Evidence map›Paper›PMID 39775318›Full record

ArticleNeuroinformatics2025

Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation.

Gangliang Zhong, Fang Jin, Liang Ma, Yongfeng Yang, Baogui Zhang, Dan Cao, Jin Li, Nianming Zuo, Lingzhong Fan, Zhengyi Yang and 1 more

Abstract read
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In one paragraph

Article in Neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Gangliang Zhong *Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Fang Jin *Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Liang MaBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Yongfeng YangDepartment of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, China.
Baogui ZhangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Dan CaoBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Jin LiBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Nianming ZuoBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Lingzhong FanBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Zhengyi YangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Tianzi JiangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jiangtz@nlpr.ia.ac.cn.

Funding

Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project 2021ZD0200200
6 · The paper itself

Abstract

The position and orientation of transcranial magnetic stimulation (TMS) coil, which we collectively refer to as coil placement, significantly affect both the assessment and modulation of cortical excitability. TMS electric field (E-field) simulation can be used to identify optimal coil placement. However, the present E-field simulation required a laborious segmentation and meshing procedure to determine optimal coil placement. We intended to create a framework that would enable us to offer optimal coil placement without requiring the segmentation and meshing procedure. We constructed the stimulation effects map (SEM) framework using the CASIA dataset for optimal coil placement. We used leave-one-subject-out cross-validation to evaluate the consistency of the optimal coil placement and the target regions determined by SEM for the 74 target ROIs in MRI data from the CASIA, HCP15 and HCP100 datasets. Additionally, we contrasted the E-norms determined by optimal coil placements using SEM and auxiliary dipole method (ADM) based on the DP and CASIA II datasets. We provided optimal coil placement in 'head-anatomy-based' (HAC) polar coordinates and MNI coordinates for the target region. The results also demonstrated the consistency of the SEM framework for the 74 target ROIs. The normal E-field determined by SEM was more significant than the value received by ADM. We created the SEM framework using the CASIA database to determine optimal coil placement without segmentation or meshing. We provided optimal coil placement in HAC and MNI coordinates for the target region. The validation of several target ROIs from various datasets demonstrated the consistency of the SEM approach. By streamlining the process of finding optimal coil placement, we intended to make TMS assessment and therapy more convenient.

Indexed as

Brain MappingMagnetic Resonance ImagingTranscranial Magnetic StimulationAdultBrainFemaleHumansMaleCoil placementE-fieldStimulation effects mappingTranscranial magnetic stimulation

Identifiers

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Registered trials

None linked

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.