Posters

  1. J. Browne et al. Forest Packing: Fast Parallel Decision Forests. 2019.
  2. J. Chung et al. Clustering Multi-Modal Connectomes. 2019.
  3. J. Chung et al. Human Structural Connectomes are Heritable. 2019.
  4. B. D. Pedigo et al. GraSPy: an Open Source Python Package for Statistical Connectomics. 2019.
  5. S. Chen et al. A Sparse High Dimensional State-Space Model with an Application to Neuroimaging Data. 2015.
  6. S. Chen et al. High Dimensional State Space Model with L-1 and L-2 Penalties. 2015.
  7. F. Collman et al. An integrated imaging and staining platform for cubic millimeter scale array tomography. 2015.
  8. E. L. Deyer et al. X-Brain: Quantifying Mesoscale Neuroanatomy Using X-ray Microtomography. 2015.
  9. S. J. Smith et al. The Open Synaptome Project: Toward a Microscopy-Based Platform for Single-synapse Analysis of Diverse Populations of CNS Synapses. 2015.
  10. J. T. Vogelstein. Open Connectome Project & NeuroData: Enabling Data-Driven Neuroscience at Scale. 2015.
  11. S. Wang et al. Optimal Design for Discovery Science: Applications in Neuroimaging. 2015.
  12. S. Sikka et al. Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac). 2014.
  13. R. D. Airan, J. T. Vogelstein and others. Reproducible differentiation of individual of individual subjects with minimal acquisition time via resting state fMRI. 2013.
  14. C. Craddock and others. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes. 2013.
  15. W. R. Gray and others. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. 2013.
  16. D. Koutra et al. Are All Brains Wired Equally? 2013.
  17. D. Mhembere and others. Multivariate Invariants from Massive Brain-Graphs. 2013.
  18. E. A. Pnevmatikakis and others. Rank-penalized nonnegative spatiotemporal deconvolution and demixing of calcium inaging data. 2013.
  19. Y. Qin and others. Robust Clustering of Adjacency Spectral Embeddings of Brain Graph Data via Lq-Likelihood. 2013.
  20. N. Sismanis and others. Feature Clustering from a Brain Graph for Voxel-to-Region Classification. 2013.
  21. D. Sussman and others. Massive Diffusion MRI Graph Structure Preserves Spatial Information. 2013.
  22. J. T. Vogelstein and C. E. Priebe. Nonparametric Two-Sample Testing on Graph-Valued Data. 2013.
  23. J. T. Vogelstein and others. Anomaly Screening and Clustering of Multi-OBject Movies via Multiscale Structure Learning. 2013.
  24. W. R. Gray and others. Towards a Fully Automatic Pipeline for Connectome Estimation from High-Resolution EM Data. 2012.
  25. J. T. Vogelstein and others. Statistical Connectomics. 2012.
  26. J. T. Vogelstein and others. BRAINSTORM towards clinically and scientifically useful neuroimaging analytics. 2012.
  27. W. R. Gray et al. Magnetic resonance connectome automated pipeline and repeatability analysis. 2011.
  28. J. T. Vogelstein et al. Large graph classification: theory and statistical connectomics applications. 2011.
  29. J. T. Vogelstein et al. Connectome Classification using statistical graph theory and machine learning. 2011.
  30. J. T. Vogelstein et al. Connectome Classification: Statistical Graph Theoretic Methods for Analysis of MR-Connectome Data. 2011.
  31. J. T. Vogelstein et al. Open Connectome Project: collectively reverse engineering the brain one synapse at a time. 2011.
  32. J. T. Vogelstein et al. Dot product embedding in large (errorfully observed) graphs with applications in statistical connectomics. 2011.
  33. W. R. Gray et al. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. 2010.
  34. J. T. Vogelstein et al. Graph-Theoretical Methods for Statistical Inference on MR Connectome Data. 2010.
  35. J. T. Vogelstein et al. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. 2010.
  36. J. T. Vogelstein et al. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. 2010.
  37. J. T. Vogelstein et al. Measuring and reconstructing the brain at the synaptic scale: towards a biofidelic human brain in silico. 2010.
  38. J. T. Vogelstein, R. Vogelstein and C. E. Priebe. A Neurocognitive Graph-Theoretical Approach to Understanding the Relationship Between Minds and Brains. 2010.
  39. J. T. Vogelstein et al. Towards Confirming Neural Circuits from Population Calcium Imaging. 2009.
  40. J. T. Vogelstein et al. Towards Inferring Neural Circuit Inference from Population Calcium Imaging. 2009.
  41. J. T. B. Vogelstein and L. Paninski. Model-Based Optimal Inference of Spike-Times and Calcium Dynamics given Noisy and Intermittent Calcium-Fluorescence Imaging. 2008.
  42. J. T. Vogelstein et al. From Calcium Sensitive Fluorescence Movies to Spike Trains. 2008.
  43. J. T. Vogelstein and L. Paninski. Inferring Spike Trains, Learning Tuning Curves, and Estimating Connectivity from Calcium Imaging. 2008.
  44. J. T. Vogelstein et al. Inferring Spike Trains, Neural Filters, and Network Circuits from in vivo Calcium Imaging. 2007.
  45. J. T. Vogelstein et al. Maximum Likelihood Inference of Neural Dynamics under Noisy and Intermittent Observations using Sequential Monnte Carlo EM Algorithms. 2007.
  46. J. T. Vogelstein and K. Zhang. A novel theory for simultaneous representation of multiple dynamic states in hippocampus. 2004.
  47. J. T. Vogelstein et al. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. 2002.
  48. J. T. Vogelstein et al. Up-down asymmetry in memory guided saccadic eye movements are independent of head orientation in space. 2002.