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001034880 0247_ $$2doi$$a10.1101/2024.12.05.24318587
001034880 037__ $$aFZJ-2025-00003
001034880 1001_ $$0P:(DE-HGF)0$$aZhang, Tongyi$$b0
001034880 245__ $$aLeveraging Stacked Classifiers for Multi-task Executive Function in Schizophrenia Yields Diagnostic and Prognostic Insights
001034880 260__ $$c2024
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001034880 520__ $$aCognitive impairment is a central characteristic of schizophrenia. Executive functioning (EF) impairments are often seen in mental disorders, particularly schizophrenia, where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of schizophrenia remains opaque. We used classification models with a stacking approach on systematically measured EFs to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions. EF assessments of inhibitory control (interference and response inhibitions), followed by working memory, evidently predicted schizophrenia diagnosis (area under the curve [AUC]=0.87) and remission status (AUC=0.81). The models highlighted the importance of interference inhibition or working memory updating in accurately identifying individuals with schizophrenia or those in remission. These identified patients had high-level negative symptoms at baseline and those who remitted showed milder cognitive symptoms at follow-up, without differences in baseline EF or symptom severity compared to non-remitted patients. Our work indicates that impairments in specific EF dimensions in schizophrenia are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be a promising tool that can aid in the clinical evaluation of this disorder.
001034880 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
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001034880 7001_ $$0P:(DE-HGF)0$$aZhao, Xin$$b1
001034880 7001_ $$0P:(DE-HGF)0$$aYeo, B. T. Thomas$$b2
001034880 7001_ $$0P:(DE-HGF)0$$aHuo, Xiaoning$$b3
001034880 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b4
001034880 7001_ $$0P:(DE-Juel1)171414$$aChen, Ji$$b5
001034880 773__ $$a10.1101/2024.12.05.24318587$$tmedRxiv$$y2024
001034880 909CO $$ooai:juser.fz-juelich.de:1034880$$pVDB
001034880 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b4$$kFZJ
001034880 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b4
001034880 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171414$$aForschungszentrum Jülich$$b5$$kFZJ
001034880 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)171414$$a Center for Brain Health and Brain Technology, Global Institute of Future Technology, Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University 800 Dongchuan Road, Shanghai, China 200240. 11 Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.$$b5
001034880 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001034880 9141_ $$y2024
001034880 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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