The European Prevention of Alzheimer’s Dementia (EPAD) MRI Dataset and Processing Workflow

Abstract

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER) , and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer’s Disease and Neurodegeneration stratified by sex

“…For the EPAD cohort(Solomon et al, 2018), which is a multisite study, T1-weighted MRIs were inversion-recovery prepare 3D gradient-echo sequences, acquired with 3T scanners. Images were segmented with Freesurfer 6.0 and subjected to a quality control procedure (Lorenzini et al, 2021).…”

“…For the EPAD cohort (Solomon et al, 2018), which is a multisite study, T1-weighted MRIs were inversion-recovery prepare 3D gradient-echo sequences, acquired with 3T scanners. Images were segmented with Freesurfer 6.0 and subjected to a quality control procedure (Lorenzini et al, 2021). For all the cohorts, subsequent to the FreeSurfer segmentation, tissue regions were parcellated into 183 different anatomical regions of interest (ROI)s using the widely-used FreeSurfer's cortical Desikan-Killiany (Desikan et al, 2006) and subcortical aseg (Fischl et al, 2002) labeling pipelines.…”

Alzheimer's Disease and Small Vessel Disease Differentially Affect White Matter Microstructure

“…MRI acquisition and preprocessing details have been detailed elsewhere. 22 Briefly, diffusion‐weighted images underwent geometric distortion correction using the opposed phase‐encoding polarities, head motion and eddy‐current correction, brain extraction, and tensor‐fitting to produce FA and MD maps.…”

Age differences in the association between sleep and Alzheimer's disease biomarkers in the EPAD cohort

“…Neuroimaging acquisition and processing procedures used in the EPAD study are detailed elsewhere 18 . Briefly, 3D T1w and FLAIR sequences were acquired across all sites to derive regional grey matter and white matter hyperintensity (WMH) volumes, respectively.…”

“…Self-reported sleep quality was first assessed with the PSQI 16 Neuroimaging acquisition and processing procedures used in the EPAD study are detailed elsewhere. 18 Briefly, 3D T1w and FLAIR sequences were acquired across all sites to derive regional grey matter and white matter hyperintensity (WMH) volumes, respectively. Hippocampal volumes (left/right averaged and corrected for each participant's intracranial volume) and WMH volumes were automatically quantified via Learning Embeddings for Atlas Propagation (LEAP).…”