A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations

Zhuoran Li; Xianqing Liu; Joshua Tatz; Umair Hassan; Jeffrey B. Wang; Corey J. Keller; Nicholas T. Trapp; Aaron D. Boes; Jing Jiang

doi:10.1101/2025.08.13.670238

Back

A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations

Preprint

Open access

A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations

Zhuoran Li, Xianqing Liu, Joshua Tatz, Umair Hassan, Jeffrey B. Wang, Corey J. Keller, Nicholas T. Trapp, Aaron D. Boes and Jing Jiang

bioRxiv

Cold Spring Harbor Laboratory

08/18/2025

DOI: 10.1101/2025.08.13.670238

PMCID: PMC12393282

PMID: 40894703

Files and links (1)

url

https://doi.org/10.1101/2025.08.13.670238View

Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Transcranial magnetic stimulation combined with intracranial EEG (TMS-iEEG) has emerged as a powerful approach for probing the causal organization and dynamics of the human brain. Despite its promise, the presence of TMS-induced artifacts poses significant challenges for accurately characterizing and interpreting evoked neural responses. In this study, we present a practical preprocessing pipeline for single pulse TMS-iEEG data, incorporating key steps of re-referencing, filtering, artifact interpolation, and detrending. Using both real and simulated data, we systematically evaluated the effects of each step and compared alternative methodological choices. Our results demonstrate that this pipeline effectively attenuated various types of artifacts and noise, yielding cleaner signals for the subsequent analysis of intracranial TMS-evoked potentials (iTEPs). Moreover, we showed that methodological choices can substantially influence iTEPs outcomes. In particular, referencing methods might strongly affect iTEP morphology and amplitude, underscoring the importance of tailoring the referencing strategy to specific signal characteristics and research objectives. For filtering, we recommend a segment-based strategy, i.e., applying filters to data segments excluding the artifact window, to minimize distortion from abrupt TMS-related transients. Overall, this work represents an important step toward establishing a general preprocessing framework for TMS-iEEG data. We hope it encourages broader adoption and methodological development in concurrent TMS-iEEG research, ultimately advancing our understanding of brain organization and TMS mechanisms.

Details

Title: Subtitle: A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations
Creators: Zhuoran Li - University of Iowa, Stead Family Department of Pediatrics
Xianqing Liu - University of Iowa
Joshua Tatz - University of Iowa
Umair Hassan - Stanford University
Jeffrey B. Wang - Johns Hopkins Hospital
Corey J. Keller - Stanford University
Nicholas T. Trapp - University of Iowa
Aaron D. Boes - University of Iowa
Jing Jiang - University of Iowa, Neurology (Pediatrics)
Resource Type: Preprint
Publication Details: bioRxiv
DOI: 10.1101/2025.08.13.670238
PMID: 40894703
PMCID: PMC12393282
eISSN: 2692-8205
Publisher: Cold Spring Harbor Laboratory
Language: English
Date posted: 08/18/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Neurology; Psychiatry; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Neurology (Pediatrics)
Record Identifier: 9984949264902771

Metrics

8 Record Views