Cerebral Blood Flow Evaluation Under Neurovascular Coupling Based on Near Field Coupling
Neurovascular coupling (NVC) function assessment has important clinical significance for guiding neurological recovery treatment and improving prognosis in stroke patients. For the cerebral blood flow (CBF) assessment under the action of NVC, this study established an experimental platform based on near-field coupling (NFC) and commonly used neural activity stimulation paradigms (memory load and SSVEP visual stimulation). Five volunteers were recruited and stimulated with two experimental paradigms to evoke functional brain neural activity. CBF during rest, stimulation, and recovery was analyzed based on the characteristics of neural activity changes at various stages. NFC-based cerebral blood flow-derived parameters were extracted, including vascular elasticity index (VEI), vascular resistance index (VRI), and peak amplitude (h). The results indicate that the amplitude of the resonance frequency (RF) representing CBF changes first increases and then decreases in both experimental paradigms. NFC-based cerebral blood flow-derived parameters first increase and then decrease, peaking during the stimulation period. During stimulation, neural activity increases, oxygen consumption increases, and CBF increases. Significant differences (P < 0.05) are obtained in volunteers' NFC-based cerebral blood flow-derived parameters before and after stimulation. NVC function induced CBF changes, with functional neural activity activating occipital lobe neuron activity, leading to increased arteriolar dilation and accelerated expansion rate, resulting in a significant CBF increase. These findings indicate that the experimental platform in this study based on NFC can measure CBF changes under NVC. With the simultaneous monitoring of neural activity signals, alternative indicators of NVC are expected to be proposed.
Authors
- Gen Li (ORCID: https://orcid.org/0000-0002-4183-4242)
- Jiawen Zhong (ORCID: https://orcid.org/0000-0002-6640-2649)
Institutions
- Chongqing University of Technology (CN)
Publication Details
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1145/3715931.3715939
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00