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نمایش مقاله

Title:

fMRI-Informed TMS for Brain Regulation by Algorithm-based Individualized Neuromodulation for Depression

Subject:

Personalized Interventions and Diagnosis of Complex Neurological Disorders

Presenter Author:
Name:	Mehran Zare-Bidoky

Author:
Name 1:	Mehran Zare-Bidoky - Corresponding - Presenter
Affiliation 1:	Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran

Name 2:	Mahak Osouli
Affiliation 2:	Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran, Iran

Name 3:	Katayoun Niki-Maleki
Affiliation 3:	Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran

Name 4:	Mohammad-Bagher Soleimani
Affiliation 4:	Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran

Name 5:	Maryam Ardeshir
Affiliation 5:	Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran

Name 6:	Hamed Ekhtiari
Affiliation 6:	Department of Psychiatry, University of Texas Southwestern (UTSW), USA; Laureate Institute for Brain Research, USA

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Abstract

Original Research

introduction :

Personalized targeting in transcranial magnetic stimulation (TMS) aims to move beyond anatomy- based stimulation toward circuit-informed neuromodulation for major depressive disorder (MDD). Stanford Neuromodulation Therapy (SNT) represents a landmark approach, but important methodological details remain incompletely accessible, limiting transparency, reproducibility, and independent advancement. We introduce fMRI-informed TMS for Brain Regulation using Algorithm-based Individualized Neuromodulation for Depression, a transparent, publicly available framework for individualized TMS targeting and stimulation optimization.

Methods:

This study integrates advanced fMRI preprocessing, network-based target identification, and subject- specific electric-field modeling. Resting-state and task-based fMRI are processed using standardized pipelines optimized for individualized connectivity analyses. Depression-relevant regions are defined using atlas-based and data-driven approaches, while target selection follows explicit algorithmic criteria prioritizing individualized fronto-limbic connectivity profiles. Coil orientation and stimulation dose are optimized through subject-specific electric-field simulations to maximize engagement of the intended cortical target while minimizing off-target stimulation. The complete pipeline was implemented in a proof-of-concept study of five individuals with treatment-resistant MDD, followed by personalized TMS based on the resulting targets and stimulation parameters.

Results:

This study successfully generated individualized targets through transparent, reproducible preprocessing and decision procedures and enabled end-to-end implementation in a clinical research setting. Compared with publicly described elements of SNT, our method provides explicit target-selection criteria and incorporates electric-field–guided optimization of coil orientation and stimulation dose. Following personalized TMS, participants demonstrated clinical improvement in depressive symptoms, supporting the feasibility and potential therapeutic relevance of the framework.

Conclusion:

This study provides a transparent and scalable framework for individualized, circuit-informed TMS. Its reproducible methodology and integration of neuroimaging with electric-field modeling may facilitate independent validation and further optimization of personalized neuromodulation. Larger controlled studies are warranted to establish clinical efficacy and generalizability. This approach may make individualized neuromodulation more accessible to clinical researchers and provide a foundation for systematic comparison of targeting strategies across patients and disorders.

Keywords:

Personalized TMS, Major Depressive Disorder (MDD), fMRI-guided neuromodulation, Electrical Field Modeling