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Towards a brain-based predictome of mental illness.

Barnaly Rashid1, Vince Calhoun2

  • 1Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.

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Neuroimaging and machine learning enable predicting mental illness using brain network features. This "predictome" approach analyzes diverse data for accurate disorder characterization and subject prediction.

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functional magnetic resonance imagingmachine learningmultimodal studiesneuroimagingpsychiatric disorder

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computer Science

Background:

  • Neuroimaging studies have advanced understanding of brain structure and function in mental illness.
  • Machine learning shows promise for individualized prediction and characterization of psychiatric disorders.

Purpose of the Study:

  • To review neuroimaging-based predictomic approaches for mental illness.
  • To discuss current trends, shortcomings, and future directions in the field.

Main Methods:

  • Utilizing multivariate brain network features from structural, functional, and diffusion MRI.
  • Incorporating features from single or multiple neuroimaging modalities into predictive models.
  • Analyzing over 250 studies focusing on various psychiatric disorders.

Main Results:

  • The "predictome" approach integrates multiple brain network features for disorder-specific prediction.
  • Subject-level prediction in psychiatric disorders is a rapidly growing research area with over 650 studies.

Conclusions:

  • Neuroimaging-based predictomics offers a powerful framework for understanding and predicting mental illness.
  • Future research should address current limitations and explore novel predictive modeling strategies.