A multivariate cognitive approach to predict social functioning in recent onset psychosis in response to computerized cognitive training

Nina Walter, Julian Wenzel, Shalaila S. Haas, Letizia Squarcina, Carolina Bonivento, Anne Ruef, Dominic Dwyer, Theresa Lichtenstein, Öznur Bastrük, Alexandra Stainton, Linda A. Antonucci, Paolo Brambilla, Stephen J. Wood, Rachel Upthegrove, Stefan Borgwardt, Rebekka Lencer, Eva Meisenzahl, Raimo K.R. Salokangas, Christos Pantelis, Alessandro BertolinoNikolaos Koutsouleris, Joseph Kambeitz, Lana Kambeitz-Ilankovic

Research output: Contribution to journalArticlepeer-review

Abstract

Clinical and neuroimaging data has been increasingly used in recent years to disentangle heterogeneity of treatment response to cognitive training (CT) and predict which individuals may achieve the highest benefits. CT has small to medium effects on improving cognitive and social functioning in recent onset psychosis (ROP) patients, who show the most profound cognitive and social functioning deficits among psychiatric patients. We employed multivariate pattern analysis (MVPA) to investigate the potential of cognitive data to predict social functioning improvement in response to 10 h of CT in patients with ROP. A support vector machine (SVM) classifier was trained on the naturalistic data of the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study sample to predict functioning in an independent sample of 70 ROP patients using baseline cognitive data. PRONIA is a part of a FP7 EU grant program that involved 7 sites across 5 European countries, designed and conducted with the main aim of identifying (bio)markers associated with an enhanced risk of developing psychosis in order to improve early detection and prognosis. Social functioning was predicted with a balanced accuracy (BAC) of 66.4% (Sensitivity 78.8%; Specificity 54.1%; PPV 60.5%; NPV 74.1%; AUC 0.64; P = 0.01). The most frequently selected cognitive features (mean feature weights > ± 0.2) included the (1) correct number of symbol matchings within the Digit Symbol Substitution Test, (2) the number of distracting stimuli leading to an error within 300 and 200 trials in the Continuous Performance Test and (3) the dynamics of verbal fluency between 15 and 30 s within the Verbal Fluency Test, phonetic part. Next, the SVM classifier generated on the PRONIA sample was applied to the intervention sample, that obtained 54 ROP patients who were randomly assigned to a social cognitive training (SCT) or treatment as usual (TAU) group and dichotomized into good (GF-S ≥ 7) and poor (GF-S 
Original languageEnglish
Article number110864
JournalProgress in Neuro-Psychopharmacology and Biological Psychiatry
Volume128
Early online date23 Sept 2023
DOIs
Publication statusPublished - 10 Jan 2024

Keywords

  • Social functioning
  • Recent onset psychosis
  • Social cognitive training
  • Machine learning
  • Treatment response

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