Assessing the information content of ERP signals in schizophrenia using multivariate decoding methods

Gi Yeul Bae, Carly J. Leonard, Britta Hahn, James M. Gold, Steven J. Luck

Research output: Contribution to journalArticle

Abstract

Multivariate pattern classification (decoding) methods are commonly employed to study mechanisms of neurocognitive processing in typical individuals, where they can be used to quantify the information that is present in single-participant neural signals. These decoding methods are also potentially valuable in determining how the representation of information differs between psychiatric and non-psychiatric populations. Here, we examined ERPs from people with schizophrenia (PSZ) and healthy control subjects (HCS) in a working memory task that involved remembering 1, 3, or 5 items from one side of the display and ignoring the other side. We used the spatial pattern of ERPs to decode which side of the display was being held in working memory. One might expect that decoding accuracy would be inevitably lower in PSZ as a result of increased noise (i.e., greater trial-to-trial variability). However, we found that decoding accuracy was greater in PSZ than in HCS at memory load 1, consistent with previous research in which memory-related ERP signals were larger in PSZ than in HCS at memory load 1. We also observed that decoding accuracy was strongly related to the ratio of the memory-related ERP activity and the noise level. In addition, we found similar noise levels in PSZ and HCS, counter to the expectation that PSZ would exhibit greater trial-to-trial variability. Together, these results demonstrate that multivariate decoding methods can be validly applied at the individual-participant level to understand the nature of impaired cognitive function in a psychiatric population.

Original languageEnglish (US)
Article number102179
JournalNeuroImage: Clinical
Volume25
DOIs
StatePublished - 2020

Keywords

  • ERP decoding
  • Schizophrenia
  • Working memory

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

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