Speech Illusions in People at Clinical High Risk for Psychosis Linked to Clinical Outcome

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Abstract

Background and hypothesis: Around 20% of people at clinical high risk (CHR) for psychosis later develop a psychotic disorder, but it is difficult to predict who this will be. We assessed the incidence of hearing speech (termed speech illusions [SIs]) in noise in CHR participants and examined whether this was associated with adverse clinical outcomes. Study design: At baseline, 344 CHR participants and 67 healthy controls were presented with a computerized white noise task and asked whether they heard speech, and whether speech was neutral, affective, or whether they were uncertain about its valence. After 2 years, we assessed whether participants transitioned to psychosis, or remitted from the CHR state, and their functioning. Study results: CHR participants had a lower sensitivity to the task. Logistic regression revealed that a bias towards hearing targets in stimuli was associated with remission status (OR = 0.21, P = 042). Conversely, hearing SIs with uncertain valence at baseline was associated with reduced likelihood of remission (OR = 7.72. P =. 007). When we assessed only participants who did not take antipsychotic medication at baseline, the association between hearing SIs with uncertain valence at baseline and remission likelihood remained (OR = 7.61, P =. 043) and this variable was additionally associated with a greater likelihood of transition to psychosis (OR = 5.34, P =. 029). Conclusions: In CHR individuals, a tendency to hear speech in noise, and uncertainty about the affective valence of this speech, is associated with adverse outcomes. This task could be used in a battery of cognitive markers to stratify CHR participants according to subsequent outcomes.

Original languageEnglish
Pages (from-to)339-349
Number of pages11
JournalSchizophrenia bulletin
Volume49
Issue number2
DOIs
Publication statusPublished - 1 Mar 2023

Keywords

  • remission
  • signal-detection
  • transition
  • uncertainty
  • white noise task

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