Center for Behavioral Brain Sciences Magdeburg
5701 F1D0 D18B 87E6 1AA3 1F3D C073 D228 7FFB 9E9B
I am heading the Psychoinformatics lab at the University of Magdeburg.
Before starting my own lab I was working with Stefan Pollmann at the Experimental psychology lab at the University of Magdeburg.
In 2009, I received a Ph.D. in Psychology from the University of Magdeburg, where I was working with my advisor Stefan Pollmann in the Department of Experimental Psychology on “Advancing the understanding of brain function with multivariate pattern analysis”.
Before that, I studied Psychology at the Martin-Luther-University Halle-Wittenberg where I received my Diploma in 2003 and worked on my thesis about the perception of motion in depth in the lab of my advisor Josef Lukas.
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. For more information about the progress made in this respect, please see the list of publications below.
U.S.-German Collaboration: Building common high-dimensional models of neural representational spaces
It is possible to decode information from brain activation patterns with multivariate analysis procedures. Despite this fact, little is known how neural codes vary across individuals. The main disadvantage of current decoding approaches is that they have to be build for each brain individually, because it is difficult to bring two brains into alignment at a fine scale. This project develops methods that allow for detecting and describing common neural representations. Individual brain activity patterns are projected into a common high-dimensional space, to build models of representational spaces of cortical areas that are valid for a range of stimuli and across individuals. This includes complex cortial networks that do not respond consistenly on direct stimulation (e.g. social cognition).
This project is a collaboration with the groups of James V. Haxby (Dartmouth College) and Peter J. Ramadge (Princeton University).
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. Some additional snippets are available in miscellaneous and things from the past.
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 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. Please also see the list of various publications about PyMVPA in the publications section below.
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 more than 8000 times. In April 2009, I joined Matthew Brett to work on its successor: NiBabel. This new project aims to be much more comprehensive than PyNIfTI and already supports a lot more file formats.
I am using the Debian operating system since 2001, and since 2003 nothing else but Debian on any machine – may it be a desktop, laptop, server or some tiny multimedia device. At some point it felt logical to start contributing to the project (potentially the largest of its kind on this planet). After having contributed to Debian for several years, I am now also an official Debian developer Meanwhile, I am maintaining a number of packages (up-to-date package list one and two).
My work in Debian is focused on making software available that is essential for psychological, and neuroscience research. To address the specific needs of this audience Yaroslav Halchenko and I started NeuroDebian. This is a platform that acts as a staging area for such software packages on their way into Debian. Sometimes that process can 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. Please also see the poster and the talk about NeuroDebian (video available) in the publications section below.
If you are developing neuroscientific software and would like to see it packaged for Debian, please contact us at firstname.lastname@example.org.
Pollmann, S., Zinke, W., Baumgartner, F., Geringswald, F. & Hanke, M. (2014). The right temporo-parietal junction contributes to visual feature binding. NeuroImage.
Hanke, M., Baumgartner, F. J., Ibe, P., Kaule, F. R., Pollmann, S., Speck, O., Zinke, W. & Stadler, J. (2014). A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Scientific Data, 1.
Kohler, P. J., Fogelson, S. V., Reavis, E. A., Meng, M., Guntupalli, J. S., Hanke, M., Halchenko, Y. O., Connolly, A. C., Haxby, J. V. & Tse, P. U. (2013). Pattern classification precedes region-average hemodynamic response in early visual cortex. NeuroImage, 78, 249-260.
Baumgartner, F., Hanke, M., Geringswald, F., Zinke, W., Speck, O. & Pollmann, S. (2013). Evidence for feature binding in the superior parietal lobule. NeuroImage, 68, 173-180.
Halchenko, Y. O. & Hanke, M. (2012). Open is not enough. Let’s take the next step: An integrated, community-driven computing platform for neuroscience. Frontiers in Neuroinformatics, 6:22.
Poline, J.-B., Breeze, J. L., Ghosh, S. S., Gorgolewski, K. F., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., Poldrack, R. A., Schwartz, Y., Ashburner, J. and Kennedy, D. N. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6:9.
Connolly, A. J., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y. C., Abdi, H. & Haxby, J. V. (2012). Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32, 2608-2618.
Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72, 404-416.
Hanke, M. & Halchenko, Y. O. (2011). Neuroscience runs on GNU/Linux. Frontiers in Neuroinformatics, 5:8.
Lee, Y. S., Janata, P., Frost, C., Hanke, M. & Granger, R. (2011). Investigation of melodic contour processing in the brain using multivariate pattern-based fMRI. NeuroImage, 57, 293–300.
Hanke, M., Halchenko, Y. O., Haxby, J. V., & Pollmann, S. (2010). Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience, 4, 38–43.
Hanke, M. (2009). Advancing the understanding of brain function with multivariate pattern analysis (Doctoral dissertation), Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. and Pollmann, S. (2009). PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.
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, 37–53. [view]
Maertens, M., Pollmann, S., Hanke, M., Mildner, T. & Möller, H.E. (2008). Retinotopic activation in response to subjective contours in primary visual cortex. Frontiers in Human Neuroscience, 2:2.
Tille, A., Möller, S., Hanke, M & Halchenko, Y. O. (2011). Debian Med: Integrated software environment for all medical purposes based on Debian GNU/Linux. In Jordanova, M. & Lievens, F. (Eds.), Global Telemedicine and eHealth Updates: Knowledge Resources, Vol. 4. Luxembourg: ISfTeH.
Halchenko, Y. O. & Hanke, M. (2010). Advancing Neuroimaging Research with Predictive Multivariate Pattern Analysis (MVPA). The Neuromorphic Engineer.
Lukas, J., & Hanke, M. (2004). Wie die Bilder laufen lernten: Kognitive Prozesse bei der Bewegungswahrnehmung. Scientia halensis, 4, 21–22.
Hanke, M. (2012). Computational and cognitive neuroscience boosted by Debian OR Just using Debian is not enough. Talk given at the workshop “Debian for Scientific Facilities Days” at the European Synchrotron Radiation Facility (ESRF), Grenoble, France.
Hanke, M. (2012). The why and how of getting packaged. Talk given at the 5th BrainScaleS CodeJam at the University of Edinburgh, Edinburgh, UK.
Hanke, M. (2012). Rock solid, brand new, everyday, for free, not a joke: NeuroDebian. Talk given at the Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, and at the Department of Biology, Ludwig-Maximilians-University Munich, Munich, Germany.
Brett, M, Gerhard, S. & Hanke, M. (2011). NiBabel: Conductor for a cacophony of neuro-imaging file formats. Talk given at the EuroSciPy 2011 satellite “Python in neuroscience”, Paris, France.
Hanke, M. & Halchenko, Y. O. (2011). More than batteries included: NeuroDebian. Talk given at the EuroSciPy 2011 satellite “Python in neuroscience”, Paris, France.
Halchenko, Y. O. & Hanke, M. (2011). The virtues and sins of PyMVPA. Talk to be given at the EuroSciPy 2011 satellite “Python in neuroscience”, Paris, France.
Hanke, M. (2011). Integrating Condor into the Debian operating system. Talk given at CondorWeek 2011, Madison, Wisconsin, 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. (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. (2008). PyMVPA: A Python toolbox for classifier-based analysis of fMRI data. Talk given at the MRC Cognition and Brain Sciences Unit, Cambridge, UK, April 2008.
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.
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 to be presented 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 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 presented at the annual meeting of the Vision Sciences Society, Naples, Florida, USA.
Connolly, A. C., Guntupalli, J. S., Hanke, M., Gobbini, I. & Haxby, J. V. (2011). More or less human: The animate-inanimate distinction in visual cortex may be more continuum than distinction. Poster presented at the annual meeting of the Cognitive Neuroscience Society, San Francisco, USA.
Hanke, M., Halchenko, Y. O., Guntupalli, J. S., Connolly, A. C. & Haxby, J. V. (2011). Unsupervised brain parcellation from functional neuroimaging data. Poster 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., & 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., 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]
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.