Top

Published in:

01-07-2014 | Software Original Article

MANIA—A Pattern Classification Toolbox for Neuroimaging Data

Authors: Dominik Grotegerd, Ronny Redlich, Jorge R. C. Almeida, Mona Riemenschneider, Harald Kugel, Volker Arolt, Udo Dannlowski

Published in: Neuroinformatics | Issue 3/2014

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.

Available only for authorised users

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

http://www.pymvpa.org/

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

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

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

http://scikit-learn.org/

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

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

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

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.

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

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.CrossRef

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.PubMedCentralPubMed

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.PubMedCrossRef

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.CrossRef

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.PubMedCentralPubMedCrossRef

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.PubMedCentralPubMedCrossRef

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.PubMedCentralPubMedCrossRef

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.

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.PubMedCentralPubMedCrossRef

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

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.PubMedCrossRef

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.PubMedCrossRef

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.CrossRef

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.PubMedCrossRef

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.CrossRef

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.PubMedCentralPubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCentralPubMedCrossRef

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.

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.PubMedCentralPubMedCrossRef

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

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.PubMedCentralPubMedCrossRef

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.PubMedCentralPubMedCrossRef

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

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCentral

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCentralPubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCentralPubMedCrossRef

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.PubMedCentralPubMedCrossRef

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

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.PubMedCentralPubMedCrossRef

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.CrossRef

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.CrossRef

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.PubMedCrossRef

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

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

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.PubMedCentralPubMedCrossRef

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.CrossRef

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.PubMedCrossRef

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.PubMedCentralPubMed

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.PubMedCentralPubMedCrossRef

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

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.

Title: MANIA—A Pattern Classification Toolbox for Neuroimaging Data
Authors: Dominik Grotegerd
Ronny Redlich
Jorge R. C. Almeida
Mona Riemenschneider
Harald Kugel
Volker Arolt
Udo Dannlowski
Publication date: 01-07-2014
Publisher: Springer US
Published in: Neuroinformatics / Issue 3/2014
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-014-9223-8

At a glance: The STEP trials

Springer Medicine

MANIA—A Pattern Classification Toolbox for Neuroimaging Data

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2014

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

Automatic Extraction of the Midsagittal Surface from Brain MR Images using the Kullback–Leibler Measure

SPIN: A Method of Skeleton-Based Polarity Identification for Neurons

Data Persistence Insurance

Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex

Structure-Based Neuron Retrieval Across Drosophila Brains