Abstract
Blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) is frequently used as a proxy for underlying neural activity. Although this is a plausible assumption for experiments where a task is performed, it may not hold to the same degree for conditions of fMRI recording in a task-free, “resting” state where neural synaptic events are weak and, hence, neurovascular coupling and endothelial vascular factors become more prominent (Hillman Annu Rev Neurosci 37:161–181, 2014, 10.1146/annurev-neuro-071013-014111). Here we investigated the magnitude of change of BOLD in consecutive samples over the acquisition time period (turnover of BOLD, “TBOLD”) by first-order differencing of single-voxel BOLD time series acquired in 70 areas of the cerebral cortex of 57 cognitively healthy women in a task-free resting state. More specifically, we evaluated (a) the variation of TBOLD among different cortical areas, (b) its dependence on age, and (c) its dependence on the presence (or absence) of the neuroprotective Human Leukocyte Antigen (HLA) gene DRB1*13 (DRB1*13:02 and DRB1*13:01). We found that TBOLD (a) varied substantially by 2.2 × among cortical areas, being highest in parahippocampal and entorhinal areas and lowest in parietal-occipital areas, (b) was significantly reduced in DRB1*13 carriers across cortical areas (from ~ 15% reduction in orbitofrontal cortex to 2% reduction in cuneus), and (c) increased with age in noncarriers of DRB1*13 but decreased with age in DRB1*13 carriers. These findings document significant dependencies of TBOLD on cortical area location, HLA DRB1*13 and age.
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References
Albin RL, Bohnen NI, Muller MLTM, Dauer WT, Sarter M, Frey KA, Koeppe RA (2018) Regional vesicular acetylcholine transporter distribution in human brain: a [18F]fluoroethoxybenzovesamicol positron emission tomography study. J Comp Neurol 526(17):2884–2897. https://doi.org/10.1002/cne.24541
Attwell D, Iadecola C (2002) The neural basis of functional brain imaging signals. Trends Neurosci 25(12):621–625. https://doi.org/10.1016/s0166-2236(02)02264-6
Barrientos RM, Kitt MM, Watkins LR, Maier SF (2015) Neuroinflammation in the normal aging hippocampus. Neuroscience 309:84–99. https://doi.org/10.1016/j.neuroscience.2015.03.007
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco
Cano P, Klitz W, Mack SJ, Maiers M, Marsh SG, Noreen H, Reed EF, Senitzer D, Setterholm M, Smith A, Fernández-Viña M (2007) Common and well-documented HLA alleles: report of the Ad-Hoc committee of the american society for histocompatiblity and immunogenetics. Hum Immunol 68(5):392–417. https://doi.org/10.1016/j.humimm.2007.01.014
Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173. https://doi.org/10.1006/cbmr.1996.0014
Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9(2):179–194. https://doi.org/10.1006/nimg.1998.0395
Desikan RS, Ségonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
DiSabato DJ, Quan N, Godbout JP (2016) Neuroinflammation: the devil is in the details. J Neurochem 139(Suppl 2):136–153. https://doi.org/10.1111/jnc.13607
Faraci FM, Breese KR (1993) Nitric oxide mediates vasodilatation in response to activation of N-methyl-D-aspartate receptors in brain. Circ Res 72(2):476–480. https://doi.org/10.1161/01.res.72.2.476
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(3):341–355. https://doi.org/10.1016/s0896-6273(02)00569-x
Garrett DD, Kovacevic N, McIntosh AR, Grady CL (2010) Blood oxygen level-dependent signal variability is more than just noise. J Neurosci 30(14):4914–4921. https://doi.org/10.1523/JNEUROSCI.5166-09.2010
Garrett DD, Kovacevic N, McIntosh AR, Grady CL (2011) The importance of being variable. J Neurosci 31(12):4496–4503. https://doi.org/10.1523/JNEUROSCI.5641-10.2011
Girouard H, Iadecola C (2006) Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. J Appl Physiol 100(1):328–335. https://doi.org/10.1152/japplphysiol.00966.2005
Heistad DD, Marcus ML, Said SI, Gross PM (1980) Effect of acetylcholine and vasoactive intestinal peptide on cerebral blood flow. Am J Physiol 239(1):H73-80. https://doi.org/10.1152/ajpheart.1980.239.1.H73
Hillman EM (2014) Coupling mechanism and significance of the BOLD signal: a status report. Annu Rev Neurosci 37:161–181. https://doi.org/10.1146/annurev-neuro-071013-014111
Ip IB, Berrington A, Hess AT, Parker AJ, Emir UE, Bridge H (2017) Combined fMRI-MRS acquires simultaneous glutamate and BOLD-fMRI signals in the human brain. Neuroimage 15(155):113–119. https://doi.org/10.1016/j.neuroimage.2017.04.030
James LM, Georgopoulos AP (2018) Persistent antigens hypothesis: the human leukocyte antigen (HLA) connection. J Neurol Neuromed 3(6):27–31. https://doi.org/10.29245/2572.942X/2018/6.1235
James LA, Georgopoulos AP (2019a) The human leukocyte antigen (HLA) DRB1*13:02 allele protects against dementia in Continental Western Europe. J Neurol Neuromed 4(5):1–6. https://doi.org/10.29245/2572.942X/2019/5.1253
James LM, Georgopoulos AP (2019b) Human leukocyte antigen as a key factor in preventing dementia and associated apolipoprotein E4 risk. Front Aging Neurosci 11:82. https://doi.org/10.3389/fnagi.2019.00082
James LM, Christova P, Engdahl BE, Lewis SM, Carpenter AF, Georgopoulos AP (2017) Human leukocyte antigen (HLA) and Gulf War Illness (GWI): HLA-DRB1*13:02 spares subcortical atrophy in Gulf War veterans. EBioMedicine 26:126–131. https://doi.org/10.1016/j.ebiom
James LM, Christova P, Lewis SM, Engdahl BE, Georgopoulos A, Georgopoulos AP (2018a) Protective effect of human leukocyte antigen (HLA) allele DRB1*13:02 on age-related brain gray matter volume reduction in healthy women. EBioMedicine 29:31–37. https://doi.org/10.1016/j.ebiom.2018.02.005
James LM, Dolan S, Leuthold AC, Engdahl BE, Georgopoulos A, Georgopoulos AP (2018b) The effects of human leukocyte antigen DRB1*13 and apolipoprotein E on age-related variability of synchronous neural interactions in healthy women. EBioMedicine 35:288–294. https://doi.org/10.1016/j.ebiom.2018.08.026
Kim SG, Hendrich K, Hu X, Merkle H, Ugurbil K (1994) Potential pitfalls of functional MRI using conventional gradient-recalled echo techniques. NMR Biomed 7:69–74. https://doi.org/10.1002/nbm.1940070111
Kukreja RC, Wei EP, Kontos HA, Bates JN (1993) Nitric oxide and S-nitroso-L-cysteine as endothelium-derived relaxing factors from acetylcholine in cerebral vessels in cats. Stroke 24(12):2010–2014. https://doi.org/10.1161/01.str.24.12.2010 (discussion 2014–2015)
Kuriyama K, Ohkuma S (1995) Role of nitric oxide in central synaptic transmission: effects on neurotransmitter release. Jpn J Pharmacol 69(1):1–8. https://doi.org/10.1254/jjp.69.1
Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843):150–157. https://doi.org/10.1038/35084005
Ownby RL (2010) Neuroinflammation and cognitive aging. Curr Psychiatry Rep 12(1):39–45. https://doi.org/10.1007/s11920-009-0082-1
Paternoster R, Brame R, Mazerolle P, Piquero A (1998) Using the correct statistical test for the equality of regression coefficients. Criminology 36(4):859–866
Perkins AE, Varlinskaya EI, Deak T (2019) From adolescence to late aging: a comprehensive review of social behavior, alcohol, and neuroinflammation across the lifespan. Int Rev Neurobiol 148:231–303. https://doi.org/10.1016/bs.irn.2019.08.001
Prast H, Philippu A (1992) Nitric oxide releases acetylcholine in the basal forebrain. Eur J Pharmacol 216(1):139–140. https://doi.org/10.1016/0014-2999(92)90223-q
Saad ZS, Reynolds RC (2012) SUMA. Neuroimage 62(2):768–773. https://doi.org/10.1016/j.neuroimage.2011.09.016
Saad ZS, Glen DR, Chen G, Beauchamp MS, Desai R, Cox RW (2009) A new method for improving functional-to-structural MRI alignment using local Pearson correlation. Neuroimage 44(3):839–848. https://doi.org/10.1016/j.neuroimage.2008.09.037
Schliebs R, Arendt T (2011) The cholinergic system in aging and neuronal degeneration. Behav Brain Res 221(2):555–563. https://doi.org/10.1016/j.bbr.2010.11.058
Scremin OU, Jenden DJ (1996) Cholinergic control of cerebral blood flow in stroke, trauma and aging. Life Sci 58(22):2011–2018. https://doi.org/10.1016/0024-3205(96)00192-0
Shi Z, Wu R, Yang PF, Wang F, Wu TL, Mishra A, Chen LM, Gore JC (2017) High spatial correspondence at a columnar level between activation and resting state fMRI signals and local field potentials. Proc Natl Acad Sci USA 114(20):5253–5258. https://doi.org/10.1073/pnas.1620520114
Taylor PA, Chen G, Glen DR, Rajendra JK, Reynolds RC, Cox RW (2018) FMRI processing with AFNI: some comments and corrections on “Exploring the impact of analysis software on task fMRI results” [Internet] p 308643. https://doi.org/10.1101/308643v1
Wei EP, Kukreja R, Kontos HA (1992) Effects in cats of inhibition of nitric oxide synthesis on cerebral vasodilation and endothelium-derived relaxing factor from acetylcholine. Stroke 23(11):1623–1628. https://doi.org/10.1161/01.str.23.11.1623 (discussion 1628–1629)
Xu Z, Tong C, Eisenach JC (1996) Acetylcholine stimulates the release of nitric oxide from rat spinal cord. Anesthesiology 85(1):107–111. https://doi.org/10.1097/00000542-199607000-00015
Yang G, Iadecola C (1996) Glutamate microinjections in cerebellar cortex reproduce cerebrovascular effects of parallel fiber stimulation. Am J Physiol 271(6 Pt 2):R1568–R1575. https://doi.org/10.1152/ajpregu.1996.271.6.R1568
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Partial funding for this study was provided by the University of Minnesota (the Anita Kunin Chair in Women's Healthy Brain Aging, the Brain and Genomics Fund, the McKnight Presidential Chair of Cognitive Neuroscience, and the American Legion Brain Sciences Chair). The sponsors had no role in the current study design, analysis or interpretation, or in the writing of this paper. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.
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APG conceived the research; PC and LMJ contributed to data acquisition; PC extracted the relevant fMRI data; APG and PC contributed to data analysis; LMJ, PC and APG wrote the paper; all authors read, edited and approved the final version of the paper.
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James, L.M., Christova, P. & Georgopoulos, A.P. BOLD turnover in task-free state: variation among brain areas and effects of age and human leukocyte antigen (HLA) DRB1*13. Exp Brain Res 240, 1967–1977 (2022). https://doi.org/10.1007/s00221-022-06382-y
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DOI: https://doi.org/10.1007/s00221-022-06382-y