Evidence map›Paper›PMID 40667307›Full record

ArticlebioRxiv : the preprint server for biology2025

On the need of individually optimizing temporal interference stimulation of human brains due to inter-individual variability.

Tapasi Brahma, Alexander Guillen, Jeffrey Moreno, Abhishek Datta, Yu Huang

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In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Tapasi BrahmaSoterix Medical Inc., Woodbridge, NJ 07095.
Alexander GuillenSoterix Medical Inc., Woodbridge, NJ 07095.
Jeffrey MorenoSoterix Medical Inc., Woodbridge, NJ 07095.
Abhishek DattaSoterix Medical Inc., Woodbridge, NJ 07095.ORCID 0000-0002-6778-8125
Yu HuangSoterix Medical Inc., Woodbridge, NJ 07095.ORCID 0000-0003-4178-0739

Funding

Virtual neuro-navigation system for personalized,community-based TMSR44MH126833 · NIMH · SOTERIX MEDICAL,INC. · PI TRUONG, DENNIS QUANGVINH · 2021 to 2023
$957k
NIMH NIH HHS R44 MH126833
6 · The paper itself

Abstract

Introduction: Transcranial temporal interference stimulation (TI, TIS, or tTIS), also known as interferential stimulation (IFS), is able to focally stimulate deep brain regions, provided it is properly optimized. We previously presented an algorithm for optimizing TI using two arrays of electrodes and showed that it can achieve more focal stimulation compared to optimized high-definition transcranial electrical stimulation (HD-TES) and conventional optimized TI using two pairs of electrodes, especially in the deep brain areas such as the hippocampus. However, those modeling studies were only performed on an averaged head (MNI152 template) and three individual heads without exploring inter-individual variability. Existing TI works in the literature mostly utilize a common (possibly optimized) montage of two pairs of electrodes on different individual heads without considering inter-individual variability. Material and method: Here we aim to study the inter-individual variability of optimized TI by applying the same optimization algorithms on N = 25 heads using their individualized head models. Specifically, we compared the focality achieved by different stimulation techniques at six different regions of interest (ROI; right hippocampus, left dorsolateral prefrontal cortex, left motor cortex, right amygdala, right caudate, and left thalamus) under both individually optimized and unoptimized montages. We also conducted numerical sensitivity analysis on the individual optimization and performed phantom recordings to test our models. Results: As expected, there is a variability in focality achieved by TI of up to 1.2 cm at the same ROI across subjects due to inter-individual differences in the head anatomy and tissue conductivity. We show that optimized TI using two arrays of electrodes achieves higher focality than that from optimized HD-TES at the same level of modulation intensity at 5 of the 6 ROIs. Compared to using a common montage either optimized from the MNI152 template or from the literature, individually optimized TI using two pairs of electrodes improves the focality by up to 4.4 cm, and by up to 1.1 cm if using two arrays of electrodes. Focality achieved by the individual optimization is sensitive to random changes and can vary up to 9.3 cm due to the non-lienarity of TI physics. Experimental recordings on a head phantom confirms the drop in TI stimulation strength when using unoptimized montages as predicted by our Conclusion: This work demonstrates the need of individually optimizing TI to target deep brain areas, and advocates against using a common head model and montage for TI modeling and experimental studies.

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PMID40667307
PMCPMC12262236

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.