Abstract
Resting-state functional MRI (rfMRI) correlates activity across brain regions to identify functional connectivity networks. The Human Connectome Project (HCP) for Early Psychosis has adopted the protocol of the HCP Lifespan Project, which collects 20 min of rfMRI data. However, because it is difficult for psychotic patients to remain in the scanner for long durations, we investigate here the reliability of collecting less than 20 min of rfMRI data. Varying durations of data were taken from the full datasets of 11 subjects. Correlation matrices derived from varying amounts of data were compared using the Bhattacharyya distance, and the reliability of functional network ranks was assessed using the Friedman test. We found that correlation matrix reliability improves steeply with longer windows of data up to 11–12 min, and ≥14 min of data produces correlation matrices within the variability of those produced by 18 min of data. The reliability of network connectivity rank increases with increasing durations of data, and qualitatively similar connectivity ranks for ≥10 min of data indicates that 10 min of data can still capture robust information about network connectivities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995)
Fox, M.D., Greicius, M.: Clinical applications of resting state functional connectivity. Front Syst. Neurosci. 4, 19 (2010). doi:10.3389/fnsys.2010.00019
Snyder, A.Z., Raichle, M.E.: A brief history of the resting state: the Washington University perspective. NeuroImage 62(2), 902–910 (2012). doi:10.1016/j.neuroimage.2012.01.044
Van Dijk, K.R.A., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L.: Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103(1), 297–321 (2010). doi:10.1152/jn.00783.2009
Anderson, J.S., Ferguson, M.A., Lopez-Larson, M., Yurgelun-Todd, D.: Reproducibility of single-subject functional connectivity measurements. AJNR Am. J. Neuroradiol. 32(3), 548–555 (2011). doi:10.3174/ajnr.A2330
Laumann, T.O., Gordon, E.M., Adeyemo, B., et al.: Functional system and areal organization of a highly sampled individual human brain. Neuron 87(3), 657–670 (2015). doi:10.1016/j.neuron.2015.06.037
Van Essen, D.C., Smith, S.M., Barch, D.M., et al.: The WU-Minn Human Connectome Project: An Overview. NeuroImage 80, 62–79 (2013). doi:10.1016/j.neuroimage.2013.05.041
Smith, S.M., Andersson, J., Auerbach, E.J., et al.: Resting-state fMRI in the Human Connectome Project. NeuroImage 80, 144–168 (2013). doi:10.1016/j.neuroimage.2013.05.039
Phase 1b Lifespan Pilot Parameters, http://lifespan.humanconnectome.org/data/phase1b-pilot-parameters.html. Accessed 09 Jun 2017
First, M.B., Williams, J.B.W., Karg, R.S., Spitzer, R.L.: Structured Clinical Interview for DSM-5—Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV). American Psychiatric Association, Arlington (2015)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., et al.: The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105–124 (2013). doi:10.1016/j.neuroimage.2013.04.127
Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L., Smith, S.M.: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014). doi:10.1016/j.neuroimage.2013.11.046
Beckmann, C.F., Smith, S.M.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23(2), 137–152 (2004). doi:10.1109/TMI.2003.822821
Glasser, M.F., Coalson, T.S., Robinson, E.C., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016). doi:10.1038/nature18933
Yeo, B.T., Krienen, F.M., Sepulcre, J., et al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011). doi:10.1152/jn.00338.2011
Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math. Soc. 35, 99–109 (1943)
Gonzalez-Castillo, J., Handwerker, D.A., Robinson, M.E., et al.: The spatial structure of resting state connectivity stability on the scale of minutes. Front Neurosci. 8, 138 (2014). doi:10.3389/fnins.2014.00138
Acknowledgments
This research was supported by National Institutes of Health (NIH) grants U01MH109977, R01MH085953, and P41EB015902.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bouix, S., Swago, S., West, J.D., Pasternak, O., Breier, A., Shenton, M.E. (2017). “Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much Is Enough?”. In: Wu, G., Laurienti, P., Bonilha, L., Munsell, B. (eds) Connectomics in NeuroImaging. CNI 2017. Lecture Notes in Computer Science(), vol 10511. Springer, Cham. https://doi.org/10.1007/978-3-319-67159-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-67159-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67158-1
Online ISBN: 978-3-319-67159-8
eBook Packages: Computer ScienceComputer Science (R0)