S.Woolridge, C. Wood-Ross, R. Voleti, G.W. Harrison, V.Berisha, C. R. Bowie
Background: Cannabis use is highly comorbid with psychotic disorders, especially among
young people experiencing their first episode of psychosis. The high rate of cannabis use in this
population has led to uncertainty between diagnoses of cannabis-induced psychosis and primary
psychosis, often with cannabis comorbidity (primary psychosis). The similarity of the clinical
presentations of these disorders can hinder intervention and treatment. Attempts to identify
differentiating features have been largely limited to self-report, patient history, and symptoms.
Despite the abundance of research identifying cognitive deficits and abnormalities in eye
movements and speech as core biological and neurological symptoms of primary psychotic
disorders (e.g., schizophrenia), limited research has pursued such biomarkers as potential targets
for differentiation in first-episode psychosis.
Purpose: The current study assessed individuals diagnosed with either cannabis-induced or
primary psychosis using cognitive, visual processing, eye movement, and speech tasks, which
are each associated with well-recognized and specific deficits in primary psychotic disorders.
Method: Sixteen participants with cannabis-induced psychosis and twelve participants with
primary psychosis were recruited in Kingston, Ontario. Cognitive performance, backward visual
masking, pro- and anti-saccade eye movements, and semantic coherence were assessed for the
first time in this population, alongside clinical symptoms, trauma, substance use, premorbid
functioning, and illness insight.
Results. Relative to individuals with primary psychosis, individuals with cannabis-induced
psychosis demonstrated significantly better performance on the pro-saccade task, faster reaction
time on both the pro- and anti-saccade task, more coherent speech, better premorbid adjustment,
more cannabis use, and a higher degree of illness insight. The use of these variables in a
discriminant analysis successfully classified cases with 88.5% accuracy. The ability to reliably
differentiate these disorders is crucial to intervention in first-episode psychosis.