*Result*: Evaluation of Cerebral Blood Flow and Cerebral Autoregulation Using Synthetic Data and In Silico Modeling.
Title:
Evaluation of Cerebral Blood Flow and Cerebral Autoregulation Using Synthetic Data and In Silico Modeling.
Authors:
Bozkurt S; School of Engineering, Ulster University, Belfast, UK.
Source:
CNS neuroscience & therapeutics [CNS Neurosci Ther] 2026 Mar; Vol. 32 (3), pp. e70821.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101473265 Publication Model: Print Cited Medium: Internet ISSN: 1755-5949 (Electronic) Linking ISSN: 17555930 NLM ISO Abbreviation: CNS Neurosci Ther Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford, UK : Wiley-Blackwell, c2008-
MeSH Terms:
References:
J. A. H. R. Claassen, D. H. J. Thijssen, R. B. Panerai, and F. M. Faraci, “Regulation of Cerebral Blood Flow in Humans: Physiology and Clinical Implications of Autoregulation,” Physiological Reviews 101, no. 4 (2021): 1487–1559, https://doi.org/10.1152/physrev.00022.2020.
F. P. Tiecks, A. M. Lam, R. Aaslid, and D. W. Newell, “Comparison of Static and Dynamic Cerebral Autoregulation Measurements,” Stroke 26, no. 6 (1995): 1014–1019, https://doi.org/10.1161/01.STR.26.6.1014.
N. A. Lassen, “Cerebral Blood Flow and Oxygen Consumption in Man,” Physiological Reviews 39, no. 2 (1959): 183–238, https://doi.org/10.1152/physrev.1959.39.2.183.
C. O. Tan, “Defining the Characteristic Relationship Between Arterial Pressure and Cerebral Flow,” Journal of Applied Physiology 113, no. 8 (2012): 1194–1200, https://doi.org/10.1152/japplphysiol.00783.2012.
C. K. Willie, Y. C. Tzeng, J. A. Fisher, and P. N. Ainslie, “Integrative Regulation of Human Brain Blood Flow,” Journal of Physiology 592, no. 5 (2014): 841–859, https://doi.org/10.1113/jphysiol.2013.268953.
P. Brassard, L. Labrecque, J. D. Smirl, et al., “Losing the Dogmatic View of Cerebral Autoregulation,” Physiological Reports 9, no. 15 (2021): e14982, https://doi.org/10.14814/phy2.14982.
Y. Wang and S. J. Payne, “Static Autoregulation in Humans,” Journal of Cerebral Blood Flow and Metabolism 44, no. 11 (2024): 1191–1207, https://doi.org/10.1177/0271678X231210430.
O. B. Paulson, S. Strandgaard, J. Olesen, and J. C. Baron, “Static Autoregulation in Humans,” Journal of Cerebral Blood Flow and Metabolism 44, no. 12 (2024): 1605–1607, https://doi.org/10.1177/0271678X241258701.
S. Moradi, H. Ferdinando, A. Zienkiewicz, et al., “Measurement of Cerebral Circulation in Human,” in Cerebral Circulation ‐ Updates on Models, Diagnostics and Treatments of Related Diseases (IntechOpen, 2022), https://doi.org/10.5772/intechopen.102383.
Z. Zi, “Sensitivity Analysis Approaches Applied to Systems Biology Models,” IET Systems Biology 5, no. 6 (2011): 336–346, https://doi.org/10.1049/iet‐syb.2011.0015.
V. C. Pezoulas, D. I. Zaridis, E. Mylona, et al., “Synthetic Data Generation Methods in Healthcare: A Review on Open‐Source Tools and Methods,” Computational and Structural Biotechnology Journal 23 (2024): 2892–2910, https://doi.org/10.1016/j.csbj.2024.07.005.
A. Tucker, Z. Wang, Y. Rotalinti, and P. Myles, “Generating High‐Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare Software,” Npj Digital Medicine 3, no. 1 (2020): 1–13, https://doi.org/10.1038/s41746‐020‐00353‐9.
L. Sala, N. Golse, A. Joosten, E. Vibert, and I. Vignon‐Clementel, “Sensitivity Analysis of a Mathematical Model Simulating the Post‐Hepatectomy Hemodynamics Response,” Annals of Biomedical Engineering 51, no. 1 (2023): 270–289, https://doi.org/10.1007/s10439‐022‐03098‐6.
S. Bozkurt, “Mathematical Modeling of Cardiac Function to Evaluate Clinical Cases in Adults and Children,” PLoS One 14, no. 10 (2019): e0224663, https://doi.org/10.1371/journal.pone.0224663.
S. Bozkurt and K. K. Safak, “Evaluating the Hemodynamical Response of a Cardiovascular System Under Support of a Continuous Flow Left Ventricular Assist Device via Numerical Modeling and Simulations,” Computational and Mathematical Methods in Medicine 2013 (2013): 986430, https://doi.org/10.1155/2013/986430.
S. Bozkurt, “Effect of Cerebral Flow Autoregulation Function on Cerebral Flow Rate Under Continuous Flow Left Ventricular Assist Device Support,” Artificial Organs 42, no. 8 (2018): 800–813, https://doi.org/10.1111/aor.13148.
B. W. Smith, S. Andreassen, G. M. Shaw, P. L. Jensen, S. E. Rees, and J. G. Chase, “Simulation of Cardiovascular System Diseases by Including the Autonomic Nervous System Into a Minimal Model,” Computer Methods and Programs in Biomedicine 86, no. 2 (2007): 153–160, https://doi.org/10.1016/j.cmpb.2007.02.001.
J. Liu, Y. S. Zhu, C. Hill, et al., “Cerebral Autoregulation of Blood Velocity and Volumetric Flow During Steady‐State Changes in Arterial Pressure,” Hypertension 62 (2013): 973–979, https://doi.org/10.1161/HYPERTENSIONAHA.113.01867.
A. S. Sainbhi, A. Gomez, L. Froese, et al., “Non‐Invasive and Minimally‐Invasive Cerebral Autoregulation Assessment: A Narrative Review of Techniques and Implications for Clinical Research,” Frontiers in Neurology 13 (2022): 872731, https://doi.org/10.3389/fneur.2022.872731.
S. Fantini, A. Sassaroli, K. T. Tgavalekos, and J. Kornbluth, “Cerebral Blood Flow and Autoregulation: Current Measurement Techniques and Prospects for Noninvasive Optical Methods,” Neurophotonics 3, no. 3 (2016): 031411, https://doi.org/10.1117/1.NPh.3.3.031411.
F. A. Sorond, D. M. Schnyer, J. M. Serrador, W. P. Milberg, and L. A. Lipsitz, “Cerebral Blood Flow Regulation During Cognitive Tasks,” Cortex 44, no. 2 (2008): 179–184, https://doi.org/10.1016/j.cortex.2006.01.003.
S. P. Klein, V. De Sloovere, G. Meyfroidt, and B. Depreitere, “Autoregulation Assessment by Direct Visualisation of Pial Arterial Blood Flow in the Piglet Brain,” Scientific Reports 9, no. 1 (2019): 13333, https://doi.org/10.1038/s41598‐019‐50046‐x.
S. P. Klein, V. D. Sloovere, G. Meyfroidt, and B. Depreitere, “Differential Hemodynamic Response of Pial Arterioles Contributes to a Quadriphasic Cerebral Autoregulation Physiology,” Journal of the American Heart Association 11 (2022): e022943, https://doi.org/10.1161/JAHA.121.022943.
S. Bozkurt, A. Volkan Yilmaz, K. Bakaya, A. Bharadwaj, and K. K. Safak, “A Novel Computational Model for Cerebral Blood Flow Rate Control Mechanisms to Evaluate Physiological Cases,” Biomedical Signal Processing and Control 78 (2022): 103851, https://doi.org/10.1016/j.bspc.2022.103851.
S. Bozkurt, “Computational Evaluation of Heart Failure and Continuous Flow Left Ventricular Assist Device Support in Anaemia,” International Journal for Numerical Methods in Biomedical Engineering 40, no. 1 (2024): e3781, https://doi.org/10.1002/cnm.3781.
J. E. Hall, Guyton and Hall Textbook of Medical Physiology E‐Book (Elsevier Health Sciences, 2010).
F. G. Zampieri, M. Park, L. C. P. Azevedo, M. B. P. Amato, and E. L. V. Costa, “Effects of Arterial Oxygen Tension and Cardiac Output on Venous Saturation: A Mathematical Modeling Approach,” Clinics (São Paulo, Brazil) 67, no. 8 (2012): 897–900, https://doi.org/10.6061/clinics/2012(08)07.
I. Giovannini, C. Chiarla, G. Boldrini, and M. Castagneto, “Calculation of Venoarterial CO2 Concentration Difference,” Journal of Applied Physiology 74, no. 2 (1993): 959–964, https://doi.org/10.1152/jappl.1993.74.2.959.
L. Labrecque, K. Rahimaly, S. Imhoff, et al., “Diminished Dynamic Cerebral Autoregulatory Capacity With Forced Oscillations in Mean Arterial Pressure With Elevated Cardiorespiratory Fitness,” Physiological Reports 5, no. 21 (2017): e13486, https://doi.org/10.14814/phy2.13486.
R. Pernice, L. Sparacino, V. Bari, et al., “Spectral Decomposition of Cerebrovascular and Cardiovascular Interactions in Patients Prone to Postural Syncope and Healthy Controls,” Autonomic Neuroscience 242 (2022): 103021, https://doi.org/10.1016/j.autneu.2022.103021.
H. Joshi and H. Edgell, “Sex Differences in the Ventilatory and Cardiovascular Response to Supine and Tilted Metaboreflex Activation,” Physiological Reports 7, no. 6 (2019): e14041, https://doi.org/10.14814/phy2.14041.
Y. Itoh and N. Suzuki, “Control of Brain Capillary Blood Flow,” Journal of Cerebral Blood Flow and Metabolism 32, no. 7 (2012): 1167–1176, https://doi.org/10.1038/jcbfm.2012.5.
O. F. Harraz, T. A. Longden, F. Dabertrand, D. Hill‐Eubanks, and M. T. Nelson, “Endothelial GqPCR Activity Controls Capillary Electrical Signaling and Brain Blood Flow Through PIP2 Depletion,” Proceedings of the National Academy of Sciences 115, no. 15 (2018): E3569–E3577, https://doi.org/10.1073/pnas.1800201115.
D. A. Hartmann, A. A. Berthiaume, R. I. Grant, et al., “Brain Capillary Pericytes Exert a Substantial but Slow Influence on Blood Flow,” Nature Neuroscience 24, no. 5 (2021): 633–645, https://doi.org/10.1038/s41593‐020‐00793‐2.
A. Silverman and N. H. Petersen, “Physiology, Cerebral Autoregulation,” in StatPearls (StatPearls Publishing, 2025), http://www.ncbi.nlm.nih.gov/books/NBK553183/.
D. C. Hoaglin, F. Mosteller, and J. W. Tukey, Exploring Data Tables, Trends, and Shapes (John Wiley & Sons, 2011).
Y. R. Lankadeva, R. G. Evans, R. Bellomo, and C. N. May, “Chapter 225—Vasoactive Drugs, Renal Function, and Acute Kidney Injury,” in Critical Care Nephrology, Third ed., ed. C. Ronco, R. Bellomo, J. A. Kellum, and Z. Ricci (Elsevier, 2019), 1344–1348, https://doi.org/10.1016/B978‐0‐323‐44942‐7.00225‐9.
N. D. Williams, R. Brady, S. Gilmore, et al., “Cardiovascular Dynamics During Head‐Up Tilt Assessed via Pulsatile and Non‐Pulsatile Models,” Journal of Mathematical Biology 79, no. 3 (2019): 987–1014, https://doi.org/10.1007/s00285‐019‐01386‐9.
M. Jones‐Muhammad and J. P. Warrington, “Redefining the Cerebral Autoregulatory Range of Blood Pressures: Not as Wide as Previously Reported,” Physiological Reports 9, no. 17 (2021): e15006, https://doi.org/10.14814/phy2.15006.
S. Ogoh, G. Moralez, T. Washio, et al., “Effect of Increases in Cardiac Contractility on Cerebral Blood Flow in Humans,” American Journal of Physiology—Heart and Circulatory Physiology 313, no. 6 (2017): H1155–H1161, https://doi.org/10.1152/ajpheart.00287.2017.
S. J. E. Lucas, Y. C. Tzeng, S. D. Galvin, K. N. Thomas, S. Ogoh, and P. N. Ainslie, “Influence of Changes in Blood Pressure on Cerebral Perfusion and Oxygenation,” Hypertension 55, no. 3 (2010): 698–705, https://doi.org/10.1161/HYPERTENSIONAHA.109.146290.
L. Zarrinkoob, K. Ambarki, A. Wåhlin, R. Birgander, A. Eklund, and J. Malm, “Blood Flow Distribution in Cerebral Arteries,” Journal of Cerebral Blood Flow and Metabolism 35, no. 4 (2015): 648–654, https://doi.org/10.1038/jcbfm.2014.241.
D. R. Enzmann, M. R. Ross, M. P. Marks, and N. J. Pelc, “Blood Flow in Major Cerebral Arteries Measured by Phase‐Contrast Cine MR,” AJNR. American Journal of Neuroradiology 15, no. 1 (1994): 123–129.
M. D. Ford, N. Alperin, S. H. Lee, D. W. Holdsworth, and D. A. Steinman, “Characterization of Volumetric Flow Rate Waveforms in the Normal Internal Carotid and Vertebral Arteries,” Physiological Measurement 26, no. 4 (2005): 477–488, https://doi.org/10.1088/0967‐3334/26/4/013.
A. Arora, J. E. Alderman, J. Palmer, et al., “The Value of Standards for Health Datasets in Artificial Intelligence‐Based Applications,” Nature Medicine 29, no. 11 (2023): 2929–2938, https://doi.org/10.1038/s41591‐023‐02608‐w.
J. L. Cross, M. A. Choma, and J. A. Onofrey, “Bias in Medical AI: Implications for Clinical Decision‐Making,” PLOS Digital Health 3, no. 11 (2024): e0000651, https://doi.org/10.1371/journal.pdig.0000651.
L. Pstras and J. Waniewski, “A Synthetic Dataset for In Silico Studies of Parameters Influencing Changes in Mean Arterial Blood Pressure During Hemodialysis,” Annual International Conference of the IEEE Engineering in Medicine & Biology Society 2025 (2025): 1–6, https://doi.org/10.1109/EMBC58623.2025.11251678.
E. Samei, “The Future of In Silico Trials and Digital Twins in Medicine,” PNAS Nexus 4, no. 5 (2025): pgaf123, https://doi.org/10.1093/pnasnexus/pgaf123.
N. Chi, H. Hu, L. Chan, et al., “Impaired Cerebral Autoregulation Is Associated With Poststroke Cognitive Impairment,” Annals of Clinical and Translational Neurology 7, no. 7 (2020): 1092–1102, https://doi.org/10.1002/acn3.51075.
A. Loewe, P. J. Hunter, and P. Kohl, “Computational Modelling of Biological Systems Now and Then: Revisiting Tools and Visions From the Beginning of the Century,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 383, no. 2296 (2025): 20230384, https://doi.org/10.1098/rsta.2023.0384.
F. P. Tiecks, A. M. Lam, R. Aaslid, and D. W. Newell, “Comparison of Static and Dynamic Cerebral Autoregulation Measurements,” Stroke 26, no. 6 (1995): 1014–1019, https://doi.org/10.1161/01.STR.26.6.1014.
N. A. Lassen, “Cerebral Blood Flow and Oxygen Consumption in Man,” Physiological Reviews 39, no. 2 (1959): 183–238, https://doi.org/10.1152/physrev.1959.39.2.183.
C. O. Tan, “Defining the Characteristic Relationship Between Arterial Pressure and Cerebral Flow,” Journal of Applied Physiology 113, no. 8 (2012): 1194–1200, https://doi.org/10.1152/japplphysiol.00783.2012.
C. K. Willie, Y. C. Tzeng, J. A. Fisher, and P. N. Ainslie, “Integrative Regulation of Human Brain Blood Flow,” Journal of Physiology 592, no. 5 (2014): 841–859, https://doi.org/10.1113/jphysiol.2013.268953.
P. Brassard, L. Labrecque, J. D. Smirl, et al., “Losing the Dogmatic View of Cerebral Autoregulation,” Physiological Reports 9, no. 15 (2021): e14982, https://doi.org/10.14814/phy2.14982.
Y. Wang and S. J. Payne, “Static Autoregulation in Humans,” Journal of Cerebral Blood Flow and Metabolism 44, no. 11 (2024): 1191–1207, https://doi.org/10.1177/0271678X231210430.
O. B. Paulson, S. Strandgaard, J. Olesen, and J. C. Baron, “Static Autoregulation in Humans,” Journal of Cerebral Blood Flow and Metabolism 44, no. 12 (2024): 1605–1607, https://doi.org/10.1177/0271678X241258701.
S. Moradi, H. Ferdinando, A. Zienkiewicz, et al., “Measurement of Cerebral Circulation in Human,” in Cerebral Circulation ‐ Updates on Models, Diagnostics and Treatments of Related Diseases (IntechOpen, 2022), https://doi.org/10.5772/intechopen.102383.
Z. Zi, “Sensitivity Analysis Approaches Applied to Systems Biology Models,” IET Systems Biology 5, no. 6 (2011): 336–346, https://doi.org/10.1049/iet‐syb.2011.0015.
V. C. Pezoulas, D. I. Zaridis, E. Mylona, et al., “Synthetic Data Generation Methods in Healthcare: A Review on Open‐Source Tools and Methods,” Computational and Structural Biotechnology Journal 23 (2024): 2892–2910, https://doi.org/10.1016/j.csbj.2024.07.005.
A. Tucker, Z. Wang, Y. Rotalinti, and P. Myles, “Generating High‐Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare Software,” Npj Digital Medicine 3, no. 1 (2020): 1–13, https://doi.org/10.1038/s41746‐020‐00353‐9.
L. Sala, N. Golse, A. Joosten, E. Vibert, and I. Vignon‐Clementel, “Sensitivity Analysis of a Mathematical Model Simulating the Post‐Hepatectomy Hemodynamics Response,” Annals of Biomedical Engineering 51, no. 1 (2023): 270–289, https://doi.org/10.1007/s10439‐022‐03098‐6.
S. Bozkurt, “Mathematical Modeling of Cardiac Function to Evaluate Clinical Cases in Adults and Children,” PLoS One 14, no. 10 (2019): e0224663, https://doi.org/10.1371/journal.pone.0224663.
S. Bozkurt and K. K. Safak, “Evaluating the Hemodynamical Response of a Cardiovascular System Under Support of a Continuous Flow Left Ventricular Assist Device via Numerical Modeling and Simulations,” Computational and Mathematical Methods in Medicine 2013 (2013): 986430, https://doi.org/10.1155/2013/986430.
S. Bozkurt, “Effect of Cerebral Flow Autoregulation Function on Cerebral Flow Rate Under Continuous Flow Left Ventricular Assist Device Support,” Artificial Organs 42, no. 8 (2018): 800–813, https://doi.org/10.1111/aor.13148.
B. W. Smith, S. Andreassen, G. M. Shaw, P. L. Jensen, S. E. Rees, and J. G. Chase, “Simulation of Cardiovascular System Diseases by Including the Autonomic Nervous System Into a Minimal Model,” Computer Methods and Programs in Biomedicine 86, no. 2 (2007): 153–160, https://doi.org/10.1016/j.cmpb.2007.02.001.
J. Liu, Y. S. Zhu, C. Hill, et al., “Cerebral Autoregulation of Blood Velocity and Volumetric Flow During Steady‐State Changes in Arterial Pressure,” Hypertension 62 (2013): 973–979, https://doi.org/10.1161/HYPERTENSIONAHA.113.01867.
A. S. Sainbhi, A. Gomez, L. Froese, et al., “Non‐Invasive and Minimally‐Invasive Cerebral Autoregulation Assessment: A Narrative Review of Techniques and Implications for Clinical Research,” Frontiers in Neurology 13 (2022): 872731, https://doi.org/10.3389/fneur.2022.872731.
S. Fantini, A. Sassaroli, K. T. Tgavalekos, and J. Kornbluth, “Cerebral Blood Flow and Autoregulation: Current Measurement Techniques and Prospects for Noninvasive Optical Methods,” Neurophotonics 3, no. 3 (2016): 031411, https://doi.org/10.1117/1.NPh.3.3.031411.
F. A. Sorond, D. M. Schnyer, J. M. Serrador, W. P. Milberg, and L. A. Lipsitz, “Cerebral Blood Flow Regulation During Cognitive Tasks,” Cortex 44, no. 2 (2008): 179–184, https://doi.org/10.1016/j.cortex.2006.01.003.
S. P. Klein, V. De Sloovere, G. Meyfroidt, and B. Depreitere, “Autoregulation Assessment by Direct Visualisation of Pial Arterial Blood Flow in the Piglet Brain,” Scientific Reports 9, no. 1 (2019): 13333, https://doi.org/10.1038/s41598‐019‐50046‐x.
S. P. Klein, V. D. Sloovere, G. Meyfroidt, and B. Depreitere, “Differential Hemodynamic Response of Pial Arterioles Contributes to a Quadriphasic Cerebral Autoregulation Physiology,” Journal of the American Heart Association 11 (2022): e022943, https://doi.org/10.1161/JAHA.121.022943.
S. Bozkurt, A. Volkan Yilmaz, K. Bakaya, A. Bharadwaj, and K. K. Safak, “A Novel Computational Model for Cerebral Blood Flow Rate Control Mechanisms to Evaluate Physiological Cases,” Biomedical Signal Processing and Control 78 (2022): 103851, https://doi.org/10.1016/j.bspc.2022.103851.
S. Bozkurt, “Computational Evaluation of Heart Failure and Continuous Flow Left Ventricular Assist Device Support in Anaemia,” International Journal for Numerical Methods in Biomedical Engineering 40, no. 1 (2024): e3781, https://doi.org/10.1002/cnm.3781.
J. E. Hall, Guyton and Hall Textbook of Medical Physiology E‐Book (Elsevier Health Sciences, 2010).
F. G. Zampieri, M. Park, L. C. P. Azevedo, M. B. P. Amato, and E. L. V. Costa, “Effects of Arterial Oxygen Tension and Cardiac Output on Venous Saturation: A Mathematical Modeling Approach,” Clinics (São Paulo, Brazil) 67, no. 8 (2012): 897–900, https://doi.org/10.6061/clinics/2012(08)07.
I. Giovannini, C. Chiarla, G. Boldrini, and M. Castagneto, “Calculation of Venoarterial CO2 Concentration Difference,” Journal of Applied Physiology 74, no. 2 (1993): 959–964, https://doi.org/10.1152/jappl.1993.74.2.959.
L. Labrecque, K. Rahimaly, S. Imhoff, et al., “Diminished Dynamic Cerebral Autoregulatory Capacity With Forced Oscillations in Mean Arterial Pressure With Elevated Cardiorespiratory Fitness,” Physiological Reports 5, no. 21 (2017): e13486, https://doi.org/10.14814/phy2.13486.
R. Pernice, L. Sparacino, V. Bari, et al., “Spectral Decomposition of Cerebrovascular and Cardiovascular Interactions in Patients Prone to Postural Syncope and Healthy Controls,” Autonomic Neuroscience 242 (2022): 103021, https://doi.org/10.1016/j.autneu.2022.103021.
H. Joshi and H. Edgell, “Sex Differences in the Ventilatory and Cardiovascular Response to Supine and Tilted Metaboreflex Activation,” Physiological Reports 7, no. 6 (2019): e14041, https://doi.org/10.14814/phy2.14041.
Y. Itoh and N. Suzuki, “Control of Brain Capillary Blood Flow,” Journal of Cerebral Blood Flow and Metabolism 32, no. 7 (2012): 1167–1176, https://doi.org/10.1038/jcbfm.2012.5.
O. F. Harraz, T. A. Longden, F. Dabertrand, D. Hill‐Eubanks, and M. T. Nelson, “Endothelial GqPCR Activity Controls Capillary Electrical Signaling and Brain Blood Flow Through PIP2 Depletion,” Proceedings of the National Academy of Sciences 115, no. 15 (2018): E3569–E3577, https://doi.org/10.1073/pnas.1800201115.
D. A. Hartmann, A. A. Berthiaume, R. I. Grant, et al., “Brain Capillary Pericytes Exert a Substantial but Slow Influence on Blood Flow,” Nature Neuroscience 24, no. 5 (2021): 633–645, https://doi.org/10.1038/s41593‐020‐00793‐2.
A. Silverman and N. H. Petersen, “Physiology, Cerebral Autoregulation,” in StatPearls (StatPearls Publishing, 2025), http://www.ncbi.nlm.nih.gov/books/NBK553183/.
D. C. Hoaglin, F. Mosteller, and J. W. Tukey, Exploring Data Tables, Trends, and Shapes (John Wiley & Sons, 2011).
Y. R. Lankadeva, R. G. Evans, R. Bellomo, and C. N. May, “Chapter 225—Vasoactive Drugs, Renal Function, and Acute Kidney Injury,” in Critical Care Nephrology, Third ed., ed. C. Ronco, R. Bellomo, J. A. Kellum, and Z. Ricci (Elsevier, 2019), 1344–1348, https://doi.org/10.1016/B978‐0‐323‐44942‐7.00225‐9.
N. D. Williams, R. Brady, S. Gilmore, et al., “Cardiovascular Dynamics During Head‐Up Tilt Assessed via Pulsatile and Non‐Pulsatile Models,” Journal of Mathematical Biology 79, no. 3 (2019): 987–1014, https://doi.org/10.1007/s00285‐019‐01386‐9.
M. Jones‐Muhammad and J. P. Warrington, “Redefining the Cerebral Autoregulatory Range of Blood Pressures: Not as Wide as Previously Reported,” Physiological Reports 9, no. 17 (2021): e15006, https://doi.org/10.14814/phy2.15006.
S. Ogoh, G. Moralez, T. Washio, et al., “Effect of Increases in Cardiac Contractility on Cerebral Blood Flow in Humans,” American Journal of Physiology—Heart and Circulatory Physiology 313, no. 6 (2017): H1155–H1161, https://doi.org/10.1152/ajpheart.00287.2017.
S. J. E. Lucas, Y. C. Tzeng, S. D. Galvin, K. N. Thomas, S. Ogoh, and P. N. Ainslie, “Influence of Changes in Blood Pressure on Cerebral Perfusion and Oxygenation,” Hypertension 55, no. 3 (2010): 698–705, https://doi.org/10.1161/HYPERTENSIONAHA.109.146290.
L. Zarrinkoob, K. Ambarki, A. Wåhlin, R. Birgander, A. Eklund, and J. Malm, “Blood Flow Distribution in Cerebral Arteries,” Journal of Cerebral Blood Flow and Metabolism 35, no. 4 (2015): 648–654, https://doi.org/10.1038/jcbfm.2014.241.
D. R. Enzmann, M. R. Ross, M. P. Marks, and N. J. Pelc, “Blood Flow in Major Cerebral Arteries Measured by Phase‐Contrast Cine MR,” AJNR. American Journal of Neuroradiology 15, no. 1 (1994): 123–129.
M. D. Ford, N. Alperin, S. H. Lee, D. W. Holdsworth, and D. A. Steinman, “Characterization of Volumetric Flow Rate Waveforms in the Normal Internal Carotid and Vertebral Arteries,” Physiological Measurement 26, no. 4 (2005): 477–488, https://doi.org/10.1088/0967‐3334/26/4/013.
A. Arora, J. E. Alderman, J. Palmer, et al., “The Value of Standards for Health Datasets in Artificial Intelligence‐Based Applications,” Nature Medicine 29, no. 11 (2023): 2929–2938, https://doi.org/10.1038/s41591‐023‐02608‐w.
J. L. Cross, M. A. Choma, and J. A. Onofrey, “Bias in Medical AI: Implications for Clinical Decision‐Making,” PLOS Digital Health 3, no. 11 (2024): e0000651, https://doi.org/10.1371/journal.pdig.0000651.
L. Pstras and J. Waniewski, “A Synthetic Dataset for In Silico Studies of Parameters Influencing Changes in Mean Arterial Blood Pressure During Hemodialysis,” Annual International Conference of the IEEE Engineering in Medicine & Biology Society 2025 (2025): 1–6, https://doi.org/10.1109/EMBC58623.2025.11251678.
E. Samei, “The Future of In Silico Trials and Digital Twins in Medicine,” PNAS Nexus 4, no. 5 (2025): pgaf123, https://doi.org/10.1093/pnasnexus/pgaf123.
N. Chi, H. Hu, L. Chan, et al., “Impaired Cerebral Autoregulation Is Associated With Poststroke Cognitive Impairment,” Annals of Clinical and Translational Neurology 7, no. 7 (2020): 1092–1102, https://doi.org/10.1002/acn3.51075.
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Contributed Indexing:
Keywords: cerebral autoregulation; cerebral blood flow; simulations; synthetic data
Entry Date(s):
Date Created: 20260313 Date Completed: 20260313 Latest Revision: 20260313
Update Code:
20260313
DOI:
10.1002/cns.70821
PMID:
41820806
Database:
MEDLINE
*Further Information*
*Aims: The classic view of static cerebral autoregulatory function suggests that cerebral blood flow (CBF) remains relatively stable across a wide range of mean arterial pressure (MAP). Recent studies propose a narrow autoregulatory range, as the methods used to manipulate MAP may also influence CBF. This study evaluates static cerebral autoregulatory function using simulations and synthetic data, providing a theoretical framework for understanding static autoregulatory mechanisms in controlled conditions.
Methods: Data for the relation between MAP and CBF were generated using a numerical model simulating the cardio and cerebrovascular systems and CBF regulation mechanisms. The influence of the cardio and cerebrovascular parameters on CBF was evaluated utilizing sensitivity analyses, and the independent effects of the hemodynamic parameters on CBF were assessed using partial regression analysis.
Results: Sensitivity analysis showed that CBF is influenced by the systemic arteriolar resistance and arterial CO<subscript>2</subscript> pressure. Partial regression analysis showed that the systemic arteriolar resistance and arterial CO<subscript>2</subscript> pressure had significant effects on CBF, and the effect of MAP on CBF was significant, with a weak correlation.
Conclusion: Synthetic data and simulations are feasible to evaluate static cerebral autoregulation and provide a theoretical framework for understanding mechanisms in controlled conditions.
(© 2026 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)*