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<p>Identifying biomarkers for serious mental illnesses (SMI) has significant implications for early intervention and prevention. The current study uses machine learning to build a model of risk prediction and transition based on multi-modal neuroimaging, clinical, and behavioral data from youth at transdiagnostic risk. Participants aged 12–25 were recruited at two sites in Canada, and followed for 4&#xa0;years. Symptom severity was measured using the Scale of Psychosis-Risk Symptoms (SOPS) and K10 Distress Scale, and a range of cognitive and behavioral measures were collected, as well as magnetic resonance imaging (MRI) data. Participants were assigned to one of 5 groups: healthy controls (HC; <i>n</i> = 42), familial risk (stage 0; <i>n</i> = 40), mild symptoms (stage 1a; <i>n</i> = 48), attenuated syndromes (stage 1b; <i>n</i> = 82), or discrete disorder (transition; <i>n</i> = 31). Constrained spherical deconvolution was used to generate whole brain tractography maps from diffusion MRI, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional MRI signal between pairs of brain regions. All measures were evaluated in a model to predict transition between groups. Random Forest analysis identified diffusion MRI-derived nodal metrics of betweenness centrality in the angular gyrus, inferior temporal gyrus, amygdala and calcarine fissure as potential features which can discriminate between the groups. Additionally, SOPS and K10 Distress Scales were useful behavioral predictors of transdiagnostic risk. Our findings show that combining neuroimaging with clinical characteristics may result in a promising predictive model for transdiagnostic risk and transition to SMI.</p>

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Prediction modeling in transdiagnostic risk: results from the PROCAN study

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Mohammed K. Shakeel,
  • Zeyad Abouyoussef,
  • Paul D. Metzak,
  • Roberto Souza,
  • Xiangyu Long,
  • Mike Lasby,
  • Signe Bray,
  • Benjamin I. Goldstein,
  • Glenda MacQueen,
  • JianLi Wang,
  • Sidney H. Kennedy,
  • Jean Addington,
  • Catherine Lebel

摘要

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for early intervention and prevention. The current study uses machine learning to build a model of risk prediction and transition based on multi-modal neuroimaging, clinical, and behavioral data from youth at transdiagnostic risk. Participants aged 12–25 were recruited at two sites in Canada, and followed for 4 years. Symptom severity was measured using the Scale of Psychosis-Risk Symptoms (SOPS) and K10 Distress Scale, and a range of cognitive and behavioral measures were collected, as well as magnetic resonance imaging (MRI) data. Participants were assigned to one of 5 groups: healthy controls (HC; n = 42), familial risk (stage 0; n = 40), mild symptoms (stage 1a; n = 48), attenuated syndromes (stage 1b; n = 82), or discrete disorder (transition; n = 31). Constrained spherical deconvolution was used to generate whole brain tractography maps from diffusion MRI, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional MRI signal between pairs of brain regions. All measures were evaluated in a model to predict transition between groups. Random Forest analysis identified diffusion MRI-derived nodal metrics of betweenness centrality in the angular gyrus, inferior temporal gyrus, amygdala and calcarine fissure as potential features which can discriminate between the groups. Additionally, SOPS and K10 Distress Scales were useful behavioral predictors of transdiagnostic risk. Our findings show that combining neuroimaging with clinical characteristics may result in a promising predictive model for transdiagnostic risk and transition to SMI.