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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Data-driven learning to identify biomarkers in bipolar disorder.

Zhuangzhuang Li1, Wenmei Li2, Wei Yan3

  • 1College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Computer Methods and Programs in Biomedicine
|September 26, 2022
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Summary

Machine learning identified distinct brain patterns in bipolar disorder (BD). The left orbital middle frontal gyrus showed significant differences between healthy individuals and BD patients, aiding in diagnosis.

Keywords:
Bipolar disorderDeep autoencoderFeature selectionStructural magnetic functional imaging

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • Bipolar disorder (BD) is a leading cause of global disability.
  • BD is often misdiagnosed due to symptom overlap with schizophrenia and major depression.
  • Understanding BD's pathogenesis is crucial for accurate diagnosis and treatment.

Purpose of the Study:

  • To develop machine learning algorithms for identifying pathological brain changes in BD.
  • To differentiate individuals with BD from healthy controls based on brain structure.
  • To pinpoint specific brain regions indicative of BD.

Main Methods:

  • Utilized an autoencoder trained on structural imaging data from 1113 healthy controls.
  • Identified BD biomarkers through reconstruction errors in brain regions.
  • Employed an FS-select framework to determine optimal feature selection and reproducible biomarkers.

Main Results:

  • The left orbital region of the middle frontal gyrus exhibited the most significant differences between healthy controls and BD patients.
  • The FS-select framework consistently identified the left orbital middle frontal gyrus as the most reproducible feature associated with BD.

Conclusions:

  • Both the autoencoder and FS-select methods consistently identified the left orbital middle frontal gyrus as a key region differentiating BD patients from healthy controls.
  • This finding highlights the potential of machine learning in identifying neurobiological markers for BD.