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Neuroimaging in Schizophrenia.

Matcheri S Keshavan1, Guusje Collin2, Synthia Guimond3

  • 1Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA 02115, USA.

Neuroimaging Clinics of North America
|November 25, 2019
PubMed
Summary
This summary is machine-generated.

Schizophrenia, a chronic brain disorder affecting 1% of people, involves distinct brain changes. Multimodal neuroimaging combined with machine learning may improve diagnosis and predict patient outcomes.

Keywords:
DiagnosisDiffusionFunctionalMagnetic resonance imagingPsychoradiologySchizophreniaSpectroscopyStructural

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Schizophrenia is a chronic psychotic disorder with a 1% lifetime prevalence, typically emerging in adolescence or early adulthood.
  • Key symptoms include positive and negative symptoms, alongside cognitive impairments.
  • Neuroimaging reveals significant structural, functional, and neurochemical alterations in the brain, particularly in association cortex and subcortical regions.

Purpose of the Study:

  • To explore the potential of neuroimaging techniques in diagnosing and predicting outcomes in schizophrenia.
  • To investigate the utility of integrating multimodal imaging datasets with machine learning for schizophrenia research.

Main Methods:

  • Review of neuroimaging studies examining brain alterations in schizophrenia.
  • Exploration of machine learning approaches applied to multimodal imaging data.

Main Results:

  • Neuroimaging shows widespread brain abnormalities in schizophrenia, but these are not specific enough for diagnosis.
  • Imaging techniques may offer predictive value for patient outcomes.
  • Multimodal imaging and machine learning show promise for enhanced diagnostic and predictive capabilities.

Conclusions:

  • While current neuroimaging findings lack diagnostic specificity for schizophrenia, they highlight key brain regions affected.
  • Future research integrating multimodal imaging and machine learning holds significant potential for improving schizophrenia diagnosis and outcome prediction.