Yaroslav O. Halchenko¶
Research Assistant Professor
Director of the Center for Open Neuroscience (CON)
Ph.D. in Computer Science, New Jersey Institute of Technology, USA
- Resume
- Professional network: LinkedIn
- Google scholar
- Neuroscience academic family tree: NeuroTree. Check Your path to me
- FOSS development vital signs: Ohloh, Debian
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. You can discover more about these and other “infrastructural” projects I am pursuing at the Center for Open Neuroscience (CON) .
Publications¶
Articles¶
| [KAB+19] | Kennedy, D. N., et al. (2019). Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging. Frontiers in Neuroinformatics, 13, 1. [URL] DOI: 10.3389/fninf.2019.00001 |
| [NHC+18] | Nastase, S. A. , Halchenko, Y. O. , Connolly, A. C. , Gobbini, M. I. and Haxby, J. V. (2018). Neural Responses to Naturalistic Clips of Behaving Animals in Two Different Task Contexts. Frontiers in Neuroscience, 12, 316. [URL] DOI: 10.3389/fnins.2018.00316, cited by: 2 |
| [EMH+17] | Eglen, S. J., et al. (2017). Toward standard practices for sharing computer code and programs in neuroscience. Nat Neurosci, 20, 770-773. DOI: 10.1038/nn.4550, cited by: 32 |
| [GPK+17] | Ghosh, S. S., Poline, J., Keator, D. B., Halchenko, Y. O. , Thomas, A. G., Kessler, D. A. and Kennedy, D. N. (2017). A very simple, re-executable neuroimaging publication. F1000Research, 6. DOI: 10.12688/f1000research.10783.1, cited by: 2 |
| [NCO+17] | Nastase, S. A. , et al. (2017). Attention selectively reshapes the geometry of distributed semantic representation. Cerebral Cortex, 27, 4277-4291. [URL] |
| [VHG+biorxiv17] | Visconti di Oleggio Castello, M., Halchenko, Y. O. , Guntupalli, J. S. , Gors, J. D. and Gobbini, M. I. (2017). The Neural Representation Of Personally Familiar And Unfamiliar Faces In The Distributed System For Face Perception. bioRxiv. [URL] DOI: 10.1101/138297 |
| [VHG+17] | Visconti di Oleggio Castello, M., Halchenko, Y. O. , Guntupalli, J. S. , Gors, J. D. and Gobbini, M. I. (2017). The Neural Representation Of Personally Familiar And Unfamiliar Faces In The Distributed System For Face Perception. Scientific Reports, 7. DOI: 10.1038/s41598-017-12559-1 |
| [CSG+16] | Connolly, A. C. , et al. (2016). How the human brain represents perceived dangerousness or “predacity” of animals. Journal of Neuroscience, 36, 5373–5384. [PDF] [URL] DOI: 10.1523/jneurosci.3395-15.2016, cited by: 19 |
| [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] DOI: 10.1038/sdata.2016.44, cited by: 101 |
| [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, cited by: 3 |
| [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, cited by: 70 |
| [NCO+biorxiv16] | Nastase, S. A. , et al. (2016). Attention selectively reshapes the geometry of distributed semantic representation. bioRxiv. [PDF] [URL] DOI: 10.1101/045252, cited by: 14 |
| [Vogel+2016] | Vogelstein, J. T., et al. (2016). To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery. Neuron, 92, 622-627. DOI: 10.1016/j.neuron.2016.10.033 |
| [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 |
| [SHA+15] | Sha, L., Haxby, J. V. , Abdi, H., Guntupalli, J. S. , Oosterhof, N. N. , Halchenko, Y. O. and Connolly, A. C. (2015). The animacy continuum in the human ventral vision pathway. Journal of Cognitive Neuroscience, 27:4, 665-678. [PDF] DOI: 10.1162/jocn_a_00733, cited by: 58 |
| [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, cited by: 24 |
| [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, cited by: 53 |
| [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, cited by: 12 |
| [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: 234 |
| [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: 82 |
| [PBG+12] | Poline, J., et al. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6. DOI: 10.3389/fninf.2012.00009, cited by: 181 |
| [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: 353 |
| [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: 31 |
| [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: 289 |
| [HH09] | Halchenko, Y. O. and Hanke, M. (2010). Advancing Neuroimaging Research with Predictive Multivariate Pattern Analysis. The Neuromorphic Engineer. [PDF] DOI: 10.2417/1200909.1683, cited by: 1 |
| [RHH+10] | Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O. , Poldrack, R. A. and Glymour, C. (2010). Six problems for causal inference from fMRI. Neuroimage, 49, 1545-58. [PDF] DOI: 10.1016/j.neuroimage.2009.08.065, cited by: 242 |
| [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: 321 |
| [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: 37 |
| [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: 226 |
| [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: 127 |
| [HHH+07] | Hanson, S. J., Hanson, C., Halchenko, Y. O. , Matsuka, T. and Zaimi, A. (2007). Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action. Brain Struct Funct, 212, 231-44. [PDF] DOI: 10.1007/s00429-007-0160-2, cited by: 15 |
| [HRH+07] | Hanson, S. J., Rebecchi, R., Hanson, C. and Halchenko, Y. O. (2007). Dense mode clustering in brain maps. Magn Reson Imaging, 25, 1249-62. [PDF] DOI: 10.1016/j.mri.2007.03.013, cited by: 6 |
| [HPH+04a] | Halchenko, Y. O. , and~S J Hanson, B. A. P. and Zaimi, A. (2004). Fusion of functional brain imaging modalities via linear programming. Biomedizinische Technik (Biomedical Engineering), 48, 102–4. [PDF] [URL] |
Editorials¶
| [HH_ed15] | Hanke, M. and Halchenko, Y. O. (2015). A communication hub for a decentralized collaboration on studying real-life cognition. F1000Research, 4. DOI: 10.12688/f1000research.6229.1, cited by: 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 |
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: 33 |
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¶
| [NHD+16] | Nastase, S. A. , Halchenko, Y. O. , Davis, B. and Hasson, U. (2016). Cross-modal searchlight classification: Methodological challenges and recommended solutions. In (Eds.) 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI). [PDF] DOI: 10.1109/PRNI.2016.7552355, cited by: 4 |
| [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 |
Miscellaneous¶
| [niceman:0.1.0] | Halchenko, Y. , Meyer, K., Travers, M., Haselgrove, C. and Buccigrossi, R. W. (2018). ReproNim/niceman v0.1.0. ”. [URL] DOI: 10.5281/zenodo.2403222 |
| [Ha12:hoxbug] | Halchenko, Y. O. (2012). Incorrect probabilities in Harvard-Oxford-sub Left hemisphere. ”. [URL] |
Interviews¶
| [SFN13] | Halchenko, Y. O. (2013). NeuroDebian: from disjoint tools and data to robust turnkey platform for neuroimaging and beyond. ”. [URL] |
Posters¶
| [CVG+OHBM18] | Contier, O. and di~Oleggio Castello, V. (2018). Temporal dynamics and effective connectivity in the distributed system of familiar face processing. Poster presented at the annual meeting of the Organization for Human Brain Mapping, Singapore. [PDF] |
| [HMP+INCF18] | Hanke, M. , Meyer, K., Poldrack, B. and Halchenko, Y. O. (2018). (Meta)Data and workflow management for your most important work and your daily routine. Poster presented at the INCF annual meeting Neuroinformatics 2018, Montreal, Canada. |
| [HVM+OHBM18] | Hanke, M. , Visconti di Oleggio Castello, M., Meyer, K., Poldrack, B. and Halchenko, Y. O. (2018). YODA: YODA’s organigram on data analysis. Poster presented at the annual meeting of the Organization for Human Brain Mapping, Singapore. |
| [NPC+OHBM18] | Nastase, S. , Philip, R., Chauhan, V., Ma, F., Taylor, M., Halchenko, Y. O., Gobbini, M. I. and Haxby, J. (2018). Decoding the neural representation of observed social and nonsocial human actions. Poster presented at the annual meeting of the Organization for Human Brain Mapping, Singapore. [PDF] |
| [TBH+OHBM18] | Travers, M., Buccigrossi, R., Haselgrove, C., Meyer, K. and Halchenko, Y. O. (2018). NICEMAN: NeuroImaging Computational Environments Manager. Poster presented at the annual meeting of the Organization for Human Brain Mapping, Singapore. |
| [YVD+OHBM18] | Yarkoni, T., et al. (2018). Pybids: Python tools for manipulation and analysis of BIDS datasets. Poster presented at the annual meeting of the Organization for Human Brain Mapping, Singapore. [PDF] |
| [VH+PYCON17] | Visconti di Oleggio Castello, M. and Halchenko, Y. O. (2017). DueCredit - automagically collect citations for software, methods, and data you use. PyCON 2017, Portland, Oregon. |
| [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] |
| [HV+OHBM16] | Halchenko, Y. O. and Visconti di Oleggio Castello, M. (2016). DueCredit - automagically collect citations for software, methods, and data you use. Organization of Human Brain Mapping Annual Meeting, Geneva, Switzerland. Talk. [PDF] |
| [NGH+SFN16] | Nastase, S. A. , Guntupalli, J. S. , Haxby, J. V. and Halchenko, Y. O. (2016). Localizing functional regions of interest based on responses to dynamic naturalistic stimuli. Poster presented at the annual meeting of the Society for Neuroscience, Chicago, IL. [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. |
| [GWH+OHBM15] | Gorgolewski, K., Wheeler, K., Halchenko, Y. O. , Poline, J. B. and Poldrack, R. (2015). The impact of shared data in neuroimaging: the case of OpenfMRI.org. Organization of Human Brain Mapping Annual Meeting, Honolulu HI, USA. Poster. [PDF] [URL] |
| [NVH+SFN15] | Nastase, S. A. , Visconti di Oleggio Castello, M., Halchenko, Y. O. , Connolly, A. C. and Oosterhof, N. N. (2015). Attention alters animal and action representation in highly-distributed functionally-defined cortical parcels. Poster presented at the annual meeting of the Society for Neuroscience, Chicago, IL. [PDF] |
| [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. |
| [VNH+SFN15] | Visconti di Oleggio Castello, M., Nastase, S. A. , Gobbini, M. I. , Haxby, J. V. and Halchenko, Y. O. (2015). Finding cortical patches of shared representations: a comparison of clustering algorithms on representational geometries, and the effect of cross-validation to reduce physiological noise. Poster presented at the annual meeting of the Society for Neuroscience, Chicago, IL. [PDF] |
| [VNH+OHBM15] | Visconti di Oleggio Castello, M., Nastase, S. A. , Haxby, J. V. , Gobbini, M. I. and Halchenko, Y. O. (2015). Clustering cortical searchlights based on shared representational geometry. Poster presented at the annual Meeting of the Organization for Human Brain Mapping, Honolulu, HI. [PDF] |
| [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] |
| [NCO+OHBM14] | Nastase, S. A. , Connolly, A. C. , Oosterhof, N. N. , Halchenko, Y. O. , Gors, J. , Gobbini, M. I. and Haxby, J. V. (2014). Attention locally modulates the discriminability of high-level, task-relevant representations. Poster presented at the annual meeting of the Organization for Human Brain Mapping, Hamburg, Germany. [PDF] |
| [NCO+VSS14] | Nastase, S. A. , Connolly, A. C. , Oosterhof, N. N. , Halchenko, Y. O. , Gors, J. , Gobbini, M. I. and Haxby, J. V. (2014). Attentional allocation locally warps representational space. Poster presented at the annual meeting of the Vision Sciences Society, St. Pete Beach, FL. [PDF] |
| [HGG+12] | Halchenko, Y. O. , Gors, J. , Guntupalli, J. S. , Haxby, J. V. and Gobbini, M. I. (2012). Investigation of the familiar face recognition model. Society for Neuroscience Annual Meeting, New Orleans. Poster. [PNG] [URL] |
![[PNG]](http://haxbylab.dartmouth.edu/publications/HGG+12_sfn12_famfaces.png){kind=link}
| [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. Vision Sciences Society Annual Meeting, Naples, FL. Poster. [PNG] |
![[PNG]](http://haxbylab.dartmouth.edu/publications/http://vss2011.png){kind=link}
| [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], cited by: 3 |
Talks¶
| [HGP+18] | Halchenko, Y. O. , and~Jean-Baptiste Poline, S. G., Jarecka, D. and Kennedy, D. (2018). Variability of the Neuroimaging Results Across OS, and How to Avoid it. Talk presented at the annual meeting of the Biological Psychiatry. |
| [VDS+OHBM18] | Visconti di Oleggio Castello, M., Dobson, J., Sackett, T., Kodiweera, C., Haxby, J., Goncalves, M., Ghosh, S. and Halchenko, Y. O. (2018). ReproIn: automatic generation of shareable, version-controlled BIDS datasets from MR scanners . Poster and talk presented at the annual meeting of the Organization for Human Brain Mapping, Singapore. [PDF] |
| [Hal+OHBM16] | Halchenko, Y. O. (2016). Resources for practicing PR4NI – pragmatic cursory overview. Organization of Human Brain Mapping Annual Meeting, Tutorials, Geneva, Switzerland. Talk. [URL] |
| [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] |
| [GH+OHBM15] | Halchenko, Y. O. (2015). Overview of statistical evaluation techniques adopted by publicly available MVPA toolboxes. Organization of Human Brain Mapping Annual Meeting, Honolulu HI, USA. Talk. [URL] |
| [NOH+OHBM15] | Nastase, S. A. , Visconti di Oleggio Castello, M., Haxby, J. V. , Gobbini, M. I. and Halchenko, Y. O. (2015). Clustering cortical searchlights based on shared representational geometry. Oral presentation at the annual meeting of the Organization for Human Brain Mapping, Honolulu, HI. |
Ph.D. Theses¶
| [Hal09] | Halchenko, Y. O. (2009). Predictive Decoding of Neural Data (Doctoral dissertation). NJIT, Newark, NJ, USA. [PDF] [URL] |
Workshops¶
Open Data Ecosystem for Neuroscience (ODEN 2016) (2016). Washington DC
NIH Data Archive workshop (2016). MIT, Cambridge, MA
More 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 the 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.
More Posters¶
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.