TY - JOUR
T1 - The value of arterial spin labelling perfusion MRI in brain age prediction
AU - Dijsselhof, Mathijs B. J.
AU - Barboure, Michelle
AU - Stritt, Michael
AU - Nordhøy, Wibeke
AU - Wink, Alle Meije
AU - Beck, Dani
AU - Westlye, Lars T.
AU - Cole, James H.
AU - Barkhof, Frederik
AU - Mutsaerts, Henk J. M. M.
AU - Petr, Jan
N1 - Funding Information: We acknowledge the following grants: the Dutch Heart Foundation 2020 T049—Mathijs B. J. Dijsselhof, Jan Petr, and Henk J. M. M. Mutsaerts—the Eurostars‐2 joint programme with co‐funding from the European Union Horizon 2020 research and innovation programme (ASPIRE E!113701), provided by the Netherlands Enterprise Agency (RvO)—Michael Stritt, Jan Petr, and Henk J. M. M. Mutsaerts—and the EU Joint Program for Neurodegenerative Disease Research, provided by the Netherlands Organisation for Health Research and Development and Alzheimer Nederland DEBBIE JPND2020‐568‐106—Jan Petr, Henk J. M. M. Mutsaerts. Lars T. Westlye is supported by The Research Council of Norway (273345, 298646, 300767), the South‐Eastern Norway Regional Health Authority (2018076, 2019101), the European Research Council under the European Union's Horizon 2020 research and Innovation program (802998). Frederik Barkhof is supported by the NIHR Biomedical Research Centre at UCLH. Open access funding enabled and organized by Projekt DEAL. Funding Information: We acknowledge the following grants: the Dutch Heart Foundation 2020 T049—Mathijs B. J. Dijsselhof, Jan Petr, and Henk J. M. M. Mutsaerts—the Eurostars-2 joint programme with co-funding from the European Union Horizon 2020 research and innovation programme (ASPIRE E!113701), provided by the Netherlands Enterprise Agency (RvO)—Michael Stritt, Jan Petr, and Henk J. M. M. Mutsaerts—and the EU Joint Program for Neurodegenerative Disease Research, provided by the Netherlands Organisation for Health Research and Development and Alzheimer Nederland DEBBIE JPND2020-568-106—Jan Petr, Henk J. M. M. Mutsaerts. Lars T. Westlye is supported by The Research Council of Norway (273345, 298646, 300767), the South-Eastern Norway Regional Health Authority (2018076, 2019101), the European Research Council under the European Union's Horizon 2020 research and Innovation program (802998). Frederik Barkhof is supported by the NIHR Biomedical Research Centre at UCLH. Open access funding enabled and organized by Projekt DEAL. Funding Information: Eurostars‐2 joint programme with co‐funding from the European Union Horizon 2020 research and innovation programme provided by the Netherlands Enterprise Agency (RvO), Grant/Award Number: ASPIRE E!113701; European Research Council under the European Union's Horizon 2020 research and Innovation program, Grant/Award Number: 802998; Dutch Heart Foundation, Grant/Award Number: 2020T049; NIHR biomedical research centre at UCLH; The Research Council of Norway, Grant/Award Numbers: 273345, 298646, 300767; South‐Eastern Norway Regional Health Authority, Grant/Award Numbers: 2018076, 2019101; EU Joint Program for Neurodegenerative Disease Research, provided by the Netherlands Organisation for Health Research and Development and Alzheimer Nederland, Grant/Award Number: DEBBIE JPND2020‐568‐106 Funding information Publisher Copyright: © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
PY - 2023/5
Y1 - 2023/5
N2 - Current structural MRI-based brain age estimates and their difference from chronological age—the brain age gap (BAG)—are limited to late-stage pathological brain-tissue changes. The addition of physiological MRI features may detect early-stage pathological brain alterations and improve brain age prediction. This study investigated the optimal combination of structural and physiological arterial spin labelling (ASL) image features and algorithms. Healthy participants (n = 341, age 59.7 ± 14.8 years) were scanned at baseline and after 1.7 ± 0.5 years follow-up (n = 248, mean age 62.4 ± 13.3 years). From 3 T MRI, structural (T1w and FLAIR) volumetric ROI and physiological (ASL) cerebral blood flow (CBF) and spatial coefficient of variation ROI features were constructed. Multiple combinations of features and machine learning algorithms were evaluated using the Mean Absolute Error (MAE). From the best model, longitudinal BAG repeatability and feature importance were assessed. The ElasticNetCV algorithm using T1w + FLAIR+ASL performed best (MAE = 5.0 ± 0.3 years), and better compared with using T1w + FLAIR (MAE = 6.0 ± 0.4 years, p <.01). The three most important features were, in descending order, GM CBF, GM/ICV, and WM CBF. Average baseline and follow-up BAGs were similar (−1.5 ± 6.3 and − 1.1 ± 6.4 years respectively, ICC = 0.85, 95% CI: 0.8–0.9, p =.16). The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most, and performed best in a cross-sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.
AB - Current structural MRI-based brain age estimates and their difference from chronological age—the brain age gap (BAG)—are limited to late-stage pathological brain-tissue changes. The addition of physiological MRI features may detect early-stage pathological brain alterations and improve brain age prediction. This study investigated the optimal combination of structural and physiological arterial spin labelling (ASL) image features and algorithms. Healthy participants (n = 341, age 59.7 ± 14.8 years) were scanned at baseline and after 1.7 ± 0.5 years follow-up (n = 248, mean age 62.4 ± 13.3 years). From 3 T MRI, structural (T1w and FLAIR) volumetric ROI and physiological (ASL) cerebral blood flow (CBF) and spatial coefficient of variation ROI features were constructed. Multiple combinations of features and machine learning algorithms were evaluated using the Mean Absolute Error (MAE). From the best model, longitudinal BAG repeatability and feature importance were assessed. The ElasticNetCV algorithm using T1w + FLAIR+ASL performed best (MAE = 5.0 ± 0.3 years), and better compared with using T1w + FLAIR (MAE = 6.0 ± 0.4 years, p <.01). The three most important features were, in descending order, GM CBF, GM/ICV, and WM CBF. Average baseline and follow-up BAGs were similar (−1.5 ± 6.3 and − 1.1 ± 6.4 years respectively, ICC = 0.85, 95% CI: 0.8–0.9, p =.16). The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most, and performed best in a cross-sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.
KW - ASL
KW - ageing
KW - brain age
KW - cerebral perfusion
KW - cerebrovascular health
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85149262789&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/hbm.26242
DO - https://doi.org/10.1002/hbm.26242
M3 - Article
C2 - 36852443
SN - 1065-9471
VL - 44
SP - 2754
EP - 2766
JO - Human brain mapping
JF - Human brain mapping
IS - 7
ER -