A Cortical Model Examining Mismatch Negativity Deficits Under Schizophrenia-Linked Variants

Abstract

Recent advances in computational modeling, genome-wide association studies, neuroimaging, and theoretical neuroscience pose better opportunities to study neuropsychiatric disorders, such as schizophrenia (SZC)[4]. However, despite a repeated examination of its well-characterized phenotypes, our understanding of SZC’s neurophysiological biomarkers or cortical dynamics remain elusive. This study presents a biophysical spiking neuron model of perceptual inference, based on the predictive coding framework [1]. The model, implemented in NetPyNE [6], incorporates various single-cell models of both excitatory and inhibitory neurons [2,8,5], mimicking the circuits of the primary auditory cortex. This model allows for the exploration of the effects bio-genetic variants (expressed via ion-channels or synaptic mechanisms alternation) have on auditory mismatch negativity (MMN) deficits, a common biomarker for SZC [3]. More particularly, the model distinguishes between repetition suppression and prediction error and examines their respective contribution to the MMN. The first part of this report establishes the model’s explanatory power using two well-known paradigms- the oddball paradigm and the cascade paradigm. Both can reproduce the electrophysiological measures of the MMN among healthy subjects. Later, via tuning the parameters of single-neuron equations or the network’s synaptic weights, the model exhibits the expected LFP changes associated with SZC [7]. Therefore, this model enables exploring how biogenetic alterations affect the underlying components of the observed MMN deficits. Novel, yet preliminary, predictions are presented and suggested future steps for validations are listed. This model could support studies exploring genetic effects on the MMN (or other aspects of predictive coding) in the auditory cortex.

Publication
In OCNS 2020