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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

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Machine learning combined with resting-state functional MRI to characterize functional brain differences in

Yuanxin Shao1,2, Chao Liang2, Dan Xu3

  • 1First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.

Frontiers in Psychiatry
|July 13, 2026
PubMed
Summary

Post-stroke depression (PSD) shows distinct resting-state functional MRI differences in brain regions like the cingulate and thalamus. Machine learning identified key imaging features, aiding in understanding PSD neurobiology.

Keywords:
anxietydepression severityfunctional connectivitymachine learningpost-stroke depressionresting-state functional magnetic resonance imaging

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Published on: July 1, 2014

Area of Science:

  • Neuroimaging
  • Neuroscience
  • Machine Learning in Medicine

Background:

  • Post-stroke depression (PSD) is a frequent complication following stroke.
  • Resting-state functional imaging correlates of PSD are not fully understood.
  • This study investigates multi-level functional brain differences in PSD patients.

Purpose of the Study:

  • To identify resting-state functional MRI differences between PSD patients and healthy controls.
  • To evaluate interpretable machine learning for identifying PSD-associated imaging features.
  • To explore the neurobiological underpinnings of PSD.

Main Methods:

  • Resting-state functional MRI was performed on 50 PSD patients and 50 controls.
  • Four imaging indices (ALFF, ReHo, DC, FC) were extracted using the AAL atlas.
  • LASSO regression and nine machine-learning classifiers were used, with SHAP for feature interpretation.

Main Results:

  • PSD patients exhibited widespread resting-state functional differences in cingulate, thalamic, prefrontal, and other regions.
  • Twenty-nine features differed between groups; LASSO identified 10 core features (AUC 0.878).
  • The Extra Trees model achieved an AUC of 0.889, with key features including left anterior cingulate DC and left thalamus ReHo.

Conclusions:

  • PSD is associated with multi-level resting-state functional brain differences.
  • Interpretable machine learning successfully identified neurobiologically relevant rs-fMRI features for PSD.
  • Further validation in larger cohorts is needed to confirm specificity and clinical utility.