stanfordpntlab.bsky.social
Deconstructing brain stimulation tools to build personalized treatments for mental health disorders.
precisionneuro.stanford.edu
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And to the rest of the amazing team and resources! 7/7
@coreykeller.bsky.social
@juhagogulski.bsky.social
@jessicamross8.bsky.social
@stanfordpntlab.bsky.social
@stanfordmedicine.bsky.social
Manjima Sarkar, Jade Truong, Lily Forman!!
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Big shout out to my co-first author 👏
@chrisclineneuro.bsky.social
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Interested in learning more? Read the full paper in Clinical Neurophysiology! doi.org/10.1016/j.cl... 5/7
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Key findings: Optimization reduced artifacts by 63% and increased early local TMS-evoked potentials (EL-TEPs), a measure of prefrontal excitability, by 75%! #EEG #TMS #TMSEEG 4/7
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We introduce a novel method for optimizing TMS parameters in the dlPFC. Based on EEG responses, this closed-loop procedure optimizes TMS coil angle, location, and intensity in real time. 3/7
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Why is this important? The dlPFC is a depression treatment target, but we need clinic-ready ways to measure stimulation effects. TMS-EEG can help, but artifacts obscure responses. 2/7
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This builds on influential work by
@foxmdphd.bsky.social
@shansiddiqi.bsky.social
@desmondoathes.bsky.social and many others. It would not have been possible without the support of UIowa, Stanford, and generous funding from the NIH/NIMH.
reposted from @esolomon.bsky.social
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It’s important to be clear that we only had two neurosurgical patients to test these effects, so these findings need replication. But these data so far seem to align with a major hypothesis in the field: DLPFC TMS specifically alters population-level neural activity in the sgACC. 5/
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We were surprised to find that, despite anticorrelated HFA signals, TMS seemed to increase phase-locking in lower frequencies (alpha and theta) between sgACC-DLPFC. Could this be a mechanism by which the DLPFC influences sgACC activity? 4/
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HFA signal in the sgACC was inversely correlated with HFA signal in the DLPFC, meaning that as DLPFC activity increased following TMS, sgACC activity decreased. This mirrors what we’ve known from fMRI for a long time, but now shown with direct in-vivo measures of neural activity. 3/
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Stimulation to the DLPFC may tamp down an overactive sgACC and relieve symptoms of depression. By stimulating the DLPFC with TMS in two neurosurgical patients with electrodes implanted within the sgACC, we found a reduction in high-frequency neural activity – a correlate of population spiking. 2/
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9/9 @juhagogulski.bsky.social @coreykeller.bsky.social @stanfordpntlab.bsky.social @jessicamross8.bsky.social @sparmi.bsky.social @stanfordmedicine.bsky.social @stanfordpress.bsky.social @nihr.bsky.social Truong J., Sarkar M.
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8/9 🔑 Key takeaways:
• Careful target selection boosts early TMS-evoked signals in dlPFC
• Most dlPFC areas can produce reliable TMS-evoked signals with optimized analysis
This work advances early TMS-evoked EEG signal as a potential biomarker for depression! #depression
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7/9 ⚡️ Surprisingly, we found that as few as 25 TMS pulses could produce reliable responses from the medial dlPFC target! This is much lower than previously thought and could allow for faster measurements.
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6/9 💪 We found that reliability was improved by using:
• Peak-to-peak amplitude
• Later time windows (20-60 ms or 30-60ms)
• Sensor-space (vs. source-space) analysis
ROI size didn't impact reliability much.
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5/9 🔍 We then examined how reliable these early TMS-evoked responses are. The most medial dlPFC target was most reliable, while the most anterior was least reliable. But with optimized analysis, most targets could produce reliable signals. #reliability
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4/9 📈 The "best" dlPFC target produced responses over 2x larger than standard clinical targets! This suggests we may be able to boost the early TMS-EEG signal by carefully selecting the stimulation location. #optimization
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3/9 📊 We found posterior & medial targets produced larger responses with less muscle artifact compared to anterior & lateral targets. This suggests some areas of the dlPFC may be better suited for measuring brain excitability with TMS.
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2/9 🎯 In the first study, we mapped early TMS-evoked brain responses across 6 targets in the dlPFC. This allowed us to compare how different areas of the dlPFC respond to TMS. #EEG #TMS
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10/10 @juhagogulski.bsky.social @coreykeller.bsky.social @jessicamross8.bsky.social @sparmi.bsky.social Truong J. Sarkar M. @stanfordmedicine.bsky.social @helsinki.fi @helsinkiupress.bsky.social Aaltosen E., Laaketieteen, National Institute of Mental Health (NIMH)
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9/10 🔑 Key takeaways:
• Careful target selection boosts early TMS-evoked signals in dlPFC
• Most dlPFC areas can produce reliable TMS-evoked signals with optimized analysis
This work advances early TMS-evoked EEG signal as a potential biomarker for depression! #depression
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8/10 ⚡️ Surprisingly, we found that as few as 25 TMS pulses could produce reliable responses from the medial dlPFC target! This is much lower than previously thought and could allow for faster measurements.
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7/10 💪 We found that reliability was improved by using:
• Peak-to-peak amplitude
• Later time windows (20-60 ms or 30-60ms)
• Sensor-space (vs. source-space) analysis
ROI size didn't impact reliability much.
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6/10 🔍 We then examined how reliable these early TMS-evoked responses are. The most medial dlPFC target was most reliable, while the most anterior was least reliable. But with optimized analysis, most targets could produce reliable signals. #reliability
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5/10 📈 The "best" dlPFC target produced responses over 2x larger than standard clinical targets! This suggests we may be able to boost the early TMS-EEG signal by carefully selecting the stimulation location. #optimization
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4/10 📊 We found posterior & medial targets produced larger responses with less muscle artifact compared to anterior & lateral targets. This suggests some areas of the dlPFC may be better suited for measuring brain excitability with TMS.
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3/10 🎯 In the first study, we mapped early TMS-evoked brain responses across 6 targets in the dlPFC. This allowed us to compare how different areas of the dlPFC respond to TMS. #EEG #TMS
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2/10 📢 Exciting new research from
@JuhaGogulski et al! 🧠 We mapped signal quality and reliability of brain excitability across different parts of the dorsolateral prefrontal cortex (dlPFC). First study: bit.ly/3xCgCHU Second study: bit.ly/3PW8RCx
@stanfordmedicine.bsky.social
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If you would like to test out #NaviNIBS or have any other questions, please message Chris on Bluesky or via email! (5/5) bsky.app/profile/chr...
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NaviNIBS is a comprehensive neuronavigation toolbox that we’re already using in multiple #TMSEEG studies! Some key improvements over existing tools include faster and more precise head re-registration procedures, advanced robotic positioning & open-source extensibility 🫨 (4/5)
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Documentation and links to source code 🧑💻👩💻 can be found on Github
🔗 precisionneurolab.github.io/navinibs-docs/ (3/5)
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🚨 Preprint describing NaviNIBS is up on bioRxiv!
🔗 www.biorxiv.org/content/10.... (2/5)
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It takes a village!
@coreykeller.bsky.social @chrisclineneuro.bsky.social @juhagogulski.bsky.social @neuro-engineer.bsky.social @sparmi.bsky.social @stanfordmedicine.bsky.social L. Forman, J. Truong, T. Fujioka, A. Pascual-Leone, W. Hartford, N.F. Chen