Introduction: Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease.
Methods: Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis.
Results: A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository.
Conclusions: The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396326 | PMC |
| http://dx.doi.org/10.1016/j.dadm.2019.01.002 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Harmonization for Parkinson's Disease Multi-Dataset T1 MRI Morphometry Classification.
NeuroSci
November 2024
National Institute of Neurological Disorders and Strokes, National Institutes of Health, Bethesda, MD 20892, USA.
Classification of disease and healthy volunteer cohorts provides a useful clinical alternative to traditional group statistics due to individualized, personalized predictions. Classifiers for neurodegenerative disease can be trained on structural MRI morphometry, but require large multi-scanner datasets, introducing confounding batch effects. We test ComBat, a common harmonization model, in an example application to classify subjects with Parkinson's disease from healthy volunteers and identify common pitfalls, including data leakage.
View Article and Find Full Text PDFReference values for the adolescent post version of the Postconcussion Symptom Inventory from the German general population.
Eur J Pediatr
December 2024
Institute of Psychology, Faculty of Psychology and Sport Science, University of Innsbruck, Innsbruck, Austria.
The present study aims at providing reference values from the general pediatric population for the German version of the 21-item self-report post version of the Postconcussion Symptom Inventory for adolescents aged 13-17 years (PCSI-SR13) following pediatric traumatic brain injury (pTBI). A total of N = 950 adolescents completed an adapted version of the PCSI-SR13. Prior to establishing reference values using percentiles, psychometric properties (i.
View Article and Find Full Text PDFSex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease.
Alzheimers Dement
December 2024
Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Introduction: The effects of sex and apolipoprotein E (APOE)-Alzheimer's disease (AD) risk factors-on white matter microstructure are not well characterized.
Methods: Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)-corrected and harmonized. This dataset included 4741 participants (age = 73.
Intracranial multimodal monitoring in neurocritical care (Neurocore-iMMM): an open, decentralized consensus.
Crit Care
December 2024
Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium.
Background: Intracranial multimodal monitoring (iMMM) is increasingly used in neurocritical care, but a lack of standardization hinders its evidence-based development. Here, we devised core outcome sets (COS) and reporting guidelines to harmonize iMMM practices and research.
Methods: An open, decentralized, three-round Delphi consensus study involved experts between December 2023 and June 2024.
Assessment of ComBat Harmonization Performance on Structural Magnetic Resonance Imaging Measurements.
Hum Brain Mapp
December 2024
Department of Neurosciences and Mental Health, Fondazione IRCS Cà Granda Ospedale Policlinico, Milano, Italy.
Data aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high-dimensional datasets to form large-scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site-related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi-scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!