A data-driven approach for stratifying psychotic and mood disorders subjects using structural magnitude resonance imaging data

Hooman Rokham, Haleh Falakshahi, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Psychotic disorders such as schizophrenia and bipolar disorder are difficult to classify because they share overlapping symptoms. Deriving biomarkers of illness using structural MRI dataset are essential because they may lead to improved diagnosis. Previous studies typically predict the diagnosis labels using supervised classifiers that rely on truly labeled dataset. Mislabeled subjects may increase the complexity of the predictive model and may impact its performance. In this work, we address the problem of inaccurate diagnosis labeling of psychotic disorders using a data-driven approach. We performed dimension reduction using PCA on the vectorized images and then k-mean clustering on the components. We evaluate our method on a structural MRI dataset, with over 900 subjects labeled using DSM-IV and biotypes. An ANOVA statistical significance test was performed after clustering based on each labelling approach and after clustering. Subjects were grouped into 5 clusters using our method, and each cluster includes all types of patients. However, we found statistically significant group differences in brain regions across 5 clusters, while for DSM and biotype, there were no significant differences. Our results also show the performance of the predictive model improved significantly using datadriven labels. Our method shows underlying biological changes associated with mental illness may be identified by studying and considering features of the brain imaging data, and annotating brain imaging data using a data-driven approach may eventually lead to improved diagnosis and advanced drug discovery and help patients.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - Jan 1 2020
Externally publishedYes
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • bipolar
  • clustering
  • data-driven
  • mood disorder
  • psychosis disorder
  • schizoaffective
  • schizophrenia
  • structural mri

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Rokham, H., Falakshahi, H., & Calhoun, V. D. (2020). A data-driven approach for stratifying psychotic and mood disorders subjects using structural magnitude resonance imaging data. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113142V] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2549680