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Schobel, S.A., Chaudhury, N.H., Khan, U.A., Paniagua, B., Styner, M.A., Asllani, I., et al. (2013) Imaging Patients with Psychosis and a Mouse Model Establishes a Spreading Pattern of Hippocampal Dysfunction and Implicates Glutamate as a Driver. Neuron, 78, 81-93.
https://doi.org/10.1016/j.neuron.2013.02.011

has been cited by the following article:

• TITLE: Machine Learning Evidence for Neurodegenerative Signatures in the Schizophrenia Spectrum: A Multimodal Longitudinal Study

  AUTHORS: Rocco de Filippis, Abdullah Al Foysal

  KEYWORDS: Schizophrenia, Neurodegeneration, Machine Learning, Longitudinal, Multimodal Neuroimaging, Biomarkers

  JOURNAL NAME: Open Access Library Journal, Vol.13 No.5, May 28, 2026

  ABSTRACT: Schizophrenia has been increasingly conceptualized as a neurodevelopmental disorder with potential neurodegenerative components, yet evidence for progressive brain changes remains controversial. We investigated longitudinal trajectories of structural, functional, and cognitive biomarkers to characterize neurodegenerative patterns in schizophrenia using machine learning approaches. We analysed longitudinal neuroimaging and cognitive data from 150 participants (75 schizophrenia patients, 75 healthy controls) across three timepoints (baseline, 2-year, and 4-year follow-up). Multimodal biomarkers included frontal and temporal cortical thickness, hippocampal volume, default mode network (DMN) functional connectivity, and cognitive performance measures. Annualized rates of change were calculated for each participant. Random Forest classification was employed to identify the neurodegenerative signature distinguishing schizophrenia from controls. Schizophrenia patients exhibited significantly accelerated decline across all biomarkers compared to controls. Frontal cortical thickness declined 4.2× faster (p