Increased noise relates to abnormal excitation-inhibition balance in schizophrenia: a combined empirical and computational study

Samira Abbasi, Annemarie Wolff, Yasir Çatal, Georg Northoff

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Electroencephalography studies link sensory processing issues in schizophrenia to increased noise level—noise here is background spontaneous activity—as measured by the signal-to-noise ratio. The mechanism, however, of such increased noise is unknown. We investigate if this relates to changes in cortical excitation-inhibition balance, which has been observed to be atypical in schizophrenia, by combining electroencephalography and computational modeling. Our electroencephalography task results, for which the local field potentials can be used as a proxy, show lower signal-to-noise ratio due to higher noise in schizophrenia. Both electroencephalography rest and task states exhibit higher levels of excitation in the functional excitation-inhibition (as a proxy of excitation-inhibition balance). This suggests a relationship between increased noise and atypical excitation in schizophrenia, which was addressed by using computational modeling. A Leaky Integrate-and-Fire model was used to simulate the effects of varying degrees of noise on excitation-inhibition balance, local field potential, NMDA current, and . Results show a noise-related increase in the local field potential, excitation in excitation-inhibition balance, pyramidal NMDA current, and spike rate. Mutual information and mediation analysis were used to explore a cross-level relationship, showing that the cortical local field potential plays a key role in transferring the effect of noise to the cellular population level of NMDA.

Original languageEnglish
Pages (from-to)10477-10491
Number of pages15
JournalCerebral Cortex
Issue number20
Publication statusPublished - Oct 15 2023
Externally publishedYes


  • EEG
  • local field potential
  • NMDA current
  • noise
  • schizophrenia

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience


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