Harmonizing Ethno-Regionally Diverse Datasets to Advance the Global Epidemiology of Dementia.

[1]  N. Jahanshad,et al.  Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses , 2022, Frontiers in Neurology.

[2]  V. Hachinski,et al.  White matter hyperintensities and longitudinal cognitive decline in cognitively normal populations and across diagnostic categories: A meta‐analysis, systematic review, and recommendations for future study harmonization , 2022, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[3]  S. Erk,et al.  ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting‐state and task‐based fMRI data , 2022, Human brain mapping.

[4]  Indira C. Turney,et al.  Cross-national harmonization of cognitive measures across HRS HCAP (USA) and LASI-DAD (India) , 2022, PloS one.

[5]  Lin F. Yang,et al.  Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019 , 2022, The Lancet. Public health.

[6]  A. Ibáñez,et al.  Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration , 2021, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[7]  David A. Rolls,et al.  Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI , 2021, Scientific Reports.

[8]  Seong Jae Hwang,et al.  A multi-scanner neuroimaging data harmonization using RAVEL and ComBat , 2021, NeuroImage.

[9]  John C. Lin,et al.  Pre-statistical harmonization of behavrioal instruments across eight surveys and trials , 2021, BMC Medical Research Methodology.

[10]  V. Ravindranath,et al.  Changing demography and the challenge of dementia in India , 2021, Nature Reviews Neurology.

[11]  R. Sacco,et al.  Pre-Statistical Considerations for Harmonization of Cognitive Instruments: Harmonization of ARIC, CARDIA, CHS, FHS, MESA, and NOMAS. , 2021, Journal of Alzheimer's disease : JAD.

[12]  Mohammad Ali Moni,et al.  Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study , 2021, BMC Medical Informatics and Decision Making.

[13]  J. Schott,et al.  Beyond the average patient: how neuroimaging models can address heterogeneity in dementia , 2021, Brain : a journal of neurology.

[14]  Alice D. Lam,et al.  Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel , 2021, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[15]  L. Smeeth,et al.  Ethnic Differences in Dementia Risk: A Systematic Review and Meta-Analysis , 2021, Journal of Alzheimer's disease : JAD.

[16]  A. Ibáñez,et al.  The Latin America and the Caribbean Consortium on Dementia (LAC-CD): From Networking to Research to Implementation Science. , 2020, Journal of Alzheimer's disease : JAD.

[17]  J. E. Iglesias,et al.  FreeSurfer‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts , 2020, Human brain mapping.

[18]  Joanne C. Beer,et al.  Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data☆ , 2020, NeuroImage.

[19]  A. Mechelli,et al.  Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners , 2020, NeuroImage.

[20]  R. Lipton,et al.  Estimating prevalence of subjective cognitive decline in and across international cohort studies of aging: a COSMIC study , 2020, medRxiv.

[21]  K. Langa,et al.  The Health and Retirement Study Harmonized Cognitive Assessment Protocol Project: Study Design and Methods , 2019, Neuroepidemiology.

[22]  Zhuo Wang,et al.  Intra-Scanner and Inter-Scanner Reproducibility of Automatic White Matter Hyperintensities Quantification , 2019, Front. Neurosci..

[23]  R. Lipton,et al.  Determinants of cognitive performance and decline in 20 diverse ethno-regional groups: A COSMIC collaboration cohort study , 2019, PLoS medicine.

[24]  C. Beckmann,et al.  Conceptualizing mental disorders as deviations from normative functioning , 2019, Molecular Psychiatry.

[25]  G. Livingston,et al.  Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data , 2019, The Lancet. Global health.

[26]  Paul M. Thompson,et al.  Scanner invariant representations for diffusion MRI harmonization , 2019, Magnetic resonance in medicine.

[27]  F. Barkhof,et al.  Harmonization of neuroimaging biomarkers for neurodegenerative diseases: A survey in the imaging community of perceived barriers and suggested actions , 2018, Alzheimer's & dementia.

[28]  M. Weissman,et al.  Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data , 2018, Human brain mapping.

[29]  P. Batterham,et al.  Assessing distress in the community: psychometric properties and crosswalk comparison of eight measures of psychological distress , 2017, Psychological Medicine.

[30]  Russell T. Shinohara,et al.  Harmonization of cortical thickness measurements across scanners and sites , 2017, NeuroImage.

[31]  W. M. van der Flier,et al.  The need for harmonisation and innovation of neuropsychological assessment in neurodegenerative dementias in Europe: consensus document of the Joint Program for Neurodegenerative Diseases Working Group , 2017, Alzheimer's Research & Therapy.

[32]  Ragini Verma,et al.  Harmonization of multi-site diffusion tensor imaging data , 2017, NeuroImage.

[33]  G. Andrews,et al.  Age-related cognitive decline and associations with sex, education and apolipoprotein E genotype across ethnocultural groups and geographic regions: a collaborative cohort study , 2017, PLoS medicine.

[34]  E. R. van den Heuvel,et al.  Comparison of Standardization Methods for the Harmonization of Phenotype Data: An Application to Cognitive Measures. , 2016, American journal of epidemiology.

[35]  Parminder Raina,et al.  Maelstrom Research guidelines for rigorous retrospective data harmonization , 2016, International journal of epidemiology.

[36]  Anbupalam Thalamuthu,et al.  The Prevalence of Mild Cognitive Impairment in Diverse Geographical and Ethnocultural Regions: The COSMIC Collaboration , 2015, PloS one.

[37]  G. Potter,et al.  Effects of education and race on cognitive decline: An integrative study of generalizability versus study-specific results. , 2015, Psychology and aging.

[38]  J. Schneider,et al.  The Statistical Modeling of Aging and Risk of Transition Project: Data Collection and Harmonization Across 11 Longitudinal Cohort Studies of Aging, Cognition, and Dementia , 2015, Observational studies.

[39]  D. Louis Collins,et al.  The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: Evidence of validity , 2015, Alzheimer's & Dementia.

[40]  Parminder Raina,et al.  Statistical approaches to harmonize data on cognitive measures in systematic reviews are rarely reported. , 2015, Journal of clinical epidemiology.

[41]  R. Green,et al.  Calibrating Longitudinal Cognition in Alzheimer's Disease Across Diverse Test Batteries and Datasets , 2014, Neuroepidemiology.

[42]  Andrew J. Saykin,et al.  A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease , 2014, Alzheimer's & Dementia.

[43]  Markus Perola,et al.  Data harmonization and federated analysis of population-based studies: the BioSHaRE project , 2013, Emerging Themes in Epidemiology.

[44]  Daniel J Bauer,et al.  Integrative data analysis in clinical psychology research. , 2013, Annual review of clinical psychology.

[45]  A. Wimo,et al.  The global prevalence of dementia: A systematic review and metaanalysis , 2013, Alzheimer's & Dementia.

[46]  H. Arrighi,et al.  Mild cognitive impairment: Disparity of incidence and prevalence estimates , 2012, Alzheimer's & Dementia.

[47]  Kaarin J Anstey,et al.  Multiple imputation was an efficient method for harmonizing the Mini-Mental State Examination with missing item-level data. , 2011, Journal of clinical epidemiology.

[48]  P. Scheltens,et al.  The use of neuropsychological tests across Europe: the need for a consensus in the use of assessment tools for dementia , 2011, European journal of neurology.

[49]  Hans Hillege,et al.  Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies , 2010, International journal of epidemiology.

[50]  Daniel J Bauer,et al.  Psychometric approaches for developing commensurate measures across independent studies: traditional and new models. , 2009, Psychological methods.

[51]  R. Stewart,et al.  The 10/66 Dementia Research Group's fully operationalised DSM-IV dementia computerized diagnostic algorithm, compared with the 10/66 dementia algorithm and a clinician diagnosis: a population validation study , 2008, BMC public health.

[52]  Wenjing Huang,et al.  Pooling data from multiple longitudinal studies: the role of item response theory in integrative data analysis. , 2008, Developmental psychology.

[53]  R. Stewart,et al.  The protocols for the 10/66 dementia research group population-based research programme , 2007, BMC Public Health.

[54]  T. Salthouse.  Localizing age-related individual differences in a hierarchical structure. , 2004, Intelligence.

[55]  V Hachinski,et al.  The effect of different diagnostic criteria on the prevalence of dementia. , 1997, The New England journal of medicine.