A biophysical modeling explaining the physiological regulatory mechanisms of neurovascular coupling
Poster No:
1374
Submission Type:
Abstract Submission
Authors:
Tongna Wang1, Liyuan Zhang1, Youjun Liu1, Mingai Li2, Bao Li1, JInping Dong1, Guangfei Li1
Institutions:
1College of Chemistry and Life Science, Beijing University of Technology, Beijing, China, 2School of Information Science and Technology, Beijing University of Technology, Beijing, China
First Author:
Co-Author(s):
Mingai Li
School of Information Science and Technology, Beijing University of Technology
Beijing, China
School of Information Science and Technology, Beijing University of Technology
Beijing, China
Introduction:
The human brain's remarkable cognitive capabilities, are supported by a tightly regulated interaction between neural activity and vascular responses. This interaction, known as neurovascular coupling (NVC), not only reflects immediate neural requirements but also contributes to long-term plasticity. This plasticity is shown both during task performance and during the resting state at the end of the task. While imaging and electrophysiological approaches offer great convenience, they often face challenges, including limited temporal or spatial resolution and the difficulty of capturing the complexity of interactions throughout the brain. Meanwhile models of the brain have the same circuit properties in different brain regions, losing the biological reality. In this study, we propose a new whole-brain model that integrates NVC dynamics across modalities. This model is designed to bridge the gap between task-induced neural activity, hemodynamic responses, NVC (rest: cerebral blood flow (CBF) & Functional connectivity strength (FCS); task: CBF & amplitude of low-frequency fluctuation (ALFF)), and the resulting plasticity (excitation-inhibition (E-I) balance) observed in the resting state. This work may provide a robust framework for understanding NVC and its role in cognition.
Methods:
This study used multimodal data for model construction: the MRI provided structural information, the scalp EEG provided functional information and served as a driver for the whole-brain model, and the fMRI validated the model's accuracy through blood oxygen level-dependent (BOLD) time series and functional connectivity matrices. The model contains multiple nodes each of which consists of a reverse neural mass model and a metabolic hemodynamic model. The nodes are connected by brain network matrix. We ensure the accuracy of the model by optimizing the parameters of the reverse neural mass model using multiple validation methods. The multimodal data is used to improve spatial and temporal resolution over any one modality. Resting state data and visual stimuli data can be downloaded from https://openneuro.org(Gu, 2022; Schultz, 2012).
Results:
This study was applied to resting before & after visuo-motor task and visual stimuli task.
Firstly, in the task state, it was found that at the subject group level, there was a significant positive correlation of NVC (CBF-FCS) in whole-brain for visual stimuli of famous faces (r=0.5083, P<0.001), cluttered faces (r=0.5957, P<0.001) and unfamiliar faces (r=0.5677, P<0.001). The NVC in whole-brain decreased continuously with the familiarity of the faces. NVC was activated in the visual cortex in brain regions. At the same time, the NVC (CBF/FCS) of the fusiform was significantly different between faces (famous faces + unfamiliar faces) and scrambled faces (P< 0.001), i.e., the NVC had different sensitivities to different visual feature integrity.
Moreover, in the resting state, it was found that we optimize the plasticity-related parameters of the brain's nodal models in a personalized way, and the parameters show different changes on the sub-networks(Tao, 2013). In addition, the E-I balance of the task-related brain regions increased, and the whole-brain NVC (CBF-ALFF) increased by 5.1%, and it suggested that the E-I balance and the NVC coordinately regulated the short-term state of the brain after the visuo-motor task.
Firstly, in the task state, it was found that at the subject group level, there was a significant positive correlation of NVC (CBF-FCS) in whole-brain for visual stimuli of famous faces (r=0.5083, P<0.001), cluttered faces (r=0.5957, P<0.001) and unfamiliar faces (r=0.5677, P<0.001). The NVC in whole-brain decreased continuously with the familiarity of the faces. NVC was activated in the visual cortex in brain regions. At the same time, the NVC (CBF/FCS) of the fusiform was significantly different between faces (famous faces + unfamiliar faces) and scrambled faces (P< 0.001), i.e., the NVC had different sensitivities to different visual feature integrity.
Moreover, in the resting state, it was found that we optimize the plasticity-related parameters of the brain's nodal models in a personalized way, and the parameters show different changes on the sub-networks(Tao, 2013). In addition, the E-I balance of the task-related brain regions increased, and the whole-brain NVC (CBF-ALFF) increased by 5.1%, and it suggested that the E-I balance and the NVC coordinately regulated the short-term state of the brain after the visuo-motor task.
Conclusions:
In this study, we develop a personalized and multimodal computational model of brain dynamics which can accurately explore and describe brain dynamics between task-induced neural activity, hemodynamic responses, and the resulting plasticity observed in the resting state. This work may provide a robust framework for understanding NVC and its role in cognition and provides theoretical support for basic research in cognitive neuroscience.
Learning and Memory:
Neural Plasticity and Recovery of Function 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
EEG/MEG Modeling and Analysis
fMRI Connectivity and Network Modeling 1
Keywords:
Computational Neuroscience
Modeling
Plasticity
Other - Neurovascular coupling
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Schultz, D. H.l. (2012). Resting-state connectivity of the amygdala is altered following Pavlovian fear conditioning. Frontiers in human neuroscience, 6, 242.
3. Tao, H.l. (2013). Depression uncouples brain hate circuit. Molecular psychiatry, 18(1), 101-111.