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Related Concept Videos

Brain Imaging01:14

Brain Imaging

824
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...
824

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Network-Guided Transcranial Magnetic Stimulation for Depression.

Marc J Dubin1, Conor Liston2, Michael A Avissar3

  • 1Department of Psychiatry, Weill Cornell Medical College, New York, NY, 10065, USA; Feil Family Mind and Brain Institute, Weill Cornell Medical College, New York, NY, 10065, USA.

Current Behavioral Neuroscience Reports
|March 21, 2017
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Summary

This review identifies biomarkers for predicting antidepressant response to transcranial magnetic stimulation (TMS). It explores how TMS engages brain networks, aiming to optimize treatment effectiveness for depression.

Keywords:
BiomarkerDepressionGABANetworkNeuroimagingNeuronavigationTMStranscranial magnetic stimulation

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Transcranial magnetic stimulation (TMS) has garnered significant interest for its antidepressant effects.
  • Functional and neurochemical imaging studies show TMS engages key brain networks implicated in depression.
  • Evidence suggests TMS gradually normalizes network function over multiple treatment sessions, mediating antidepressant effects.

Purpose of the Study:

  • Identify predictive biomarkers for antidepressant response to TMS using neuroimaging literature.
  • Review the impact of TMS on brain networks involved in depression.
  • Discuss optimizing TMS antidepressant effects through enhanced network engagement understanding.

Main Methods:

  • Literature review of neuroimaging studies on TMS and depression.
  • Analysis of functional neuroimaging and neurochemical imaging data.
  • Exploration of network-based models of TMS action.

Main Results:

  • Candidate predictive biomarkers for TMS antidepressant response identified.
  • TMS engagement of depression-related brain networks confirmed.
  • A model of gradual network normalization by TMS presented.

Conclusions:

  • Optimizing TMS antidepressant effects may involve precise targeting of relevant brain networks.
  • Functional and neurochemical imaging offer methods for network-specific TMS targeting.
  • Further research can refine TMS protocols for enhanced clinical outcomes.