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Updated: Jan 21, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A symptom network approach to schizophrenia in the CATIE study: processing speed as the central cognitive impairment
Khan Buchwald1, Richard J Siegert2, Matthieu Vignes3
1School of Sciences, Auckland University of Technologyhttps://ror.org/01zvqw119, Auckland, New Zealand.
Background:
People diagnosed with schizophrenia can have functional impairments in multiple domains. Cognitive impairment is central to schizophrenia and has substantial prognostic value compared with other symptoms of schizophrenia. However, no study has previously investigated directed relationships in a complex system of cognitive, sociodemographic, clinical and quality of life (QOL) variables in people diagnosed with schizophrenia.
Aims:
To identify the complex relationships of components of cognition with other cognitive components, as well as with clinical and QOL variables.
Method:
This study included data from 1450 participants in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The present study reconstructed a Bayesian network from this data using cognition, clinical, sociodemographic and QOL variables.
Results:
Processing speed was centrally associated with all other cognitive domains. Cognitive domains were conditionally independent of positive symptoms but moderately associated with negative symptoms (β = -0.25; P < 0.001). The positive symptoms subscale was independent of QOL, conditioning on third variables. Negative symptoms were moderately associated with QOL (β = -0.33; P < 0.001), and processing speed had a weak association with QOL (β = -0.12; P < 0.001). Processing speed was a central variable in the network.
Conclusions:
Intervening with respect to processing speed may be the most beneficial way of improving other cognitive functions. More research is needed on directed networks that include social cognition and global levels of functioning.
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