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Subtyping schizophrenia based on symptomatology and cognition using a data driven approach.

Luis Fs Castro-de-Araujo1, Daiane B Machado2, Maurício L Barreto3

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Biology

Background:

  • Schizophrenia is a complex disorder with varied symptoms and progression, hindering research and treatment.
  • Current diagnostic criteria for schizophrenia lack specificity, limiting clinical utility.
  • Reducing heterogeneity is crucial for advancing schizophrenia classification and understanding.

Purpose of the Study:

  • To apply machine learning (k-means clustering) to identify biologically distinct subtypes of schizophrenia.
  • To investigate if symptom and cognitive clusters correlate with neuroanatomical differences.
  • To explore novel approaches for subtyping schizophrenia beyond traditional phenomenology.

Main Methods:

  • K-means clustering was employed using symptom and cognitive measures from schizophrenia patients.
  • Brain volumetric data (MRI-derived) were analyzed across identified clusters.
  • Analysis of Covariance (ANCOVA) controlled for age and intracranial volume to compare brain structures.

Main Results:

  • Three distinct clusters emerged: high cognitive performance, high positive symptoms, and low positive symptoms.
  • Significant differences in six brain volumes were observed between these clusters.
  • Specific regions including the left caudate and right lateral pars opercularis showed notable variations.

Conclusions:

  • Machine learning-driven subtyping of schizophrenia reveals potential biological distinctions.
  • Identified clusters suggest a neuroanatomical basis for schizophrenia heterogeneity.
  • Further research is needed to validate these subtypes and confirm their clinical significance.