The Hopf whole-brain model,based on structural connectivity,overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters,quantifying dynamic brai...The Hopf whole-brain model,based on structural connectivity,overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters,quantifying dynamic brain characteristics in healthy and diseased states.Traditional parameter fitting techniques lack precision,restricting broader use.To address this,we validated parameter fitting methods using simulated networks and synthetic models,introducing improvements such as individual-specific initialization and optimized gradient descent,which reduced individual data loss.We also developed an approximate loss function and gradient adjustment mechanism,enhancing parameter fitting accuracy and stability.Applying this refined method to datasets for major depressive disorder(MDD)and autism spectrum disorder(ASD),we identified differences in brain regions between patients and healthy controls,explaining related anomalies.This rigorous validation is crucial for clinical application,paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research,demonstrating substantial potential in clinical neurology.展开更多
基金supported by the National Natural Science Foundation for Young Scholars of China(grant number 12205229 to J.J.and Q.-Y.Z.)STI 2030-Major Projects(no.2021ZD0201300 to J.J.,Q.-Y.Z.and Z.-G.H.)+1 种基金STI 2030-Major Projects(no.2022ZD0208500 to C.-W.S.)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JQ-010 to C.-W.S.).
文摘The Hopf whole-brain model,based on structural connectivity,overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters,quantifying dynamic brain characteristics in healthy and diseased states.Traditional parameter fitting techniques lack precision,restricting broader use.To address this,we validated parameter fitting methods using simulated networks and synthetic models,introducing improvements such as individual-specific initialization and optimized gradient descent,which reduced individual data loss.We also developed an approximate loss function and gradient adjustment mechanism,enhancing parameter fitting accuracy and stability.Applying this refined method to datasets for major depressive disorder(MDD)and autism spectrum disorder(ASD),we identified differences in brain regions between patients and healthy controls,explaining related anomalies.This rigorous validation is crucial for clinical application,paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research,demonstrating substantial potential in clinical neurology.
