Simulating the N400 congruence effect in Alzheimer’s disease

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disorder afflicting millions worldwide. Abnormal N400 Event-Related Potentials (ERP) have been proposed as biomarkers indicative of AD progression. Specifically, in the semantic category judgment task, the N400 congruency effect diminishes in Mild Cognitive Impairment Patients, who progress to AD. Pathological hallmarks of AD such as amyloid and tau protein accumulation, synaptic and neuronal atrophy, and their secondary effects like Calcium ion (Ca2+) and Magnesium ion (Mg2+) dysfunction, acetylcholine (ACh) depletion and glutamate excess could be the underlying causes of these ERP abnormalities. However, models of the N400 in literature do not account for these neuronal properties. Nor is there consensus in the literature on the exact cognitive functions or specific neural mechanisms of the N400. Here we propose, to our knowledge, the first biologically detailed and plausible connectionist spiking neural network architecture to model the N400 ERP in the semantic category judgment task and simulate neuronal effects of AD. The results suggest that the N400 for healthy participants is generated in the model due to its neuronal organization, whereas the congruence effect requires the spike frequency adaptation mechanism in addition to its neuronal organization. In the model, the abnormal N400 congruence effect seen in AD is simulated by synaptic and neuronal atrophy, acetylcholine depletion, and glutamate accumulation. While the N400 congruence effect is also modulated by calcium dysfunction and magnesium excess, they do not simulate the abnormal N400 congruency effect as seen in AD. This may have implications for AD research as it could help better classify and diagnose AD patients and aid efforts to develop medical interventions and tools for personalized care.