• Study
  • Data Usage
    • Access & download data
    • Responsible use
    • Acknowledgment
  • Documentation
    • Curation & structure
    • Non-imaging
    • Imaging
    • Substudies
    • Release notes
  • Tools
    • Data tools
    • R Packages
  • Info
    • FAQs
    • Report issues
    • Changelog
    • Cite this website
  • Version
    • empty
  1. Imaging data
  2. Scan types
  3. Documentation
  4. Imaging
  5. Resting-state fMRI
  • Curation & structure
    • Data structure
    • Curation standards
    • Naming convention
    • Metadata
  • Non-imaging data
    • ABCD (General)
    • Friends, Family, & Community
    • Genetics
    • Linked External Data
    • Mental Health
    • Neurocognition
    • Novel Technologies
    • Physical Health
    • Substance Use
  • Imaging data
    • Administrative tables
    • Data types
      • Documentation
        • Imaging
          • Concatenated
          • MRI derivatives data documentation
          • Source data / raw data
          • Supplementary tables
    • Scan types
      • Documentation
        • Imaging
          • Diffusion MRI
          • MRI Quality Control
          • Resting-state fMRI
          • Structural MRI
          • Task-based fMRI
          • Task-based fMRI (Behavioral performance)
          • Trial level behavioral performance during task-based fMRI
    • ABCD BIDS Community Collection (ABCC)
      • Documentation
        • Imaging
          • ABCD-BIDS community collection
          • BIDS conversion
          • Data processing
          • Derivatives
          • Quality control procedures
  • Substudy data
    • COVID-19 rapid response research
    • Endocannabinoid
    • IRMA
    • MR Spectroscopy
  • Release notes
    • 6.0 data release

On this page

  • Domain overview
    • Image processing (common to all fMRI)
      • rs-fMRI specific pre-processing
    • Regions of interest (ROI)
    • Functional connectivity analysis
  • Methods
  • Notes
    • Effects of scanner instance and software version
  1. Imaging data
  2. Scan types
  3. Documentation
  4. Imaging
  5. Resting-state fMRI

Resting-state fMRI

Domain overview

Please scroll horizontally to view the number of variables and events of administration for the displayed tables.

The Resting State fMRI (rsfMRI) data contains field maps, spin echo images with opposite phase encode polarity, and multi-frame, gradient echo, echo-planar imaging.

Responsible Use Warning: Head motion

Head motion is a serious issue for neuroimaging, and especially for resting state fMRI. It creates brain-wide artifactual effects including elevated short-distance connectivity and attenuated long-distance connectivity (Power et al. 2012). In order to guard against artifactual effects due to head motion, researchers typically implement a variety of strategies that operate at multiple points of the data collection and processing pipeline, with guidance regularly evolving over time (Power et al. 2014; Power, Schlaggar, and Petersen 2015; Satterthwaite et al. 2013; Siegel et al. 2017; Gratton et al. 2020). Some of these strategies include discarding entire runs of data that exceed certain motion thresholds and discarding individual functional imaging frames that are proximal to motion events (i.e., “motion censoring”). These strategies in particular typically lead to the exclusion of some participants from further analysis for lack of sufficient data. Levels of head motion differ according to demographic factors such as sex, race/ethnicity, and SES (Cosgrove et al. 2022). Therefore, strategies to deal with head motion may lead to differential exclusions across demographic groups. In addition, data quality procedures cause sessions to vary by the amount of data remaining. Such variability may continue to inflate findings especially in the presence of conditions that may correlate with the motion artifact like autism or ADHD (Eggebrecht et al. 2017). One strategy that avoids this confound is to strictly control the degrees of freedom, where functional connectivity measures are calculated with the exact same amount of data. To aid researchers in exploring this impact further, the ABCD study has released 5 minute- and 10 minute-trimmed and untrimmed functional connectivity datasets. Researchers interested in examining brain-behavior associations or multivariate predictions should follow strategies such as those in Eggebrecht et al. (2017):

  1. Assess how missing data impacts dependent, independent variables and covariates.
  2. Examine the association between the degrees of freedom and non-FC variables.
  3. Use trimmed FC measures when needed to mitigate artifacts due to data quality.

Image processing (common to all fMRI)

  • Head motion corrected by registering each frame to the first using AFNI’s 3dvolreg (Cox 1996)
  • B0 distortions were corrected using the reversing gradient method with FSL’s topup (Andersson, Skare, and Ashburner 2003; Smith et al. 2004)
  • Displacement field estimated from spin-echo field map scans
  • Applied to gradient-echo images after adjustment for between-scan head motion
  • Corrected for gradient nonlinearity distortions (Jovicich et al. 2006)
  • Between scan motion correction across all fMRI scans in imaging event
  • Registration between T2-weighted, spin-echo B0 calibration scans and T1-weighted structural images performed using mutual information (Wells et al. 1996)

rs-fMRI specific pre-processing

Note: not included in “minimal processing”

  • Removal of initial volumes
    • Siemens: 8 TRs
    • Philips: 8 TRs
    • GE DV25: 5 TRs
    • GE DV26 and later (e.g., RX28, DV29, etc.): 16 TRs
  • Normalization and demean
    • Divide by the mean of each voxel, subtract 1, multiply by 100
  • Regression
    • Linear regression to remove quadratic trends and signals correlated with motion and mean time courses of cerebral white matter, ventricles, and whole brain, plus first derivatives (Power et al. 2014; Satterthwaite et al. 2012)
    • Motion regression included 6 parameters plus derivatives and squares
    • Frames with displacement > 0.3 mm were excluded from the regression (Power et al. 2014)
    • Values for censored frames were replaced using linear interpolation
  • Temporal filtering
    • Band-pass filtered between 0.009 and 0.08 Hz (Hallquist, Hwang, and Luna 2013)
  • Pre-processed time courses were sampled onto the cortical surface
    • Projecting 1mm into cortical gray matter along surface normal vector
  • Motion censoring to reduce residual effects of head motion (Power et al. 2012, 2014)
    • Motion estimates filtered to attenuate signals (0.31 - 0.43 Hz) associated with respiration (18.6 - 25.7 respirations / minute)
    • Time points with FD > 0.2 mm excluded from variance and correlation calculations
    • Time periods with < 5 contiguous, sub-threshold time points also excluded
    • Time points that were outliers in standard deviation across ROIs also excluded

Regions of interest (ROI)

  • Subcortical structures labeled with atlas-based segmentation (Fischl et al. 2002)
  • Cortical regions labeled with the Desikan atlas-based classification (Desikan et al. 2006)
  • Cortical regions labeled with the Destrieux atlas-based classification (Destrieux et al. 2010)
  • Functionally-defined parcels derived resting-state functional connectivity patterns (Gordon et al. 2016)

Functional connectivity analysis

  • Seed-based, correlational approach (Van Dijk et al. 2010)
    • Adapted for cortical surface based analysis (Seibert and Brewer 2011)
    • Networks defined as pre-defined groups of cortical parcels (Gordon et al. 2016) (e.g., default, fronto-parietal, dorsal attention, etc.)
    • Calculation of average within- and between-network mean correlation (Van Dijk et al. 2010)
      • Correlation between each pair of ROIs
      • Fisher transformed to z-statistics
      • Averaged to provide measure of network correlation strength
    • Calculation of correlation between each network and each subcortical ROI
  • Variance across time calculated for each ROI
    • Magnitude of low frequency oscillations

Methods

Image processing and analysis methods corresponding to ABCD Release 2.0.1 are described in Hagler et al. (2019). Changes to image processing and analysis methods in Release 3.0 and Release 4.0 are documented in Resting-State fMRI Data Documentation. No significant changes were made to the processing pipeline for Release 5.0 or Release 6.0.

Notes

Effects of scanner instance and software version

Multisite longitudinal MRI imaging data are potentially susceptible to influences of scanning parameters, scanner manufacturer, scanner model, and software version, which vary across sites, and sometimes across time. For more information, please refer to “Effects of scanner instance and software version” in Imaging Overview Data Documentation.

Additional references:

  • Fair et al. (2007)
  • Holland, Kuperman, and Dale (2010)
  • Pruim, Mennes, Van Rooij, et al. (2015)
  • Pruim, Mennes, Buitelaar, et al. (2015)
  • Ramduny et al. (2024)
  • Wells et al. (1996)

References

Andersson, Jesper L. R., Stefan Skare, and John Ashburner. 2003. NeuroImage 20 (2): 870–88. doi:10.1016/S1053-8119(03)00336-7.
Cosgrove, Kelly T., Timothy J. McDermott, Evan J. White, Matthew W. Mosconi, Wesley K. Thompson, Martin P. Paulus, Carlos Cardenas-Iniguez, and Robin L. Aupperle. 2022. Brain Imaging and Behavior 16 (4): 1919–25. doi:10.1007/s11682-022-00665-2.
Cox, Robert W. 1996. Computers and Biomedical Research 29 (3): 162–73. doi:10.1006/cbmr.1996.0014.
Desikan, Rahul S., Florent Ségonne, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, et al. 2006. NeuroImage 31 (3): 968–80. doi:10.1016/j.neuroimage.2006.01.021.
Destrieux, Christophe, Bruce Fischl, Anders Dale, and Eric Halgren. 2010. NeuroImage 53 (1): 1–15. doi:10.1016/j.neuroimage.2010.06.010.
Eggebrecht, Adam T, Jed T Elison, Eric Feczko, Alexandre Todorov, Jason J Wolff, Sridhar Kandala, Chloe M Adams, et al. 2017. Cerebral Cortex 27 (3): 1709–20. doi:10.1093/cercor/bhw403.
Fair, Damien A., Bradley L. Schlaggar, Alexander L. Cohen, Francis M. Miezin, Nico U. F. Dosenbach, Kristin K. Wenger, Michael D. Fox, Abraham Z. Snyder, Marcus E. Raichle, and Steven E. Petersen. 2007. NeuroImage 35 (1): 396–405. doi:10.1016/j.neuroimage.2006.11.051.
Fischl, Bruce, David H. Salat, Evelina Busa, Marilyn Albert, Megan Dieterich, Christian Haselgrove, Andre Van Der Kouwe, et al. 2002. Neuron 33 (3): 341–55. doi:10.1016/S0896-6273(02)00569-X.
Gordon, Evan M., Timothy O. Laumann, Babatunde Adeyemo, Jeremy F. Huckins, William M. Kelley, and Steven E. Petersen. 2016. Cerebral Cortex 26 (1): 288–303. doi:10.1093/cercor/bhu239.
Gratton, Caterina, Ally Dworetsky, Rebecca S. Coalson, Babatunde Adeyemo, Timothy O. Laumann, Gagan S. Wig, Tania S. Kong, et al. 2020. NeuroImage 217 (August): 116866. doi:10.1016/j.neuroimage.2020.116866.
Hagler, Donald J., SeanN. Hatton, M. Daniela Cornejo, Carolina Makowski, Damien A. Fair, Anthony Steven Dick, Matthew T. Sutherland, et al. 2019. NeuroImage 202 (November): 116091. doi:10.1016/j.neuroimage.2019.116091.
Hallquist, Michael N., Kai Hwang, and Beatriz Luna. 2013. NeuroImage 82 (November): 208–25. doi:10.1016/j.neuroimage.2013.05.116.
Holland, Dominic, Joshua M. Kuperman, and Anders M. Dale. 2010. NeuroImage 50 (1): 175–83. doi:10.1016/j.neuroimage.2009.11.044.
Jovicich, Jorge, Silvester Czanner, Douglas Greve, Elizabeth Haley, Andre Van Der Kouwe, Randy Gollub, David Kennedy, et al. 2006. NeuroImage 30 (2): 436–43. doi:10.1016/j.neuroimage.2005.09.046.
Power, Jonathan D., Kelly A. Barnes, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2012. NeuroImage 59 (3): 2142–54. doi:10.1016/j.neuroimage.2011.10.018.
Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. NeuroImage 84 (January): 320–41. doi:10.1016/j.neuroimage.2013.08.048.
Power, Jonathan D., Bradley L. Schlaggar, and Steven E. Petersen. 2015. NeuroImage 105 (January): 536–51. doi:10.1016/j.neuroimage.2014.10.044.
Pruim, Raimon H. R., Maarten Mennes, Jan K. Buitelaar, and Christian F. Beckmann. 2015. NeuroImage 112 (May): 278–87. doi:10.1016/j.neuroimage.2015.02.063.
Pruim, Raimon H. R., Maarten Mennes, Daan Van Rooij, Alberto Llera, Jan K. Buitelaar, and Christian F. Beckmann. 2015. NeuroImage 112 (May): 267–77. doi:10.1016/j.neuroimage.2015.02.064.
Ramduny, Jivesh, Lucina Q. Uddin, Tamara Vanderwal, Eric Feczko, Damien A. Fair, Clare Kelly, and Arielle Baskin-Sommers. 2024. doi:10.1101/2024.06.22.600221.
Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. NeuroImage 64 (January): 240–56. doi:10.1016/j.neuroimage.2012.08.052.
Satterthwaite, Theodore D., Daniel H. Wolf, James Loughead, Kosha Ruparel, Mark A. Elliott, Hakon Hakonarson, Ruben C. Gur, and Raquel E. Gur. 2012. NeuroImage 60 (1): 623–32. doi:10.1016/j.neuroimage.2011.12.063.
Seibert, Tyler M., and James B. Brewer. 2011. Journal of Neuroscience Methods 198 (2): 301–11. doi:10.1016/j.jneumeth.2011.04.010.
Siegel, Joshua S., Anish Mitra, Timothy O. Laumann, Benjamin A. Seitzman, Marcus Raichle, Maurizio Corbetta, and Abraham Z. Snyder. 2017. Cerebral Cortex 27 (9): 4492–502. doi:10.1093/cercor/bhw253.
Smith, Stephen M., Mark Jenkinson, Mark W. Woolrich, Christian F. Beckmann, Timothy E. J. Behrens, Heidi Johansen-Berg, Peter R. Bannister, et al. 2004. NeuroImage 23 (January): S208–19. doi:10.1016/j.neuroimage.2004.07.051.
Van Dijk, Koene R. A., Trey Hedden, Archana Venkataraman, Karleyton C. Evans, Sara W. Lazar, and Randy L. Buckner. 2010. Journal of Neurophysiology 103 (1): 297–321. doi:10.1152/jn.00783.2009.
Wells, William M., Paul Viola, Hideki Atsumi, Shin Nakajima, and Ron Kikinis. 1996. Medical Image Analysis 1 (1): 35–51. doi:10.1016/S1361-8415(01)80004-9.
 

ABCD Study®, Teen Brains. Today’s Science. Brighter Future.® and the ABCD Study Logo are registered marks of the U.S. Department of Health & Human Services (HHS). Adolescent Brain Cognitive Development℠ Study is a service mark of the U.S. Department of Health & Human Services (HHS).