Computational Modelling of Novelty Detection in the Mismatch Negativity Protocols and Its Impairments in Schizophrenia

TL;DR

This paper develops a computational model to understand how the brain detects novel sounds and how this process is impaired in schizophrenia.

Contribution

A novel integrate-and-fire spiking network model is introduced to simulate auditory novelty detection and its disruption in schizophrenia.

Findings

The model reliably reproduces MMN-like novelty detection and can test SCZ-related cellular alterations.

Reduced pyramidal cell excitability and decreased spine density impair novelty detection, with spine density loss causing stronger deficits.

Phase locking in a synfire chain network with STDP can theoretically achieve rhythmic stimulus entrainment.

Abstract

The human auditory system rapidly distinguishes between novel and familiar sounds, a process reflected in mismatch negativity (MMN), an electroencephalogram (EEG)‐based biomarker of auditory novelty detection. MMN is impaired in psychiatric conditions, most notably schizophrenia (SCZ), yet the neuronal mechanisms underlying this deficit remain unclear. Here, we combined computational modelling and genetic analyses to investigate how SCZ‐associated cellular abnormalities affect auditory novelty detection. We developed an integrate‐and‐fire spiking network model capable of detecting four types of auditory novelty, including stimulus omission. Based on assumptions of short‐term depressing synapses between the subpopulations of the network and the existence of neuronal inputs that are phase‐locked to the rhythm of the recently experienced stimulus sequence, we showed that the model reliably reproduced MMN‐like novelty detection and allowed systematic testing of SCZ‐related cellular alterations. We also demonstrated that the required phase locking can theoretically be achieved in a synfire chain network exhibiting spike‐timing dependent plasticity (STDP) in its feedback synapses that becomes entrained to the rhythmic stimulus. Simulations of our novelty‐detecting network revealed that both reduced pyramidal cell excitability, linked to ion channel dysfunction, and decreased spine density impaired novelty detection, with the latter producing stronger deficits. Our work provides a flexible spiking network model of auditory novelty detection that can link cellular‐level abnormalities to measurable MMN deficits, improving their mechanistic interpretation and helping to explain the heterogeneity of SCZ.

The mechanisms of novelty detection in the mismatch negativity (MMN) protocol and its disturbances in schizophrenia (SCZ) remain unclear. Here, we developed an integrate‐and‐fire (IAF) spiking neuronal network model to detect multiple types of auditory novelty. For this, we assumed the existence of excitatory (ES) and inhibitory (IS) stimulus‐encoding populations, an excitatory population that receives inputs phase‐locked to the stimulus rate (EP), and an output population (EO) that receives synaptic contacts exhibiting short‐term depression from the excitatory populations (left). With additional populations for alternative stimulus frequencies and durations, we showed that the network is able to detect four types of deviants: frequency deviants, stimulus omissions, duration deviants where the stimulus duration was longer for a deviant, and inverse duration deviants where it was shorter (upper right). Combining the model with postmortem genetics data on ion‐channel expression in SCZ and control subjects and a biophysically detailed single‐cell model that helped to convert these gene‐expression differences to a parameter change in the IAF model, we showed that the model predicted an impaired novelty‐detection response in most MMN protocols for SCZ (lower left). A generic decrease in spine density had a similar effect (lower right). Although yet a theoretical model, our framework provides an example of a network capable of multiple types of novelty detection with neurobiologically plausible mechanisms that can be used to explore the cellular and network level phenotypes in psychiatric disorders and their effects on MMN.

Figures