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Published on: May 15, 2020
Relationships between cognitive event-related brain potential measures in patients at clinical high risk for
Jennifer R Lepock1, Sarah Ahmed1, Romina Mizrahi2
1Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
Abstract:
Neurophysiological measures of cognitive functioning that are abnormal in patients with schizophrenia are promising candidate biomarkers for predicting development of psychosis in individuals at clinical high risk (CHR). We examined the relationships among event-related brain potential (ERP) measures of early sensory, pre-attentional, and attention-dependent cognition, in antipsychotic-naïve help-seeking CHR patients (n = 36) and healthy control participants (n = 22). These measures included the gamma auditory steady-state response (ASSR; early sensory); mismatch negativity (MMN) and P3a (pre-attentional); and N400 semantic priming effects - a measure of using meaningful context to predict related items - over a shorter and a longer time interval (attention-dependent). Compared to controls, CHR patients had significantly smaller P3a amplitudes (d = 0.62, p = 0.03) and N400 priming effects over the long interval (d = 0.64, p = 0.02). In CHR patients, gamma ASSR evoked power and phase-locking factor were correlated (r = 0.41, p = 0.03). Reductions in mismatch negativity (MMN) and P3a amplitudes were also correlated (r = -0.36, p = 0.04). Moreover, lower gamma ASSR evoked power correlated with smaller MMN amplitudes (r = -0.45, p = 0.02). MMN amplitude reduction was also associated with reduced N400 semantic priming over the shorter but not the longer interval (r = 0.52, p < 0.002). This pattern of results suggests that, in a subset of CHR patients, impairment in pre-attentional measures of early information processing may contribute to deficits in attention-dependent cognition involving rapid, more automatic processing, but may be independent from pathological processes affecting more controlled or strategic processing. Thus, combining neurophysiological indices of cognitive deficits in different domains offers promise for improving their predictive power as prognostic biomarkers of clinical outcome.
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