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Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data

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Abstract

It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.

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References

  • Abbott, C., Juarez, M., White, T., Gollub, R. L., Pearlson, G. D., Bustillo, J., et al. (2011). Antipsychotic dose and diminished neural modulation: A multi-site fMRI study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 35(2), 473–482.

    CAS  PubMed  Google Scholar 

  • Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., et al. (2017). Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. Neuroimage, 147, 736–745.

    PubMed  Google Scholar 

  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95–113.

  • Baria, A. T., Baliki, M. N., Parrish, T., & Apkarian, A. V. (2011). Anatomical and functional assemblies of brain BOLD oscillations. The Journal of Neuroscience, 31(21), 7910–7919.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Beall, E. B., & Lowe, M. J. (2010). The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T. Journal of Neuroscience Methods, 191(2), 263–276.

    PubMed  Google Scholar 

  • 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 (4):537–541.

  • Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., Dogonowski, A. M., Ernst, M., Fair, D., Hampson, M., Hoptman, M. J., Hyde, J. S., Kiviniemi, V. J., Kötter, R., Li, S. J., Lin, C. P., Lowe, M. J., Mackay, C., Madden, D. J., Madsen, K. H., Margulies, D. S., Mayberg, H. S., McMahon, K., Monk, C. S., Mostofsky, S. H., Nagel, B. J., Pekar, J. J., Peltier, S. J., Petersen, S. E., Riedl, V., Rombouts, S. A., Rypma, B., Schlaggar, B. L., Schmidt, S., Seidler, R. D., Siegle, G. J., Sorg, C., Teng, G. J., Veijola, J., Villringer, A., Walter, M., Wang, L., Weng, X. C., Whitfield-Gabrieli, S., Williamson, P., Windischberger, C., Zang, Y. F., Zhang, H. Y., Castellanos, F. X., & Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4734–4739.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Boubela, R. N., Kalcher, K., Huf, W., Kronnerwetter, C., Filzmoser, P., & Moser, E. (2013). Beyond noise: Using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Frontiers in Human Neuroscience, 7.

  • Caramanos, Z., Fonov, V. S., Francis, S. J., Narayanan, S., Pike, G. B., Collins, D. L., & Arnold, D. L. (2010). Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change. NeuroImage, 49(2), 1601–1611.

    PubMed  Google Scholar 

  • Castellanos, F. X., Di Martino, A., Craddock, R. C., Mehta, A. D., & Milham, M. P. (2013). Clinical applications of the functional connectome. Neuroimage, 80, 527–540. https://doi.org/10.1016/j.neuroimage.2013.04.083.

    Article  CAS  PubMed  Google Scholar 

  • Chen, N. K., Dickey, C. C., Yoo, S. S., Guttmann, C. R., & Panych, L. P. (2003). Selection of voxel size and slice orientation for fMRI in the presence of susceptibility field gradients: Application to imaging of the amygdala. NeuroImage, 19, 817–825.

    PubMed  Google Scholar 

  • Cheng, W., Palaniyappan, L., Li, M., Kendrick, K. M., Zhang, J., Luo, Q., et al. (2015). Voxel-based, brain-wide association study of aberrant functional connectivity in schizophrenia implicates thalamocortical circuitry. NPJ Schizophrenia, 1, 15016.

    PubMed  PubMed Central  Google Scholar 

  • Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., et al. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR. American Journal of Neuroradiology, 21, 1636–1644.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Dagli, M. S., Ingeholm, J. E., & Haxby, J. V. (1999). Localization of cardiac-induced signal change in fMRI. Neuroimage, 9, 407–415.

    CAS  PubMed  Google Scholar 

  • Dansereau, C., Benhajali, Y., Risterucci, C., Pich, E. M., Orban, P., Arnold, D., & Bellec, P. (2017). Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage, 149, 220–232.

    PubMed  Google Scholar 

  • Deichmann, R., Gottfried, J. A., Hutton, C., & Turner, R. (2003). Optimized EPI for fMRI studies of the orbitofrontal cortex. NeuroImage, 19, 430–441.

    CAS  PubMed  Google Scholar 

  • Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., et al. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667.

    PubMed  Google Scholar 

  • Edward, V., Windischberger, C., Cunnington, R., Erdler, M., Lanzenberger, R., Mayer, D., Endl, W., & Beisteiner, R. (2000). Quantification of fMRI artifact reduction by a novel plaster cast head holder. Human Brain Mapping, 11(3), 207–213.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Fair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U., et al. (2012). 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.

    PubMed  Google Scholar 

  • Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A. R., Fox, P. T., Eickhoff, S. B., Yu, C., & Jiang, T. (2016). The human Brainnetome atlas: A new brain atlas based on connectional architecture. Cerebral Cortex, 26, 3508–3526.

    PubMed  PubMed Central  Google Scholar 

  • Feinberg, D. A., & Yacoub, E. (2012). The rapid development of high speed, resolution and precision in fMRI. Neuroimage, 62(2), 720–725.

    PubMed  Google Scholar 

  • Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Gunther, M., Glasser, M. F., Miller, K. L., Ugurbil, K., & Yacoub, E. (2010). Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One, 5(12), e15710.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience, 4, 19.

    PubMed  PubMed Central  Google Scholar 

  • Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8(9), 700–711.

    CAS  PubMed  Google Scholar 

  • Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., Raichle, M.E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the U S A 102(27), 9673–9678.

  • Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15–29.

    PubMed  PubMed Central  Google Scholar 

  • Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35, 346–355.

    CAS  PubMed  Google Scholar 

  • Fryer, S. L., Roach, B. J., Ford, J. M., Turner, J. A., van Erp, T. G., Voyvodic, J., et al. (2015). Relating intrinsic low-frequency BOLD cortical oscillations to cognition in schizophrenia. Neuropsychopharmacology, 40(12), 2705–2714.

    PubMed  PubMed Central  Google Scholar 

  • Glover, G. H. (2012). Spiral imaging in fMRI. Neuroimage, 62(2), 706–712.

    PubMed  Google Scholar 

  • Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253–258.

    CAS  PubMed  Google Scholar 

  • Greicius, M. D., Kiviniemi, V., Tervonen, O., Vainionpaa, V., Alahuhta, S., Reiss, A. L., et al. (2008). Persistent default-mode network connectivity during light sedation. Human Brain Mapping, 29(7), 839–847.

    PubMed  PubMed Central  Google Scholar 

  • Hohenfeld, C., Werner, C. J., & Reetz, K. (2018). Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker? Neuroimage Clin, 18, 849–870.

    PubMed  PubMed Central  Google Scholar 

  • Jao, T., Vertes, P. E., Alexander-Bloch, A. F., Tang, I. N., Yu, Y. C., Chen, J. H., et al. (2013). Volitional eyes opening perturbs brain dynamics and functional connectivity regardless of light input. Neuroimage, 69, 21–34.

    PubMed  Google Scholar 

  • Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841.

    PubMed  Google Scholar 

  • Jia, X. Z., Sun, J. W., Ji, G. J., Liao, W., Lv, Y. T., Wang, J., Wang, Z., Zhang, H., Liu, D. Q., & Zang, Y. F. (2020). Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level. PLoS One, 15(1), e0227021 https://doi.org/10.1371/journal.pone.0227021.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jiang, X., Dai, X., Kale Edmiston, E., Zhou, Q., Xu, K., Zhou, Y., Wu, F., Kong, L., Wei, S., Zhou, Y., Chang, M., Geng, H., Wang, D., Wang, Y., Cui, W., Wang, F., & Tang, Y. (2017). Alteration of cortico-limbic-striatal neural system in major depressive disorder and bipolar disorder. Journal of Affective Disorders, 221, 297–303.

    PubMed  Google Scholar 

  • Kirilina, E., Lutti, A., Poser, B. A., Blankenburg, F., & Weiskopf, N. (2016). The quest for the best: The impact of different EPI sequences on the sensitivity of random effect fMRI group analyses. NeuroImage, 126, 49–59.

    PubMed  Google Scholar 

  • Liu, D., Dong, Z., Zuo, X., Wang, J., & Zang, Y. (2013). Eyes-open/eyes-closed dataset sharing for reproducibility evaluation of resting state fMRI data analysis methods. Neuroinformatics, 11(4), 469–476.

    PubMed  Google Scholar 

  • Lowe, M. J., Mock, B. J., & Sorenson, J. A. (1998). Functional connectivity in single and multislice Echoplanar imaging using resting-state fluctuations. Neuroimage, 7, 119–132.

    CAS  PubMed  Google Scholar 

  • Lui, S., Yao, L., Xiao, Y., Keedy, S. K., Reilly, J. L., Keefe, R. S., Tamminga, C. A., Keshavan, M. S., Pearlson, G. D., Gong, Q., & Sweeney, J. A. (2015). Resting-state brain function in schizophrenia and psychotic bipolar probands and their first-degree relatives. Psychological Medicine, 45(1), 97–108.

    CAS  PubMed  Google Scholar 

  • McAvoy, M., Larson-Prior, L., Nolan, T. S., Vaishnavi, S. N., Raichle, M. E., & d'Avossa, G. (2008). Resting states affect spontaneous BOLD oscillations in sensory and paralimbic cortex. Journal of Neurophysiology, 100(2), 922–931.

    PubMed  PubMed Central  Google Scholar 

  • Merboldt, K. D., Fransson, P., Bruhn, H., & Frahm, J. (2001). Functional MRI of the human amygdala? Neuroimage, 14(2), 253–257.

    CAS  PubMed  Google Scholar 

  • Moeller, S., Yacoub, E., Olman, C. A., Auerbach, E., Strupp, J., Harel, N., & Uğurbil, K. (2010). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magnetic Resonance in Medicine, 63(5), 1144–1153.

    PubMed  PubMed Central  Google Scholar 

  • Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., et al. (2013). Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in Human Neuroscience, 7, 599.

    PubMed  PubMed Central  Google Scholar 

  • Orban, P., Dansereau, C., Desbois, L., Mongeau-Perusse, V., Giguere, C. E., Nguyen, H., et al. (2018). Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity. Schizophrenia Research, 192, 167–171.

    PubMed  Google Scholar 

  • Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2014). Studying brain organization via spontaneous fMRI signal. Neuron, 84(4), 681–696.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Shang, C. Y., Yan, C. G., Lin, H. Y., Tseng, W. Y., Castellanos, F. X., & Gau, S. S. (2016). Differential effects of methylphenidate and atomoxetine on intrinsic brain activity in children with attention deficit hyperactivity disorder. Psychological Medicine, 46(15), 3173–3185.

    CAS  PubMed  Google Scholar 

  • Tadayonnejad, R., Yang, S., Kumar, A., & Ajilore, O. (2015). Clinical, cognitive, and functional connectivity correlations of resting-state intrinsic brain activity alterations in unmedicated depression. Journal of Affective Disorders, 172, 241–250.

    PubMed  Google Scholar 

  • Tam, A., Dansereau, C., Badhwar, A., Orban, P., Belleville, S., Chertkow, H., et al. (2015). Common effects of amnestic mild cognitive impairment on resting-state connectivity across four independent studies. Frontiers in Aging Neuroscience, 7, 242.

    PubMed  PubMed Central  Google Scholar 

  • Teipel, S. J., Wohlert, A., Metzger, C., Grimmer, T., Sorg, C., Ewers, M., Meisenzahl, E., Klöppel, S., Borchardt, V., Grothe, M. J., Walter, M., & Dyrba, M. (2017). Multicenter stability of resting state fMRI in the detection of Alzheimer's disease and amnestic MCI. Neuroimage Clin, 14, 183–194.

    PubMed  PubMed Central  Google Scholar 

  • Van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.

    PubMed  Google Scholar 

  • Wang, Z., Yan, C., Zhao, C., Qi, Z., Zhou, W., Lu, J., He, Y., & Li, K. (2011). Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer's disease: A resting-state functional MRI study. Human Brain Mapping, 32(10), 1720–1740.

    PubMed  Google Scholar 

  • Wang, J. B., Zheng, L. J., Cao, Q. J., Wang, Y. F., Sun, L., Zang, Y. F., et al. (2017). Inconsistency in abnormal brain activity across cohorts of ADHD-200 in children with attention deficit hyperactivity disorder. Frontiers in Neuroscience, 11, 320.

    PubMed  PubMed Central  Google Scholar 

  • Wang, J., Zhang, J. R., Zang, Y. F., & Wu, T. (2018). Consistent decreased activity in the putamen in Parkinson's disease: A meta-analysis and an independent validation of resting-state fMRI. Gigascience, 7(6).

  • Weiskopf, N., Hutton, C., Josephs, O., & Deichmann, R. (2006). Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: A whole-brain analysis at 3 T and 1.5 T. Neuroimage, 33(2), 493–504.

    PubMed  Google Scholar 

  • Xia, M., Si, T., Sun, X., Ma, Q., Liu, B., Wang, L., et al. (2019). Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals. Neuroimage, 289, 700–714.

    Google Scholar 

  • Yan, C. G., Craddock, R. C., Zuo, X. N., Zang, Y. F., & Milham, M. P. (2013). Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage, 80, 246–262.

    PubMed  Google Scholar 

  • Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data Processing & Analysis for (resting-state) brain imaging. Neuroinformatics, 14(3), 339–351.

    PubMed  Google Scholar 

  • Yang, H., Long, X. Y., Yang, Y., Yan, H., Zhu, C. Z., Zhou, X. P., et al. (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage, 36(1), 144–152.

    PubMed  Google Scholar 

  • Yu, R., Chien, Y. L., Wang, H. L., Liu, C. M., Liu, C. C., Hwang, T. J., Hsieh, M. H., Hwu, H. G., & Tseng, W. Y. (2014). Frequency-specific alternations in the amplitude of low-frequency fluctuations in schizophrenia. Human Brain Mapping, 35(2), 627–637.

    PubMed  Google Scholar 

  • Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., Tian, L. X., Jiang, T. Z., & Wang, Y. F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 29(2), 83–91.

    PubMed  Google Scholar 

  • Zhang, D., & Raichle, M. E. (2010). Disease and the brain's dark energy. Nature Reviews. Neurology, 6(1), 15–28.

    PubMed  Google Scholar 

  • Zhang, Z., Lu, G., Zhong, Y., Tan, Q., Chen, H., Liao, W., Tian, L., Li, Z., Shi, J., & Liu, Y. (2010). fMRI study of mesial temporal lobe epilepsy using amplitude of low-frequency fluctuation analysis. Human Brain Mapping, 31(12), 1851–1861.

    PubMed  PubMed Central  Google Scholar 

  • Zhang, Z., Liao, W., Zuo, X. N., Wang, Z., Yuan, C., Jiao, Q., Chen, H., Biswal, B. B., Lu, G., & Liu, Y. (2011). Resting-state brain organization revealed by functional covariance networks. PLoS One, 6(12), e28817.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang, Z., Xu, Q., Liao, W., Wang, Z., Li, Q., Yang, F., Zhang, Z., Liu, Y., & Lu, G. (2015). Pathological uncoupling between amplitude and connectivity of brain fluctuations in epilepsy. Human Brain Mapping, 36(7), 2756–2766.

    PubMed  PubMed Central  Google Scholar 

  • Zou, Q. H., Zhu, C. Z., Yang, Y., Zuo, X. N., Long, X. Y., Cao, Q. J., Wang, Y. F., & Zang, Y. F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141.

    PubMed  PubMed Central  Google Scholar 

  • Zuo, X. N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., et al. (2010). The oscillating brain: Complex and reliable. Neuroimage, 49(2), 1432–1445.

    PubMed  Google Scholar 

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Acknowledgments

This work was supported by the Natural Science Foundation of China (81201083) and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (16YJCZH057).

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  1. Xinbo Wang and Qing Wang contributed equally to this work.

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  1. Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China

    Xinbo Wang, Qing Wang, Peiwen Zhang, Shufang Qian, Shiyu Liu & Dong-Qiang Liu

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Wang, X., Wang, Q., Zhang, P. et al. Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data. Neuroinform 19, 23–38 (2021). https://doi.org/10.1007/s12021-020-09463-x

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