The HaxbyLab@Dartmouth

Michael Hanke

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Michael Hanke

Post-doctoral research fellow

Ph.D. Otto-von-Guericke University Magdeburg, Germany

Contact

Address:Otto-von-Guericke University, Magdeburg, Germany
Phone:TBA
Email:michael.hanke@gmail.com

Research focus

My research is focused on the representation of information in the human brain. The application of multivariate pattern analysis, with its roots in statistical learning theory, seems to be a promising tool to investigate this topic. To facilitate such research, I have started a project, called PyMVPA that aims to provide a flexible environment to apply novel analysis techniques to brain imaging data.

Software

Over the years I have started numerous software projects. Most of them took the common path: enthusiastic start, followed by slow death. However, some of them are alive and deserve to be mentioned here.

PyMVPA

PyMVPA is a Python package that provides a comprehensive environment for the statistical learning analysis of neuroscientific or psychophysical data. Although originally started by myself, it could only grow into what it is now after Yaroslav O. Halchenko had joined the project. We are fortunate to have a number of people contributing to the project. More recently, even users started publishing articles on analyses done with PyMVPA.

PyNIfTI -> NiBabel

In 2006, I wrote PyNIfTI, a free software Python module to access the NIfTI file format from within Python. Since then the source code has been downloaded several thousand times. Since April 2009, I am working with Matthew Brett on its successor: NiBabel. This new project supports a lot more file formats than PyNIfTI.

NeuroDebian

I am maintaining a number of packages (up-to-date package list) for the Debian project. In particular, I am trying to make software available in Debian that is essential for psychological, or neuroscience research. To address the specific needs of this audience Yaroslav O. Halchenko and I started NeuroDebian. This is a platform that acts as a staging area for such packages on their way into Debian. That way can sometimes be quiet long, due to complicated licenses and other technicalities. NeuroDebian tries to make software accessible to neuroscientists, even if a particular package does not (yet) meet all of Debian’s standard, and tries to offer scientists with the possibility to have a stable operating system (maybe even Ubuntu) and nevertheless up-to-date research tools.

Publications

Articles

[EMH+biorxiv16]Eglen, S., et al. (2016). Towards standard practices for sharing computer code and programs in neuroscience. bioRxiv. [PDF] [URL] DOI: 10.1101/045104
[GAC+16]Gorgolewski, K. J., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044. [PDF] [URL]
[GAC+biorxiv16]Gorgolewski, K. J., et al. (2016). The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments. bioRxiv. [URL] DOI: 10.1101/034561
[GHH+16]Guntupalli, J. S. , Hanke, M. , Halchenko, Y. O. , Connolly, A. C. , Ramadge, P. J. and Haxby, J. V. (2016). A Model of Representational Spaces in Human Cortex. Cerebral Cortex, 2919-2934. DOI: 10.1093/cercor/bhw068
[SKG+SD16]Sengupta, A., Kaule, F. R., Guntupalli, J. S. , Hoffmann, M. B. and H, (2016). A studyforrest extension, retinotopic mapping and localization of higher visual areas. Scientific Data, 3, 160093.
[HH15]Halchenko, Y. O. and Hanke, M. (2015). Four aspects to make science open “by design” and not as an after-thought. GigaScience, 4. [PDF] DOI: 10.1186/s13742-015-0072-7
[HHH+arxiv13]Halchenko, Y. (2013). Transmodal Analysis of Neural Signals. ArXiv e-prints. [URL]
[KFR+13]Kohler, P. J., et al. (2013). Pattern classification precedes region-average hemodynamic response in early visual cortex.. Neuroimage, 78C, 249-260. [PDF] DOI: 10.1016/j.neuroimage.2013.04.019
[CGG+12]Connolly, A. C. , Guntupalli, J. S. , Gors, J. , Hanke, M. , Halchenko, Y. O., Wu, Y. , Abdi, H. and Haxby, J. V. (2012). Representation of biological classes in the human brain. Journal of Neuroscience, 32, 2608-2618. [PDF] DOI: 10.1523/JNEUROSCI.5547-11.2012, cited by: 16
[HH12]Halchenko, Y. O. and Hanke, M. (2012). Open is not enough. Let’s take the next step: An integrated, community-driven computing platform for neuroscience. Frontiers in Neuroinformatics, 6. [PDF] DOI: 10.3389/fninf.2012.00022, cited by: 1
[PBG+12]Poline, J., et al. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6. DOI: 10.3389/fninf.2012.00009, cited by: 6
[HH11]Hanke, M. and Halchenko, Y. O. (2011). Neuroscience runs on GNU/Linux. Front. Neuroinform., 5, 8. [PDF] DOI: 10.3389/fninf.2011.00008, cited by: 7
[HGC+11]Haxby, J. V. , Guntupalli, J. S. , Connolly, A. C. , Halchenko, Y. O. , Conroy, B. R., Gobbini, M. I. , Hanke, M. and Ramadge, P. J. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404-416. [PDF] [PDF:Supp] DOI: 10.1016/j.neuron.2011.08.026, cited by: 21
[LJF+11]Lee, Y., Janata, P., Frost, C., Hanke, M. and Granger, R. (2011). Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI. NeuroImage. [PDF] DOI: 10.1016/j.neuroimage.2011.02.006, cited by: 10
[HH09]Halchenko, Y. O. and Hanke, M. (2010). Advancing Neuroimaging Research with Predictive Multivariate Pattern Analysis (MVPA). The Neuromorphic Engineer. [PDF] DOI: 10.2417/1200909.1683
[HHS+09a]Hanke, M. , Halchenko, Y. O. , Sederberg, P. B., Hanson, S. J., Haxby, J. V. and Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37-53. [PDF] DOI: 10.1007/s12021-008-9041-y, cited by: 70
[HHS+09b]Hanke, M. , et al. (2009). PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. Frontiers in Neuroinformatics, 3, 3. [PDF] DOI: 10.3389/neuro.11.003.2009, cited by: 36
[MPH+08]Maertens, M., Pollmann, S., Hanke, M. , Mildner, T. and M”oller, H. (2008). Retinotopic activation in response to subjective contours in primary visual cortex. Frontiers in Human Neuroscience, 2, 2. [PDF] DOI: 10.3389/neuro.09.002.2008, cited by: 14
[LH04]Lukas, J. and Hanke, M. (2004). Wie die Bilder laufen lernten: Kognitive Prozesse bei der Bewegungswahrnehmung. Scientia halensis, 4, 21-22.

Editorials

[HH_ed15]Hanke, M. and Halchenko, Y. O. (2015). A communication hub for a decentralized collaboration on studying real-life cognition [version 1; referees: not peer reviewed] . F1000Research, 4. DOI: 10.12688/f1000research.6229.1

Review Articles

[HHH+10]Hanke, M. , Halchenko, Y. O. , Haxby, J. V. and Pollmann, S. (2010). Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience, 4, 38-43. [PDF] DOI: 10.3389/neuro.01.007.2010

Conference Proceedings

[TMH+11]Tille, A., Moeller, S., Hanke, M. and Halchenko, Y. O. (2011). Debian Med: Integrated software environment for all medical purposes based on Debian GNU/Linux. In Jordanova, M. and Lievens, F. (Eds.) Global Telemedicine and eHealth Updates: Knowledge Resources, Luxembourg: ISfTeH.

Miscellaneous

Posters

[HPH+OHBM16b]Halchenko, Y. O. , Poldrack, B. and Hanke, M. (2016). DataLad – decentralized data distribution for consumption and sharing of scientific datasets. Organization of Human Brain Mapping Annual Meeting, Geneva, Switzerland. Talk. [PDF]
[GAG+OHBM15]Ghosh, S., et al. (2015). NIDM-Workflow - The Evolution of Provenance in Neuroimaging. Organization of Human Brain Mapping Annual Meeting, Honolulu HI, USA. Poster.
[NKM+OHBM15]Nichols, N., et al. (2015). Application of the Neuroimaging Data Model to Represent and Exchange Primary and Derived Data. Organization of Human Brain Mapping Annual Meeting, Honolulu HI, USA. Poster.
[PDK+OHBM15]Poline, J. B., et al. (2015). How to make brain imaging research efficient and reproducible: building software and standards. Organization of Human Brain Mapping Annual Meeting, Honolulu HI, USA. Poster.
[HH14]Halchenko, Y. O. and Hanke, M. (2014). Converging catalogues, warehouses, and deployment logistics into a federated ‘data distribution’. Annual PI meeting of Collaborative Research in Computational Neuroscience (CRCNS) program, Tempe, AZ. [PDF]
[CGH+11]Connolly, A. C. , Guntupalli, J. S. , Hanke, M. , Gobbini, M. I. and Haxby, J. V. (2011). More or less human: The animate-inanimate distinction in visual cortex may be more continuum than distinction. Cognitive Neuroscience Society 2011 Annual Meeting, San Francisco, CA. Poster. [PNG]
[HHH10]Hanke, M. , Halchenko, Y. O. , Haxby, J. V. and Pollmann, S. (2010). Improving efficiency in cognitive neuroscience research with NeuroDebian. Annual meeting of the Cognitive Neuroscience Society, Montreal, Canada. [PDF]

Talks

[HPH+OHBM16a]Halchenko, Y. O. , Poldrack, B. and Hanke, M. (2016). DataLad – decentralized data distribution for consumption and sharing of scientific datasets. Organization of Human Brain Mapping Annual Meeting, Geneva, Switzerland. Talk. [URL]

Ph.D. Theses

[Han09]Hanke, M. (2009). Advancing the Understanding of Brain Function with Multivariate Pattern Analysis (Doctoral dissertation). Otto-von-Guericke-University, Magdeburg, Germany. [PDF] [URL]

Conference Contributions

Lohmann, G., Tuerke, E., Reimer, E., Proeger, T., Hellrung, L., Goldhahn, D., Mueller, K., Hanke, M., Margulies, D., Villringer, A. & Turner, R. (2011). Lipsia 2.0 – a software package for analyzing MRI/fMRI/rs-fMRI data. Poster submitted for presentation at the 17th Annual Meeting of the Organization for Human Brain Mapping, Quebec City, Canada.

Hanke, M., Halchenko, Y. O., & Haxby, J. V. (2011). NeuroDebian: versatile platform for brain imaging research. Poster submitted for presentation at the 17th Annual Meeting of the Organization for Human Brain Mapping, Quebec City, Canada.

Fogelson, S. V., Kohler, P. J., Hanke, M., Halchenko, Y. O., Haxby, J. V., Granger, R. H. & Tse, P. U. (2011). STMVPA: Spatiotemporal multivariate pattern analysis permits fine- grained visual categorization. Poster to be presented at the annual meeting of the Vision Sciences Society, Naples, Florida, USA.

Baumgartner, F., Hanke, M., Geringswald, F., Speck, O. & Pollmann, S. (2011). Representation of visual feature conjunctions in the superior parietal lobule. Poster to be presented at the annual meeting of the Vision Sciences Society, Naples, Florida, USA.

Hanke, M., Halchenko, Y. O., & Haxby, J. V. (2011). Unsupervised brain parcellation from functional neuroimaging data. Poster to be presented at the annual meeting of the Cognitive Neuroscience Society, San Francisco, USA.

Halchenko, Y. O., Hanke, M., Haxby, J. V., Pollmann, S. & Raizada, R. D. (2010). Having trouble getting your Nature paper? Maybe you are not using the right tools? Poster presented at the annual meeting of the Society for Neuroscience, San Diego, USA.

Hanke, M., Halchenko, Y. O. (2010). Debian: The ultimate platform for neuroimaging research. Talk given at DebConf10, New York City, USA. [video: low resolution, high resolution]

Hanke, M., Halchenko, Y. O., & Olivetti, E. (2010). PyMVPA 0.5: A Major Update Of The Comprehensive Framework For Statistical Learning Analysis Of Neural Data. Poster presented at the workshop “Concepts, Actions, and Objects: Functional and Neural Perspectives”, Rovereto, Italy.

Halchenko, Y. O., Hanke, M, Haxby, J. V., Hanson, S. J. & Herrmann, C. (2010). Neural activity localization by predictive mapping between imaging modalities. Poster presented at the annual meeting of the Cognitive Neuroscience Society, Montréal, Canada.

Hanke, M., Halchenko, Y. O., Haxby, J. V. & Pollmann, S. (2010). Improving efficiency in cognitive neuroscience research with NeuroDebian. Poster presented at the annual meeting of the Cognitive Neuroscience Society, Montréal, Canada.

Hanke, M. (2009). An Introduction into fMRI data analysis with (Py)MVPA. Talk given at the ISMRM workshop: fMRI Advanced Issues and Processing Software, Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, USA, April 2009.

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for machine-learning based data analysis. Poster presented at the annual meeting of the Society for Neuroscience, Washington, USA.

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for classifier-based analysis. Poster presented at the annual meeting of the German Society for Psychophysiology and its Application, Magdeburg, Germany. [Winner of the poster prize of the German Society of Psychophysiology and its Application]

Hanke, M. & Pollmann, S. (2006). Classification of dimension-change-related brain activation patterns with neural networks. Talk given at the “Tagung experimentell arbeitender Psychologen”, TeAP, Mainz, Germany.

Lukas, J. & Hanke, M. (2005). Reversed phi with random dot cinematograms under luminance and color contrast reversal. Poster presented at the European Conference on Visual Perception, A Coruña, Spain.

Hanke, M. & Lukas, J. (2003). Die Wahrnehmung der Bewegungsrichtung beim binokularen Tiefensehen: Zum Einfluss von Disparitatsänderung und monokularer Bildgeschwindigkeit. In H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich., F.A. Wichmann (Eds), Beiträge zur 6. Tübinger Wahrnehmungskonferenz (p. 94). Knirsch: Kirchentellinsfurt.

Lukas, J. & Hanke, M. (2002). Bewegungswahrnehmung bei Reizdarbietung mit dynamischen Random-dot- Stereogrammen. In M. Baumann, A. Keinath & J.F. Krems (Eds.), Experimentelle Psychologie (p. 163). Regensburg: Roderer.

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