Assessing the Impact of Hypercapnic Stimulation on Brain Connectivity Metrics During Functional Magnetic Resonance Imaging

Year 2024, , 54 - 60, 24.04.2024

İdiz İset , Ali Bayram

https://doi.org/10.26650/experimed.1425820

Abstract

Objective: Functional connectivity serves as a widely employed metric in neuroscience research focusing on the dynamics of the brain. Additionally, non-neuronal physiological oscillations are acknowledged as being able to impact functional connectivity. This study aimed to explore the effects of non-neuronal hypercapnic stimulation on the activity of intrinsic connectivity networks (ICNs), as well as the dynamic changes in connectivity between them.

Materials and Methods: The study involved 10 healthy participants, encompassed their functional magnetic resonance imaging (fMRI) scans with carbon dioxide-enriched air stimuli in a block paradigm, with group independent component analysis (GICA) being used for defining ICNs. Similarity analysis has been conducted between the connectivity changes in the network components and the end-tidal partial pressure of carbon dioxide (PETCO2).

Results: The study has identified 40 components representing 10 ICNs. Of these, 11 components representing seven ICNs were found to have significantly correlated time courses with PETCO2. Among the networks without correlated components, the dynamic functional connectivity metrics of the language network and the subcortical network have been found to be significantly modulated by PETCO2.

Conclusion: The cerebrovascular reactivity to a hypercapnic stimulus is a factor that influences changes in the blood oxygenation level-dependent fMRI signal. This non-neuronal effect is detectable for ICN components derived by the GICA technique and must be considered when making inferences about network connectivity metrics.

Keywords

Cerebrovascular reactivity, hypercapnic stimulation, functional magnetic resonance imaging, functional network connectivity, intrinsic connectivity networks

Ethical Statement

Ethics committee approval was not obtained because the data was downloaded from an open access database (Oxford University Research Archive, doi:10.5287/bodleian:Xk48adQAO)

Supporting Institution

This study was funded by the Scientific and Technological Research Council of Turkey (TUBITAK) ARDEB 3501 Grant No 122S188

Project Number

TUBITAK ARDEB 3501 #122S188

References

• Friston KJ. Functional and effective connectivity in neuroimaging: A synthesis. Hum Brain Mapp 1994; 2(1-2): 56-78. google scholar
• Biswal BB, Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34(4): 537-41. google scholar
• Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007; 8(9): 700-11. google scholar
• Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001; 98(2): 676-82. google scholar
• Calhoun VD, Adali T, Stevens MC, Kiehl KA, Pekar JJ. Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. Neuroimage 2005; 25(2): 527-38. google scholar
• Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 2004; 23(2): 137-52. google scholar
• Sleight E, Stringer MS, Marshall I, Wardlaw JM, Thrippleton MJ. Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review. Front Physiol 2021; 12: 643468. google scholar
• Liu TT. Reprint of “Noise contributions to the fMRI signal: An Overview”. Neuroimage 2017; 154: 4-14. google scholar
• Blockley NP, Harkin JW, Bulte DP. Rapid cerebrovascular reactivity mapping: Enabling vascular reactivity information to be routinely acquired. Neuroimage 2017; 159: 214-23. google scholar
• Sobczyk O, Battisti-Charbonney A, Poublanc J, Crawley AP, Sam K, Fierstra J, et al. Assessing cerebrovascular reactivity abnormality by comparison to a reference atlas. J Cereb Blood Flow Metab 2015; 35(2): 213-20. google scholar
• Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002; 17(2): 825-41. google scholar
• Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage 2012; 62(2): 782-90. google scholar
• Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 2009; 48(1): 6372. google scholar
• Smith SM, Brady JM. SUSAN-A new approach to low level image processing. Int J Comput Vis 1997; 23(1): 45-78. google scholar
• Calhoun VD, Adali T. Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng 2012; 5: 60-73. google scholar
• Du Y, Fan Y. Group information guided ICA for fMRI data analysis. Neuroimage 2013; 69: 157-97. google scholar
• Griffanti L, Douaud G, Bijsterbosch J, Evangelisti S, Alfaro-Almagro F, Glasser MF, et al. Hand classification of fMRI ICA noise components. Neuroimage 2017; 154: 188-205. google scholar
• Nieto-Castanon A. Handbook of functional connectivity magnetic resonance imaging methods in CONN. Boston, MA: Hilbert Press; 2020. google scholar
• Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011; 106(3): 1125-65. google scholar
• Frederick B. Rapidtide, ver. 2.2.7 [computer program]. Belmont (MA): GitHub; 2022. [cited 20 January 2024]. Available from: https://github.com/bbfrederick/rapidtide. google scholar
• Allen EA, Erhardt EB, Wei Y, Eichele T, Calhoun VD. Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study. Neuroimage 2012; 59(4): 414159. google scholar
• Vergara VM, Mayer AR, Kiehl KA, Calhoun VD. Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning. Neuroimage Clin 2018; 19: 30-7. google scholar
• Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. Neuroimage 2014; 92: 381-97. google scholar
• Hou X, Liu P, Gu H, Chan MY, Li Y, Peng S, et al. Estimation of brain functional connectivity from hypercapnia BOLD MRI data: Validation in a lifespan cohort of 170 subjects. Neuroimage 2019; 186: 455-63. google scholar
• Lewis N, Lu H, Liu P, Hou X, Damaraju E, Iraji A, et al. Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study. Brain Behav 2020; 10(6): e01516. google scholar
• Tong Y, Hocke LM, Fan X, Janes AC, Frederick Bd. Can apparent resting state connectivity arise from systemic fluctuations? Front Hum Neurosci 2015; 9: 285. google scholar