Neuroimaging standards for research into small vessel disease-advances since 2013

Duering, Marco; Biessels, Geert Jan; Brodtmann, Amy; Chen, Christopher; Cordonnier, Charlotte; de Leeuw, Frank-Erik; Debette, Stephanie; Frayne, Richard; Jouvent, Eric; Rost, Natalia S.; ter Telgte, Annemieke; Salman, Rustam Al-Shahi; Backes, Walter H.; Bae, Hee-Joon; Brown, Rosalind; Chabriat, Hugues; De Luca, Alberto; deCarli, Charles; Dewenter, Anna; Doubal, Fergus N.; Ewers, Michael; Field, Thalia S.; Ganesh, Aravind; Greenberg, Steven; Helmer, Karl G.; Hilal, Saima; Jochems, Angela C. C.; Jokinen, Hanna; Kuijf, Hugo; Lam, Bonnie Y. K.; Lebenberg, Jessica; MacIntosh, Bradley J.; Maillard, Pauline; Mok, Vincent C. T.; Pantoni, Leonardo; Rudilosso, Salvatore; Satizabal, Claudia L.; Schirmer, Markus D.; Schmidt, Reinhold; Smith, Colin; Staals, Julie; Thrippleton, Michael J.; van Veluw, Susanne J.; Vemuri, Prashanthi; Wang, Yilong; Werring, David; Zedde, Marialuisa; Akinyemi, Rufus O.; Del Brutto, Oscar H.; Markus, Hugh S.; Zhu, Yi-Cheng; Smith, Eric E.; Dichgans, Martin und Wardlaw, Joanna M. (2023): Neuroimaging standards for research into small vessel disease-advances since 2013. In: Lancet Neurology, Bd. 22, Nr. 7: S. 602-618

Abstract

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Munich Cluster for Systems Neurology (SyNergy)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198