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Published on: April 23, 2014
Cross-sectional study comparing cognitive function in treatment responsive versus treatment non-responsive
Edward Millgate1, Eugenia Kravariti1,2, Alice Egerton1,2
1Department of Psychosis Studies, King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK.
Antipsychotic treatment resistance in schizophrenia did not show significant cognitive differences between responders and non-responders. Further research with machine learning on first-episode schizophrenia is needed to explore cognitive subtypes.
Area of Science:
- Neuroscience
- Psychiatry
- Cognitive Science
Background:
- Antipsychotic treatment resistance affects 70-84% of schizophrenia patients from the first episode.
- Emerging evidence suggests potential cognitive differences, particularly in verbal memory and language, between treatment-resistant and treatment-responsive schizophrenia.
- This study aimed to validate these findings by comparing cognitive performance in antipsychotic non-responders (NR) versus responders (R).
Purpose of the Study:
- To compare cognitive performance between antipsychotic non-responders (NR) and responders (R) in schizophrenia.
- To specifically investigate differences in verbal memory and language functions using a brief cognitive battery.
- To determine if cognitive profiles differ based on antipsychotic treatment response.
Main Methods:
- A cross-sectional study involving 106 participants (52 NR, 54 R) aged 18-65 with schizophrenia or schizophreniform disorder.
- Cognitive performance was assessed using the Brief Assessment of Cognition in Schizophrenia (BACS).
- Univariable and multivariable linear regressions, adjusted for age, gender, and illness duration, were used to analyze group differences.
Main Results:
- No significant differences in cognitive performance were found between the antipsychotic responder (R) and non-responder (NR) groups on any BACS measures.
- Specifically, verbal memory (ß=-1.99, p=0.398) and verbal fluency (ß=1.23, p=0.510) showed no significant group differences in univariable models.
- These findings remained consistent in multivariable regression analyses.
Conclusions:
- The lack of cognitive differences between groups may stem from insufficient clinical distinction between responders and non-responders in this sample.
- Future research should employ machine learning on longitudinal, first-episode schizophrenia samples.
- Identifying responder subtypes and their interaction with cognitive factors is crucial for advancing schizophrenia treatment.
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