Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks

Zohreh Doborjeh; Maryam Doborjeh; Alexander Sumich; Balkaran Singh; Alexander Merkin; Sugam Budhraja; Wilson Goh; Edmund M-K Lai; Margaret Williams; Samuel Tan; Jimmy Lee; Nikola Kasabov

doi:10.1038/s41537-023-00335-2

Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks

Schizophrenia (Heidelb). 2023 Feb 15;9(1):10. doi: 10.1038/s41537-023-00335-2.

Authors

Zohreh Doborjeh^{1

2

3}, Maryam Doborjeh⁴, Alexander Sumich^{5

6}, Balkaran Singh⁷, Alexander Merkin^{8

9}, Sugam Budhraja⁷, Wilson Goh^{10

11

12}, Edmund M-K Lai⁷, Margaret Williams¹³, Samuel Tan¹¹, Jimmy Lee^{11

14}, Nikola Kasabov^{7

15

16}

Affiliations

¹ Audiology Department, School of Population Health, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand. Zohreh.doborjeh@auckland.ac.nz.
² Centre for Brain Research, The University of Auckland, Auckland, New Zealand. Zohreh.doborjeh@auckland.ac.nz.
³ School of Psychology, The University of Waikato, Hamilton, New Zealand. Zohreh.doborjeh@auckland.ac.nz.
⁴ Knowledge Engineering and Discovery Research Institute, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand. mgholami@aut.ac.nz.
⁵ School of Psychology, Nottingham Trent University, Nottingham, UK.
⁶ Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
⁷ Knowledge Engineering and Discovery Research Institute, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand.
⁸ Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand.
⁹ Research Methods, Assessment & Science, Department of Psychology, University of Konstanz, Konstanz, Germany.
¹⁰ School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
¹¹ Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
¹² Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore.
¹³ Department of Public Health and Psychosocial Studies, Auckland University of Technology, Auckland, New Zealand.
¹⁴ Institute of Mental Health & Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
¹⁵ Intelligent Systems Research Centre, Ulster University, Londonderry, UK.
¹⁶ Institute for Information and Communication Technologies (IICT), Bulgarian Academy of Sciences, Sofia, Bulgaria.

Abstract

Finding 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.