---
title: "COFITAGE: Multimodal Longitudinal Neuroimaging Dataset for Brain Aging (Ages 50–70)"
id: "biorxiv-15-cognitive-fitness-in-ageing-cofitage-a-multimodal-and-longitudinal-neuroimaging"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-15-cognitive-fitness-in-ageing-cofitage-a-multimodal-and-longitudinal-neuroimaging"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.24.746635v1?rss=1"
published_at: "2026-08-27T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# COFITAGE: Multimodal Longitudinal Neuroimaging Dataset for Brain Aging (Ages 50–70)
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-15-cognitive-fitness-in-ageing-cofitage-a-multimodal-and-longitudinal-neuroimaging
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.24.746635v1?rss=1)
- **Published At:** 2026-08-27T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The COFITAGE study provides an open-access, **multimodal** and longitudinal neuroimaging dataset focused on adults aged 50–69 to study early brain aging processes. - Baseline cohort: 101 community-dwelling participants who underwent 3T MRI with high-resolution structural (T1, T2), quantitative multiparametric acquisitions including B1 mapping, and multi-shell diffusion-weighted imaging. - PET imaging for amyloid-beta was obtained in all participants using **[18F]Flutemetamol** or **[18F]Florbetapir**; a subset also had **[18F]THK-5351** PET to assess tau-related signals or neuroinflammation. - Extensive phenotyping accompanies imaging: sleep assessments, neuropsychological testing, and genotype data from genetic analysis. - Longitudinal cognitive data: 66 participants completed a 2-year cognitive follow-up, enabling analysis of cognitive trajectories over time. - Data acquisition and curation followed standardized procedures with systematic quality control to support robust cross-sectional and longitudinal research. - All data are distributed in a **BIDS-compliant** format and released open-access via EBRAINS. - Potential uses include multimodal analyses to identify imaging biomarkers of early cognitive decline, comparison of quantitative MRI models, and prediction or monitoring of brain aging progression. - The dataset fills a coverage gap by focusing on ages 50–70, a transitional period often underrepresented in public healthy-subject datasets.
## Clinical Analysis & Structured Key Points
Cognitive Fitness in Ageing (COFITAGE): A Multimodal and Longitudinal Neuroimaging Dataset | bioRxiv Skip to main content New Results Cognitive Fitness in Ageing (COFITAGE): A Multimodal and Longitudinal Neuroimaging Dataset Antoine Jacquemin , Jiqing Huang , Nikita Beliy , Christian Degueldre , Francois Meyer , Daphne Chylinski , Justinas Narbutas , Maxime Van Egroo , Eric Salmon , Puneet Talwar , Fabienne Collette , Gilles Vandewalle , Christine Bastin , Mohamed Ali Bahri , View ORCID Profile Christophe Phillips doi: https://doi.org/10.64898/2026.08.24.746635 Antoine Jacquemin 1 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jiqing Huang 1 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nikita Beliy 2 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Psychology, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian Degueldre 3 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Francois Meyer 4 IGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Memory Clinic, Department of Neurology, University Hospital of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daphne Chylinski 5 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Neuropsychology and Speech Therapy, Hopital Universitaire de Bruxelles (H.U.B.), Brussels, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Justinas Narbutas 6 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors (IfADo) Dortmund, Germany; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maxime Van Egroo 7 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Faculty of Health, Medicine and Life Sciences, Mental Health and Neuroscience Research Institute, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric Salmon 8 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Memory Clinic, Department of Neurology, University Hospital of Liege, Liege, Belgium ; 8Department of Clinical Sciences, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Puneet Talwar 9 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Biology, Ecology and Evolution, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fabienne Collette 2 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Psychology, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gilles Vandewalle 9 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Biology, Ecology and Evolution, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine Bastin 2 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Psychology, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohamed Ali Bahri 10 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Department of Physics, University of Liege, Liege, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christophe Phillips 1 GIGA - CRC Human Imaging, University of Liege, Liege, Belgium ; Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christophe Phillips For correspondence: c.phillips{at}uliege.be Abstract Info/History Metrics Preview PDF Abstract Purpose: Brain aging involves interrelated changes in molecular processes, neuroinflammatory mechanisms, brain macro- and microstructure, sleep physiology, and cognition. The 50 to 70 years age range represents a critical transition period, in which these subtle alterations may precede measurable cognitive decline and the onset of clinical neurodegenerative disease. To allow systematic investigation of these early alterations and the subsequent progression in brain aging, we provide an open-access data resource from a multidisciplinary longitudinal study integrating neuroimaging, genetics, sleep, and neuropsychological phenotyping with assessments at baseline and at 2-year follow-up. Acquisition and Validation Methods: The baseline cohort comprises 101 community-dwelling participants (50-69 years old) who underwent magnetic resonance imaging (MRI) using a 3T protocol that included high-resolution structural imaging (T1- and T2-weighted), quantitative multi-parametric acquisitions with B1 mapping, and multi-shell diffusion-weighted imaging. Moreover, positron emission tomography (PET) imaging was performed using [18F]Flutemetamol or [18F]Florbetapir (amyloid-beta tracers) in all participants, with a subset also undergoing [18F]THK-5351 PET (tau-related/neuroinflammation). The dataset was complemented by extensive phenotypic data, including sleep and neuropsychological assessments, and by genotype data through genetic analysis. 66 participants underwent a 2-year cognitive follow-up, enabling longitudinal analyses of cognitive trajectories. Data acquisition and curation were performed using standardized procedures, with systematic quality control to support reliable cross-sectional and longitudinal analyses. Data Format and Usage Notes: All data are distributed in a BIDS-compliant format, and released in open-access (EBRAINS). Potential Applications: This dataset supports multimodal analyses, allowing the identification of interpretable patterns characterizing brain aging from multiple perspectives. It enables the comparison of different models to derive (semi)quantitative MRI parameters, the discovery of imaging biomarkers associated with early cognitive decline, and the monitoring or prediction of brain aging progression. In addition, it offers focused coverage of adults aged 50-70 years, which is often underrepresented in existing healthy subjects public datasets. Competing Interest Statement The authors have declared no competing interest. Funder Information Declared FRS-FNRS, Belgium Actions de Recherche Concertees of the Federation Wallonie-Bruxelles, Belgium University of Liège, https://ror.org/00afp2z80 Fondation Simone et Pierre Clerdent, Belgium European Regional Development Fund (ERDF, Radiomed Project) GE Healthcare Ltd. , ISS290 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Back to top Previous Posted August 27, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Cognitive Fitness in Ageing (COFITAGE): A Multimodal and Longitudinal Neuroimaging Dataset Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. 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