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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.

Additional file 2: Table S3. List of mapped genes.

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, �� 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, �� 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, �� 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, �� 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'

Additional file 2: Table S1. Probability of tract reaching brain region (Reach probability). Table S2. Brain region mapping between Desikan-Killany atlas and post-mortem brain labels. Table S3. Number of Tissue-to-Tissue (TTC) correlated genes for each pair of Region-of-interests (ROIs). Table S4. Pathway list with annotated subtypes. Table S5. Pathway interactions in brain region pairs that are that are significantly different in tract-bound. Table S6. Pathway interactions in brain region pairs that are that are significantly different in AD-tract-bound. Table S7. Association between covariates and diffusion measures in each tract. Table S8. Effect sizes for associations in ADNI3 and ADNI2. Table S9. Pathway over-representation analysis between brain region pairs connected by white matter tracts and region pairs not connected by tracts. Table S10. Pathway interaction graph (degree). Table S11. Pathway over-representation analysis of symmetric gene synchronization in brain region pairs connected by white matter tracts. Table S12. Association between gene expression of Toll receptor signaling in the blood and diffusion measures in the brain. Table S13. Number of subjects in each study per site.

Additional file 2. The R script to draw a heatmap for GTEx dataset.

Additional file 1. Supplementary tables.

imaging and cognitive data from 86 subjects: ws2-based tau measure (tau), global amyloid PET SUVR (abeta), regional amyloid PET SUVR in temporal lobe (temp), cingulate cortex, parietal lobe (par), frontal lobe(front) , tau PET SUVRs (ibraak1, ibraak34, ibraak56), standard objective cognitive performance (mmse, adas, adef, admem), and subjective cognitive scores (emem,elan, eviso, eplan, eorg, ediv) THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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.

This dataset contains key characteristics about the data described in the Data Descriptor Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.