Michael Hanke¶
Academic Whereabouts¶
I am currently a post-doctoral fellow in the lab of James V. Haxby at the Department of Psychological and Brain Sciences at Dartmouth College.
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.
Research¶
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.
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. Some additional snippets are available in miscellaneous and things from the past.
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 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.
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.
Debian¶
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).
NeuroDebian¶
Meanwhile, I am maintaining a number of packages (up-to-date package list). I am especially trying to make software available in Debian that is essential for psychological, or 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 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. Please also see the poster about NeuroDebian in the publications section below.
If you are developing neuroscientific software and would like to see it packaged for Debian, please contact us at team@neuro.debian.net.
Publications¶
Peer-reviewed Publications¶
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.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J., & 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.
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.
Additional Publications¶
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.
Conference Contributions¶
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 to be presented at the anual meeting of the Society for Neuroscience, San Diego, USA.
Hanke, M, Halchenko, Y. O. (2010). Debian: The ultimate platform for neuroimaging research. Talk to be given at DebConf10, New York City, 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. (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.
Articles Referring to PyMVPA¶
For an up-to-date list, please see the corresponding page on the PyMVPA website.
