Investigation of Social and Cognitive Predictors in Non-transition Ultra-high-risk' Individuals for Psychosis Using Spiking Neural Networks

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aut.relation.articlenumber10
aut.relation.issue1
aut.relation.journalSchizophrenia (Heidelb)
aut.relation.startpage10
aut.relation.volume9
dc.contributor.authorDoborjeh, Zohreh
dc.contributor.authorDoborjeh, Maryam
dc.contributor.authorSumich, Alexander
dc.contributor.authorSingh, Balkaran
dc.contributor.authorMerkin, Alexander
dc.contributor.authorBudhraja, Sugam
dc.contributor.authorGoh, Wilson
dc.contributor.authorLai, Edmund M-K
dc.contributor.authorWilliams, Margaret
dc.contributor.authorTan, Samuel
dc.contributor.authorLee, Jimmy
dc.contributor.authorKasabov, Nikola
dc.date.accessioned2023-02-27T01:14:48Z
dc.date.available2023-02-27T01:14:48Z
dc.date.copyright2023
dc.date.issued2023-02-15
dc.description.abstractFinding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56-64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis.
dc.identifier.citationSchizophrenia (Heidelb), ISSN: 2754-6993 (Print); 2754-6993 (Online), Springer Science and Business Media LLC, 9(1), 10-. doi: 10.1038/s41537-023-00335-2
dc.identifier.doi10.1038/s41537-023-00335-2
dc.identifier.issn2754-6993
dc.identifier.issn2754-6993
dc.identifier.urihttps://hdl.handle.net/10292/15902
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttp://dx.doi.org/10.1038/s41537-023-00335-2
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPrevention
dc.subjectPediatric
dc.subjectNeurosciences
dc.subjectBehavioral and Social Science
dc.subjectClinical Research
dc.subjectBrain Disorders
dc.subjectBasic Behavioral and Social Science
dc.subjectMental Health
dc.subjectMental health
dc.titleInvestigation of Social and Cognitive Predictors in Non-transition Ultra-high-risk' Individuals for Psychosis Using Spiking Neural Networks
dc.typeJournal Article
pubs.elements-id492994
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