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

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

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/

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/

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/

🚀 Exciting week at #BrainStimConf in Japan 🇯🇵!

@coreykeller.bsky.social @juhagogulski.bsky.social & @chrisclineneuro.bsky.social are presenting on #TMSEEG, plasticity & precision neuromodulation 🧠⚡

See the full lineup below! 📸✨ @stanfordmedicine.bsky.social #neuroscience

🧠 We are #hiring! Postdoc @stanfordmedicine.bsky.social: help us develop closed-loop EEG platforms to improve TMS treatment for depression! Coding and neuroscience / engineering backgrounds required. Join our team! precisionneuro.stanford.edu email: [email protected]

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.

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.

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

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

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.

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

We're excited to share NaviNIBS, our new open-source tool 🔨 for neuronavigated noninvasive #brainstim 🧠⚡️

🎉Congrats to our co-director Chris Cline, lead developer of NaviNIBS!

We hope to enable new possibilities for innovations in neuronavigation #TMSEEG @stanfordmedicine.bsky.social (1/5)

Next, TMS + music! 🎶🧠

We show that timing ⏱️ TMS with our musical sounds increased 📈 the size of motor-evoked potentials (MEPs) compared to standard single pulse TMS and an auditory control condition (same sounds, different timing). (5/10)

First, we recorded #EEG brain activity during passive listening to musical rhythms 🎶 to confirm that our sounds induce relevant brain 🧠 changes. Listening to the sounds desychronized inhibitory sensorimotor brain rhythms (mu oscillations) just before each rhythmic event. (4/10)

Here we introduce Sensory Entrained TMS #seTMS that uses musical rhythms 🎵 paired with TMS ⚡️ to enhance the corticomotor response (3/10)