machine learning models can be found in 'master data sheet.xls'. With the exception of the derived PLS Derived GM all data were downloaded from the ADNI repository http://adni.loni.usc.edu/. For description on derivation of the PLS Dervived GM see ‘Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).’ in the final publication DATA SETS: 1.) ‘Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).’ Data available: RIDS: The ADNI identifier, DIAG(1CN, 2MCI): Baseline diagnosis (1:cognitively normal, 2: MCI) ADNI Mem: ADNI Memory composite measure used as outcome variable for the PLSr-RFE, PLS Derived GM: Variable derived from the PLSr-RFE procedure. These data are presented in ‘Results: Composite grey matter score for predicting cross-modality associations’ 2.) ‘Statistical Validation: Out-of-Sample validation[cross-modality associations ]’ Data available: RIDS: The ADNI identifier, DIAG(1CN, 2DEM, 3MCI): Baseline diagnosis (1:cognitively normal, 2:demented, 3: MCI), PLS Derived GM: Variable derived out-of-sample. FTP Braak(12): tau PET SUVR for Braak stage (1,2), FTP Braak(34): tau PET SUVR for Braak stage (3,4), FTP Braak(56): tau PET SUVR for Braak stage (5,6). These data are presented in ‘Results: Composite grey matter score for predicting cross-modality associations’ 3.)‘Statistical Validation: Out-of-Sample validation [Cross-modal associations -adni mem]’ Data available: RIDS: The ADNI identifier ADNI Mem: ADNI Memory composite measure used as outcome variable. These data are presented in ‘Results: Composite grey matter score for predicting cross-modality associations’ 4.) ‘ Methods:GMLVQ Cognitive model’ Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ‘Results: Cognitive Classification Models for predicting sMCI vs pMCI’ 5.) ‘ Methods:GMLVQ Biological model’ Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ‘Results: Biological Classification Models for predicting sMCI vs pMCI’ 6.) ‘ Methods: GMLVQ-Scalar Projection Cognitive model
Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, Δ ADNI-Mem: Change in ADNI mem from baseline. 7.) ‘ Methods: GMLVQ-Scalar Projection Biological model
Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, Δ ADNI-Mem: Change in ADNI mem from baseline. ‘Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline. 8.) ’Methods: Statistical Validation: Out-of-Sample-[Cognitive model]’ Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, Δ ADNI-Mem: Change in ADNI mem from baseline. 9.) ’Methods: Statistical Validation: Out-of-Sample-[Biological model]’ : RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, Δ ADNI-Mem: Change in ADNI mem from baseline. ‘Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline.’

For a more detailed description of the populations these data were extracted for see 'description of uploaded files.doc'

Version

Software / Usage instructions

MATLAB, SPM

Keywords

Machine learning, Mild cognitive impairment, Alzheimer's disease brain imaging, Cognition

Publisher

Sponsorship
European Commission (290011)
Wellcome Trust (205067/Z/16/Z)
Biotechnology and Biological Sciences Research Council (BB/P021255/1)
Alan Turing Institute (EP/N510129/1)
Alan Turing Institute (SF\087)
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