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Related Experiment Video

Updated: May 26, 2026

A Novel Scalp Acupuncture-based Method to Target the Hand Motor Hotspot for Non-invasive Brain Stimulation
07:07

A Novel Scalp Acupuncture-based Method to Target the Hand Motor Hotspot for Non-invasive Brain Stimulation

Published on: December 19, 2025

Network-based near-scalp personalized brain stimulation targets.

Ru Kong1,2,3,4, Aihuiping Xue1,2,3,4, Jingwen Cheng1,2,3,4

  • 1Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Imaging Neuroscience (Cambridge, Mass.)
|May 25, 2026
PubMed

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Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets.

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Convergent Network Localization of Brain Stimulation Targets for Trait Anxiety.

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Summary

This study introduces a new algorithm for personalized transcranial magnetic stimulation (TMS) targeting, optimizing functional connectivity and scalp proximity for better depression and anxiety treatment. The method demonstrates improved reliability and reduced stimulation intensity.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Functional connectivity (FC) is crucial for personalized transcranial magnetic stimulation (TMS) target identification.
  • Existing methods often neglect individual whole-cortex network variations and scalp proximity.
  • Optimizing target localization for patient comfort and efficacy is an ongoing challenge.

Purpose of the Study:

  • To develop a novel algorithm for simultaneous optimization of FC and scalp proximity in personalized TMS target localization.
  • To improve the generalizability and reliability of TMS targeting across diverse populations.
  • To compare the new algorithm's performance against existing methods in healthy individuals and patients with treatment-resistant depression.

Main Methods:

Keywords:
brain stimulationfunctional connectivityindividual brain networkspersonalized targetpsychiatric disorderstranscranial magnetic stimulation

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  • Utilized a multi-session hierarchical Bayesian model (MS-HBM) for high-quality, individual-specific cortical network estimation.
  • Employed a tree-based algorithm for optimal target selection, balancing FC and scalp proximity.
  • Validated the approach on test-retest datasets from healthy individuals (US, Singapore) and patients with treatment-resistant depression.

    • Main Results:

    • The tree-based MS-HBM reliably identified personalized TMS targets near the scalp for depression and anxiety.
    • Compared to cluster and cone algorithms, MS-HBM showed superior reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC).
    • In treatment-resistant depression, MS-HBM targets suggested potential reductions in stimulation intensity (15% and 5%) and exhibited optimal electric field characteristics.

    Conclusions:

    • Tree-based MS-HBM offers a robust and generalizable framework for estimating near-scalp personalized TMS targets.
    • The algorithm's ability to optimize both FC and scalp proximity enhances treatment efficacy and patient tolerance.
    • This approach holds promise for improving outcomes in psychiatric neuromodulation therapies.