Synopsis

Keywords: Contrast mechanisms: fMRI, Neuro: Brain function, Neuro: Cerebrovascular

Blood-oxygen-level-dependent (BOLD) fMRI is the most-widely used technique to measure brain function non-invasively. BOLD however is a surrogate measure of brain function as the signals reflect local changes in hemodynamics. Understanding the relationship between neuronal activity and hemodynamics is therefore critical in interpreting BOLD data. This lecture will discuss neuronal and vascular contributions to BOLD in terms of spatial and temporal specificity, and address factors that affect the variability and linearity of the BOLD response. It will also discuss the importance of signal quality and data analysis methods, and the role of models, in interpreting BOLD fMRI data.

Lecture overview

Blood-oxygen-level-dependent (BOLD) fMRI is the most-widely used non-invasive neuroimaging technique to measure brain function. BOLD fMRI though is a surrogate measure of brain function, as the signals reflect local changes in blood oxygenation, flow, and volume, that are associated with neuronal activity [1]. The BOLD contrast arises from the resulting changes in the magnetic susceptibility of venous blood due to changes in the concentration of deoxy-hemoglobin associated with oxygen consumption in the tissue [2-5]. Understanding the relationship between neuronal activity and hemodynamics can aid in extracting neurophysiological information from BOLD fMRI. In addition, signal quality and the analysis methods used also affect the neurophysiological information that can be extracted from the signals measured. This lecture will address these topics as follows:
· Describe the hemodynamics underlying the BOLD response across the cortical vascular tree.
· Discuss the neuronal and vascular contributions to the BOLD response in terms of spatial and temporal specificity.
· Discuss systematic factors (such as data acquisition parameters, field strength) affecting the spatial and temporal specificity of the BOLD response to the underlying neuronal activity
· Explore the linearity and variability in the BOLD response, including baseline physiology, vascular anatomy, and the coupling between neuronal activity and cerebral blood flow (CBF), oxygenation (O2), and metabolic rate of oxygen consumption (CMRO2).
· Give examples of connecting BOLD data with mechanistic knowledge gained from invasive electrophysiological and optical imaging studies.
· Emphasize the importance of signal quality and data analysis methods in drawing reliable conclusions from BOLD fMRI studies.
· Examine the role of biophysical models in interpreting BOLD fMRI data.
In summary, this lecture will address the capabilities and limitations of interpreting BOLD fMRI data. As BOLD fMRI has evolved from primarily topographical analysis to studying cognitive processing in both normal and abnormal neural systems, it is essential to recognize the dynamic nature of the field and the ongoing advancements in both knowledge and methodologies.

Acknowledgements

No acknowledgement found.

References

1. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

2. Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990;87(24):9868-72.

3. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R ( 1992) Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci USA 89: 5675–5679

4. 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 USA 89: 5951–5955

5. Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS ( 1992) Time course EPI of human brain function during task activation. Magn Reson Med 25: 390–397

6. Duvernoy HM, Delon S, Vannson JL. Cortical blood vessels of the human brain. Brain Res Bull. 1981; 7:519–579

7. Kim SG, Ogawa S. Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J Cereb Blood Flow Metab. 2012; 32:1188–1206.

8. Tian P, Teng IC, May LD, Kurz R, Lu K, Scadeng M, Hillman EM, De Crespigny AJ, D’Arceuil HE, Mandeville JB, Marota JJ, Rosen BR, Liu TT, Boas DA, Buxton RB, Dale AM, Devor A. Cortical depth-specific microvascular dilation underlies laminar differences in blood oxygenation level dependent functional MRI signal. Proc Natl Acad Sci U S A. 2010; 107:15246–15251.

9. Buxton RB, Griffeth VE, Simon AB, Moradi F, Shmuel A. Variability of the coupling of blood flow and oxygen metabolism responses in the brain: a problem for interpreting BOLD studies but potentially a new window on the underlying neural activity. Front Neurosci. 2014 Jun 11;8:139.

10. Drew PJ. Vascular and neural basis of the BOLD signal. Curr Opin Neurobiol. 2019 Oct;58:61-69.

11. Piantoni G, Hermes D, Ramsey N, Petridou N. Size of the spatial correlation between ECoG and fMRI activity. Neuroimage. 2021 Nov 15;242:118459.

12. Siero JC, Hermes D, Hoogduin H, Luijten PR, Ramsey NF, Petridou N. BOLD matches neuronal activity at the mm scale: a combined 7T fMRI and ECoG study in human sensorimotor cortex. Neuroimage. 2014 Nov 1;101:177-84.

13. Hermes D, Nguyen M, Winawer J. Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential. PLoS Biol. 2017 Jul 24;15(7):e2001461.

14. Special issue of the Philosophical Transactions of the Royal Society B, “Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity”, volume 376, issue 1815, 2021

15. Special issue of the Philosophical Transactions of the Royal Society B, “Interpreting BOLD: towards a dialogue between cognitive and cellular neuroscience”, volume 371, issue 1705, 2016

16. Special issue of Progress in Neurobiology, “How high spatiotemporal resolution fMRI can advance neuroscience”, volume 207, 2021

17. Computational and Network Modeling of Neuroimaging Data, Elsevier, 1^st edition, June 2024, editor: Kendrick Kay