Excessive censoring degrades individual-specific cortical parcellations and personalized TMS targets
14 affiliations
- 1Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- 2Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- 3Department of Medicine, Healthy Longevity Translational Research Programme, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- 4N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- 5Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- 6Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- 7School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, Queensland, Australia
- 8Department of Psychiatry, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, United States
- 9Center for Brain Circuit Therapeutics, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, United States
- 10Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- 11Centre for Integrative Neuroimaging (OxCIN), FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- 12Department of Statistics, Indiana University, Bloomington, IN, United States
- 13Department of Psychological Medicine, National University Hospital, Singapore, Singapore
- 14Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
Abstract
Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized “ground-truth” references from ≥1 hour of low-motion data per participant. We then simulate 10-minute or 20-minute rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 minutes under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to—or even better than—those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.
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BibTeX
@article{tan2026excessive,
title = {{Excessive censoring degrades individual-specific cortical parcellations and personalized TMS targets}},
author = {Trevor Wei Kiat Tan and Ru Kong and Aihuiping Xue and Jingwen Cheng and Bjorn Burgher and Luca Cocchi and Shan H. Siddiqi and Thomas E. Nichols and Amanda F. Mejia and Phern-Chern Tor and B.T. Thomas Yeo},
journal = {Imaging Neuroscience},
year = {2026},
month = nov,
doi = {10.1162/imag.a.1422},
url = {https://doi.org/10.1162/imag.a.1422},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Excessive censoring degrades individual-specific cortical parcellations and personalized TMS targets
AU - Trevor Wei Kiat Tan
AU - Ru Kong
AU - Aihuiping Xue
AU - Jingwen Cheng
AU - Bjorn Burgher
AU - Luca Cocchi
AU - Shan H. Siddiqi
AU - Thomas E. Nichols
AU - Amanda F. Mejia
AU - Phern-Chern Tor
AU - B.T. Thomas Yeo
PY - 2026
DA - 2026/11/02
JO - Imaging Neuroscience
DO - 10.1162/imag.a.1422
UR - https://doi.org/10.1162/imag.a.1422
AB - Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized “ground-truth” references from ≥1 hour of low-motion data per participant. We then simulate 10-minute or 20-minute rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 minutes under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to—or even better than—those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.
SN - 2837-6056
ER -