Neural Noise

Precision TMS through neuroimaging and machine learning for personalized targets

3 min read

  • Precision TMS integrates neuroimaging and machine learning to optimize stimulation targets for personalized treatment (Frontiers).1
  • The approach supports personalized neuromodulation and transferable methods for stimulation dosing.1 1

Weekly enrichment (2026-07-20)

  • The source is a 2025 Frontiers in Human Neuroscience review (Bi et al., doi:10.3389/fnhum.2025.1682852) arguing that conventional TMS targeting via the “5-cm rule” or EMG-based motor-evoked-potential hotspots ignores inter-individual differences in cortical morphology, functional connectivity, and white-matter pathways.2
  • The established personalization gold standard, from Fox et al. (2012), uses resting-state fMRI to target the dorsolateral prefrontal cortex (DLPFC) subregion most functionally anticorrelated with the subgenual anterior cingulate cortex (sgACC) to improve antidepressant response.2
  • The review cites Stanford Neuromodulation Therapy (SNT, formerly SAINT), which pairs fMRI-guided sgACC-anticorrelated targeting with accelerated high-dose intermittent theta-burst stimulation over 5 days; a double-blind RCT reported remission rates near 80% in treatment-resistant depression.2 3
  • The review reports that fMRI-optimized targeting improved TMS treatment response by over 30% versus conventional targeting (citing Cash et al., 2021), and that a deep-learning model fusing EEG, fMRI, and clinical data reached an area under the ROC curve (AUC) of 0.87 for predicting response (Kale et al., 2024).2
  • The review frames diffusion tensor imaging (DTI) as complementary to fMRI for validating white-matter tracts, with DTI-guided targeting yielding higher response rates than anatomical methods in depression and better site selection in Parkinson’s disease.2
  • A separate 2025 Human Brain Mapping study (Lynch et al., doi:10.1002/hbm.70266) modeled the TMS electric field with precision functional networks and found that homotopic scalp positions (left F3, right F4) engage different networks across individuals, and that DLPFC sites anticorrelated with sgACC most often target ventral-striatum (reward) circuitry but miss it in some people.4
  • The Human Brain Mapping work showed precision E-field targeting is feasible in major depressive disorder patients from a single low-burden MRI session, supporting translation where patient burden and robustness matter.4
  • A 2025 Brain Stimulation multisite study (doi:10.1016/j.brs.2025.04.003) of the Beam/F3 scalp method in the VA B-SMART-fMRI trial used personalized E-field models and acquired MRI before treatment, after 5 sessions, and after all 30 sessions, finding network effects emerge early and that Beam/F3 can engage sgACC-related functional mechanisms.5
  • The review flags open challenges for AI-driven TMS: small single-center training datasets, poor multi-center generalizability, “black box” deep-learning interpretability, and neuroimaging data-privacy/ethics constraints; exact multi-center validation metrics are not reported.2

Footnotes

  1. https://news.google.com/rss/articles/CBMinAFBVV95cUxPUVRWTTNhdHZUMGcxdVhTSkNqMlY5U09iLTItSllOdXpVUHZIdkNmbUhfR2Q1Y1VLRXZ2TlhadzRYd3hrUS1LQ09mak9sTEVNbUYzNUxWeS1rXzJudUgxaXRRRnBNbFhjTHFfVERiN0RHMzE4M1MtT25MRkxRRnFBdG5hODZ1QzZfbW14Z1p3b2hLeTV0T1ZFMXQ0Z04?oc=5 ↩ ↩2 ↩3

  2. https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1682852/full ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  3. https://doi.org/10.1176/appi.ajp.2019.19070720 ↩

  4. https://doi.org/10.1002/hbm.70266 ↩ ↩2

  5. https://doi.org/10.1016/j.brs.2025.04.003 ↩

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