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Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate

Yufen Li1, Li Tao1, Huiyue Chen1

  • 1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Frontiers in Human Neuroscience
|October 29, 2021
PubMed
Summary

Machine learning and brain connectivity mapping can identify depression in essential tremor (ET) patients. Global brain connectivity changes in cerebellar-prefrontal circuits reveal the underlying brain network pathogenesis of depression in ET.

Keywords:
depressionessential tremorglobal brain connectivitymultivariate pattern analysisresting-state functional magnetic resonance imaging

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Depression is a common non-motor symptom in essential tremor (ET).
  • The pathogenesis and diagnostic biomarkers for depression in ET remain unknown.
  • Resting-state fMRI with machine learning offers a novel approach to identify depressed ET patients and understand brain network alterations.

Purpose of the Study:

  • To investigate the utility of global brain connectivity (GBC) mapping and machine learning for identifying depression in essential tremor (ET) patients.
  • To reveal the brain network pathogenesis underlying depression in ET.
  • To identify potential diagnostic biomarkers for depressed ET.

Main Methods:

  • Utilized global brain connectivity (GBC) mapping from resting-state fMRI data.
  • Analyzed data from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls.
  • Employed multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) for pattern analysis and classification.

Main Results:

  • While overall four-class GPC accuracy was low (40.45%), it could discriminate depressed ET from other groups (sensitivity 70.73%, P < 0.001).
  • Binary SVM showed higher sensitivities: 73.17% for depressed ET vs. non-depressed ET, 80.49% vs. primary depression, and 75.61% vs. healthy controls (P < 0.001).
  • Discriminative features were primarily in cerebellar-motor-prefrontal cortex circuits; GBC values in specific regions correlated with depression severity.

Conclusions:

  • Global brain connectivity mapping combined with machine learning multivariate pattern analysis (MVPA) can effectively identify depressed ET patients.
  • Altered GBC in cerebellar-prefrontal cortex circuits are significant discriminative features.
  • These findings contribute to understanding the network pathogenesis of depression in essential tremor.