Table 1.
Application of NDM in neurodegenerative diseases.
Table 2.
The extended models of NDM.
Table 3.
Application of ESM in AD.
Table 4.
Application of SIR model in neurodegenerative disease.
Table 5.
Comparison of the 3 basic connectome-based biophysical models.
Fig 1.
Individual difference in connectivity pattern and vulnerability mechanisms.
The horizontal axis represents individual difference in the ROI size and location, along with the difference in structural connectivity patterns. The vertical axis represents the distribution of vulnerability factors within the brain of the same individual, including morphological attributes (gray matter, white matter/myelin sheath, synaptic density, etc.), energy metabolism (glucose, blood flow, aerobic glycolysis, oxygen saturation of blood, etc.), neurotransmitter systems (receptors, transporters), and immunity (activated microglia, activated astrocytes). The distribution of vulnerability factors vary across different ROIs within the same individual. In the brain graph, circles represent ROIs, line represent structural connections between ROIs. The size of the circles indicates the size of the ROIs, the color from light to dark represents the distribution of vulnerability factors across different ROIs and the thickness of the lines represents the strength of structural connections. The brain of left hemisphere sketch in Fig 1 is downloaded from the open-source site Servier (URL: https://smart.servier.com/smart_image/brain/), which is licensed under CC BY 4.0.
Fig 2.
The unified framework for model evaluation.
Database: The unified database includes both raw data and parameter libraries, which are divided into individual-level data and population-level data, animal and human brain images, cross-sectional data and longitudinal data with multiple time points. Models: These data are used for model f(x), where researchers select appropriate biophysical models (NDM, ESM, and SIR) for individual-level or population-level analysis as appropriate. The selected model was fitted using the data provided by the parameter library and the simulated propagation process of pathological proteins/brain atrophy was obtained. Evaluation: The simulation results are compared with the real propagation process of pathological proteins/brain atrophy from both temporal and spatial perspective, utilizing measures of model performance (e.g., Pearson’s r, MAE), significance (spatial autocorrelation-preserving null models), and importance of model parameters (null models). The human and mouse icons are free icons provided by Microsoft PowerPoint.