Poster Z11 - Intrinsic neural oscillations influence individual response to transcranial magnetic stimulation

Saturday, October 26, 2024

10:00 AM - 11:00 AM EST

Location: Exhibit Hall E, F & G

Primary Investigator(s)

Jingna Jin, PhD

associate researcher
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, United States

Co-Author(s)

XZ

Xinyu Zhao

junior engineer
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, United States
JW

Jiankang Wu

junior engineer
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, United States
YL

Ying Li

Associate Researcher
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, United States
Xin Wang

Associate Researcher
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, United States
HW

He Wang

Associate Researcher
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, United States
Zhipeng Liu, PhD (she/her/hers)

Professor
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, United States

Presenting Author(s)

Tao Yin, MA

Professor
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, United States

Introduction: Transcranial magnetic stimulation (TMS) is a non-invasive method to modulate brain activity and has been widely used in the treatment of neuropsychiatric disorders. However, the promotion of TMS in clinical practice is significantly hampered by the fact that its effectiveness varies widely from person to person, resulting in variable clinical outcomes and even many individuals who do not respond to TMS.
By stimulating the primary motor cortex with TMS, the motor evoked potential (MEP) and rest motor threshold (RMT) of the individual can be recorded. RMT and MEP are commonly considered to reflect individual corticospinal excitability, and the response to TMS. Therefore, it is possible to understand the reasons of individual response to TMS, by investigating the inter-individual variable components of RMT and MEP. At present, the majority of studies analyzing the inter-individual difference of TMS effects were at the group level, and there are currently few studies examining this at the individual level by using the RMT and MEP amplitudes as a reflection of the individual response to TMS.
The fundamental characteristics of brain oscillations at rest are linked to human motor and cognitive activities. In light of the fact that resting electroencephalography (EEG) can detect the intrinsic oscillation activity of the brain in the resting state, we utilize RMT and MEP as indicators of individual response to TMS, study the inter-individual variation in TMS effects, and investigate the causes of inter-individual variability.

Materials and

Methods: Seventeen healthy subjects (mean age = 22.88, SD =1.32, female = 9) volunteered, and all subjects provided informed consent prior to inclusion in the study. First, we recorded 5 min resting-state EEG with eyes open. Then, the individual RMT was measured. Finally, single-pulse TMS over left primary motor cortex was performed and the MEP evoked by single-pulse TMS was recorded. the TMS intensity is 120% RMT, the interval between two pulses is 3-5 s and total 180 pulses.
The amplitude of MEP was detected in the 15-50 ms time window after each TMS output. EEG data was preprocessed in order to remove artifact including muscular, eye movements and so on. The fast Fourier transform was used to calculate the power spectral density (PSD) of delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), gamma1 (30-45 Hz), and gamma2 (55-100 Hz) bands. Finally, the global PSD is obtained by averaging all the channels.
Statistical analyses were performed using SPSS and MATLAB. The Shapiro–Wilk normality test was performed on all variables. For variables with normal distribution, Pearson's correlation was used to assess the correlation between MEP amplitude and RMT, and the correlation between MEP amplitudes and RMT and EEG PSD in rhythms. Otherwise, Spearman’s correlation analysis was applied. The significance level was set at 0.05 unless otherwise stated.

Results, Conclusions, and Discussions: First, since both RMT and MEP amplitudes reflect the degree of individual response to TMS, the correlation between RMT and MEP amplitudes was assessed, and the results are presented in Fig. 1. We find a significant negative correlation between RMT and MEP (r=0.5782, p=0.0151). Larger RMT leads to smaller MEP, vice versa.
Second, in order to investigate the effect of individual's intrinsic brain oscillatory PSD on their RMT, we calculated the correlation between RMT and global PSD in each rhythm, and the results are shown in Fig. 2. We found a negative correlation with global delta (r=-0.4593, p=0.0637) and theta PSD (r=-0.4374, p=0.0791), even if there were not significant. The correlation between RMT and global PSD in the total, alpha, beta, gamma1 and gamma2 are not statistically significant.
Finally, we also calculated the correlation between MEP amplitude and global PSD in each rhythm, and the results are presented in Fig. 3. MEP amplitude is positively correlated with global total (r=0.5537, p=0.0211), delta (r=0.5473, p=0.0230), theta (r=0.6076, p=0.0097), and beta (r=0.5894, p=0.0128) PSD, and the correlation is statistically significant. The correlation between MEP amplitude and alpha, gamma1, and gamma2 PSD is not statistically significant.
In conclusion, the inter-individual difference is closely related to the intrinsic oscillation features of the delta and theta rhythm, which might be the inherent parameters controlling the degree of an individual’s response to TMS. The findings of our study have important significance for understanding the causes of inter-individual differences in response to TMS.

Acknowledgements (Optional): the National Key Research and Development Program of China (2022YFC2402202, 2023YFC2412503)