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

Iset I., BAYRAM A.

Experimed, vol.14, no.1, pp.54-60, 2024 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.26650/experimed.1425820
  • Journal Name: Experimed
  • Journal Indexes: Scopus
  • Page Numbers: pp.54-60
  • Keywords: Cerebrovascular reactivity, functional magnetic resonance imaging, functional network connectivity, hypercapnic stimulation, intrinsic connectivity networks
  • Istanbul University Affiliated: Yes


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.