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Brain simulation techniques have demonstrated undisputable therapeutic effects on neural diseases. Invasive stimulation techniques like deep brain stimulation (DBS) and noninvasive techniques like transcranial magnetic stimulation (TMS) have been approved by FDA as treatments for many drug resist neural disorders and diseases. Developing noninvasive, deep, and targeted brain stimulation techniques is currently one of the important tasks in brain researches. Transcranial direct current stimulation (tDCS) and transcranial alternative current stimulation (tACS) techniques have the advantages of low cost and portability. However, neither of them can produce targeted stimulation due to lacking of electrical field focusing mechanism. Recently, Grossman et al. reported using the down beating signals of two tACS signals to accomplish focused stimulation. By sending two sine waves running at slightly different high frequencies (~2kHz), they demonstrated that they can modulate a “localized” neuron group at the difference frequency of the two sine waves and at the same time avoid excitation of neurons at other locations. As a result, equivalent focusing effect was accomplished by such beating mechanism. In this work, we show neither theoretically nor experimentally the beating mechanism can produce “focusing effect” and the beating signal spread globally across the full brain. The localized modulation effect likely happened right at the electrode contact sites when the electrode contact area is small and the current is concentrated. We conclude that to accomplish noninvasive and focused stimulation at current stage the only available tool is the focused TMS system we recently demonstrated.
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This content will become publicly available on October 4, 2025
Towards tDCS Digital Twins Using Deep Learning-Based Direct Estimation of Personalized Electrical Field Maps from T1-Weighted MRI
Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation method that applies neuromodulatory effects to the brain via low-intensity, direct current. It has shown possible positive effects in areas such as depression, substance use disorder, anxiety, and pain. Unfortunately, mixed trial results have delayed the field’s progress. Electrical current field approximation provides a way for tDCS researchers to estimate how an individual will respond to specific tDCS parameters. Publicly available physics-based stimulators have led to much progress; however, they can be error-prone, susceptible to quality issues (e.g., poor segmentation), and take multiple hours to run. Digital functional twins provide a method of estimating brain function in response to stimuli using computational methods. We seek to implement this idea for individualized tDCS. Hence, this work provides a proof-of-concept for generating electrical field maps for tDCS directly from T1-weighted magnetic resonance images (MRIs). Our deep learning method employs special loss regularizations to improve the model’s generalizability and calibration across individual scans and electrode montages. Users may enter a desired electrode montage in addition to the unique MRI for a custom output. Our dataset includes 442 unique individual heads from individuals across the adult lifespan. The pipeline can generate results on the scale of minutes, unlike physics-based systems that can take 1–3 hours. Overall, our methods will help streamline the process of individual current dose estimations for improved tDCS interventions. To support open science, the code that is associated with this paper is available at: https://github.com/lab-smile/tDCS-DT.
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- Award ID(s):
- 1908299
- PAR ID:
- 10579446
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-72069-7
- Page Range / eLocation ID:
- 465 to 475
- Subject(s) / Keyword(s):
- transcranial direct current stimulation (tDCS) deep learning digital twin
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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