Abstract
Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Between and within individuals, microstates can differ both in terms of characteristic topographies and temporal dynamics, which leads to analytic challenges as the measurement of microstate dynamics is dependent on assumptions about their topographies. Here we focus on the analysis of group differences, using simulations seeded on real data from healthy control subjects to compare approaches that derive separate sets of maps within subgroups versus a single set of maps applied uniformly to the entire dataset. In the absence of true group differences in either microstate maps or temporal metrics, we found that using separate subgroup maps resulted in substantially inflated type I error rates. On the other hand, when groups truly differed in their microstate maps, analyses based on a single set of maps confounded topographic effects with differences in other derived metrics. We propose an approach to alleviate both classes of bias, based on a paired analysis of all subgroup maps. We illustrate the qualitative and quantitative impact of these issues in real data by comparing waking versus non-rapid eye movement sleep microstates. Overall, our results suggest that even subtle chance differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.
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Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgments
GRINS Consortium members: Clinical Research Team—Jun Wang, Chenguang Jiang, Guanchen Gai, Kai Zou, Zhe Wang, Xiaoman Yu, Guoqiang Wang, Shuping Tan, Michael Murphy, Mei Hua Hall, Wei Zhu, Zhenhe Zhou. Molecular Genetics— Lu Shen, Shenying Qin. Hailiang Huang. Electrophysiology data analyses—Nataliia Kozhemiako, Lei A Wang, Yining Wang, Lin Zhou, Shen Li, Jun Wang, Robert Law, Minitrios Mylonas, Michael Murphy, Robert Stickgold, Dara Manoach, Mei-Hua Hall, Jen Q. Pan, Shaun M. Purcell. Project management—Zhenglin Guo, Sinead Chapman, Hailiang Huang, Jun Wang, Chenaugnag Jiang, Zhenhe Zhou, Jen Q. Pan. Principal Investigators—Mei Hua Hall, Hailiang Huang, Dara Manoach, Jen Q. Pan, Shaun M. Purcell, Zhenhe Zhou.
Funding
This work was funded by the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and MIT (Jen Q. Pan, Shaun M. Purcell, the GRINS consortium). Additional funding was provided by the National Institute of Mental Health (K23MH11865 to Michael Murphy, R01 MH115045-01 and R01MH118298 to Jen Q. Pan, and R01MH092638 and UG3 MH125273 to other GRINS consortium members), the National Institute of Neurological Disorders and Stroke (NS108874 to Jen Q Pan), the National Heart, Lung, and Blood Institute (R01HL146339 to Shaun M. Purcell), the National Institute on Minority Health and Health Disparities (R21 MD012738 to Shaun M. Purcell), and the Top Talent Support Program for Young and Middle-aged People of Wuxi Health Committee (HB2020077 to Jun Wang). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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MM and SP wrote the main manuscript text and prepared the figures. SP, LAW, NK, YW, and MM analyzed the data. JP edited the manuscript. All authors reviewed the manuscript.
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Murphy, M., Wang, J., Jiang, C. et al. A Potential Source of Bias in Group-Level EEG Microstate Analysis. Brain Topogr 37, 232–242 (2024). https://doi.org/10.1007/s10548-023-00992-7
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DOI: https://doi.org/10.1007/s10548-023-00992-7