Temporal Variability of Scalp EEG as a Predictor for Long-term Efficacy of rTMS Treatment in ASD

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Abstract Submission 

Runyang He¹, Dezhong Yao², Fali Li³, Lin Jiang¹, Peng Xu³

¹University of Electronic Science and Technology of China, Chengdu, Sichuan, ²The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, China, ³School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, Sichuan

Runyang He  
University of Electronic Science and Technology of China
Chengdu, Sichuan

Dezhong Yao  
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation
Chengdu, China

Fali Li  
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, Sichuan

Lin Jiang  
University of Electronic Science and Technology of China
Chengdu, Sichuan

Peng Xu  
School of life Science and technology, University of Electronic Science and Technology of China
Chengdu, Sichuan

Repetitive transcranial magnetic stimulation (rTMS) emerges as a useful therapy for autism spectrum disorder (ASD) clinically. Whereas the neural mechanisms of rTMS therapy on ASD are not fully understood, and no biomarkers until now are available to reliably predict the follow-up rTMS efficacy in clinical practice. Advancements in the analysis and processing of electroencephalogram (EEG) signals have transformed EEG into a convenient, accurate, and highly sensitive research tool for exploring the underlying mechanisms and identifying the relevant biomarkers. In the current work, we investigated the rhythmic fluctuating modes of resting-state networks over time to reveal the evidence accounting for the clinical improvement induced by rTMS for ASD patients. Afterwards, the potential relationships between the fluctuating properties and clinical scales were investigated, from which robust biomarkers were identified and models were further established to predict the longterm treatment response of rTMS intervention.

The time-resolved resting-state network within the delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands were first constructed for both ASDs and TDs, respectively. Specifically, using a 20 s sliding-window approach with an overlapping of 98%, we constructed the time-varying networks with a 400 ms temporal resolution. Thereinto, for each segment, the phase-locking value (PLV) that can estimate the inter-regional phase synchronization was adopted to assess the synchronized strengths (Li, et al 2015). Brain temporal variability was investigated in the resting-state EEG of ASD patients, and the nonlinear complexity of related time-varying networks was accordingly evaluated by fuzzy entropy. Based on related temporal variability indexes, a stepwise multiple linear regression model was established to predict the clinical scores at the four weeks after the rTMS treatment of ASD patients.

Here, Fig.1(a) presents the distinct topologies underlying network variability between pretreatment ASD patients and TDs within four concerned bands revealed by two-way repeated ANOVAs, where the green solid line presents stronger connectivity of ASDs than that of TDs. Specifically, in comparison with TDs, the stronger long-range variability connectivity spanning the distributed frontal and posterior lobes was found for pre-treatment ASDs, especially within the theta and alpha bands. Of note, in contrast to pre-treatment ASDs, after three week rTMS treatments, patients experienced reduced temporal variability in the longrange frontal-parietal and frontal-occipital linkages within the theta and alpha bands (Fig.1(b)). In short, Fig.1 demonstrated the hyper-variability in the resting-state networks of ASD patients, while three-week rTMS treatment alleviates the hyper fluctuations occurring in the frontal-parietal and frontal-occipital connectivity and further contributes to the ameliorative ASD symptoms. In addition, the changes in variability network properties are significantly correlated with clinical scores, which further serve as potential predictors to track the long-term rTMS efficacy for ASD. As proved, the predicted and actual clinical scores of ASD patients at the follow-up stage were found to be significantly correlated in Fig.2, signifying a satisfactory prediction performance.

The findings validated that the temporal variability of time-varying networks of ASD patients could be modulated by rTMS, and related variability properties also help predict follow-up rTMS efficacy, which provides the potential for formulating individualized treatment strategies for ASD.

TMS ²

Neurodevelopmental/ Early Life (eg. ADHD, autism) ¹

EEG

Autism

Electroencephaolography (EEG)

Transcranial Magnetic Stimulation (TMS)

Treatment