Abstract
Functional connectivity-based analysis of functional magnetic resonance imaging data (fMRI) is an emerging technique for human brain mapping. One powerful method for the investigation of functional connectivity is independent component analysis (ICA) of concatenated data. However, this research field is evolving toward processing increasingly larger database taking into account inter-individual variability. Concatenated data analysis only handles these features using some additional procedures such as bootstrap or including a model of between-subject variability during the preprocessing step of the ICA. In order to alleviate these limitations, we propose a method based on group analysis of individual ICA components, using a multi-scale clustering (MICCA). MICCA start with two steps repeated several times: 1) single subject data ICA followed by 2) clustering of all subject independent components according to a spatial similarity criterion. A final third step consists in selecting reproducible clusters across the repetitions of the two previous steps. The core of the innovation lies in the multi-scale and unsupervised clustering algorithm built as a chain of three processes: robust proto-cluster creation, aggregation of the proto-clusters, and cluster consolidation. We applied MICCA to the analysis of 310 fMRI resting state dataset. MICCA identified 28 resting state brain networks. Overall, the cluster neuroanatomical substrate included 98% of the cerebrum gray matter. MICCA results proved to be reproducible in a random splitting of the data sample and more robust than the classical concatenation method.
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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, 1207–1216.
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26, 839–851.
Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. Medical Imaging, IEEE Transactions on, 23, 137–152.
Beckmann, C. F., & Smith, S. M. (2005). Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage, 25, 294–311.
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 1001.
Beckmann, C., Mackay, C., Filippini, N., & Smith, S. (2009). Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. NeuroImage, 47, S148.
Biswal, B. B., & Ulmer, J. L. (1999). Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. Journal of Computer Assisted Tomography, 23, 265–271.
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.
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., et al. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences, 107, 4734–4739.
Buckner, R. L., & Vincent, J. L. (2007). Unrest at rest: default activity and spontaneous network correlations. Neuroimage, 37, 1091–1096.
Calhoun, V. D., Adali, T., McGinty, V. B., Pekar, J. J., Watson, T. D., & Pearlson, G. D. (2001a). fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis. NeuroImage, 14, 1080–1088.
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001b). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14, 140–151.
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, 828–838.
Chang, C., & Glover, G. H. (2010). Time–frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50, 81–98.
Chen, S., Ross, T. J., Zhan, W., Myers, C. S., Chuang, K. S., Heishman, S. J., Stein, E. A., & Yang, Y. (2008). Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Research, 1239, 141–151.
Cole, D. M., Smith, S. M., & Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 8.
Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J. S., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Rombouts, S. A. R. B. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18, 1856–1864.
Damoiseaux, J. S., Rombouts, S., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences, 103, 13848.
Daubechies, I., Roussos, E., Takerkart, S., Benharrosh, M., Golden, C., D’Ardenne, K., Richter, W., Cohen, J. D., & Haxby, J. (2009). Independent component analysis for brain fMRI does not select for independence. Proceedings of the National Academy of Sciences, 106, 10415–10422.
De Luca, M., Beckmann, C. F., De Stefano, N., Matthews, P. M., & Smith, S. M. (2006). fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage, 29, 1359–1367.
De Martino, F., Gentile, F., Esposito, F., Balsi, M., Di Salle, F., Goebel, R., & Formisano, E. (2007). Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers. NeuroImage, 34, 177–194.
Esposito, F., Aragri, A., Pesaresi, I., Cirillo, S., Tedeschi, G., Marciano, E., Goebel, R., & Di Salle, F. (2008). Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI. Magnetic Resonance Imaging, 26, 905–913.
Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., Tedeschi, G., Goebel, R., Seifritz, E., & Di Salle, F. (2005). Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25, 193–205.
Friston, K. J., Holmes, A. P., Price, C. J., Büchel, C., & Worsley, K. J. (1999). Multisubject fMRI studies and conjunction analyses. Neuroimage, 10, 385–396.
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences, 101, 4637–4642.
Himberg, J., Hyvärinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage, 22, 1214–1222.
Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. Neural Networks, IEEE Transactions on, 10, 626–634.
Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: Wiley.
Jutten, C., & Herault, J. (1991). Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1–10.
Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., Veijola, J., Moilanen, I., Isohanni, M., Zang, Y.-F., & Tervonen, O. (2009). Functional segmentation of the brain cortex using high model order group PICA. Human Brain Mapping, 30, 3865–3886.
Lee, J.-H., Lee, T.-W., Jolesz, F. A., & Yoo, S.-S. (2008). Independent vector analysis (IVA): multivariate approach for fMRI group study. NeuroImage, 40, 86–109.
Li, Y.-O., Adalı, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28, 1251–1266.
Makeig, S., Jung, T. P., Bell, A. J., Ghahremani, D., & Sejnowski, T. J. (1997). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences of the United States of America, 94, 10979.
Mazoyer, B., Zago, L., Mellet, E., Bricogne, S., Etard, O., Houde, O., Crivello, F., Joliot, M., Petit, L., & Tzourio-Mazoyer, N. (2001). Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Research Bulletin, 54, 287–298.
McKeown, M. (2003). Independent component analysis of functional MRI: what is signal and what is noise? Current Opinion in Neurobiology, 13, 620–629.
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.
Minka, T., (2000). Automatic choice of dimensionality for PCA (Technical report No. 514). MIT.
Ojemann, J. G., Akbudak, E., Snyder, A. Z., McKinstry, R. C., Raichle, M. E., & Conturo, T. E. (1997). Anatomic localization and quantitative analysis of gradient refocused echo-planar fMRI susceptibility artifacts. NeuroImage, 6, 156–167.
Perlbarg, V., Marrelec, G., Doyon, J., Pélégrini-Issac, M., Lehéricy, S., Benali, H. (2008). NEDICA: Detection of group functional networks in FMRI using spatial independent component analysis. Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on 1247–1250.
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98, 676.
Schöpf, V., Kasess, C. H., Lanzenberger, R., Fischmeister, F., Windischberger, C., & Moser, E. (2010). Fully exploratory network ICA (FENICA) on resting-state fMRI data. Journal of Neuroscience Methods, 192, 207–213.
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23, 208–219.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & 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.
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, 288–299.
Vigário, R., Sarela, J., Jousmiki, V., Hamalainen, M., & Oja, E. (2002). Independent component approach to the analysis of EEG and MEG recordings. Biomedical Engineering, IEEE Transactions on, 47, 589–593.
Acknowledgements
The authors are deeply indebted to Guy Perchey and Mathieu Vigneau for their help with fMRI data acquisition and analysis.
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Appendix
Simulation of Hub in the Data: Comparison of MICCA and Concat-ICA Processing
We adapted the simulation framework developed by Daubechies et al. (2009) for the group ICA analysis using the Matlab Fast ICA toolbox (http://www.cis.hut.fi/projects/ica/fastica/). We consider two independent components, C 1 and C 2 , and two “observations” at times t 1 and t 2 . “Activation process” is simulated by a random variable with a cumulative density function (cdf) equal to:
whereas “background process” was associated to a random variable with a cdf equal to:
“Activations” were restricted to a sparse square of the simulated space (space: 100 × 100 pixels, C 1 (“activated”): 20x20 pixels and C 2 (“activated”): 25 × 20 pixels). Activated pixels of component C 1 were positioned at the coordinates {41,…,60} × {31,…,50}. Activated pixels of component C 2 were gradually shifted according to a parameter α, ranging from -15 to 15, and located at the coordinates {57 + α, …, 81 + α} × {46 + α,…,65 + α}. α measured the overlap between the activated regions of the two independent components from completely separated (α > 10) to highly overlapping (α < -10). α = 0 corresponds to the mathematical independence. Both components were simulated for 100 subjects (for each α value) and mixed by the same times-series: 0.5 C 1 + 0.5 C 2 at t 1 and 0.3 C 1 + 0.7 C 2 at t 2 . These observations (100 subjects x 2 time points) were then used as input for both Concat-ICA and MICCA. We used the spatial correlation to quantify the quality of the decomposition by the norm λ = || ρ(S ic ,S ic )–ρ(S ic ,E ic )|| of the difference between the autocorrelation matrix of the simulated components (S ic ), and the cross correlation matrix between the estimated components (E ic ) and the simulated components (S ic ). For accurate decomposition, λ is close to 0. Because λ depends on the particular realization of C 1 and C 2 , analyses were performed 100 times for each α value. We report the mean and standard deviation of λ. By visual inspection of “success separation”, we considered the components well separated when λ < 0.2 and not when λ > 0.3.
Figure 11 indicates the success of the separation between the two components for both Concat-ICA and MICCA. Under this sparse condition, individual ICA should not be dependent on the overlap value (Daubechies et al. 2009). In the case of independence or no overlap (α ≥ 0), both group approaches successfully estimate the components (λ < 0.2). Concat-ICA was not able to separate components when they shared a large region of “activation” (α ≤ -8). In other words, regions sharing functional connectivity with many networks (known as a “hub”) cannot be well separated using Concat-ICA; thus, networks are not successfully detected.
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Naveau, M., Doucet, G., Delcroix, N. et al. A Novel Group ICA Approach Based on Multi-scale Individual Component Clustering. Application to a Large Sample of fMRI Data. Neuroinform 10, 269–285 (2012). https://doi.org/10.1007/s12021-012-9145-2
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DOI: https://doi.org/10.1007/s12021-012-9145-2