Abstract
There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.
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Abbott, C., Juarez, M., White, T., Gollub, R.L., Pearlson, G.D., Bustillo, J.R., Lauriello, J., Ho, B.C., Bockholt, H.J., Clark, V.P., Magnotta, V., & Calhoun, V.D. (2011). Antipsychotic dose and diminished neural modulation: a multi-site fMRI study. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 35, 473–482.
Abou-Elseoud, A., Starck, T., Remes, J., Nikkinen, J., Tervonen, O., & Kiviniemi, V. (2010). The effect of model order selection in group PICA. Human Brain Mapping, 31(8), 1207–1216.
Alkan, Y., Biswal, B.B., Taylor, P.A., & Alvarez, T.L. (2011). Segregation of frontoparietal and cerebellar components within saccade and vergence networks using hierarchical independent component analysis of fMRI. Vision Neuroscience, 28(3), 247–261.
Allen, E., Erhardt, E., Damaraju, E., Gruner, W., Segall, J., Silva, R., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., Michael, A., Turner, J., Eichele, T., Adelsheim, S., Bryan, A., Bustillo, J.R., Clark, V.P., Feldstein, S., Filbey, F.M., Ford, C., Hutchison, K., Jung, R., Kiehl, K.A., Kodituwakku, P., Komesu, Y., Mayer, A.R., Pearlson, G.D., Phillips, J., Sadek, J., Stevens, M., Teuscher, U., Thoma, R.J., & Calhoun, V.D. (2011). A baseline for the multivariate comparison of resting state networks. Frontiers in Systems Neuroscience, 5(2), 12.
Andrews-Hanna, J.R., Reidler, J.S., Sepulcre, J., Poulin, R., & Buckner, R.L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65(4), 550–562.
Beckmann, C.F., & Smith, S.M. (2005). Tensorial extensions of independent component analysis for multisubject FMRI analysis. NeuroImage, 25(1), 294–311.
Bell, A.J., & Sejnowski, T.J. (1995). An information maximisation approach to blind separation and blind deconvolution. Neural Computing, 7(6), 1129–1159.
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., Kotter, 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.
Bockholt, H.J., Scully, M., Courtney, W., Rachakonda, S., Scott, A., Caprihan, A., Fries, J., Kalyanam, R., Segall, J., De la Garza, R., Lane, S., & Calhoun, V.D. (2010). Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources. Frontiers in Neuroinformatics, 3(36), 1–10.
Calhoun, V.D., & Adali, T. (2009). Feature-based fusion of medical imaging data. IEEE Transactions on Information Technology in Biomedicine, 13(5), 1–10.
Calhoun, V.D., Adali, T., Kiehl, K.A., Astur, R.S., Pekar, J.J., & Pearlson, G.D. (2006). A method for multi-task fMRI data fusion applied to schizophrenia. Human Brain Mapping, 27(7), 598–610.
Calhoun, V.D., Adali, T., Pearlson, G.D., & Pekar, J.J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151.
Calhoun, V.D., Adali, T., Pekar, J.J., & Pearlson, G.D. (2003). Latency (in)sensitive ICA: group independent component analysis of fMRI data in the temporal frequency domain. NeuroImage, 20(3), 1661–1669.
Calhoun, V.D., Kiehl, K.A., & Pearlson, G.D. (2008). Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Human Brain Mapping, 29(7), 828–838.
Calhoun, V.D., Liu, J., & Adali, T. (2009). A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage, 45, 163–172.
Caprihan, A., Abbott, C., Yamamoto, J., Pearlson, G.D., Bizzozero, N., Sui, J., & Calhoun, V.D. (2011). Source-based morphometry analysis of group differences in fractional anisotropy in schizophrenia. Brain Connectivity, 1(2), 133–145.
Damoiseaux, J.S., Beckmann, C.F., Arigita, E.J., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., & Rombouts, S.A. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18(8), 1856–1864.
Erhardt, E., Allen, E., Damaraju, E., & Calhoun, V.D. (2011a). On network derivation, classification, and visualization: a response to Habeck and Moeller. Brain Connectivity, 1(2), 1–19.
Erhardt, E., Allen, E., Wei, Y., Eichele, T., & Calhoun, V.D. (2012). SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability. NeuroImage, 59, 4160–4167.
Erhardt, E., Rachakonda, S., Bedrick, E., Adali, T., & Calhoun, V.D. (2011b). Comparison of multi-subject ICA methods for analysis of fMRI data. Human Brain Mapping, 12, 2075–2095.
Franco, A.R., Pritchard, A., Calhoun, V.D., & Mayer, A.R. (2009). Inter-rater and inter-method reliability of default mode network selection. Human Brain Mapping, 30(7), 2293–2303.
Friston, K., Ashburner, J., Frith, C.D., Poline, J.P., Heather, J.D., & Frackowiak, R.S. (1995a). Spatial registration and normalization of images. Human Brain Mapping, 2, 165–189.
Friston, K.J., Frith, C.D., Turner, R., & Frackowiak, R.S. (1995b). Characterizing evoked hemodynamics with fMRI. NeuroImage, 2(2), 157–165.
Himberg, J., Hyvarinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22(3), 1214–1222.
Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., Veijola, J., Moilanen, I., Isohanni, M., & Zang, Y.F. (2009). Functional segmentation of the brain cortex using high model order group PICA. Human Brain Mapping, 30, 3865–3886.
Laird, A.R., Fox, P.M., Eickhoff, S.B., Turner, J.A., Ray, K.L., McKay, D.R., Glahn, D.C., Beckmann, C.F., Smith, S.M., & Fox, P.T. (2011). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience, 23(12), 4022–4037.
Li, Y., Adali, T., & Calhoun, V.D. (2007). Estimating the number of independent components for fMRI data. Human Brain Mapping, 28(11), 1251–1266.
McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., & Sejnowski, T.J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6, 160–188.
Michael, A., Baum, S., White, T., Demirci, O., Andreasen, N.C., Segall, J.M., Jung, R.E., Pearlson, G.D., Clark, V.P., Gollub, R.L., Schulz, S.C., Roffmann, J., Lim, K.O., Ho, B.C., Bockholt, H.J., & Calhoun, V.D. (2010). Does function follow form?: methods to fuse structural and functional brain images show decreased linkage in schizophrenia. Human Brain Mapping, 49(3), 2626–2637.
Scott, A., Courtney, W., Wood, D., De la Garza, R., Lane, S., Wang, R., Roberts, J., Turner, J.A., & Calhoun, V.D. (2011). COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Frontiers in Neuroinformatics, 5(33), 1–15.
Segall, J., & Calhoun, V.D. (2011). Structural and functional networks in the human brain. Paper presented at the Proc. HBM, Quebec City, Canada.
Seifritz, E., Esposito, F., Hennel, F., Mustovic, H., Neuhoff, J.G., Bilecen, D., Tedeschi, G., Scheffler, K., & Salle, F.D. (2002). Spatiotemporal pattern of neural processing in the human auditory cortex. Science, 297(6), 1706–1708.
Shehzad, Z., Kelly, A.M., Reiss, P.T., Gee, D.G., Gotimer, K., Uddin, L.Q., Lee, S.H., Margulies, D.S., Roy, A.K., Biswal, B.B., Petkova, E., Castellanos, F.X., & Milham, M.P. (2009). The resting brain: unconstrained yet reliable. Cerebral Cortex, 19(10), 2209–2229.
Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., & Beckmann, C.F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045.
Sui, J., Adali, T., Clark, V.P., Pearlson, G., & Calhoun, V.D. (2009). A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework. Human Brain Mapping, 30(9), 2953–2970.
Sui, J., Adali, T., Yu, Q., & Calhoun, V.D. (2012). A review of multivariate methods for multimodal fusion of brain imaging data. Journal of Neuroscience Methods, 204(1), 68–81.
Svensen, M., Kruggel, F., & Benali, H. (2002). ICA of fMRI group study data. NeuroImage, 16, 551–563.
Van Dijk, K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., & Buckner, R.L. (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology, 103(1), 297–321.
Varoquaux, G., Sadaghiani, S., Pinel, P., Kleinschmidt, A., Poline, J.B., & Thirion, B. (2010). A group model for stable multi-subject ICA on fMRI datasets. NeuroImage, 51(1), 288–299.
Xu, L., Groth, K., Pearlson, G., Schretlen, D., & Calhoun, V. (2009). Source based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Human Brain Mapping, 30, 711–724.
Ystad, M., Eichele, T., Lundervold, A.J., & Lundervold, A. (2010). Subcortical functional connectivity and verbal episodic memory in healthy elderly—a resting state fMRI study. NeuroImage, 52(1), 379–388.
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.
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This research was supported in part by the National Institute of Health (NIH), under grants 1 R01 EB 000840, 1 R01 EB 005846, and 1 R01 EB 006841.
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Calhoun, V.D., Allen, E. Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis. Psychometrika 78, 243–259 (2013). https://doi.org/10.1007/s11336-012-9291-3
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DOI: https://doi.org/10.1007/s11336-012-9291-3