Complex visual cognitive function based on a large-scale neurovascular and metabolic coupling mechanisms model in whole brain

Tongna Wang; Bao Li; Youjun Liu; Ruoyao Xu; Yuejuan Xu; Yang Yang; Liyuan Zhang; Tongna Wang; Bao Li; Youjun Liu; Ruoyao Xu; Yuejuan Xu; Yang Yang; Liyuan Zhang

doi:10.3934/era.2025107

Electronic Research Archive

2025, Volume 33, Issue 4: 2412-2432. doi: 10.3934/era.2025107

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Complex visual cognitive function based on a large-scale neurovascular and metabolic coupling mechanisms model in whole brain

Department of Biomedical Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China

Received: 12 January 2025 Revised: 24 March 2025 Accepted: 08 April 2025 Published: 22 April 2025

The neurovascular and metabolic coupling (NVMC) mechanism constitutes a critical physiological foundation for visual information processing. However, multimodal studies remain confined to phenomenological descriptions and fail to provide deeper theoretical investigations, preventing precise assessment of individuals. To address these limitations, we developed a heterogeneous whole-brain computational model of NVMC that integrates task-based EEG-MRI-fMRI multimodal data to simulate the cascading processes from neural mass firing to metabolic-hemodynamic responses. The model was validated against 33 resting-state simultaneous EEG-fMRI datasets. It was found that at the regional level, the fusiform exhibited stronger functional connectivity associated with face recognition, and its NVMC (CBF/FCS) demonstrated statistically significant differences between face stimuli (famous and unfamiliar faces) and scrambled faces (P < 0.001). Whole brain level analyses revealed reduced NVMC (CBF-FCS) with increasing face regularity and familiarity, despite nonsignificant differences in network indices. Subnetwork-level investigations further identified pronounced heterogeneity in functional interactions across distinct neural circuits. In this study, we developed a whole-brain-scale computational model to investigate the heterogeneity of NVMC during face-specific stimulus processing. The model provides an interpretable computational framework for enabling personalized assessments of visual cognitive tasks.
- neurovascular and metabolic coupling,
- neurodynamic response,
- hemodynamics,
- visual stimulation,
- whole-brain-scale computational model
Citation: Tongna Wang, Bao Li, Youjun Liu, Ruoyao Xu, Yuejuan Xu, Yang Yang, Liyuan Zhang. Complex visual cognitive function based on a large-scale neurovascular and metabolic coupling mechanisms model in whole brain[J]. Electronic Research Archive, 2025, 33(4): 2412-2432. doi: 10.3934/era.2025107

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Abstract

The neurovascular and metabolic coupling (NVMC) mechanism constitutes a critical physiological foundation for visual information processing. However, multimodal studies remain confined to phenomenological descriptions and fail to provide deeper theoretical investigations, preventing precise assessment of individuals. To address these limitations, we developed a heterogeneous whole-brain computational model of NVMC that integrates task-based EEG-MRI-fMRI multimodal data to simulate the cascading processes from neural mass firing to metabolic-hemodynamic responses. The model was validated against 33 resting-state simultaneous EEG-fMRI datasets. It was found that at the regional level, the fusiform exhibited stronger functional connectivity associated with face recognition, and its NVMC (CBF/FCS) demonstrated statistically significant differences between face stimuli (famous and unfamiliar faces) and scrambled faces (P < 0.001). Whole brain level analyses revealed reduced NVMC (CBF-FCS) with increasing face regularity and familiarity, despite nonsignificant differences in network indices. Subnetwork-level investigations further identified pronounced heterogeneity in functional interactions across distinct neural circuits. In this study, we developed a whole-brain-scale computational model to investigate the heterogeneity of NVMC during face-specific stimulus processing. The model provides an interpretable computational framework for enabling personalized assessments of visual cognitive tasks.

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