Posters

  1. T. L. Athey, J. Sulam, J. Vogelstein, D. Tward, and M. Miller. Automated Neuron Tracing of Sparse Fluorescently Labeled Neurons. Neuromatch 3, 2020.
  2. E. W. Bridgeford, M. Powell, A. Alyakin, B. Caffo, and J. T. Vogelstein. Batch Effects are Causal Effects: Applications in Human Functional Connectomes. Neuromatch 3, 2020.
  3. J. Chung, J. Dey, G. Kiar, C. E. Priebe, and J. T. Vogelstein. Human Structural Connectomes are Heritable. Neuromatch 3, 2020.
  4. V. Gopalakrishnan, J. Chung, E. Bridgeford, J. Arroyo, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. Statistical Methods for Multiscale Comparative Connectomics. Neuromatch 3, 2020.
  5. T. Liu, B. D. Pedigo, T. L. Athey, and J. T. Vogelstin. Hierarchical stochastic block modeling in the Drosophila connectome. Neuromatch 3, 2020.
  6. B. D. Pedigo, M. Winding, T. Orujlu, M. Zlatic, Cardona,Albert, C. E. Priebe, and J. T. Vogelstein. A quantitative comparison of a complete connectome to artificial intelligence architectures. NAIsys, Cold Spring Harbor, NY, USA, 2020.
  7. B. D. Pedigo, M. Winding, A. Saad-Eldin, T. Liu, A. Cardona, M. Zlatic, C. E. Priebe, and J. T. Vogelstein. Statistical tools for nanoscale connectomics: clustering neurons in Drosophila larva brain and other applications. Neuromatch 3, 2020.
  8. R. Perry, J. Zorn, S. Czajko, D. S. Margulies, and J. T. Vogelstein. Permutation-corrected independence testing for high-dimensional fMRI data. Neuromatch 3, 2020.
  9. A. Saad-Eldin, B. D. Pedigo, Y. Park, C. E. Priebe, and J. T. Vogelstein. NeuroGraphMatch. Neuromatch 3, 2020.
  10. J. T. Vogelstein, H. Helm, B. D. Pedigo, R. Mehta, C. E. Priebe, and C. White. A Biological Implementation of Lifelong Learning in the Pursuit of Artificial General Intelligence. NAIsys, Cold Spring Harbor, NY, USA, 2020.
  11. J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. Impact of Concatenating fMRI Data on reliability for Functional Connectomics. OHBM and Resting State, Fairmont, Dallas, TX, USA, 2020.
  12. J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. Developing a gradient flow framework to guide the optimization of reliability for the study of individual differences. OHBM and Resting State, Fairmont, Dallas, TX, USA, 2020.
  13. J. Hecheng, J. S. Ramirez, J. T. Vogelstein, M. P. Milham, and T. Xu. Assessing functional connectivity beyond Pearson's correlation. Fairmont, Dallas, TX, USA, 2020.
  14. X. Li, J. Cho, M. P. Milham, and T. Xu. Improving brain-behavior prediction using individual-specific components from connectivity-based shared response model. Resting State, Fairmont, Dallas, TX, USA, 2020.
  15. E. Bridgeford and J. T. Vogelstein. Optimal Experimental Design for Big Data: Applications in Brain Imaging. OHBM, 2020.
  16. J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. Impact of Concatenating fMRI Data on reliability for Functional Connectomics. OHBM and Resting State, Fairmont, Dallas, TX, USA, 2020.
  17. J. Cho, A. Korchmaros, J. T. Vogelstein, M. P. Milham, and T. Xu. Developing a gradient flow framework to guide the optimization of reliability for the study of individual differences. OHBM and Resting State, Fairmont, Dallas, TX, USA, 2020.
  18. R. Perry and J. T. Vogelstein. Identifying Differences Between Expert and Novice Meditator Brain Scans via Multiview Embedding. OHBM, 2020.
  19. B. Falk and J. T. Vogelstein. NeuroData's Open Data Cloud Ecosystem. Harvard University, Cambridge, MA, USA, 2019.
  20. J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. Clustering Multi-Modal Connectomes. OHBM, Rome Italy, 2019.
  21. J. Chung, B. D. Pedigo, C. E. Priebe, and J. T. Vogelstein. Human Structural Connectomes are Heritable. OHBM, Rome Italy, 2019.
  22. J. Browne, D. Mhembere, T. M. Tomita, J. T. Vogelstein, and R. Burns. Forest Packing: Fast Parallel Decision Forests. SIAM International Conference on Data Mining, Calgary, Alberta, Canada, 2019.
  23. T. L. Athey and J. T. Vogelstein. Low-level Neuron Segmentation in Sub-Micron Resolution Images of the Complete Mouse Brain. Brain Initiative Investigators Meeting, Washington DC, USA, 2019.
  24. T. L. Athey and J. T. Vogelstein. Investigating Neuron Trajectories with Splines. Brain Initiative Investigators Meeting, Washington DC, USA, 2019.
  25. B. D. Pedigo, J. Chung, E. W. Bridgeford, B. Varjavand, C. E. Priebe, and J. T. Vogelstein. GraSPy: an Open Source Python Package for Statistical Connectomics. Max Planck /HHMI Connectomics Meeting Berlin, Germany, 2019.
  26. A. Baden, E. Perlman, F. Collman, S. Smith, J. T. Vogelstein, and R. Burns. Processing and Analyzing Terascale Conjugate Array Tomography Data. Berlin, Germany, 2017.
  27. E. Perlman. NEURODATA: ENABLING BIG DATA NEUROSCIENCE. Kavli, Baltimore, MD, USA, 2017.
  28. S. J. Smith, R. Burns, M. Chevillet, E. Lein, G. Sapiro, W. Seeley, J. Trimmer, J. T. Vogelstein, and R. Weinberg. The Open Synaptome Project: Toward a Microscopy-Based Platform for Single-synapse Analysis of Diverse Populations of CNS Synapses. Society for Neuroscience, Chicago, IL, USA, 2015.
  29. J. T. Vogelstein. Open Connectome Project & NeuroData: Enabling Data-Driven Neuroscience at Scale. Society for Neuroscience, Chicago, IL, USA, 2015.
  30. S. Chen, J. T. Vogelstein, S. Lee, M. Lindquist, and B. Caffo. High Dimensional State Space Model with L-1 and L-2 Penalties. ENAR 2015, Miami, FL, USA, 2015.
  31. S. Chen, K. Liu, Y. Yuguang, L. Seonjoo, M. Lindquist, B. Caffo, and J. T. Vogelstein. A Sparse High Dimensional State-Space Model with an Application to Neuroimaging Data. Figshare, 2015.
  32. E. L. Deyer, H. L. Fernandes, W. G. Roncal, D. Gursoy, J. T. Vogelstein, X. Xiao, C. Jacobsen, K. P. Kording, and N. Kasthuri. X-Brain: Quantifying Mesoscale Neuroanatomy Using X-ray Microtomography. Figshare, 2015.
  33. S. Wang, Z. Yang, X. Zuo, M. Milham, C. Craddock, C. E. Priebe, and J. T. Vogelstein. Optimal Design for Discovery Science: Applications in Neuroimaging. Figshare, 2015.
  34. S. A. C. Sikka. Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac). 5th INCF Congress of Neuroinformatics, Munich, Germany, 2014.
  35. J. T. Vogelstein and C. E. Priebe. Nonparametric Two-Sample Testing on Graph-Valued Data. Duke Workshop on Sensing and Analysis of HighDimensional Data, Durham, NC, USA, 2013.
  36. W. A. K. Gray Roncal. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. OHBM, Seattle, WA, USA, 2013.
  37. D. A. B. Mhembere. Multivariate Invariants from Massive Brain-Graphs. OHBM, Seattle, WA, USA, 2013.
  38. Y. Qin, D. Mhembere, S. Ryman, R. Jung, R. J. Vogelstein, R. Burns, J. Vogelstein, and C. . Priebe. Robust Clustering of Adjacency Spectral Embeddings of Brain Graph Data via Lq-Likelihood. OHBM, Seattle, WA, USA, 2013.
  39. D. L. Sussman, D. Mhembere, S. Ryman, R. Jung, R. J. Vogelstein, R. Burns, J. T. Vogelstein, and C. E. . Priebe. Massive Diffusion MRI Graph Structure Preserves Spatial Information. OHBM, Seattle, WA, USA, 2013.
  40. D. Koutra, Y. Gong, S. Ryman, R. Jung, J. T. Vogelstein, and C. Faloutsos. Are All Brains Wired Equally? Proceedings of the 19th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Seattle, WA, USA, 2013.
  41. A. A. V. Raag D. Reproducible differentiation of individual of individual subjects with minimal acquisition time via resting state fMRI. Proc ISMRM, Salt Lake City, UT, USA, 2013.
  42. N. A. S. Sismanis. Feature Clustering from a Brain Graph for Voxel-to-Region Classification. 5th Panhellic Conference on Biomedical Technology, Athens, Greece, 2013.
  43. E. A. A. M. Pnevmatikakis. Rank-penalized nonnegative spatiotemporal deconvolution and demixing of calcium inaging data. COSYNE, Salt Lake City, UT, USA, 2013.
  44. J. T. Vogelstein and others. Anomaly Screening and Clustering of Multi-OBject Movies via Multiscale Structure Learning. DARPA XDATA Colloquium, 2013.
  45. J. A. S. Vogelstein. BRAINSTORM towards clinically and scientifically useful neuroimaging analytics. Neuroinformatics, Munich, Germany, 2012.
  46. J. T. A. B. Vogelstein. Statistical Connectomics. Janelia Farm conference, Statistical Inference and Neuroscience, Loudoun County, VA, USA, 2012.
  47. W. R. A. K. Gray. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. Cold Spring Harbor Laboratory, Neuronal Circuits, Cold Spring Harbor, NY, USA, 2012.
  48. W. R. Gray, J. A. Bogovic, J. T. Vogelstein, C. Ye, B. A. Landman, J. L. Prince, and R. J. Vogelstein. Magnetic resonance connectome automated pipeline and repeatability analysis. Society for Neuroscience, Washington DC, USA, 2011.
  49. J. T. Vogelstein, D. E. Fishkind, D. L. Sussman, and C. E. Priebe. Large graph classification: theory and statistical connectomics applications. IMA conference on Large Graphs, University of Minnesota, Minneapolis, MN, USA, 2011.
  50. J. T. Vogelstein, W. Gray, J. G. Martin, G. C. Coppersmith, M. Dredze, J. Bogovic, J. L. Prince, S. M. Resnick, C. E. Priebe, and R. J. Vogelstein. Connectome Classification using statistical graph theory and machine learning. Society for Neuroscience, Washington DC, USA, 2011.
  51. J. T. Vogelstein, D. L. Sussman, M. Tang, D. E. Fishkind, and C. E. Priebe. Dot product embedding in large (errorfully observed) graphs with applications in statistical connectomics. IMA conference on Large Graphs, University of Minnesota, Minneapolis, MN, USA, 2011.
  52. J. T. Vogelstein, E. Perlman, D. Bock, W. C. Lee, M. Chang, B. Kasthuri, M. Kazhdan, C. Reid, J. Lichtman, R. Burns, and R. J. Vogelstein. Open Connectome Project: collectively reverse engineering the brain one synapse at a time. None 2011.
  53. J. T. Vogelstein, W. R. Gray, R. J. Vogelstein, J. Bogovic, S. Resnick, J. Prince, and C. E. Priebe. Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data. Organization for Human Brain Mapping, Quebec City, Canada, 2011.
  54. W. R. Gray, J. T. Vogelstein, J. Bogovic, A. Carass, J. L. Prince, B. Landman, D. Pham, L. Ferrucci, S. M. Resnick, C. E. Priebe, and R. J. Vogelstein. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. DARPA Neural Engineering, Science and Technology Forum, San Diego, CA, USA, 2010.
  55. J. T. Vogelstein, C. E. Priebe, R. Burns, R. J. Vogelstein, and J. Lichtman. Measuring and reconstructing the brain at the synaptic scale: towards a biofidelic human brain in silico. DARPA Neural Engineeering, Science and Technology Forum, San Diego, CA, USA, 2010.
  56. J. T. Vogelstein, J. Bogovic, A. Carass, W. Gray, J. Prince, B. Landman, D. Pham, L. Ferrucci, S. Resnick, C. E. Priebe, and R. Vogelstein. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. Organization for Human Brain Mapping, Barcelona, Spain, 2010.
  57. J. T. Vogelstein, R. Vogelstein, and C. E. Priebe. A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains. CSHL conference on Neural Circuits, Cold Shore Harbor, NY, USA, 2010.
  58. J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. COSYNE, Salt Lake City, UT, USA, 2010.
  59. J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. COSYNE, Salt Lake City, UT, USA, 2010.
  60. J. T. Vogelstein, Y. Mishchchenko, A. M. Packer, T. A. Machado, R. Yuste, and L. Paninski. Towards Confirming Neural Circuits from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging, Whistler, BC, Canada, 2009.
  61. J. T. Vogelstein, Y. Mishchenki, A. Packer, T. Machado, R. Yuste, and L. Paninski. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. COSYNE, Salt Lake City, UT, USA, 2009.
  62. J. T. Vogelstein, B. Babadi, B. Watson, R. Yuste, and L. Paninski. From Calcium Sensitive Fluorescence Movies to Spike Trains. Society for Neuroscience, Washington DC, USA, 2008.
  63. J. T. Vogelstein, B. Babadi, and L. Paninski. Model-Based Optimal Inference of Spike-Times and Calcium Dynamics given Noisy and Intermittent Calcium-Fluorescence Imaging. COSYNE, Salt Lake City, UT, USA, 2008.
  64. J. T. Vogelstein and L. Paninski. Inferring Spike Trains, Learning Tuning Curves, and Estimating Connectivity from Calcium Imaging. Integrative Approaches to Brain Complexity, 2008.
  65. J. T. Vogelstein, B. Jedynak, K. Zhang, and L. Paninski. Inferring Spike Trains, Neural Filters, and Network Circuits from in vivo Calcium Imaging. Society for Neuroscience, San Diego, CA, USA, 2007.
  66. J. T. Vogelstein, K. Zhang, B. Jedynak, and L. Paninski. Maximum Likelihood Inference of Neural Dynamics under Noisy and Intermittent Observations using Sequential Monnte Carlo EM Algorithms. COSYNE, Salt Lake City, UT, USA, 2007.
  67. J. T. Vogelstein and K. Zhang. A novel theory for simultaneous representation of multiple dynamic states in hippocampus. Society for Neuroscience, San Diego, CA, USA, 2004.
  68. J. T. Vogelstein, L. Snyder, M. Warchol, and D. Angelaki. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. Society for Neuroscience, Orlando, FL, USA, 2002.