Predicting response to repetitive transcranial magnetic stimulation in patients with chronic insomnia disorder using electroencephalography: A pilot study

Zhu，Lin1; Pei，Zian2; Dang，Ge1; Shi，Xue1; Su，Xiaolin1; Lan，Xiaoyong2; Lian，Chongyuan2; Yan，Nan3,4; Guo，Yi1,2

2024

10.1016/j.brainresbull.2023.110851

Brain Research Bulletin   影响因子和分区

0361-9230

1873-2747

Predicting responsvienss to repetitive transcranial magnetic stimulation (rTMS) can facilitate personalized treatments with improved efficacy; however, predictive features related to this response are still lacking. We explored whether resting-state electroencephalography (rsEEG) functional connectivity measured at baseline or during treatment could predict the response to 10-day rTMS targeted to the right dorsolateral prefrontal cortex (DLPFC) in 36 patients with chronic insomnia disorder (CID). Pre- and post-treatment rsEEG scans and the Pittsburgh Sleep Quality Index (PSQI) were evaluated, with an additional rsEEG scan conducted after four rTMS sessions. Machine-learning approaches were employed to assess the ability of each connectivity measure to distinguish between responders (PSQI improvement > 25%) and non-responders (PSQI improvement ≤ 25%). Furthermore, we analyzed the connectivity trends of the two subgroups throughout the treatment. Our results revealed that the machine learning model based on baseline theta connectivity achieved the highest accuracy (AUC = 0.843) in predicting treatment response. Decreased baseline connectivity at the stimulated site was associated with higher responsiveness to TMS, emphasizing the significance of functional connectivity characteristics in rTMS treatment. These findings enhance the clinical application of EEG functional connectivity markers in predicting treatment outcomes.

Chronic insomnia disorder (CID) Functional connectivity Repetitive transcranial magnetic stimulation (rTMS) Response prediction

SCI

英语

第一 ; 通讯

NEUROSCIENCE & BEHAVIOR

2-s2.0-85182264473

Scopus

1.Department of Neurology,Shenzhen People's Hospital,The Second Clinical Medical College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,518020,China
2.Shenzhen Bay Laboratory,Shenzhen,Guangdong,518020,China
3.CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
4.Shenzhen College of Advanced Technology,University of Chinese Academy of Sciences,Shenzhen,518055,China

Zhu，Lin,Pei，Zian,Dang，Ge,et al. Predicting response to repetitive transcranial magnetic stimulation in patients with chronic insomnia disorder using electroencephalography: A pilot study[J]. Brain Research Bulletin,2024,206.

Zhu，Lin.,Pei，Zian.,Dang，Ge.,Shi，Xue.,Su，Xiaolin.,...&Guo，Yi.(2024).Predicting response to repetitive transcranial magnetic stimulation in patients with chronic insomnia disorder using electroencephalography: A pilot study.Brain Research Bulletin,206.

Zhu，Lin,et al."Predicting response to repetitive transcranial magnetic stimulation in patients with chronic insomnia disorder using electroencephalography: A pilot study".Brain Research Bulletin 206(2024).