Understanding circuit-level imbalances in the cortex can yield mechanistic insights into Alzheimer's disease (AD), supporting both diagnosis and therapeutic development. We present a computational framework that integrates the causal interpretability of mechanistic modeling with the predictive power of simulation-based inference (SBI) to identify candidate neuroimaging biomarkers of cortical circuit dysfunction in AD. Using a spiking cortical circuit model with recurrent excitatory and inhibitory populations, we generated a comprehensive dataset of two million simulations and produced realistic electroencephalography (EEG) signals through biophysically grounded causal filtering of spiking activity. From these signals, we extracted EEG features serving as potential biomarkers of cortical dysregulation and trained SBI models optimized for accuracy and efficiency. Comparisons across feature sets revealed that multi-feature SBI models achieved higher inference accuracy than single-feature approaches in predicting various cortical parameters, suggesting that no single biomarker is sufficient to fully characterize the neural processes underlying the EEG signal. Applying the best-performing models to real EEG data from AD patients at varying stages uncovered distinct patterns of cortical dysfunction, including a progressive reduction in cortico-cortical connectivity, linked to the accelerated breakdown of synaptic connections widely reported in AD progression. A reduction in the efficacy of the excitatory time constant was also observed, likely reflecting a shift in the excitation/inhibition (E/I) balance toward inhibition in later stages of the disease. Our framework provides a scalable and interpretable bridge between local-scale mechanistic brain modeling and clinical neuroimaging, advancing the identification of physiologically meaningful biomarkers of cortical dysfunction in AD.
Sánchez et al. (Fri,) studied this question.