Aline Carsin-Vu1, Isabelle Corouge2, Olivier Commowick3, Guillaume Bouzillé4, Christian Barillot5, Jean-Christophe Ferré6, Maia Proisy7. 1. Univ Rennes1, Faculté de Médecine, F-35043 Rennes, France; INSERM, U 1228, ERL VISAGES, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VISAGES Project-Team, F-35042 Rennes, France; CHU Reims, Département de Radiologie, F-51092 Reims, France. Electronic address: acarsinvu@gmail.com. 2. Univ Rennes1, Faculté de Médecine, F-35043 Rennes, France; INSERM, U 1228, ERL VISAGES, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VISAGES Project-Team, F-35042 Rennes, France. Electronic address: isabelle.corouge@irisa.fr. 3. Univ Rennes1, Faculté de Médecine, F-35043 Rennes, France; INSERM, U 1228, ERL VISAGES, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VISAGES Project-Team, F-35042 Rennes, France. Electronic address: olivier.commowick@irisa.fr. 4. INSERM, U 1414, CIC, F-35033 Rennes, France; CHU Rennes, Centre de Données Cliniques, F-35033 Rennes, France. Electronic address: guillaume.bouzille@chu-rennes.fr. 5. Univ Rennes1, Faculté de Médecine, F-35043 Rennes, France; INSERM, U 1228, ERL VISAGES, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VISAGES Project-Team, F-35042 Rennes, France. Electronic address: christian.barillot@irisa.fr. 6. Univ Rennes1, Faculté de Médecine, F-35043 Rennes, France; INSERM, U 1228, ERL VISAGES, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VISAGES Project-Team, F-35042 Rennes, France; CHU Rennes, Département de Radiologie, F-35033 Rennes, France. Electronic address: jean-christophe.ferre@chu-rennes.fr. 7. Univ Rennes1, Faculté de Médecine, F-35043 Rennes, France; INSERM, U 1228, ERL VISAGES, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VISAGES Project-Team, F-35042 Rennes, France; CHU Rennes, Département de Radiologie, F-35033 Rennes, France. Electronic address: maia.proisy@chu-rennes.fr.
Abstract
OBJECTIVES: To investigate changes in cerebral blood flow (CBF) in gray matter (GM) between 6 months and 15 years of age and to provide CBF values for the brain, GM, white matter (WM), hemispheres and lobes. METHODS: Between 2013 and 2016, we retrospectively included all clinical MRI examinations with arterial spin labeling (ASL). We excluded subjects with a condition potentially affecting brain perfusion. For each subject, mean values of CBF in the brain, GM, WM, hemispheres and lobes were calculated. GM CBF was fitted using linear, quadratic and cubic polynomial regression against age. Regression models were compared with Akaike's information criterion (AIC), and Likelihood Ratio tests. RESULTS: 84 children were included (44 females/40 males). Mean CBF values were 64.2 ± 13.8 mL/100 g/min in GM, and 29.3 ± 10.0 mL/100 g/min in WM. The best-fit model of brain perfusion was the cubic polynomial function (AIC = 672.7, versus respectively AIC = 673.9 and AIC = 674.1 with the linear negative function and the quadratic polynomial function). A statistically significant difference between the tested models demonstrating the superiority of the quadratic (p = 0.18) or cubic polynomial model (p = 0.06), over the negative linear regression model was not found. No effect of general anesthesia (p = 0.34) or of gender (p = 0.16) was found. CONCLUSION: we provided values for ASL CBF in the brain, GM, WM, hemispheres, and lobes over a wide pediatric age range, approximately showing inverted U-shaped changes in GM perfusion over the course of childhood.
OBJECTIVES: To investigate changes in cerebral blood flow (CBF) in gray matter (GM) between 6 months and 15 years of age and to provide CBF values for the brain, GM, white matter (WM), hemispheres and lobes. METHODS: Between 2013 and 2016, we retrospectively included all clinical MRI examinations with arterial spin labeling (ASL). We excluded subjects with a condition potentially affecting brain perfusion. For each subject, mean values of CBF in the brain, GM, WM, hemispheres and lobes were calculated. GM CBF was fitted using linear, quadratic and cubic polynomial regression against age. Regression models were compared with Akaike's information criterion (AIC), and Likelihood Ratio tests. RESULTS: 84 children were included (44 females/40 males). Mean CBF values were 64.2 ± 13.8 mL/100 g/min in GM, and 29.3 ± 10.0 mL/100 g/min in WM. The best-fit model of brain perfusion was the cubic polynomial function (AIC = 672.7, versus respectively AIC = 673.9 and AIC = 674.1 with the linear negative function and the quadratic polynomial function). A statistically significant difference between the tested models demonstrating the superiority of the quadratic (p = 0.18) or cubic polynomial model (p = 0.06), over the negative linear regression model was not found. No effect of general anesthesia (p = 0.34) or of gender (p = 0.16) was found. CONCLUSION: we provided values for ASL CBF in the brain, GM, WM, hemispheres, and lobes over a wide pediatric age range, approximately showing inverted U-shaped changes in GM perfusion over the course of childhood.