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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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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...
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Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
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Predicting Antidepressant Treatment Response Using Functional Brain Controllability Analysis.

Feng Fang1, Beata Godlewska2,3, Sudhakar Selvaraj4

  • 1Department of Biomedical Engineering, University of Houston, Houston, Texas, USA.

Brain Connectivity
|November 10, 2022
PubMed
Summary
This summary is machine-generated.

Predicting antidepressant response in depression is crucial. Functional brain network controllability (fBNC) analysis identified pretreatment biomarkers for treatment responders versus nonresponders, aiding personalized depression treatment.

Keywords:
antidepressantbrain connectivitybrain controllabilitydepressionneuromodulation

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomarker Discovery

Background:

  • Predicting antidepressant response remains a significant clinical challenge.
  • Existing methods lack precision in identifying treatment responders early.
  • Functional brain network controllability (fBNC) offers a novel approach to analyze brain dynamics.

Purpose of the Study:

  • To investigate the utility of fBNC in predicting antidepressant treatment response in depression patients.
  • To identify pretreatment brain network characteristics differentiating responders from nonresponders.
  • To explore the potential of fBNC as a biomarker for personalized depression treatment.

Main Methods:

  • Employed functional brain network controllability (fBNC) analysis on resting-state fMRI data.
  • Analyzed pretreatment data from 20 unmedicated depression patients undergoing a 6-week escitalopram trial.
  • Assessed treatment outcomes using Hamilton Depression Rating Scale (HAMD) scores.

Main Results:

  • Treatment responders exhibited higher global average controllability and lower global modal controllability.
  • Responders showed greater regional average controllability and smaller regional modal controllability within the default mode network.
  • No significant differences in neuromodulation effects were found between responders and nonresponders.

Conclusions:

  • fBNC measures serve as potential novel biomarkers for predicting antidepressant response in depression.
  • Findings support the use of neuromodulation strategies for treating antidepressant nonresponders.
  • Early identification of treatment response can personalize therapy, reduce suffering, and lower costs.