Skip to main content

Advertisement

Log in

MANIA—A Pattern Classification Toolbox for Neuroimaging Data

  • Software Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Conventional univariate statistics are common and widespread in neuroimaging research. However, functional and structural MRI data reveal a multivariate nature, since neighboring voxels are highly correlated and different localized brain regions activate interdependently. Multivariate pattern classification techniques are capable of overcoming shortcomings of univariate statistics. A rising interest in such approaches on neuroimaging data leads to an increasing demand of appropriate software and tools in this field. Here, we introduce and release MANIA—Machine learning Application for NeuroImaging Analyses. MANIA is a Matlab based software toolbox enabling easy pattern classification of neuroimaging data and offering a broad assortment of machine learning algorithms and feature selection methods. Between groups classifications are the main scope of this software, for instance the differentiation between patients and controls. A special emphasis was placed on an intuitive and easy to use graphical user interface allowing quick implementation and guidance also for clinically oriented researchers. MANIA is free and open source, published under GPL3 license. This work will give an overview regarding the functionality and the modular software architecture as well as a comparison between other software packages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://code.google.com/p/princeton-mvpa-toolbox/

  2. http://www.pymvpa.org/

  3. http://www.mlnl.cs.ucl.ac.uk/pronto/prtsoftware.html

  4. http://www.brainmap.co.uk/probid.htm

  5. http://www.lacontelab.org/3dsvm.htm

  6. http://scikit-learn.org/

  7. http://www.cs.waikato.ac.nz/ml/weka/

  8. http://www.uni-marburg.de/fb12/kebi/research/software/nifti_importer

  9. http://www.fil.ion.ucl.ac.uk/spm

References

  • Almeida, J. R. C., Mourao-Miranda, J., Aizenstein, H. J., Versace, A., Kozel, F., Lu, H., et al. (2013). Pattern recognition analysis of anterior cingulate cortex blood flow to classify depression polarity. The British Journal of Psychiatry, 203(4), 310-311. doi:10.1192/bjp.bp.112.122838.

    Google Scholar 

  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. doi:10.1007/BF00058655.

    Google Scholar 

  • Chang, C.-C., & Lin, C.-J. (2011). LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27:1–27:27.

    Article  Google Scholar 

  • Colby, J. B., Rudie, J. D., Brown, J. A., Douglas, P. K., Cohen, M. S., & Shehzad, Z. (2012). Insights into multimodal imaging classification of ADHD. Frontiers in Systems Neuroscience, 6(August), 59. doi:10.3389/fnsys.2012.00059.

    PubMed Central  PubMed  Google Scholar 

  • Cox, 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(2), 261–270. doi:10.1016/S1053-8119(03)00049-1.

    Article  PubMed  Google Scholar 

  • Craddock, R. C., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine, 62(6), 1619–1628. doi:10.1002/mrm.22159.

    Article  Google Scholar 

  • Deshpande, G., Li, Z., Santhanam, P., Coles, C. D., Lynch, M. E., Hamann, S., et al. (2010). Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLoS One, 5(12), e14277. doi:10.1371/journal.pone.0014277.

    Article  PubMed Central  PubMed  Google Scholar 

  • Ding, Y., & Wilkins, D. (2006). Improving the performance of SVM-RFE to select genes in microarray data. BMC Bioinformatics, 7(Suppl 2), S12. doi:10.1186/1471-2105-7-S2-S12.

    Article  PubMed Central  PubMed  Google Scholar 

  • Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. In Advances in neural information processing systems 9 (Vol. 9, pp. 155–161).

  • Dybowski, J. N., Riemenschneider, M., Hauke, S., Pyka, M., Verheyen, J., Hoffmann, D., et al. (2011). Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers. BioData Mining, 4(1), 26. doi:10.1186/1756-0381-4-26.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Fan, C., Hsieh, W., & Lin. (2008). LIBLINEAR: a library for large linear classification. Journal of Machine Learning Research, 9(6/1/2008), 1871–1874. doi:10.1038/oby.2011.351.

    Google Scholar 

  • Forbes, E. E., Brown, S. M., Kimak, M., Ferrell, R. E., Manuck, S. B., & Hariri, A. R. (2009). Genetic variation in components of dopamine neurotransmission impacts ventral striatal reactivity associated with impulsivity. Molecular Psychiatry, 14(1), 60–70. doi:10.1038/sj.mp.4002086.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(1), 256–285.

    Article  Google Scholar 

  • Fu, C. H. Y., Mourao-Miranda, J., Costafreda, S. G., Khanna, A., Marquand, A. F., Williams, S. C. R., et al. (2008). Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biological Psychiatry, 63(7), 656–662. doi:10.1016/j.biopsych.2007.08.020.

    Article  PubMed  Google Scholar 

  • Grotegerd, D., Stuhrmann, A., Kugel, H., Schmidt, S., Redlich, R., Zwanzger, P., et al. (2013). Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression—an fMRI and pattern classification study. Human brain mapping, in press.

  • Grotegerd, D., Suslow, T., Bauer, J., Ohrmann, P., Arolt, V., Stuhrmann, A., et al. (2013b). Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study. European Archives of Psychiatry and Clinical Neuroscience, 263(2), 119–131. doi:10.1007/s00406-012-0329-4.

    Article  PubMed  Google Scholar 

  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1–3), 389–422.

    Article  Google Scholar 

  • Hahn, T., Marquand, A. F., Ehlis, A.-C., Dresler, T., Kittel-Schneider, S., Jarczok, T., et al. (2011). Integrating neurobiological markers of depression. Archives of General Psychiatry, 68(4), 361–368. doi:10.1001/archgenpsychiatry.2010.178.

    Article  PubMed  Google Scholar 

  • Hall, M., National, H., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., et al. (2009). The WEKA data mining software : an update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.

    Article  Google Scholar 

  • Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009). PyMVPA: a python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7(1), 37–53. doi:10.1007/s12021-008-9041-y.

    Article  PubMed Central  PubMed  Google Scholar 

  • Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating (ROC) curvel characteristic. Radiology, 143(1), 29–36.

    Article  CAS  PubMed  Google Scholar 

  • Hanson, S. J., Matsuka, T., & Haxby, J. V. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? NeuroImage, 23(1), 156–166. doi:10.1016/j.neuroimage.2004.05.020.

    Article  PubMed  Google Scholar 

  • Hardoon, D. R., Ettinger, U., Mourão-Miranda, J., Antonova, E., Collier, D., Kumari, V., et al. (2009). Correlation-based multivariate analysis of genetic influence on brain volume. Neuroscience Letters, 450(3), 281–286. doi:10.1016/j.neulet.2008.11.035.

    Article  CAS  PubMed  Google Scholar 

  • Hastie, T., Tibshirani, R., Sherlock, G., Brown, P., Botstein, D., & Eisen, M. (1999). Imputing missing data for gene expression arrays imputation using the SVD, 1–9.

  • 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. doi:10.1126/science.1063736.

    Article  CAS  PubMed  Google Scholar 

  • Haynes, J.-D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews. Neuroscience, 7(7), 523–534. doi:10.1038/nrn1931.

    Article  CAS  PubMed  Google Scholar 

  • Heider, D., Hauke, S., Pyka, M., & Kessler, D. (2010). Insights into the classification of small GTPases. Advances and Applications in Bioinformatics and Chemistry : AABC, 3, 15–24.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Joachims, T. (1999). Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods—support vector learning. Cambridge: MIT-Press.

    Google Scholar 

  • Kamitani, Y., & Tong, F. (2006). Decoding seen and attended motion directions from activity in the human visual cortex. Current Biology, 16(11), 1096–1102. doi:10.1016/j.cub.2006.04.003.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Kononenko, I., Simec, E., & Sikonja, M. R. (1997). Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, 7, 39–55.

    Article  Google Scholar 

  • Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3863–3868. doi:10.1073/pnas.0600244103.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12(5), 535–540. doi:10.1038/nn.2303.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Kuncheva, L. I. (2004). Combining pattern classifiers—methods and algorithms. Hoboken: Wiley.

    Book  Google Scholar 

  • Kuncheva, L. I., & Rodríguez, J. J. (2010). Classifier ensembles for fMRI data analysis: an experiment. Magnetic Resonance Imaging, 28(4), 583–593. doi:10.1016/j.mri.2009.12.021.

    Article  PubMed  Google Scholar 

  • Kuncheva, L. I., Rodriguez, J. J., Plumpton, C. O., Linden, D. E. J., & Johnston, S. J. (2010). Random subspace ensembles for FMRI classification. IEEE Transactions on Medical Imaging, 29(2), 531–542. doi:10.1109/TMI.2009.2037756.

    Article  PubMed  Google Scholar 

  • LaConte, S., Strother, S., Cherkassky, V., Anderson, J., & Hu, X. (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage, 26(2), 317–329. doi:10.1016/j.neuroimage.2005.01.048.

    Article  PubMed  Google Scholar 

  • Langleben, D. D., Loughead, J. W., Bilker, W. B., Ruparel, K., Childress, A. R., Busch, S. I., et al. (2005). Telling truth from lie in individual subjects with fast event-related fMRI. Human Brain Mapping, 26(4), 262–272. doi:10.1002/hbm.20191.

    Article  PubMed  Google Scholar 

  • Lee, S., Halder, S., Kübler, A., Birbaumer, N., & Sitaram, R. (2010). Effective functional mapping of fMRI data with support-vector machines. Human Brain Mapping. doi:10.1002/hbm.20955.

    PubMed Central  Google Scholar 

  • Lehrl, S. (1995). Mehrfachwahl-Wortschatz-Intelligenztest MWT-B. Göttingen: Hogrefe.

  • Marquand, A., Howard, M., Brammer, M., Chu, C., Coen, S., & Mourão-Miranda, J. (2010). Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. NeuroImage, 49(3), 2178–2189. doi:10.1016/j.neuroimage.2009.10.072.

    Article  PubMed  Google Scholar 

  • Martino, F. D., 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(1), 44–58. doi:10.1016/j.neuroimage.2008.06.037.

    Article  PubMed  Google Scholar 

  • Modinos, G., Mechelli, A., Pettersson-Yeo, W., Allen, P., McGuire, P., & Aleman, A. (2013). Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor. PeerJ, 1(Mdd), e42. doi:10.7717/peerj.42.

    Article  PubMed Central  PubMed  Google Scholar 

  • 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 MRI data. NeuroImage, 28(4), 980–995. doi:10.1016/j.neuroimage.2005.06.070.

    Article  PubMed  Google Scholar 

  • Mourão-Miranda, J., Reynaud, E., McGlone, F., Calvert, G., & Brammer, M. (2006). The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data. NeuroImage, 33(4), 1055–1065. doi:10.1016/j.neuroimage.2006.08.016.

    Article  PubMed  Google Scholar 

  • Mourão-Miranda, J., Friston, K. J., & Brammer, M. (2007). Dynamic discrimination analysis: a spatial-temporal SVM. NeuroImage, 36(1), 88–99. doi:10.1016/j.neuroimage.2007.02.020.

    Article  PubMed  Google Scholar 

  • Mourão-Miranda, J., Almeida, J. R. C., Hassel, S., de Oliveira, L., Versace, A., Marquand, A. F., et al. (2012a). Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disorders, 14(4), 451–460. doi:10.1111/j.1399-5618.2012.01019.x.

    Article  PubMed Central  PubMed  Google Scholar 

  • Mourão-Miranda, J., Oliveira, L., Ladouceur, C. D., Marquand, A., Brammer, M., Birmaher, B., et al. (2012b). Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS One, 7(2), e29482. doi:10.1371/journal.pone.0029482.

    Article  PubMed Central  PubMed  Google Scholar 

  • Pedregosa, F., Weiss, R., & Brucher, M. (2011). Scikit-learn : machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.

    Google Scholar 

  • Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. NeuroImage, 45(1 Suppl), S199–S209. doi:10.1016/j.neuroimage.2008.11.007.

    Article  PubMed Central  PubMed  Google Scholar 

  • Pessoa, L., & Padmala, S. (2007). Decoding near-threshold perception of fear from distributed single-trial brain activation. Cerebral Cortex (New York, N.Y. : 1991), 17(3), 691–701. doi:10.1093/cercor/bhk020.

    Article  Google Scholar 

  • Polyn, S. M., Natu, V. S., Cohen, J. D., & Norman, K. A. (2005). Category-specific cortical activity precedes retrieval during memory search. Science (New York, N.Y.), 310(5756), 1963–1966. doi:10.1126/science.1117645.

    Article  CAS  Google Scholar 

  • Pyka, M., Balz, A., Jansen, A., Krug, A., & Hüllermeier, A. (2012a). A WEKA interface for fMRI data. Neuroinformatics, 10(4), 409–413. doi:10.1007/s12021-012-9144-3.

    Article  CAS  PubMed  Google Scholar 

  • Pyka, M., Hahn, T., Heider, D., Krug, A., Sommer, J., Kircher, T., et al. (2012b). Baseline activity predicts working memory load of preceding task condition. Human Brain Mapping. doi:10.1002/hbm.22121.

    PubMed  Google Scholar 

  • Rasmussen, C. E., & Nickisch, H. (2010). Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research, 11, 3011–3015.

    Google Scholar 

  • Ryali, S., Supekar, K., Abrams, D. A., & Menon, V. (2010). Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage, 51(2), 752–764. doi:10.1016/j.neuroimage.2010.02.040.

    Article  PubMed Central  PubMed  Google Scholar 

  • Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics (Oxford, England), 23(19), 2507–2517. doi:10.1093/bioinformatics/btm344.

    Article  CAS  Google Scholar 

  • Sato, J. R., Fujita, A., Thomaz, C. E., Martin, M. D. G. M., Mourão-Miranda, J., Brammer, M. J., et al. (2009). Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction. NeuroImage, 46(1), 105–114. doi:10.1016/j.neuroimage.2009.01.032.

    Article  PubMed  Google Scholar 

  • Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., & Ashburner, J. (2013). PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics. doi:10.1007/s12021-013-9178-1.

    PubMed Central  PubMed  Google Scholar 

  • Shinkareva, S. V., Mason, R. A., Malave, V. L., Wang, W., Mitchell, T. M., & Just, M. A. (2008). Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings. PLoS One, 3(1), e1394. doi:10.1371/journal.pone.0001394.

    Article  PubMed Central  PubMed  Google Scholar 

  • Vapnik, V., & Chervonenkis, A. (1974). Theory of pattern recognition [in Russian]. Moscow: Nauka.

    Google Scholar 

  • Wang, X., Hutchinson, R., & Mitchell, T. (2003). Training fMRI classifiers to detect cognitive states across multiple human subjects. In Proceedings of the Conference on Neural Information Processing Systems.

  • Wittchen, H.-U., Wunderlich, U., Gruschwitz, S., & Zaudig, M. (1997). SKID-I. Strukturiertes Klinisches Interview für DSM-IV. Göttingen: Hogrefe.

    Google Scholar 

Download references

Conflict of Interest

All authors state that they have no conflicts of interest to declare, financial or otherwise for any aspect of the submitted work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Udo Dannlowski.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Suppl. Table 1

(DOCX 15 kb)

Suppl. Table 2

(DOCX 15 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Grotegerd, D., Redlich, R., Almeida, J.R.C. et al. MANIA—A Pattern Classification Toolbox for Neuroimaging Data. Neuroinform 12, 471–486 (2014). https://doi.org/10.1007/s12021-014-9223-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-014-9223-8

Keywords

Navigation