Individual differences in haemoglobin concentration influence bold fMRI functional connectivity and its correlation with cognition

Phillip G D Ward, Edwina R Orchard, Stuart Oldham, Aurina Arnatkevičiūtė, Francesco Sforazzini, Alex Fornito, Elsdon Storey, Gary F Egan, Sharna D Jamadar, Phillip G D Ward, Edwina R Orchard, Stuart Oldham, Aurina Arnatkevičiūtė, Francesco Sforazzini, Alex Fornito, Elsdon Storey, Gary F Egan, Sharna D Jamadar

Abstract

Resting-state connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions, and is commonly measured using BOLD-fMRI. The BOLD response follows neuronal activity, when changes in the relative concentration of oxygenated and deoxygenated haemoglobin cause fluctuations in the MRI T2* signal. Since the BOLD signal detects changes in relative concentrations of oxy/deoxy-haemoglobin, individual differences in haemoglobin levels may influence the BOLD signal-to-noise ratio in a manner independent of the degree of neural activity. In this study, we examined whether group differences in haemoglobin may confound measures of functional connectivity. We investigated whether relationships between measures of functional connectivity and cognitive performance could be influenced by individual variability in haemoglobin. Finally, we mapped the neuroanatomical distribution of the influence of haemoglobin on functional connectivity to determine where group differences in functional connectivity are manifest. In a cohort of 518 healthy elderly subjects (259 men), each sex group was median-split into two groups with high and low haemoglobin concentration. Significant differences were obtained in functional connectivity between the high and low haemoglobin groups for both men and women (Cohen's d 0.17 and 0.03 for men and women respectively). The haemoglobin connectome in males showed a widespread systematic increase in functional connectivity correlation values, whilst the female connectome showed predominantly parietal and subcortical increases and temporo-parietal decreases. Despite the haemoglobin groups having no differences in cognitive measures, significant differences in the linear relationships between cognitive performance and functional connectivity were obtained for all 5 cognitive tests in males, and 4 out of 5 tests in females. Our findings confirm that individual variability in haemoglobin levels that give rise to group differences are an important confounding variable in BOLD-fMRI-based studies of functional connectivity. Controlling for haemoglobin variability as a potentially confounding variable is crucial to ensure the reproducibility of human brain connectome studies, especially in studies that compare groups of individuals, compare sexes, or examine connectivity-cognition relationships.

Trial registration: ClinicalTrials.gov NCT01038583.

Keywords: Fmri; Functional connectivity; Functional connectome; Haematocrit; Haemoglobin.

Conflict of interest statement

Declaration of Competing Interest The authors have no competing or conflicting interests.

Copyright © 2020. Published by Elsevier Inc.

Figures

Fig. 1.
Fig. 1.
The effect of haemoglobin on resting-state functional connectivity for (A) females and (B) males. Values are Cohen’s d effect sizes of the difference between high-haemoglobin and low-haemoglobin groups. Abbreviation: Hb, haemoglobin.
Fig. 2.
Fig. 2.
Correlation between resting-state functional connectivity and haemoglobin values in (A) Females and (B) Males. Upper plots (i.) show distribution of linear coefficients (t-values). Middle plots (ii.) show distribution of explained variance, and lower panel (iii.) shows distribution of p-values. Each panel compares the distribution of obtained values to a null distribution, calculated by randomly permuting haemoglobin values between subjects and refitting the identical model.
Fig. 3.
Fig. 3.
Spatial representation of regions strongly influenced by haemoglobin in resting-state functional connectivity (region degree, blue-green colour bar), and the probabilistic location of the major draining veins (see Ward et al., 2018; red-yellow colour bar), for (A) females and (B) males. Degree is defined by the number of edges connected to a region with correlation in the 90th percentile. Supplementary Figure 2 show results at different thresholds and standardised to a t-distribution.
Fig. 4.
Fig. 4.
Correlation between resting-state functional connectivity and cognitive test performance (y-axis) in five domains, (top-bottom) 3MS, Stroop, SDMT, COWAT. Each dot denotes an edge in the functional connectivity matrix correlated with the cognitive test score. The black line shows the relationship between the cognitive score and functional connectivity calculated for the entire cohort. The order of the edges (x-axis) is denoted by the coefficients on the entire cohort. The blue points show the relationship between the cognitive score and functional connectivity for the low haemoglobin group. The red points show the relationship between the cognitive score and functional connectivity for the high haemoglobin group. Abbreviations: 3MS, modified mini-mental state exam; SDMT, symbol-digit modalities test; COWAT, controlled word association test; Hb, haemoglobin.

References

    1. Aanerud J, Borghammer P, Chakravarty MM, Vang K, Rodell AB, Jónsdottir KY, Møller A, Ashkanian M, Vafaee MS, Iversen P, Johannsen P, Gjedde A, 2012. Brain energy metabolism and blood flow differences in healthy aging. J. Cereb. Blood Flow Metab. Off. J. Int. Soc. Cereb. Blood Flow Metab 32, 1177–1187. doi: 10.1038/jcbfm.2012.18.
    1. Amin MG, Tighiouart H, Weiner DE, Stark PC, Griffith JL, MacLeod B, Salem DN, Sarnak MJ, 2004. Hematocrit and left ventricular mass: the Framingham Heart study. J. Am. Coll. Cardiol 43, 1276–1282. doi: 10.1016/j.jacc.2003.10.048.
    1. Andrews-Hanna JR, Snyder AZ, Vincent JL, Lustig C, Head D, Raichle ME, Buckner RL, 2007. Disruption of Large-Scale Brain Systems in Advanced Aging. Neuron 56, 924–935. doi: 10.1016/j.neuron.2007.10.038.
    1. ASPREE Investigator Group, 2013. Study design of ASPirin in Reducing Events in the Elderly (ASPREE): a randomized, controlled trial. Contemp. Clin. Trials 36, 555–564. doi: 10.1016/j.cct.2013.09.014.
    1. Avants BB, Epstein CL, Grossman M, Gee JC, 2008. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal 12, 26–41. doi: 10.1016/j.media.2007.06.004.
    1. Avcioglu G, Nural C, Yilmaz FM, Baran P, Erel Ö, Yilmaz G, 2018. Comparison of noninvasive and invasive point-of-care testing methods with reference method for hemoglobin measurement. J. Clin. Lab. Anal 32, e22309. doi: 10.1002/jcla.22309.
    1. Bäckman S, Larjo A, Soikkeli J, Castrén J, Ihalainen J, Syrjälä M, 2016. Season and time of day affect capillary blood hemoglobin level and low hemoglobin deferral in blood donors: analysis in a national blood bank. Transfusion (Paris) 56, 1287–1294. doi: 10.1111/trf.13578.
    1. Beckmann CF, DeLuca M, Devlin JT, Smith SM, 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B Biol. Sci 360, 1001–1013. doi: 10.1098/rstb.2005.1634.
    1. Birn RM, 2012. The role of physiological noise in resting-state functional connectivity. Neuroimage 62, 864–870. doi: 10.1016/j.neuroimage.2012.01.016, 20 YEARS OF fMRI.
    1. Biswal B, Yetkin FZ, Haughton VM, Hyde JS, 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn. Reson. Med 34, 537–541. doi: 10.1002/mrm.1910340409.
    1. Buxton RB, Frank LR, 1997. A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J. Cereb. Blood Flow Metab. Off. J. Int. Soc. Cereb. Blood Flow Metab 17, 64–72. doi: 10.1097/00004647-199701000-00009.
    1. Buxton RB, Uludağ K, Dubowitz DJ, Liu TT, 2004. Modeling the hemodynamic response to brain activation. NeuroImage, Mathematics in Brain Imaging 23, S220–S233. doi: 10.1016/j.neuroimage.2004.07.013.
    1. Calamante F, Ahlgren A, Osch van MJ, Knutsson L, 2015. A novel approach to measure local cerebral haematocrit using MRI. J. Cereb. Blood Flow Metab doi: 10.1177/0271678X15606143, 0271678X15606143.
    1. Chang C, Cunningham JP, Glover GH, 2009. Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44, 857–869. doi: 10.1016/j.neuroimage.2008.09.029.
    1. Choi JB, Loredo JS, Norman D, Mills PJ, Ancoli-Israel S, Ziegler MG, Dimsdale JE, 2006. Does obstructive sleep apnea increase hematocrit? Sleep Breath 10, 155–160. doi: 10.1007/s11325-006-0064-z.
    1. Cohen J, 2013. Statistical Power Analysis For the Behavioral Sciences. Routledge.
    1. Cordes D, Turski PA, Sorenson JA, 2000. Compensation of susceptibility-induced signal loss in echo-planar imaging for functional applications☆. Magn. Reson. Imaging 18, 1055–1068. doi: 10.1016/S0730-725X(00)00199-5.
    1. Cox RW, 1996. AFNI: software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Comput. Biomed. Res 29, 162–173. doi: 10.1006/cbmr.1996.0014.
    1. Cruickshank JM, 1970. Some Variations in the Normal Haemoglobin Concentration. Br. J. Haematol 18, 523–530. doi: 10.1111/j.1365-2141.1970.tb00773.x.
    1. Davis S, Lambrinoudaki I, Lumsden M, Mishra G, Pal L, et al., 2015. Menopause. Nature Reviews Disease Primers, 15004.
    1. D’Elia L, 1996. Color Trails test Professional Manual. PAR Psychological Assessmant Resources. Lutz, Florida. USA.
    1. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ, 2006. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980. doi: 10.1016/j.neuroimage.2006.01.021.
    1. Dong X, Mendes de Leon C, Artz A, Tang Y, Shah R, Evans D, 2008. A population-based study of hemoglobin, race, and mortality in elderly persons. J. Gerontol. A doi: 10.1093/gerona/63.8.873.
    1. Drinka P, 2013. Hematocrit Elevation Associated With Testosterone Administration. J. Am. Med. Dir. Assoc 14, 848. doi: 10.1016/j.jamda.2013.08.006.
    1. Dutton DB, 1979. Hematocrit levels and race: an argument against the adoption of separate standards in screening for anemia. J. Natl. Med. Assoc 71, 945–954.
    1. Fallon J, Ward P, Parkes L, Oldham S, Arnatkevičiūtė A, Fornito A, Fulcher BD, 2019. Timescales of spontaneous activity fluctuations relate to structural connectivity in the brain. bioRxiv 655050. 10.1101/655050 (accepted Network Neuroscience, May 2020).
    1. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM, 2002. Whole Brain Segmentation: automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron 33, 341–355. doi: 10.1016/S0896-6273(02)00569-X.
    1. Fornito A, Bullmore ET, 2010. What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders? Curr. Opin. Psychiatry 23, 239–249. doi: 10.1097/YCO.0b013e328337d78d.
    1. Fornito A, Zalesky A, Bassett DS, Meunier D, Ellison-Wright I, Yücel M, Wood SJ, Shaw K, O’Connor J, Nertney D, Mowry BJ, Pantelis C, Bullmore ET, 2011. Genetic influences on cost-efficient organization of human cortical functional networks. J. Neurosci. Off. J. Soc. Neurosci 31, 3261–3270. doi: 10.1523/JNEU-ROSCI.4858-10.2011.
    1. Fox MD, Raichle ME, 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci 8, 700–711. doi: 10.1038/nrn2201.
    1. Fox MD, Snyder AZ, Vincent JL, Raichle ME, 2007. Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron 56, 171–184. doi: 10.1016/j.neuron.2007.08.023.
    1. Friston KJ, Holmes AP, Worsley KJ, Poline J-P, Frith CD, Frackowiak RSJ, 1994. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp 2, 189–210. doi: 10.1002/hbm.460020402.
    1. Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R, 1996. Movement-related effects in fMRI time-series. Magn. Reson. Med 35, 346–355. doi: 10.1002/mrm.1910350312.
    1. Gao J-H, Liu H-L, 2012. Inflow effects on functional MRI. Neuroimage 62, 1035–1039. doi: 10.1016/j.neuroimage.2011.09.088.
    1. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD, 2007. Aberrant “default mode ” functional connectivity in schizophrenia. Am. J. Psychiatry 164, 450–457. doi: 10.1176/ajp.2007.164.3.450.
    1. Glahn DC, Winkler AM, Kochunov P, Almasy L, Duggirala R, Carless MA, Curran JC, Olvera RL, Laird AR, Smith SM, Beckmann CF, Fox PT, Blangero J, 2010. Genetic control over the resting brain. Proc. Natl. Acad. Sci. U. S. A 107, 1223–1228. doi: 10.1073/pnas.0909969107.
    1. Glover GH, 2011. Overview of Functional Magnetic Resonance Imaging . Neurosurg. Clin. N. Am 22, 133–139. doi: 10.1016/j.nec.2010.11.001.
    1. Glover GH, Li TQ, Ress D, 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med 44, 162–167 10.1002/1522-2594(200007)44:1<162::aid-mrm23>;2-e.
    1. Goodwin L, Leech N, 2006. Understanding correlation: factors that affect the size of r. J. Exp. Educ doi: 10.3200/JEXE.74.3.249-266.
    1. Greicius MD, Srivastava G, Reiss AL, Menon V, 2004. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. U. S. A 101, 4637–4642. doi: 10.1073/pnas.0308627101.
    1. Guensch DP, Nadeshalingam G, Fischer K, Stalder AF, Friedrich MG, 2016. The impact of hematocrit on oxygenation-sensitive cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson 18, 42. doi: 10.1186/s12968-016-0262-1.
    1. Gustard S, Williams EJ, Hall LD, Pickard JD, Carpenter TA, 2003. Influence of baseline hematocrit on between-subject BOLD signal change using gradient echo and asymmetric spin echo EPI. Magn. Reson. Imaging 21, 599–607. doi: 10.1016/S0730-725X(03)00083-3.
    1. Hayashi T, Watabe H, Kudomi N, Kim KM, Enmi J-I, Hayashida K, Iida H, 2003. A Theoretical Model of Oxygen Delivery and Metabolism for Physiologic Interpretation of Quantitative Cerebral Blood Flow and Metabolic Rate of Oxygen. J. Cereb. Blood Flow Metab 23, 1314–1323. doi: 10.1097/01.WCB.0000090506.76664.00.
    1. Jamadar SD, Egan GF, Calhoun VD, Johnson B, Fielding J, 2016. Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: a Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance. Brain Connect. 6, 505–517. doi: 10.1089/brain.2015.0411.
    1. Jamadar SD, Sforazzini F, Raniga P, Ferris NJ, Paton B, Bailey MJ, Brodtmann A, Yates PA, Donnan GA, Ward SA, Woods RL, Storey E, McNeil JJ, Egan GF, 2018. Sexual Dimorphism of Resting-State Network Connectivity in Healthy Ageing. J. Gerontol. Ser. B doi: 10.1093/geronb/gby004.
    1. Jern C, Wadenvik H, Mark H, Hallgren J, Jern S, 1989. Haematological changes during acute mental stress. Br. J. Haematol 71, 153–156. doi: 10.1111/j.1365-2141.1989.tb06290.x.
    1. Jin Y-Z, Zheng D-H, Duan Z-Y, Lin Y-Z, Zhang X-Y, Wang J-R, Han S, Wang G-F, Zhang Y-J, 2015. Relationship Between Hematocrit Level and Cardiovascular Risk Factors in a Community-Based Population. J. Clin. Lab. Anal 29, 289–293. doi: 10.1002/jcla.21767.
    1. Koons NJ, Suresh MR, Schlotman TE, Convertino VA, 2019. Interrelationship Between Sex, Age, Blood Volume, and Vo2max [WWW Document]. info:10.3357/AMHP.5255.2019
    1. Levin JM, Frederick de BB, Ross MH, Fox JF, von Rosenberg HL, Kaufman MJ, Lange N, Mendelson JH, Cohen BM, Renshaw PF, 2001. Influence of baseline hematocrit and hemodilution on BOLD fMRI activation. Magn. Reson. Imaging 19, 1055–1062. doi: 10.1016/S0730-725X(01)00460-X.
    1. Li W, Liu P, Lu H, Strouse JJ, Zijl van PCM, Qin Q, 2017. Fast measurement of blood T1 in the human carotid artery at 3T: accuracy, precision, and reproducibility. Magn. Reson. Med 77, 2296–2302. doi: 10.1002/mrm.26325.
    1. Liang X, Zou Q, He Y, Yang Y, 2013. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc. Natl. Acad. Sci 110, 1929–1934. doi: 10.1073/pnas.1214900110.
    1. Liu TT, 2017. Reprint of ‘Noise contributions to the fMRI signal: an Overview”.’ NeuroImage. Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies 154, 4–14. doi: 10.1016/j.neuroimage.2017.05.031.
    1. McGrath CL, Kelley ME, Holtzheimer PE, Dunlop BW, Craighead WE, Franco AR, Craddock RC, Mayberg HS, 2013. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70, 821–829. doi: 10.1001/jamapsychiatry.2013.143.
    1. Mchedlishvili G, Varazashvili M, 1987. Hematocrit in cerebral capillaries and veins under control and ischemic conditions. J. Cerebr. Blood Flow Metab
    1. McNeil JJ, Woods RL, Nelson MR, Murray AM, Reid CM, Kirpach B, Storey E, Shah RC, Wolfe RS, Tonkin AM, Newman AB, Williamson JD, Lockery JE, Margolis KL, Ernst ME, Abhayaratna WP, Stocks N, Fitzgerald SM, Trevaks RE, Orchard SG, Beilin LJ, Donnan GA, Gibbs P, Johnston CI, Grimm RH, 2017. Baseline Characteristics of Participants in the ASPREE (ASPirin in Reducing Events in the Elderly) Study. J. Gerontol. Ser. A 72, 1586–1593. doi: 10.1093/gerona/glw342.
    1. Muldoon MF, Herbert TB, Patterson SM, Kameneva M, Raible R, Manuck SB, 1995. Effects of Acute Psychological Stress on Serum Lipid Levels, Hemoconcentration, and Blood Viscosity. Arch. Intern. Med 155, 615–620. doi: 10.1001/archinte.1995.00430060077009.
    1. Nkrumah B, Nguah SB, Sarpong N, Dekker D, Idriss A, May J, Adu-Sarkodie Y, 2011. Hemoglobin estimation by the HemoCue® portable hemoglobin photometer in a resource poor setting. BMC Clin. Pathol 11, 5. doi: 10.1186/1472-6890-11-5.
    1. Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, Ugurbil K, 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci 89, 5951–5955.
    1. Parkes L, Fulcher B, Yücel M, Fornito A, 2018. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 171, 415–436. doi: 10.1016/j.neuroimage.2017.12.073.
    1. Patriat R, Molloy E, Meier T, Kirk G, Nair V, et al., 2013. The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated. Neuroimage doi: 10.1016/j.neuroimage.2013.04.013.
    1. Patterson SM, Matthews KA, Allen MT, Owens JF, 1995. Stress-induced hemo-concentration of blood cells and lipids in healthy women during acute psychological stress. Health Psychol 14, 319–324. doi: 10.1037/0278-6133.14.4.319.
    1. Peng S-L, Dumas J, Park D, Liu P, Filbey F, et al., 2014. Age-related increase of resting metabolic rate in the human brain. Neuroimage 98, 176–183. doi: 10.1016/j.neuroimage.2014.04.078.
    1. Phillips AA, Chan FH, Zheng MMZ, Krassioukov AV, Ainslie PN, 2016. Neurovascular coupling in humans: physiology, methodological advances and clinical implications. J. Cereb. Blood Flow Metab 36, 647–664. doi: 10.1177/0271678X15617954.
    1. Power JD, Plitt M, Gotts SJ, Kundu P, Voon V, Bandettini PA, Martin A, 2018. Ridding fMRI data of motion-related influences: removal of signals with distinct spatial and physical bases in multiecho data. Proc. Natl. Acad. Sci. U. S. A 115, E2105–E2114. doi: 10.1073/pnas.1720985115.
    1. Power JD, Plitt M, Laumann TO, Martin A, 2017. Sources and implications of whole-brain fMRI signals in humans. Neuroimage 146, 609–625. doi: 10.1016/j.neuroimage.2016.09.038.
    1. Quinto L, Aponte JJ, Menendex C, Sacarlal J, Aide P, Espasa M, et al., 2006. Relationship between haemoglobin and haematocrit in the definition of anaemia. Trop. Med. Int. Health 11, 1295–1302. doi: 10.1111/j.1365-3156.2006.01679.x.
    1. Ruff RM, Light RH, Parker SB, Levin HS, 1996. Benton controlled oral word association test: reliability and updated norms. Arch. Clin. Neuropsychol 11, 329–338. doi: 10.1093/arclin/11.4.329.
    1. Rushton DH, Barth JH, 2010. What is the evidence for gender differences in ferritin and haemoglobin? Crit. Rev. Oncol. Hematol 73, 1–9. doi: 10.1016/j.critrevonc.2009.03.010.
    1. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM, 2014. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90, 449–468. doi: 10.1016/j.neuroimage.2013.11.046.
    1. Salive ME, Cornoni‐Huntley J, Guralnik JM, Phillips CL, Wallace RB, Ostfeld AM, Cohen HJ, 1992. Anemia and Hemoglobin Levels in Older Persons: relationship with Age, Gender, and Health Status. J. Am. Geriatr. Soc 40, 489–496. doi: 10.1111/j.1532-5415.1992.tb02017.x.
    1. Singh A, Dubey A, Sonker A, Chaudhary R, 2015. Evaluation of various methods of point-of-care testing of haemoglobin concentration in blood donors. Blood Transfus 13, 233–239. doi: 10.2450/2014.0085-14.
    1. Smith A, 1982. Symbol Digit Modalities Test. Western Psychological Services, Los Angeles, CA.
    1. Smith SM, 2002. Fast robust automated brain extraction. Hum. Brain Mapp 17, 143–155. doi: 10.1002/hbm.10062.
    1. Stack SW, Berger SA, 2009. The effects of high hematocrit on arterial flow—A phenomenological study of the health risk implications. Chem. Eng. Sci., Morton Denn Festschrift 64, 4701–4706. doi: 10.1016/j.ces.2009.07.017.
    1. Teng EL, Chui HC, 1987. The Modified Mini-Mental State (3MS) examination. J. Clin. Psychiatry 48, 314–318.
    1. Thirup P, 2003. Haematocrit. Sports Med 33, 231–243. doi: 10.2165/00007256-200333030-00005.
    1. Tomasi D, Wang G-J, Volkow ND, 2013. Energetic cost of brain functional connectivity. Proc. Natl. Acad. Sci 110, 13642–13647. doi: 10.1073/pnas.1303346110.
    1. Troyer AK, Leach L, Strauss E, 2006. Aging and Response Inhibition: normative Data for the Victoria Stroop Test. Aging Neuropsychol. Cogn 13, 20–35. doi: 10.1080/138255890968187.
    1. van der Veen P, Muller M, Vincken K, Westerink J, Mali W, et al., 2015. Hemoglobin, hematocrit, and changes in cerebral blood flow: the Second Manifestations of ARTerial disease-Magnetic Resonance study. Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2014.12.019.
    1. Ward PGD, Ferris NJ, Raniga P, Dowe DL, Ng ACL, Barnes DG, Egan GF, 2018. Combining images and anatomical knowledge to improve automated vein segmentation in MRI. Neuroimage 165, 294–305. doi: 10.1016/j.neuroimage.2017.10.049.
    1. Ward SA, Raniga P, Ferris NJ, Woods RL, Storey E, Bailey MJ, Brodtmann A, Yates PA, Donnan GA, Trevaks RE, Wolfe R, Egan GF, McNeil JJ, 2017. ASPREE-NEURO study protocol: a randomized controlled trial to determine the effect of low-dose aspirin on cerebral microbleeds, white matter hyperintensities, cognition, and stroke in the healthy elderly. Int. J. Stroke 12, 108–113. doi: 10.1177/1747493016669848.
    1. Weiss EM, Siedentopf C, Hofer A, Deisenhammer EA, Hoptman MJ, Kremser C, Golaszewski S, Felber S, Fleischhacker WW, Delazer M, 2003. Brain activation pattern during a verbal fluency test in healthy male and female volunteers: a functional magnetic resonance imaging study. Neurosci. Lett 352, 191–194. doi: 10.1016/j.neulet.2003.08.071.
    1. Xu F, Li W, Liu P, Hua J, Strouse JJ, Pekar JJ, Lu H, Zijl van PCM, Qin Q, 2018. Accounting for the role of hematocrit in between-subject variations of MRI-derived baseline cerebral hemodynamic parameters and functional BOLD responses. Hum. Brain Mapp 39, 344–353. doi: 10.1002/hbm.23846.
    1. Yang Z, Craddock RC, Milham MP, 2015. Impact of hematocrit on measurements of the intrinsic brain. Front. Neurosci 8. doi: 10.3389/fnins.2014.00452.
    1. Zauber NP, Zauber AG, 1987. Hematologic Data of Healthy Very Old People. JAMA 257, 2181–2184. doi: 10.1001/jama.1987.03390160067028.

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