The HaxbyLab@Dartmouth

Yaroslav O. Halchenko

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Yaroslav O. Halchenko

Senior Research Associate

Ph.D. in Computer Science, New Jersey Institute of Technology, USA

Contact

Address:6207 Moore Hall, Rm419 Hanover, NH03755
Phone:(+1)(603) 646-9834
Email:yoh@dartmouth.edu

Research focus

With the goal of contributing to our understanding of the brain function, I am interested in developing new and formalizing existing analysis methodologies and software solutions in the domain of computational and cognitive neuroscience.

Contemporary instrumental methods, such as MRI and EEG, deliver us tremendous amounts of data reflecting undergoing neural processes. To better understand functioning of the brain, it is necessary not only to increase computational capacities but also to develop adequate methodologies and software infrastructure. To address the primary high-level question of How does brain processes, encodes and represents information? I am investigating How could we decode neural processes and their organization at different spatial and temporal scales given available instrumentation? What research questions and findings are adequate given experimental designs and acquired data? How developed methodologies could be made available in a way suitable for quick adoption by the research community?

Research projects

Seeking for the answers to above questions I have tackled problems of multimodal (e.g. EEG/fMRI) data analysis [HHP05], visual perception [HH08], large-scale decoding of the mental states [PHH09], and causal structure inference [RHH+10]. To streamline my own analysis and to help other researchers with answering former questions, I have joined the efforts with Michael Hanke to develop PyMVPA [HHS+09a] - a flexible and versatile Python platform for the analysis of neural data through employing recent advances in statistical learning methods. It is really inspiring to see the PyMVPA being used productively by hundreds of researchers around the globe.

To address the later question of software deployment, methods popularization and results reproducibility in neuroscience, together with the same old Michael Hanke we founded the NeuroDebian project. Relying on and contributing back to the Debian project, we equip neuroscience research community with a free, reliable and versatile research platform. Nowadays Debian and its derivatives are used by thousands of researchers as their main operating systems. The NeuroDebian repository became for them the ultimate source of recent developments in neuroscience software and canonical datasets necessary for their day-to-day research.

Publications

Articles

[GGH+13]Gobbini, M. I. , Gors, J. D. , Halchenko, Y. O. , Hughes, H. C. and Cipolli, C. (2013). Processing of invisible social cues. Consciousness and Cognition, 22, 765-770. [PDF] DOI: 10.1016/j.concog.2013.05.002
[GGH+13b]Gobbini, M. I. , Gors, J. D. , Halchenko, Y. O. , Rogers, C. , Guntupalli, J. S. , Hughes, H. and Cipolli, C. (2013). Prioritized Detection of Personally Familiar Faces. PLoS ONE, 8. DOI: 10.1371/journal.pone.0066620
[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
[GBM+11]Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O. , Waskom, M. L. and Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform., 5, 13. [PDF] DOI: 10.3389/fninf.2011.00013, cited by: 15
[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
[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
[PHH09]Poldrack, R. A., Halchenko, Y. O. and Hanson, S. J. (2009). Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals. Psychological Science, 20, 1364-1372. [PDF] DOI: 10.1111/j.1467-9280.2009.02460.x, cited by: 75
[HH08]Hanson, S. J. and Halchenko, Y. O. (2008). Brain reading using full brain support vector machines for object recognition: there is no “face’’ identification area. Neural Computation, 20, 486-503. [PDF] DOI: 10.1162/neco.2007.09-06-340, cited by: 66

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

Book Chapters

[HHP05]Halchenko, Y. O. , Hanson, S. J. and Pearlmutter, B. A. (2005). Advanced Image Processing in Magnetic Resonance Imaging: fMRI, MRI, EEG, MEG. In Landini, Luigi and Positano, Vincenzo and Santarelli, Maria Filomena (Eds.) (pp. 223-65): CRC Press. [PDF] [URL], cited by: 15

Collection Chapters

[TSM+98]Tymchenko, L. I., Scorukova, J., Markov, S. and Halchenko, Y. O. (1998). Image Segmentation on the basis of spatial connected features. In (Eds.) Visnyk VSTU (pp. 39-43), Vinnytsya, Ukraine: VSTU University Press.
[MKH97]Martinyuk, T. B., Kogemiako, A. V. and Halchenko, Y. O. (1997). The model of associative processor for numerical data sorting. In (Eds.) Visnyk VSTU (pp. 19-23), Vinnytsya, Ukraine: VSTU University Press.

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.
[HHP04]Halchenko, Y. O. , Hanson, S. J. and Pearlmutter, B. A. (2004). Fusion of Functional Brain Imaging Modalities using L-Norms Signal Reconstruction. In (Eds.) Proceedings of the Annual Meeting of the Cognitive Neuroscience Society. [PDF]
[HMH+04]Hanson, S. J., Matsuka, T., Hanson, C., Rebbechi, D., Halchenko, Y. O. , Zaimi, A. and Pearlmutter, B. A. (2004). Structural Equation Modeling of Neuroimaging Data: Exhaustive Search and Markov Chain Monte Carlo. In (Eds.) Human Brain Mapping. [PDF]
[TKG+00]Timchenko, L. I., Kutaev, Y. F., Gertsiy, A. A., Halchenko, Y. O. , Zahoruiko, L. V. and Mansur, T. (2000). Method for image coordinate definition on extended laser paths. In Gurevich, S. B. and Nazarchuk, Z. T. and Muravsky, L. I. (Eds.) Optoelectronic and Hybrid Optical/Digital Systems for Image and Signal Processing, : SPIE. [URL] DOI: 10.1117/12.388446, cited by: 1
[TKG+99]Timchenko, L. I., Kutaev, Y. F., Gertsiy, A. A., Zahoruiko, L. V., Halchenko, Y. O. and Mansur, T. (1999). Approach to parallel-hierarchical network learning for real-time image sequence recognition. In Miller, J. W. V. and Solomon, S. S. and Batchelor, B. G. (Eds.) Machine Vision Systems for Inspection and Metrology VIII, : SPIE. DOI: 10.1117/12.360283

Miscelaneous

Posters

[CWH+11]Connolly, A. C. , Wu, Y. , Halchenko, Y. O. , Guntupalli, J. S. and Haxby, J. V. (2011). Scanning parameters for optimal decoding using a 32-channel head coil for fMRI. . [PNG] [URL]
[HHH10]Hanke, M. , Halchenko, Y. O. , Haxby, J. V. and Pollmann, S. (2010). Improving efficiency in cognitive neuroscience research with NeuroDebian. . [PDF] `[URL] <Poster presented at the annual meeting of the Cognitive Neuroscience Society, Montr{\’{e}}al, Canada>`__

Ph.D. Theses

[Hal09]Halchenko, Y. O. (2009). Predictive Decoding of Neural Data (Doctoral dissertation). NJIT, Newark, NJ, USA. [PDF] [URL]

Talks

Halchenko Y.O. & Hanke, M. (2013). Open is not enough: benefits from Debian as an integrated, community-driven computing platform. Talk given at SEA-2013 conference, University Corporation for Atmospheric Research (UCAR), Boulder CO, USA.

Halchenko Y.O. (2012). Applied NeuroDebian: Python in Neuroimaging. Talk given at Ph.D. students symposium for Bernstein Conference 2012. Munich, Germany.

Halchenko Y.O. & Hanke, M. (2012). Environments for efficient contemporary research in neuroimaging: PyMVPA and NeuroDebian. Talk given at the University of Pennsylvania School of Medicine, Philadelphia, PA, USA.

Halchenko Y.O, Hanke, M. & Haxby, J. V. (2011). Multivariate analysis strategies of neuroimaging data in PyMVPA. Talk given at Annual Meeting of Society for NeuroScience, Washington DC, USA.

Halchenko, Y.O & Hanke, M. (2011) The virtues and sins of PyMVPA. Talk given at EuroScipy satellite “Python in Neuroscience”, Paris, France.

Halchenko, Y.O (2011) π’s in Debian or Scientific Debian: NumPy, SciPy and beyond Talk given at EuroScipy”, Paris, France.

Möller S., Booth T., Prins P., Krabbenhöft H., Williams A., Mestiashvili A., Rice P., Sallou S., Halchenko Y. O., Holland R., Spooner W., Procter J., Alteholz T., Plessy C., Ucko A. M., Tille A. (2011). Debian Med: individuals’ expertise and their sharing of package build instructions Talk abstract submitted for presentation at the 12th Annual Bioinformatics Open Source Conference (BOSC 2011), Vienna, Austria.

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]

Halchenko, Y. O. & Hanke, M. (2009). An ecosystem of neuroimaging, statistical learning, and open-source software to make research more efficient, more open, and more fun. Talk given at PBS Department, Dartmouth College, November 2009.

Halchenko, Y. O. (2009). PyMVPA: Fathom Brain Function through Multivariate Pattern Analysis Talk given at the University of Hawaii, Seminar Series in Machine Learning and Computational Biology Honolulu, USA, April 2009.

Halchenko, Y.O & Hanke, M. (2009). Reliable Decoding of Neural Data. Talk given at Berkeley Brain Imaging Center, California, USA April 2009.

Interviews

FLOSS for Neuroscience: An interview with the NeuroDebian team By floss4science.com. November, 2011.

NeuroDebian: the value of an integrated tool suite By INCF. October, 2011.

Posters

Halchenko, Y. O., Gors, J., Guntupalli, J. S., Haxby, J. V. & Gobbini, M. I. (2012). Investigation of the familiar face recognition model. Poster to be presented at the SfN 2012 Annual Meeting, New Orleans, Louisiana, USA.

Connolly, A., Wu, Y.-C., Halchenko, Y. O., Guntupalli, S. & Haxby, J. V. (2011). Scanning parameters for optimal decoding using 32-channel head coil for fMRI. Poster presented at the Vision Sciences Society 11th Annual Meeting, Naples, Florida, USA.

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

Garyfallidis E., Brett M., Amirbekian B., Nguyen C., Yeh F-C, Olivetti E., Halchenko Y. O., Nimmo-Smith I.(2011) Dipy - a novel software library for diffusion MR and tractography. 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 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 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.

Ghosh, S., Burns, C., Clark, D., Gorgolewski, K., Halchenko, Y. O., Madison, C., Tungaraza R., Millman J. (2010). Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools. Poster presented at 16th Annual Meeting of the Organization for Human Brain Mapping

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]

Halchenko, Y. O., Hanson, S. J. & Pearlmutter, B. A. (2004). Fusion of Functional Brain Imaging Modalities using L-Norms Signal Reconstruction. Poster presented at the annual meeting of the Cognitive Neuroscience Society.

Hanson, S. J., Matsuka, T., Hanson, C., Rebbechi, D., Halchenko, Y. O. O., Zaimi, A. & Pearlmutter, B. A. (2004). Structural Equation Modeling of Neuroimaging Data: Exhaustive Search and Markov Chain Monte Carlo. Poster presented at the annual meeting of Human Brain Mapping society.

Halchenko, Y. O., Pearlmutter, B.A., Hanson, S.J. & Zaimi, A. (2003) “Fusion of Functional Brain Imaging Modalities via Linear Programming” Poster presented at NFSI-2003. Chiety Italy.

References

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