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Combined multivariate pattern analysis with frequency-dependent intrinsic brain activity to identify essential

Xiaoyu Zhang1, Huiyue Chen1, Li Tao1

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

Neuroscience Letters
|March 8, 2022
PubMed
Summary

Essential tremor (ET) diagnosis can be improved using brain imaging. Multivariate pattern analysis of resting-state fMRI data reveals distinct brain activity patterns in ET patients, offering potential diagnostic biomarkers.

Keywords:
Essential tremorFrequency-dependentIntrinsic brain activityMultivariate pattern analysisResting-state functional MRI

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biomarkers

Background:

  • Essential tremor (ET) is a common neurological disorder with unclear intrinsic brain activity and diagnostic biomarkers.
  • Resting-state functional MRI (Rs-fMRI) combined with multivariate pattern analysis (MVPA) shows promise for identifying neurological disease biomarkers.

Purpose of the Study:

  • To investigate the potential of voxel-level frequency-dependent brain activity measures from Rs-fMRI as diagnostic biomarkers for Essential Tremor.
  • To differentiate ET patients from healthy controls (HCs) using MVPA.

Main Methods:

  • Rs-fMRI data from 162 ET patients and 153 HCs were analyzed using MVPA (binary support vector machine, SVM).
  • Voxel-level amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were computed across three frequency bands (classical, slow-5, slow-4).

Main Results:

  • MVPA successfully differentiated ET from HCs using ALFF and DC across frequency bands, and ReHo in classical and slow-5 bands.
  • ReHo in the slow-4 band showed lower classification performance but highlighted subcortical structure changes, particularly in the thalamus.
  • Discriminative features were primarily located in the cerebello-thalamo-cortical pathway, correlating with ET clinical features.

Conclusions:

  • Frequency-dependent ALFF, ReHo, and DC derived from Rs-fMRI are effective in discriminating ET from HCs.
  • These neuroimaging metrics can reveal intrinsic brain activity changes in ET, serving as potential diagnostic biomarkers.