Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data

  • Juan E. Arco
  • Paloma Díaz-Gutiérrez
  • Javier Ramírez
  • María RuzEmail author
Original Article


Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employed. In the current study, we examined the validity of two different alternatives to Searchlight: an atlas-based local averaging method (ABLA, Schrouff et al. Neuroinformatics 16, 117–143, 2013a) and a Multi-Kernel Learning (MKL, Rakotomamonjy et al. Journal of Machine Learning 9, 2491–2521, 2008) approach, in a scenario where the goal is to find the informative brain regions that support certain mental operations. These methods employ weights to measure the informativeness of a brain region and highly reduce the large computational cost that Searchlight entails. We evaluated their performance in two different scenarios where the differential BOLD activation between experimental conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations. Results show that both methods were able to localize informative regions when differences between conditions were large, demonstrating a large sensitivity and stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provided the directionality of univariate approaches. However, when differences were small, only ABLA localized informative regions. Thus, our results show that atlas-based methods are useful alternatives to Searchlight, but that the nature of the classification to perform should be taken into account when choosing the specific method to implement.


Multi-voxel pattern analysis Multiple-kernel learning Searchlight Atlas-based local averaging fMRI Permutation testing 



We are grateful to Janaina Mourão-Miranda for her kind help during the development of the algorithms employed in the current research.


This work was supported by the Spanish Ministry of Science and Innovation through grant PSI2016–78236-P to M.R and the Spanish Ministry of Economy and Competitiveness through grant BES-2014-069609 to J.E.A.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


  1. Abdulrahman, H., & Henson, R. N. (2016). Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis. NeuroImage, 125, 756–766.CrossRefGoogle Scholar
  2. Adeli, E., Guorong, W., Saghafi, B., An, L., Shi, F., & Shen, D. (2017). Kernel-based joint feature selection and max-margin classification for early diagnosis of Parkinson’s disease. Scientific Reports, 7, 41069.CrossRefGoogle Scholar
  3. Arco, J.E., Ramírez, J., Puntonet, C.G., Górriz, J.M., Ruz, M., 2015. Short-term prediction of MCI to AD conversion based on longitudinal MRI analysis and neuropsychological tests. Innovation in medicine healthcare, 385-394.Google Scholar
  4. Arco, J.E., González-García, C., Ramírez, J., Ruz, M., 2016. Comparison of different methods for brain decoding from fMRI beta maps. Poster presented at 22nd annual meeting of the Organization for Human Brain Mapping, Geneve, (Switzerland).Google Scholar
  5. Arco, J. E., González-García, C., Díaz-Gutiérrez, P., Ramírez, J., & Ruz, M. (2018). Influence of activation pattern estimates and statistical significance tests in fMRI decoding analysis. Journal of Neuroscience Methods, 308, 248–260.CrossRefGoogle Scholar
  6. Balci, S. K., Sabuncu, M. R., Yoo, J., Ghosh, S. S., Whitfield-Gabrieli, S., Gabrieli, J. D., & Golland, P. (2008). Prediction of successful memory encoding from fMRI data. Med Image Comput Assist Inter, 11, 97–104.Google Scholar
  7. Baldassarre, L., Pontil, M., & Mourão-Miranda, J. (2017). Combining accuracy and stability for model selection in brain decoding. Frontiers in Neuroscience, 11, 62.CrossRefGoogle Scholar
  8. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51, 1126–1139.CrossRefGoogle Scholar
  9. Bennett, K.P., Blue, J.A., 1998. A support vector machine approach to decision trees. 1998 IEEE international joint conference in neural networks proceedings.Google Scholar
  10. Bhandari, A., Gagne, C., & Badre, D. (2018). Just above chance: Is it harder to decode information from prefrontal cortex hemodynamic activity patterns? Journal of Cognitive Neuroscience, 30(10), 1473–1498.CrossRefGoogle Scholar
  11. Blankertz, B., Dornhege, G., Kraudelat, M., Müller, K. R., & Curio, G. (2007). The non-invasive Berlin brain-computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage, 37(2), 539–550.CrossRefGoogle Scholar
  12. Bode, S., & Haynes, J.-D. (2009). Decoding sequential stages of task preparation in the human brain. NeuroImage, 45(2), 606–613.CrossRefGoogle Scholar
  13. Boser, B.E., Guyon, I., Vapnik, V., 1992. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on computational learning theory, 144-152.Google Scholar
  14. Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M., 2010. The balanced accuracy and its posterior distribution. 2010 20th international conference on pattern recognition.Google Scholar
  15. Brodersen, K. H., Schofield, T. M., Leff, A. P., Ong, C. S., Lomakina, E. I., Buhmann, J. M., & Stephan, K. E. (2011). Generative embedding for model-based classification of fMRI data. PLoS Computational Biology, 7(6), e1002079.CrossRefGoogle Scholar
  16. Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167.CrossRefGoogle Scholar
  17. Chanel, G., Pichon, S., Conty, L., Berthoz, S., Chevallier, C., & Grèzes, J. (2016). Classification of autistic individuals and controls using cross-task characterization of fMRI activity. NeuroImage: Clinical, 10, 76–88.Google Scholar
  18. Chang, L. J., & Sanfey, A. G. (2013). Great expectations: Neural computations underlying the use of social norms in decision-making. Social Cognitive and Affective Neuroscience, 8(3), 277–284.CrossRefGoogle Scholar
  19. Chen, Y., Namburi, P., Elliott, L., Heinzle, J., Soon, C., Chee, M., & Haynes, J. (2011). Cortical surface-based searchlight decoding. NeuroImage, 56, 582–592.CrossRefGoogle Scholar
  20. Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. Nature Neuroscience, 20(1), 115–125.CrossRefGoogle Scholar
  21. Choi, H., Ha, S., Im, H. J., Paek, S. H., & Lee, D. S. (2017). Refining diagnosis of Parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. NeuroImage: Clinical, 16, 586–594.CrossRefGoogle Scholar
  22. Cichy, R. M., Pantazis, D., & Oliva, A. (2016). Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition. Cerebral Cortex, 26(8), 3563–3579.CrossRefGoogle Scholar
  23. Coutanche, M. N., Thompson-Schill, S. L., & Schultz, R. T. (2011). Multi-voxel pattern analysis of MRI data predicts clinical symptom severity. NeuroImage, 57(1), 113–123.CrossRefGoogle Scholar
  24. Cox, D. D., & Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) “brain reading”: Detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19, 261–270.CrossRefGoogle Scholar
  25. Dai, D., Wang, J., Hua, J., & He, H. (2012). Classification of ADHD children through multimodal magnetic resonance imaging. Frontiers in Systems Neuroscience, 6(63).Google Scholar
  26. De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008). Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage, 43, 44–58.CrossRefGoogle Scholar
  27. Del Gaizo, J., Mofrad, N., Jensen, J. H., Clark, D., Glenn, R., Helpern, J., & Bonilha, L. (2017). Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain and Behavior: A Cognitive Neuroscience Perspective, 7(10), e00801.CrossRefGoogle Scholar
  28. Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 968–980.CrossRefGoogle Scholar
  29. Di Russo, F., Berchicci, M., Bozzacchi, C., Perri, R. L., Pitzalis, S., & Spinelli, D. (2017). Beyond the “Bereitschaftspotential”: Action preparation behind cognitive functions. Neuroscience and Biobehavioral Reviews, 78, 57–81.CrossRefGoogle Scholar
  30. Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., et al. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358–1361.CrossRefGoogle Scholar
  31. Dubois, J., de Berker, A. O., & Tsao, D. Y. (2015). Single-unit recordings in the macaque face patch system reveal limitations of fMRI MVPA. The Journal of Neuroscience, 35(6), 2791–2802.CrossRefGoogle Scholar
  32. Etzel, J. A., Zacks, J. M., & Braver, T. S. (2013). Searchlight analysis: Promise, pitfalls, and potential. NeuroImage, 78, 261–269.CrossRefGoogle Scholar
  33. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., & Lin, C.-J. (2008). LIBLINEAR: A library for large linear classification. Journal March Learning Res, 9, 1871–1874.Google Scholar
  34. Fan, L., Wang, J., Zhang, Y., Han, W., Yu, C., & Jiang, T. (2014). Connectivity-based parcellation of the human temporal pole using diffusion tensor imaging. Cerebral Cortex, 24, 3365–3378.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. Filippone, M., Marquand, A. F., Blain, C. R. V., Williams, S. C. R., Mourão-Miranda, J., & Girolami, M. (2013). Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities. The Annals of Applied Statistics, 6(4), 1883–1905.CrossRefGoogle Scholar
  37. Forman, S., Cohen, J., Fitzgerald, M., Eddy, W., Mintum, M., & Noll, D. (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): Use of a cluster-size threshold. Magnetic Resonance in Medicine, 33, 636–647.CrossRefGoogle Scholar
  38. Fort, G., & Lambert-Lacroix, S. (2005). Classification using partial least squares with penalized logistic regression. Bioinformatics, 21, 1104–1111.CrossRefGoogle Scholar
  39. Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P., Frith, C. D., & Frackowiak, R. S. J. (1995). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2, 189–210.CrossRefGoogle Scholar
  40. Gabay, A. S., Radua, J., Kempton, M. J., & Mehta, M. A. (2014). The ultimatum game and the brain: A meta-analysis. Neuroscience and Biobehavioral Reviews, 47, 549–558.CrossRefGoogle Scholar
  41. Gaertig, C., Moser, A., Alguacil, S., & Ruz, M. (2012). Social information and economic decisión-making in the ultimatum game. Frontiers in Neuroscience, 6(103).Google Scholar
  42. Gaonkar, B., Shinohara, R., Davatzikos, C., & Initiative, A. D. N. (2015). Interpreting support vector machine models for multivariate group analysis in neuroimaging. Medical Image Analysis, 24(1), 190–204.CrossRefGoogle Scholar
  43. González-García, C., Mas-Herrero, E., de Diego-Balaguer, R., & Ruz, M. (2016). Task-specific preparatory neural activations in low-inference contexts. Brain Structure & Function, 8, 3997–4006.CrossRefGoogle Scholar
  44. González-García, C., Arco, J. E., Palenciano, A. F., Ramírez, J., & Ruz, M. (2017). Encoding, preparation and implementation of novel complex verbal instructions. NeuroImage, 148, 264–273.CrossRefGoogle Scholar
  45. Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., Hampton, J. M., Coalson, R. S., Nguyen, A. L., McDermott, K. B., Shimony, J. S., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Nelson, S. M., & Dosenbach, N. U. F. (2017). Precision functional mapping of individual human brains. Neuron, 95(4), 791–807.CrossRefGoogle Scholar
  46. Grecucci, A., Giorgetta, C., van’t Wout, M., Bonini, N., & Sanfey, A. G. (2013). Reappraising the ultimatum: An fMRI study of emotion regulation and decision making. Cerebral Cortex, 23(2), 399–410.CrossRefGoogle Scholar
  47. Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., & Bießmann, F. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87, 96–110.CrossRefGoogle Scholar
  48. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430.CrossRefGoogle Scholar
  49. Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37, 435–456.CrossRefGoogle Scholar
  50. Haynes, J.-D., & Rees, G. (2005). Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience, 8(5), 686–691.CrossRefGoogle Scholar
  51. Haynes, J.-D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews. Neuroscience, 7, 523–534.CrossRefGoogle Scholar
  52. Hebart, M. N., & Baker, C. I. (2017). Deconstructing multivariate decoding for the study of brain function. NeuroImage, 180, 4–18. Scholar
  53. Henson, R., 2005. Design efficiency in fMRI. URL VII._Should_I_treat_my_trials_as_events_or_epochs_.3F.
  54. Illan, I. A., Górriz, J. M., Ramírez, J., & Meyer-Base, A. (2014). Spatial component analysis of fMRI ata for Alzheimer’s disease diagnosis: A Bayesian network approach. Frontiers in Computational Neuroscience, 26, 156.Google Scholar
  55. Joliot, M., Jobard, G., Naveau, M., Delcroix, N., Petit, L., Zago, L., Crivello, F., Mellet, E., Mazoyer, B., & Tzourio-Mazoyer, N. (2015). AICHA: An atlas of intrinsic connectivity of homotopic areas. Journal of Neuroscience Methods, 254, 46–59.CrossRefGoogle Scholar
  56. Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8(5), 679–685.CrossRefGoogle Scholar
  57. Khedher, L., Illán, I.A., Górriz, J.M., Ramírez, J., Meyer-Baese, A., 2017. Independent component analysis-support vector machine-based computer aided diagnosis system for Alzheimer’s disease with visual support. International journal of neural systems 27(3), 8 1650050.Google Scholar
  58. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. PNAS, 103, 3863–3868.CrossRefGoogle Scholar
  59. Kuzmanovic, B., Rigoux, L., & Tittgemeyer, M. (2018). Influence of vmPFC on dmPFC predicts valence-guided belief formation. The Journal of Neuroscience, 38(37), 7996–8010.CrossRefGoogle Scholar
  60. Lanckriet, G. R. G., Cristianini, N., Bartlett, P., El Ghaoui, L., & Jordan, M. I. (2004). Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5, 27–72.Google Scholar
  61. Lindquist, K., Satpute, A., Wager, T., Weber, J., & Barrett, L. (2015). The brain basis of positive and negative affect: Evidence from a meta-analysis of the human neuroimaging literature. Cerebral Cortex, 26(5), 1910–1922.CrossRefGoogle Scholar
  62. Liu, H., Stufflebeam, S. M., Sepulcre, J., Hedden, T., & Buckner, R. L. (2009). Evidence from intrinsic activity that asymmetry of the human brain is controlled by multiple factors. Proceedings of the National Academy of Sciences, 106(48), 20499–20503.CrossRefGoogle Scholar
  63. Liu, H., Qin, W., Li, W., Fan, L., Wang, J., Jiang, T., & Yu, C. (2013). Connectivity-based parcellation of the human frontal pole with diffusion tensor imaging. The Journal of Neuroscience, 33, 6782–6790.CrossRefGoogle Scholar
  64. Loose, L. S., Wisniewski, D., Rusconi, M., Goschke, T., & Haynes, J.-D. (2017). Switch-independent task representations in frontal and parietal lobe. The Journal of Neuroscience, 37(33), 8033–8042.CrossRefGoogle Scholar
  65. Misaki, M., Kim, Y., Bandettini, P., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. NeuroImage, 53(1), 103–118.CrossRefGoogle Scholar
  66. Moser, A., Gaertig, C., & Ruz, M. (2014). Social information and personal interests modulate neural activity during economic decision-making. Frontiers in Human Neuroscience, 8, 31.Google Scholar
  67. Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H., & Stetter, M. (2005). Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional fMRI data. NeuroImage, 25, 980–995.CrossRefGoogle Scholar
  68. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi- voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10, 424–430.CrossRefGoogle Scholar
  69. Nurse, E. S., Karoly, P. J., Grayden, D. B., & Freestone, D. R. (2015). A generalizable brain-computer-Interface (BCI) using machine learning for feature discovery. PLoS One, 10(6), 1–22.CrossRefGoogle Scholar
  70. Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overvie. NeuroImage, 45, S199–S209.CrossRefGoogle Scholar
  71. Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourão-Miranda, J., Bokde, A. W., Hampel, H., & Ewers, M. (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. NeuroImage, 50(1), 162–174.CrossRefGoogle Scholar
  72. Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2(1), 67–70.CrossRefGoogle Scholar
  73. Poldrack, R. A., & Farah, M. J. (2015). Progress and challenges in probing the human brain. Nature, 526, 371–379.CrossRefGoogle Scholar
  74. Qiao, L., Zhang, L., Chen, A., & Egner, T. (2017). Dynamic trial-by trial recoding of task-set representations in the frontoparietal cortex mediates behavioral flexibility. The Journal of Neuroscience, 37(45), 11037–11050.CrossRefGoogle Scholar
  75. Qureshi, M. N. I., Oh, J., Min, B., Jo, H. J., & Lee, B. (2017). Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Frontiers in Human Neuroscience, 11(157).Google Scholar
  76. Rakotomamonjy, A., Bach, F., Canu, S., & Grandvalet, Y. (2008). SimpleMKL. Journal of Machine Learning, 9, 2491–2521.Google Scholar
  77. Sakai, K. (2008). Task set and prefrontal cortex. Annual Review of Neuroscience, 31, 219–245.CrossRefGoogle Scholar
  78. Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., & Eickhoff, S. B. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 1–20.Google Scholar
  79. Schrouff, J., Cremers, J., Garraux, G., Baldassarre, L., Mourão-Miranda, J., Phillips, C., 2013a. Localizing and comparing weight maps generated from linear kernel machine learning models. IEEE Explore,
  80. Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., & Mourão-Miranda, J. (2013b). Localizing and comparing weight maps generated fromlinear kernel machine learning models. Proceedings of the 3rd workshop on Pattern Recognition in NeuroImaging,
  81. Schrouff, J., Monteiro, J. M., Portugal, L., Rosa, M. J., Phillips, C., & Mourão-Miranda, J. (2018). Embedding anatomical or functional knowledge in whole-brain multiple kernel learnig models. Neuroinformatics, 16, 117–143.CrossRefGoogle Scholar
  82. Sona, D., Veeramachaneni, S., Olivetti, E., & Avesani, P. (2007). Inferring cognition from fMRI brain images. Int Conf Artif Neural Netw, 869–878.Google Scholar
  83. Stelzer, J., Chen, Y., & Turner, R. (2013). Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control. NeuroImage, 65, 69–82.CrossRefGoogle Scholar
  84. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1), 267–288.Google Scholar
  85. Turner, B., Mumford, J., Poldrack, R., & Ashby, F. (2012). Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs. NeuroImage, 62(3), 1429–1438.CrossRefGoogle Scholar
  86. Urchs, S., Dansereau, C., Benhajali, Y., Bellec, P. (2015) Group multiscale functional template generated with BASC on the Cambridge sample
  87. Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Kuncheng, L., Baker, J. T., Stufflebeam, S. M., Wang, K., Wang, X., Hong, B., & Liu, H. (2015). Parcellating cortical functional networks in individuals. Nature Neuroscience, 18, 1853–1860.CrossRefGoogle Scholar
  88. Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125–1165.CrossRefGoogle Scholar
  89. Yu, S., Falck, T., Daemen, A., Tranchevent, L. C., Suykens, J. A., De Moor, B., & Moreau, Y. (2010). L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinformatics, 11, 309.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Juan E. Arco
    • 1
  • Paloma Díaz-Gutiérrez
    • 1
  • Javier Ramírez
    • 2
  • María Ruz
    • 1
    Email author
  1. 1.Mind, Brain and Behavior Research Center (CIMCYC)University of GranadaGranadaSpain
  2. 2.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain

Personalised recommendations