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.

Alzheimer's disease (AD) is an age-related neurodegenerative disorder that is difficult to diagnose early. Traditional methods can only confirm the diagnosis when symptoms are evident, but by that time, brain damage is irreversible. Recent research has found that changes in brain activity patterns occur early in AD. Using brain imaging and computational modeling techniques, we can hopefully detect these changes before the onset of symptoms, enabling early intervention. In this paper, we constructed a neurovascular coupling (NVC) whole-brain dynamic model by integrating brain structural data with biophysical modeling methods, aiming to explore the association between dynamical parameters and biomarkers of AD and to reveal their relationship with the overall brain function and network dynamic changes. First, the model simulates properly the resting-state functional connectivity (FC) at various stages of AD development; second, the strength of circulatory connectivity and NVC parameters generated by the brain simulation may be some potential new indicators for the early diagnosis of AD; and finally, the predictive ability of the indicator is quantified using the area under the curve (AUC) values of the receiver operating characteristic (ROC) curves, which suggests a dual strategy for the early diagnosis of biomarkers. These new indicators not only deepen our understanding of the mechanisms of disease progression but also provide an important theoretical basis and technical support for the formulation of early intervention strategies and the development of novel therapeutic approaches.