The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity
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Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability.
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The study tested the robustness of its findings by adjusting functional connectivity processing steps and using a different motion-censoring method in a supplementary analysis.
“we tested the robustness of our findings by adjusting the functional connectivity processing steps and employing a different method of motion censoring in a supplementary analysis (see Supplementary Methods), as suggested by prior reports (Hallquist et al., 2013, Power et al., 2014).”
From Developmental sex differences in resting state functional connectivity of amygdala sub-regions (2015) · cites this paper as Hallquist et al., 2013
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Global Network Organization of the Fetal Functional Connectome
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- Authors
- Hallquist, M. N., Hwang, K., & Luna, B.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Hallquist, Michael N.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-11
- Volume
- 82
- Pages
- 208-225
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3759585
- OpenAlex
- W2068375947
- Cited by
- 753
- References
- 55
- Citations by year
- 2026: 75 · 2025: 127 · 2024: 85 · 2023: 54 · 2022: 56 · 2021: 63 · 2020: 61 · 2019: 42 · 2018: 38 · 2017: 42 · 2016: 37 · 2015: 38 · 2014: 30 · 2013: 4
- FWCI
- 19.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH080243, K01MH097091, F32MH090629)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- resting-state fMRI, functional connectivity, bandpass filtering, head movement, cardiac artifacts, noise correction, data preprocessing
- MeSH terms
- Adolescent, Brain Mapping, Child, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Artifacts, Signal-To-Noise Ratio
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