Neuronavigation maximizes accuracy and precision in TMS positioning: evidence from 11,230 distance, angle, and electric field modeling measurements
Open accessAbstract
BACKGROUND: Researchers and clinicians have traditionally relied on elastic caps with markings to reposition the transcranial magnetic stimulation (TMS) coil between trains and sessions. Newer neuronavigation technology co-registers the patient's head and structural magnetic resonance imaging (MRI) scan, providing the researcher with real-time feedback about how to adjust the coil to be on-target. However, there has been no head to head comparison of accuracy and precision across treatment sessions. OBJECTIVE: /Hypothesis: In this two-part study, we compared elastic cap and neuronavigation targeting methodologies on distance, angle, and electric field (E-field) magnitude values. METHODS: In 42 participants receiving up to 50 total accelerated rTMS sessions in 5 days, we compared cap and neuronavigation targeting approaches in 3408 distance and 6816 angle measurements. In Experiment 1, TMS administrators saved an on-target neuronavigation location at Beam F3, which served as the landmark for all other measurements. Next, the operators placed the TMS coil based on cap markings or neuronavigation software to measure the distance and angle differences from the on-target sample. In Experiment 2, we saved each XYZ coordinate of the TMS coil from cap and neuronavigation targeting in 12 participants to compare the E-field magnitude differences at the cortical prefrontal target in 1106 cap and neuronavigation models. RESULTS: Cap targeting was significantly off-target for distance, placing the coil an average of 10.66 mm off-target (Standard error of the mean; SEM = 0.19 mm) compared to 0.3 mm (SEM = 0.03 mm) for neuronavigation (p < 0.0001). Cap targeting also significantly deviated for angles off-target, averaging 7.79 roll/pitch degrees (SEM = 1.07°) off-target and 5.99 yaw degrees (SEM = 0.12°) off-target; in comparison, neuronavigation targeting positioned the coil 0.34 roll/pitch degrees (SEM = 0.01°) and 0.22 yaw (SEM = 0.004°) off-target (both p < 0.0001). Further analyses revealed that there were significant inter-operator differences on distance and angle positioning for F3 (all p < 0.05), but not neuronavigation. Lastly, cap targeting resulted in significantly lower E-fields at the intended prefrontal cortical target, with equivalent E-fields as 110.7% motor threshold (MT; range = 58.3-127.4%) stimulation vs. 119.9% MT (range = 115-123.3%) from neuronavigated targeting with 120% MT stimulation applied (p < 0.001). CONCLUSIONS: Cap-based targeting is an inherent source of target variability compared to neuronavigation. Additionally, cap-based coil placement is more prone to differences across operators. Off-target coil placement secondary to cap-based measurements results in significantly lower amounts of stimulation reaching the cortical target, with some individuals receiving only 48.6% of the intended on-target E-field. Neuronavigation technology enables more precise and accurate TMS positioning, resulting in the intended stimulation intensities at the targeted cortical level.
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A Brainsight Neuronavigation system ensured accuracy, reliability, and reproducibility of coil placement both within and between sessions.
“A Brainsight Neuronavigation system (Version 2.4.8) ensured accuracy, reliability, and reproducibility of coil placement both within and between sessions [35].”
From A phase I trial of accelerated intermittent theta burst rTMS for amnestic MCI (2024) · cites this paper as [35]
The Results (Experiment 1A–B; page unavailable) report substantially smaller coil-placement deviations with neuronavigation than with cap targeting across sessions, supporting improved accuracy and consistency. The Methods (Neuronavigation procedure; page unavailable) identify the system as Brainsight Version 2.4.7, not the citing sentence’s Version 2.4.8. The paper supports improved placement rather than a guarantee that placement was accurate and reproducible within and between sessions. (LLM, conf=0.84)
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- Authors
- Caulfield, K. A., Fleischmann, H. H., Cox, C. E., Wolf, J. P., George, M. S., & McTeague, L. M.
- Affiliations
- Medical University of South Carolina (US)
- Corresponding author
- Caulfield, Kevin A.
- Venue
- Brain Stimulation
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 15
- Issue
- 5
- Pages
- 1192-1205
- ISSN
- 1876-4754 (ISSN-L)
- 1935-861X
- PMC
- PMC10026380
- OpenAlex
- W4293512447
- Cited by
- 145
- References
- 55
- Citations by year
- 2026: 38 · 2025: 55 · 2024: 39 · 2023: 13
- FWCI
- 9.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain stimulation (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Alliance for Research on Schizophrenia and Depression
- National Institutes of Health (F31 NRSA)
- National Institute of General Medical Sciences (P20GM109040)
- National Institute of Neurological Disorders and Stroke (F31NS126019)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD086844)
- Topics
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Physical Sciences › Engineering › Biomedical Engineering › Ultrasound and Hyperthermia Applications
- Keywords
- neuronavigation, transcranial magnetic stimulation, targeting accuracy, electric field modeling, inter-operator variability, prefrontal cortex stimulation
- MeSH terms
- Humans, Magnetic Resonance Imaging, Software, Prefrontal Cortex, Neuronavigation, Transcranial Magnetic Stimulation
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