Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

Centre de Recherche Cerveau et Cognition Universite Paul Sabatier Toulouse France

Department of Computing and Control Engineering University of Chemistry and Technology Prague Czech Republic

Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

Institute for Clinical and Experimental Medicine Prague Czech Republic

Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic

National Institute of Mental Health Klecany Czech Republic

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Arslan, S. , Ktena, S. I. , Makropoulos, A. , Robinson, E. C. , Rueckert, D. , & Parisot, S. (2018). Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage, 170, 5–30. 10.1016/j.neuroimage.2017.04.014 PubMed DOI

Aurich, N. K. , Filho, A. , O, J. , da Silva, M. , M, A. , & Franco, A. R. (2015). Evaluating the reliability of different preprocessing steps to estimate graph theoretical measures in resting state fMRI data. Frontiers in Neuroscience, 9, 48 10.3389/fnins.2015.00048 PubMed DOI PMC

Barton, M. , Marecek, R. , Krajcovicova, L. , Slavicek, T. , Kasparek, T. , Zemankova, P. , … Mikl, M. (2019). Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation. Human Brain Mapping, 40(4), 1114–1138. 10.1002/hbm.24433 PubMed DOI PMC

Beall, E. B. , & Lowe, M. J. (2014). SimPACE: Generating simulated motion corrupted BOLD data with synthetic‐navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction. NeuroImage, 101, 21–34. 10.1016/j.neuroimage.2014.06.038 PubMed DOI PMC

Behzadi, Y. , Restom, K. , Liau, J. , & Liu, T. T. (2007). A component‐based noise correction method (CompCor) for BOLD and perfusion‐based fMRI. NeuroImage, 37(1), 90–101. 10.1016/j.neuroimage.2007.04.042 PubMed DOI PMC

Bianciardi, M. , Fukunaga, M. , van Gelderen, P. , Horovitz, S. G. , de Zwart, J. A. , Shmueli, K. , & Duyn, J. H. (2009). Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: A 7 T study. Magnetic Resonance Imaging, 27(8), 1019–1029. 10.1016/j.mri.2009.02.004 PubMed DOI PMC

Biswal, B. B. , Mennes, M. , Zuo, X.‐N. , Gohel, S. , Kelly, C. , Smith, S. M. , … Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences, 107(10), 4734–4739. 10.1073/pnas.0911855107 PubMed DOI PMC

Biswal, B. , Yetkin, F. Z. , Haughton, V. M. , & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magnetic Resonance in Medicine, 34, 537–541. 10.1002/mrm.1910340409. PubMed DOI

Bodurka, J. , Ye, F. , Petridou, N. , Murphy, K. , & Bandettini, P. A. (2007). Mapping the MRI voxel volume in which thermal noise matches physiological noise—Implications for fMRI. NeuroImage, 34(2), 542–549. 10.1016/j.neuroimage.2006.09.039 PubMed DOI PMC

Bright, M. G. , & Murphy, K. (2013). Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data. NeuroImage, 64, 526–537. 10.1016/j.neuroimage.2012.09.043 PubMed DOI PMC

Buckner, R. L. , Krienen, F. M. , & Yeo, B. T. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. Nature Neuroscience, 16(7), 832–837. 10.1038/nn.3423 PubMed DOI

Burgess, G. C. , Kandala, S. , Nolan, D. , Laumann, T. O. , Power, J. D. , Adeyemo, B. , … Barch, D. M. (2016). Evaluation of denoising strategies to address motion‐correlated artifacts in resting‐state functional magnetic resonance imaging data from the human Connectome project. Brain Connectivity, 6(9), 669–680. 10.1089/brain.2016.0435 PubMed DOI PMC

Caballero‐Gaudes, C. , & Reynolds, R. C. (2017). Methods for cleaning the BOLD fMRI signal. NeuroImage, 154, 128–149. 10.1016/j.neuroimage.2016.12.018 PubMed DOI PMC

Carbonell, F. , Bellec, P. , & Shmuel, A. (2011). Global and system‐specific resting‐state fMRI fluctuations are uncorrelated: Principal component analysis reveals anti‐correlated networks. Brain Connectivity, 1(6), 496–510. 10.1089/brain.2011.0065 PubMed DOI PMC

Chang, C. , & Glover, G. H. (2009). Relationship between respiration, end‐tidal CO2, and BOLD signals in resting‐state fMRI. NeuroImage, 47(4), 1381–1393. 10.1016/j.neuroimage.2009.04.048 PubMed DOI PMC

Ciric, R. , Wolf, D. H. , Power, J. D. , Roalf, D. R. , Baum, G. L. , Ruparel, K. , … Satterthwaite, T. D. (2017). Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage, 154, 174–187. 10.1016/j.neuroimage.2017.03.020 PubMed DOI PMC

Craddock, R. C. , James, G. A. , Holtzheimer, P. E. , Hu, X. P. , & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928. 10.1002/hbm.21333 PubMed DOI PMC

de Winter, J. , de Samuel, C. F. , Gosling, D. , & Potter, J. (2016). Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods, 21(3), 273–290. 10.1037/met0000079 PubMed DOI

Eickhoff, S. B. , Yeo, B. T. T. , & Genon, S. (2018). Imaging‐based parcellations of the human brain. Nature Reviews Neuroscience, 19(11), 672–686. 10.1038/s41583-018-0071-7 PubMed DOI

Engelhardt, L. E. , Roe, M. A. , Juranek, J. , DeMaster, D. , Harden, K. P. , Tucker‐Drob, E. M. , & Church, J. A. (2017). Children's head motion during fMRI tasks is heritable and stable over time. Developmental Cognitive Neuroscience, 25, 58–68. 10.1016/j.dcn.2017.01.011 PubMed DOI PMC

Fair, D. A. , Miranda‐Dominguez, O. , Snyder, A. Z. , Perrone, A. , Earl, E. A. , Van, A. N. , … Dosenbach, N. U. F. (2020). Correction of respiratory artifacts in MRI head motion estimates. NeuroImage, 208, 116400 10.1016/j.neuroimage.2019.116400 PubMed DOI PMC

Fair, D. A. , Nigg, J. T. , Iyer, S. , Bathula, D. , Mills, K. L. , Dosenbach, N. U. F. , … Milham, M. P. (2013). Distinct neural signatures detected for ADHD subtypes after controlling for micro‐movements in resting state functional connectivity MRI data. Frontiers in Systems Neuroscience, 6, 80 10.3389/fnsys.2012.00080 PubMed DOI PMC

Friston, K. J. , Frith, C. D. , Liddle, P. F. , & Frackowiak, R. S. J. (1993). Functional connectivity: The principal‐component analysis of large (PET) data sets. Journal of Cerebral Blood Flow & Metabolism, 13(1), 5–14. 10.1038/jcbfm.1993.4 PubMed DOI

Friston, K. J. , Williams, S. , Howard, R. , Frackowiak, R. S. J. , & Turner, R. (1996). Movement‐related effects in fMRI time‐series. Magnetic Resonance in Medicine, 35(3), 346–355. 10.1002/mrm.1910350312 PubMed DOI

Gratton, C. , Dworetsky, A. , Coalson, R. S. , Adeyemo, B. , Laumann, T. O. , Wig, G. S. , … Campbell, M. C. (2020). Removal of high frequency contamination from motion estimates in single‐band fMRI saves data without biasing functional connectivity. NeuroImage, 217, 116866 10.1016/j.neuroimage.2020.116866 PubMed DOI PMC

Hajnal, J. V. , Myers, R. , Oatridge, A. , Schwieso, J. E. , Young, I. R. , & Bydder, G. M. (1994). Artifacts due to stimulus correlated motion in functional imaging of the brain. Magnetic Resonance in Medicine, 31(3), 283–291. 10.1002/mrm.1910310307 PubMed DOI

Hallquist, M. N. , Hwang, K. , & Luna, B. (2013). The nuisance of nuisance regression: Spectral misspecification in a common approach to resting‐state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage, 82, 208–225. 10.1016/j.neuroimage.2013.05.116 PubMed DOI PMC

Hartman, D. , Hlinka, J. , Palus, M. , Mantini, D. , & Corbetta, M. (2011). The role of nonlinearity in computing graph‐theoretical properties of resting‐state functional magnetic resonance imaging brain networks. Chaos, 21(1), 013119 10.1063/1.3553181 PubMed DOI PMC

Hlinka, J. , Alexakis, C. , Hardman, J. G. , Siddiqui, Q. , & Auer, D. P. (2010). Is sedation‐induced BOLD fMRI low‐frequency fluctuation increase mediated by increased motion? Magnetic Resonance Materials in Physics, Biology and Medicine, 23(5–6), 367–374. 10.1007/s10334-010-0199-6 PubMed DOI

Hlinka, J. , Palus, M. , Vejmelka, M. , Mantini, D. , & Corbetta, M. (2011). Functional connectivity in resting‐state fMRI: Is linear correlation sufficient? NeuroImage, 54(3), 2218–2225. 10.1016/j.neuroimage.2010.08.042 PubMed DOI PMC

Lee, M. H. , Smyser, C. D. , & Shimony, J. S. (2013). Resting‐state fMRI: A review of methods and clinical applications. American Journal of Neuroradiology, 34(10), 1866–1872. 10.3174/ajnr.A3263 PubMed DOI PMC

Maclaren, J. , Herbst, M. , Speck, O. , & Zaitsev, M. (2013). Prospective motion correction in brain imaging: A review. Magnetic Resonance in Medicine, 69(3), 621–636. 10.1002/mrm.24314 PubMed DOI

Mowinckel, A. M. , Espeseth, T. , & Westlye, L. T. (2012). Network‐specific effects of age and in‐scanner subject motion: A resting‐state fMRI study of 238 healthy adults. NeuroImage, 63(3), 1364–1373. 10.1016/j.neuroimage.2012.08.004 PubMed DOI

Murphy, K. , Birn, R. M. , & Bandettini, P. A. (2013). Resting‐state fMRI confounds and cleanup. NeuroImage, 80, 349–359. 10.1016/j.neuroimage.2013.04.001 PubMed DOI PMC

Muschelli, J. , Nebel, M. B. , Caffo, B. S. , Barber, A. D. , Pekar, J. J. , & Mostofsky, S. H. (2014). Reduction of motion‐related artifacts in resting state fMRI using aCompCor. NeuroImage, 96, 22–35. 10.1016/j.neuroimage.2014.03.028 PubMed DOI PMC

Nir, Y. , Hasson, U. , Levy, I. , Yeshurun, Y. , & Malach, R. (2006). Widespread functional connectivity and fMRI fluctuations in human visual cortex in the absence of visual stimulation. NeuroImage, 30(4), 1313–1324. 10.1016/j.neuroimage.2005.11.018 PubMed DOI

Parkes, L. , Fulcher, B. , Yücel, M. , & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting‐state functional MRI. NeuroImage, 171, 415–436. 10.1016/j.neuroimage.2017.12.073 PubMed DOI

Patel, A. X. , Kundu, P. , Rubinov, M. , Jones, P. S. , Vértes, P. E. , Ersche, K. D. , … Bullmore, E. T. (2014). A wavelet method for modeling and despiking motion artifacts from resting‐state fMRI time series. NeuroImage, 95(100), 287–304. 10.1016/j.neuroimage.2014.03.012 PubMed DOI PMC

Poldrack, R. A. , Mumford, J. A. , & Nichols, T. E. (2011). Handbook of functional MRI data analysis, Cambridge, England: Cambridge University Press.

Ponsoda, V. , Martínez, K. , Pineda‐Pardo, J. A. , Abad, F. J. , Olea, J. , Román, F. J. , … Colom, R. (2017). Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis. Human Brain Mapping, 38(2), 803–816. 10.1002/hbm.23419 PubMed DOI PMC

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. 10.1016/j.neuroimage.2011.10.018 PubMed DOI PMC

Power, J. D. , Barnes, K. A. , Snyder, A. Z. , Schlaggar, B. L. , & Petersen, S. E. (2013). Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to carp. NeuroImage, 76, 439–441. 10.1016/j.neuroimage.2012.03.017 PubMed DOI PMC

Power, J. D. , Lynch, C. J. , Silver, B. M. , Dubin, M. J. , Martin, A. , & Jones, R. M. (2019). Distinctions among real and apparent respiratory motions in human fMRI data. NeuroImage, 201, 116041 10.1016/j.neuroimage.2019.116041 PubMed DOI PMC

Power, J. D. , Mitra, A. , Laumann, T. O. , Snyder, A. Z. , Schlaggar, B. L. , & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341. 10.1016/j.neuroimage.2013.08.048 PubMed DOI PMC

Power, J. D. , Plitt, M. , Gotts, S. J. , Kundu, P. , Voon, V. , Bandettini, P. A. , & Martin, A. (2018). Ridding fMRI data of motion‐related influences: Removal of signals with distinct spatial and physical bases in multiecho data. Proceedings of the National Academy of Sciences, 115(9), E2105–E2114. 10.1073/pnas.1720985115 PubMed DOI PMC

Power, J. D. , Plitt, M. , Kundu, P. , Bandettini, P. A. , & Martin, A. (2017). Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection. PLoS One, 12(9), e0182939 10.1371/journal.pone.0182939 PubMed DOI PMC

Power, J. D. , Schlaggar, B. L. , & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage, 105, 536–551. 10.1016/j.neuroimage.2014.10.044 PubMed DOI PMC

Power, J. D. , Silver, B. M. , Silverman, M. R. , Ajodan, E. L. , Bos, D. J. , & Jones, R. M. (2019). Customized head molds reduce motion during resting state fMRI scans. NeuroImage, 189, 141–149. 10.1016/j.neuroimage.2019.01.016 PubMed DOI

Saad, Z. S. , Reynolds, R. C. , Jo, H. J. , Gotts, S. J. , Chen, G. , Martin, A. , & Cox, R. W. (2013). Correcting brain‐wide correlation differences in resting‐state FMRI. Brain Connectivity, 3(4), 339–352. 10.1089/brain.2013.0156 PubMed DOI PMC

Satterthwaite, T. D. , Ciric, R. , Roalf, D. R. , Davatzikos, C. , Bassett, D. S. , & Wolf, D. H. (2019). Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies. Human Brain Mapping, 40(7), 2033–2051. 10.1002/hbm.23665 PubMed DOI PMC

Satterthwaite, T. D. , Elliott, M. A. , Gerraty, R. T. , Ruparel, K. , Loughead, J. , Calkins, M. E. , … Wolf, D. H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting‐state functional connectivity data. NeuroImage, 64, 240–256. 10.1016/j.neuroimage.2012.08.052 PubMed DOI PMC

Satterthwaite, T. D. , Wolf, D. H. , Loughead, J. , Ruparel, K. , Elliott, M. A. , Hakonarson, H. , … Gur, R. E. (2012). Impact of in‐scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. NeuroImage, 60(1), 623–632. 10.1016/j.neuroimage.2011.12.063 PubMed DOI PMC

Schölvinck, M. L. , Maier, A. , Ye, F. Q. , Duyn, J. H. , & Leopold, D. A. (2010). Neural basis of global resting‐state fMRI activity. Proceedings of the National Academy of Sciences, 107(22), 10238–10243. 10.1073/pnas.0913110107 PubMed DOI PMC

Shen, X. , Tokoglu, F. , Papademetris, X. , & Constable, R. T. (2013). Groupwise whole‐brain parcellation from resting‐state fMRI data for network node identification. NeuroImage, 82, 403–415. 10.1016/j.neuroimage.2013.05.081 PubMed DOI PMC

Shmueli, K. , van Gelderen, P. , de Zwart, J. A. , Horovitz, S. G. , Fukunaga, M. , Jansma, J. M. , & Duyn, J. H. (2007). Low‐frequency fluctuations in the cardiac rate as a source of variance in the resting‐state fMRI BOLD signal. NeuroImage, 38(2), 306–320. 10.1016/j.neuroimage.2007.07.037 PubMed DOI PMC

Siegel, J. S. , Mitra, A. , Laumann, T. O. , Seitzman, B. A. , Raichle, M. , Corbetta, M. , & Snyder, A. Z. (2017). Data quality influences observed links between functional connectivity and behavior. Cerebral Cortex, 27(9), 4492–4502. 10.1093/cercor/bhw253 PubMed DOI PMC

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. 10.1093/cercor/bhq035 PubMed DOI PMC

Spisák, T. , Jakab, A. , Kis, S. A. , Opposits, G. , Aranyi, C. , Berényi, E. , & Emri, M. (2014). Voxel‐wise motion artifacts in population‐level whole‐brain connectivity analysis of resting‐state fMRI. PLoS One, 9(9), e104947 10.1371/journal.pone.0104947 PubMed DOI PMC

Tomeček, D. , Androvičová, R. , Fajnerová, I. , Děchtěrenko, F. , Rydlo, J. , Horáček, J. , … Hlinka, J. (2020). Personality reflection in the Brain's intrinsic functional architecture remains elusive. PLoS One, 15(6), e0232570 10.1371/journal.pone.0232570 PubMed DOI PMC

Tyszka, J. M. , Kennedy, D. P. , Paul, L. K. , & Adolphs, R. (2014). Largely typical patterns of resting‐state functional connectivity in high‐functioning adults with autism. Cerebral Cortex, 24(7), 1894–1905. 10.1093/cercor/bht040 PubMed DOI PMC

Tzourio‐Mazoyer, N. , Landeau, B. , Papathanassiou, D. , Crivello, F. , Etard, O. , Delcroix, N. , … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single‐subject brain. NeuroImage, 15, 273–289. 10.1006/nimg.2001.0978 PubMed DOI

Uğurbil, K. , Xu, J. , Auerbach, E. J. , Moeller, S. , Vu, A. T. , Duarte‐Carvajalino, J. M. , … WU‐Minn HCP Consortium . (2013). Pushing spatial and temporal resolution for functional and diffusion MRI in the human Connectome project. NeuroImage, 80, 80–104. 10.1016/j.neuroimage.2013.05.012 PubMed DOI PMC

Van Dijk, K. R. A. , Hedden, T. , Venkataraman, A. , Evans, K. C. , Lazar, S. W. , & Buckner, R. L. (2009). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103(1), 297–321. 10.1152/jn.00783.2009 PubMed DOI PMC

Van Dijk, K. R. A. , Sabuncu, M. R. , & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59(1), 431–438. 10.1016/j.neuroimage.2011.07.044 PubMed DOI PMC

Van Essen, D. C. , Smith, S. M. , Barch, D. M. , Behrens, T. E. J. , Yacoub, E. , Ugurbil, K. , & WU‐Minn HCP Consortium . (2013). The WU‐Minn human Connectome project: An overview. NeuroImage, 80, 62–79. 10.1016/j.neuroimage.2013.05.041 PubMed DOI PMC

Venkatesh, M. , Jaja, J. , & Pessoa, L. (2020). Comparing functional connectivity matrices: A geometry‐aware approach applied to participant identification. NeuroImage, 207, 116398 10.1016/j.neuroimage.2019.116398 PubMed DOI PMC

Waheed, S. H. , Mirbagheri, S. , Agarwal, S. , Kamali, A. , Yahyavi‐Firouz‐Abadi, N. , Chaudhry, A. , … Sair, H. I. (2016). Reporting of resting‐state functional magnetic resonance imaging preprocessing methodologies. Brain Connectivity, 6(9), 663–668. 10.1089/brain.2016.0446 PubMed DOI

Williams, J.C. , & Snellenberg, J.X.V. (2019). Motion denoising of multiband resting state functional connectivity MRI data: An improved volume censoring method. bioRxiv 860635. 10.1101/860635 DOI

Yan, C.‐G. , Cheung, B. , Kelly, C. , Colcombe, S. , Craddock, R. C. , Di Martino, A. , … Milham, M. P. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage, 76, 183–201. 10.1016/j.neuroimage.2013.03.004 PubMed DOI PMC

Yan, C.‐G. , Craddock, R. C. , He, Y. , & Milham, M. P. (2013). Addressing head motion dependencies for small‐world topologies in functional connectomics. Frontiers in Human Neuroscience, 7, 910 10.3389/fnhum.2013.00910 PubMed DOI PMC

Zalesky, A. , Fornito, A. , Harding, I. H. , Cocchi, L. , Yücel, M. , Pantelis, C. , & Bullmore, E. T. (2010). Whole‐brain anatomical networks: Does the choice of nodes matter? NeuroImage, 50(3), 970–983. 10.1016/j.neuroimage.2009.12.027 PubMed DOI

Zar, J. H. (1999). Biostatistical analysis, Upper Saddle River, NJ: Prentice Hall.

Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression