Abstract #1219

Cerebrovascular brain-age

M.B.J. Dijsselhof¹, M. Barboure¹, M. Stritt², W. Nordhøy³, A.M. Wink¹, L.T. Westlye^4,5,6, J.H. Cole⁷, F. Barkhof^1,8, J. Petr⁹, and H.J.M.M. Mutsaerts¹

¹Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, Netherlands, ²Mediri GmbH, Heidelberg, Germany, ³Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, ⁴Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway, ⁵Department of Psychology, University of Oslo, Oslo, Norway, ⁶KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway, ⁷Dementia Research Centre, Queen Square Institute of Neurology; Centre for Medical Image Computing, University College London, London, United Kingdom, ⁸Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom, ⁹Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany

Synopsis

The structural brain-age makes predictions based on changes in tissue integrity. Adding cerebrovascular MRI biomarkers may add sensitivity to physiological and metabolic changes, hence complementing structural brain-age, and possibly improving its early pathology sensitivity. Baseline and follow-up T1w, FLAIR, and ASL data of 233 healthy participants and combinations of features and algorithms were used to predict ‘Cerebrovascular brain-age’.

The ExtraTrees algorithm utilising T1w, ASL, and FLAIR features performed best and showed good longitudinal reproducibility.

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