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

Overview

1. Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
2. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
3. Institute of Clinical Medicine, University of Oslo, Oslo, Norway
4. Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
5. Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
6. School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
7. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
8. Department of Physics, Norwegian University of Life Sciences, Ås, Norway
9. Department of Physics, University of Oslo, Oslo, Norway
10. Department of Biosciences, University of Oslo, Oslo, Norway

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Code Availability

All simulations were run using Brian 2 (Stimberg et al. 2019) or NEURON simulator (Hines and Carnevale 2001) v. 8.2.6, using Python (3.9.20) interface. Our simulation scripts are available at ModelDB, accession number 2019882 (https://modeldb.science/2019882).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability Statement

The data that support the findings of this study are available from the ModelDB model repository (https://modeldb.science/2019882).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 8 MeSH terms, 2 funders, 75 references.

Cite

This paper

Eissa, A., Fredrik Kismul, J., Pentz, A. B., Elvsåshagen, T., Metzner, C., Akkouh, I., Djurovic, S., Shadrin, A., Linne, M., Einevoll, G. T., Andreassen, O. A., & Mäki‐Marttunen, T. (2026). Computational Modelling of Novelty Detection in the Mismatch Negativity Protocols and Its Impairments in Schizophrenia. The European journal of neuroscience, 63(6), e70453. https://doi.org/10.1111/ejn.70453

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