Multi-origins of pathological theta oscillation from neuron to network inferred by a hybrid data and model study with cubature Kalman filter

Abstract The brain rhythm is strongly associated with the brain function. Alzheimer’s disease (AD) is majorly reflected by the brain rhythm switching from the alpha band (9–12 Hz) to the theta band (4–8 Hz), accompanying with the loss of brain function. However, extracting the implicating intrinsic characteristic variations of the brain network by utilizing the Electroencephalogram (EEG) information is extremely difficult. Kaman observer, as an effective Bayesian technique, can provide a visualization service for probing the intrinsic characteristics underlying the pathological theta oscillations. This work first establishes an excitation-inhibitory neural network model and explores the role of the proportion of the inhibitory neurons and inhibitory synapses in the pathological theta oscillation. The results indicate that the apoptosis of inhibitory neurons and accompanied loss of inhibitory synaptic weight are the main neural bases of the frequency decrease of neural oscillation. Then, we further explore the intrinsic spiking characteristic by considering spike frequency adaptation (SFA) to the inhibitory neurons. The results show that the SFA reduces the firing rate of neurons, which facilitates the theta rhythm. The enhancement of SFA current by increasing time constant of its gating variable can further decrease the theta frequency from 7 Hz to 4 Hz. Finally, for this high-dimensional nonlinear excitation-inhibitory neural network model, cubature Kalman filter (CKF) is employed to estimate the above potential variations from the noisy EEG information. The observation results show that both the proportion of inhibitory neurons and the inhibitory SFA current present descending trends as the degree of AD increases. Collectively, the generation of AD state is speculated to rely on multi-origin inhibitory intrinsic characteristics: a significant attenuation on the proportion of inhibitory neurons, synaptic weight and SFA current. The observation result by CKF from EEG verifies the simulation results from the model. We investigate the parameter effects from both the forward model simulation and the inverse estimation process of network parameters using EEG data. This work enhances the understanding of the role of inhibitory intrinsic characteristics on pathological theta oscillation and provides an effective method to decode the dynamics underlying the neural activities.