Motion Insensitive Resting-State Analysis (MIRA)
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1595
Submission Type:
Late-Breaking Abstract Submission
Authors:
Pravesh Parekh1, Diliana Pecheva2, Alison Rigby3, Janosch Linkersdörfer2, Carolina Makowski4, Nadine Parker1, Hugh Garavan5, Terry Jernigan3, Wesley Thompson6, Donald Hagler Jr4, Thomas Nichols7, Ole Andreassen1, Anders Dale4
Institutions:
1University of Oslo, Oslo, Oslo, 2University of California, San Diego, San Diego, CA, 3University of California, San Diego, La Jolla, CA, 4University of California San Diego, San Diego, CA, 5University of Vermont College of Medicine, Burlington, VT, 6Laureate Institute for Brain Research, Tulsa, OK, 7University of Oxford, Oxford, Oxfordshire
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Eduardo Garza-Villarreal, M.D., Ph.D.
Universidad Nacional Autónoma de México campus Juriquilla
Juriquilla, Querétaro
Universidad Nacional Autónoma de México campus Juriquilla
Juriquilla, Querétaro
Ruby Kong
Computational Brain Imaging Group, Yong Loo Lin School of Medicine, National University of Singapor
Singapore, Singapore
Computational Brain Imaging Group, Yong Loo Lin School of Medicine, National University of Singapor
Singapore, Singapore
Introduction:
Head motion significantly impacts imaging-derived phenotypes (IDPs), particularly in resting-state functional MRI. A common approach to mitigate this is censoring high-motion frames using metrics like framewise displacement (FD, Power et al., 2012) and DVARS (Afyouni & Nichols, 2018; Smyser et al., 2010). However, this raises concerns about potential bias due to disproportionate censoring across participant groups (Peverill et al., 2025).
To address this, we introduce Motion Insensitive Resting-State Analysis (MIRA), a new method to quantify and correct motion-related bias in resting-state correlation matrices (commonly referred to as "functional connectivity"). Using data from the Adolescent Brain Cognitive Development℠ (ABCD) Study, processed using the standard pipeline (Hagler et al., 2019), we demonstrate MIRA's effectiveness in removing the association between motion and various outcomes, thereby reducing concerns about participant selection bias.
To address this, we introduce Motion Insensitive Resting-State Analysis (MIRA), a new method to quantify and correct motion-related bias in resting-state correlation matrices (commonly referred to as "functional connectivity"). Using data from the Adolescent Brain Cognitive Development℠ (ABCD) Study, processed using the standard pipeline (Hagler et al., 2019), we demonstrate MIRA's effectiveness in removing the association between motion and various outcomes, thereby reducing concerns about participant selection bias.
Methods:
MIRA involves computing the mean correlation matrices (582 regions of interest) for equally spaced percentile ranges (25 bins) of mean FD values across subjects and visits (1000 visits per bin). Then, we quantify the motion-related bias for each of these bins as the difference in the mean correlation matrix between each bin and the lowest motion bin (less than 4th percentile). Next, we perform a singular value decomposition on the matrix of vectorized percentile bin correlation matrix bias estimates. The first six principal components are then used to define the correlation matrix projection vectors, which collectively define the subspace associated with motion confounds of the estimated correlation matrices. The motion loadings are computed by projecting bias estimates of correlation matrices onto these components, thus providing estimates of motion contamination per subject and visit, based on the observed correlation matrices. These loadings (six per subject) can be used as confounders in a general linear model or to compute motion-denoised correlation matrices. Note that this is different from using the mean FD as a single covariate, as the mean FD collapses many different modes of motion, each with its own characteristic effect on correlation matrix contamination. MIRA estimates each of these modes of motion contamination, thereby more fully accounting for motion confounds.
Results:
We evaluated the effectiveness of MIRA using the ABCD Study release 6.0 resting-state correlation matrices. We found that the Motion-Related Bias (MRB) metric, defined as the root mean square difference between estimated correlation matrices for a given mean FD percentile range and the low-motion bin, showed a dramatic reduction across all FD threshold values (0.1 to 2.0 mm) with MIRA (Fig. 1).
Next, we examined the association maps between correlation matrices and various demographic and behavioral measures (Age, Child Opportunity Index, BMI, Area Disadvantage Index, NIH Toolbox Total Composite Score, and Sex), with standard processing and with MIRA processing. We find that the apparent motion confounds in the correlation maps substantially overlap with a large fraction of the uncorrected correlation maps (see Max Diff vs. Max Corr for each outcome measure in Fig. 2).
Next, we examined the association maps between correlation matrices and various demographic and behavioral measures (Age, Child Opportunity Index, BMI, Area Disadvantage Index, NIH Toolbox Total Composite Score, and Sex), with standard processing and with MIRA processing. We find that the apparent motion confounds in the correlation maps substantially overlap with a large fraction of the uncorrected correlation maps (see Max Diff vs. Max Corr for each outcome measure in Fig. 2).
Conclusions:
We have developed a method for quantifying and correcting motion related biases in resting-state functional MRI analyses. Our results show that there is substantial reduction in motion contamination in the association maps with measures like subject age and other variables previously shown to be confounded with motion. Thus, our novel method, MIRA, effectively controls and corrects for motion-related confounds in resting-state functional MRI analysis. It mitigates motion-related adverse effects, eliminates selection bias risk due to FD-scrubbing, and reduces the risk of discovering spurious associations in resting-state analyses.
Modeling and Analysis Methods:
Methods Development 2
Motion Correction and Preprocessing 1
Keywords:
FUNCTIONAL MRI
Statistical Methods
1|2Indicates the priority used for review
Abstract Information
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Hagler, D. J., Hatton, SeanN., Cornejo, M. D., Makowski, C., Fair, D. A., Dick, A. S., Sutherland, M. T., Casey, B. J., Barch, D. M., Harms, M. P., Watts, R., Bjork, J. M., Garavan, H. P., Hilmer, L., Pung, C. J., Sicat, C. S., Kuperman, J., Bartsch, H., Xue, F., … Dale, A. M. (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage, 202, 116091. https://doi.org/10.1016/j.neuroimage.2019.116091
Peverill, M., Russell, J. D., Keding, T. J., Rich, H. M., Halvorson, M. A., King, K. M., Birn, R. M., & Herringa, R. J. (2025). Balancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive DevelopmentSM (ABCD Study) Across Quality Control Pathways. Human Brain Mapping, 46(1), e70094. https://doi.org/10.1002/hbm.70094
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Smyser, C. D., Inder, T. E., Shimony, J. S., Hill, J. E., Degnan, A. J., Snyder, A. Z., & Neil, J. J. (2010). Longitudinal Analysis of Neural Network Development in Preterm Infants. Cerebral Cortex, 20(12), 2852–2862. https://doi.org/10.1093/cercor/bhq035