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

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

doi:10.1016/j.neuroimage.2025.121677

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

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

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

NeuroImage (Orlando, Fla.), Vol.325, 121677

01/2026

DOI: 10.1016/j.neuroimage.2025.121677

PMCID: PMC12817620

PMID: 41448514

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Abstract

•We presented a practical preprocessing pipeline for single pulse TMS-iEEG data, incorporating key steps of re-referencing, filtering, artifact interpolation, and detrending.•The pipeline effectively attenuates multiple types of artifacts and noise, enabling accurate characterization of evoked neural responses.•Methodological alternatives for each preprocessing step were evaluated using real and/or simulated datasets.•Re-referencing substantially affects the morphology and amplitude of intracranial TMS-evoked potentials and requires careful consideration.•A segment-based filtering strategy is recommended to better minimize distortion from TMS-related artifacts. 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.

artifacts removal

Concurrent TMS-iEEG

detrending

filtering

re-referencing

signal preprocessing

TMS-evoked potentials

Details

Title: Subtitle: A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations
Creators: Zhuoran Li - University of Iowa
Xianqing Liu - University of Iowa
Joshua Tatz - University of Iowa
Eric W. Tsang - University of Nebraska Medical Center
Umair Hassan - Stanford Medicine
Jeffrey B Wang - Johns Hopkins Hospital
Corey J. Keller - Stanford Medicine
Nicholas T. Trapp - University of Iowa
Aaron D. Boes - University of Iowa
Jing Jiang - University of Iowa Stead Family Department of Pediatrics, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: NeuroImage (Orlando, Fla.), Vol.325, 121677
DOI: 10.1016/j.neuroimage.2025.121677
PMID: 41448514
PMCID: PMC12817620
NLM abbreviation: Neuroimage
ISSN: 1053-8119
eISSN: 1095-9572
Publisher: Elsevier Inc
Grant note: National Institutes of Health: R01MH136197, R01MH126639, R01MH129018, R21MH120441, R01NS114405 Brain and Behavior Research Foundation Young Investigator: 29441 Burroughs Welcome Fund Career Award for Medical ScientistsNational Institute of Mental Health: 1K23MH125145 Brain and Behavior Research Foundation: 31275, 32270 Roy J. Carver TrustNIMH: R01MH132074, R01MH139650 MRI instrument: 1S10OD025025-01
JJ is supported by the National Institutes of Health (R01MH136197) and the Brain and Behavior Research Foundation Young Investigator grant (29441). CJK is supported by grants from the National Institutes of Health (R01MH126639, R01MH129018), and Burroughs Welcome Fund Career Award for Medical Scientists. NTT is supported by grants from the National Institute of Mental Health (1K23MH125145), and the Brain and Behavior Research Foundation (31275 and 32270). ADB is supported by the National Institutes of Health (R21MH120441, R01NS114405) and Roy J. Carver Trust. Other funding includes NIMH R01MH132074 and R01MH139650 (CJK, NTT, ADB). This work was conducted, in part, on an MRI instrument funded by 1S10OD025025-01.
Language: English
Electronic publication date: 12/23/2025
Date published: 01/2026
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Neurology; Psychiatry; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Neurology (Pediatrics); Health, Sport, and Human Physiology
Record Identifier: 9985093888102771

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