Diffusion Tensor Analysis
Point Set Registration Using Havrda–Charvat–Tsallis Entropy Measures|
N.J. Tustison, S.P. Awate, G. Song, T.S. Cook, J.C. Gee. In IEEE Transactions on Medical Imaging, Vol. 30, No. 2, pp. 451--460. 2011.
FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics|
H. Zhu, L. Kong, R. Li, M.S. Styner, G. Gerig, W. Lin, J.H. Gilmore. In NeuroImage, Vol. 56, No. 3, pp. 1412--1425. 2011.
PubMed ID: 21335092
DTI registration in atlas based fiber analysis of infantile Krabbe disease|
Y. Wang, A. Gupta, Z. Liu, H. Zhang, M.L. Escolar, J.H. Gilmore, S. Gouttard, P. Fillard, E. Maltbie, G. Gerig, M. Styner. In Neuroimage, pp. (in print). 2011.
PubMed ID: 21256236
Twin-Singleton Differences in Neonatal Brain Structure|
R.C. Knickmeyer, C. Kang, S. Woolson, K.J. Smith, R.M. Hamer, W. Lin, G. Gerig, M. Styner, J.H. Gilmore. In Twin Research and Human Genetics, Vol. 14, No. 3, pp. 268--276. 2011.
Early Brain Overgrowth in Autism Associated with an Increase in Cortical Surface Area Before Age 2|
H.C. Hazlett, M. Poe, G. Gerig, M. Styner, C. Chappell, R.G. Smith, C. Vachet, J. Piven. In Arch of Gen Psych, Vol. 68, No. 5, pp. 467--476. 2011.
Optimal data-driven sparse parameterization of diffeomorphisms for population analysis|
S. Durrleman, M.W. Prastawa, G. Gerig, S. Joshi. In Proceedings of the IPMI 2011 conference, Springer LNCS, Vol. 6801/2011, pp. 123--134. July, 2011.
PubMed ID: 20516153
Spatial Intensity Prior Correction for Tissue Segmentation in the Developing human Brain|
S.H. Kim, V. Fonov, J. Piven, J. Gilmore, C. Vachet, G. Gerig, D.L. Collins, M. Styner. In Proceedings of IEEE ISBI 2011, pp. 2049--2052. 2011.
CENTS: Cortical Enhanced Neonatal Tissue Segmentation|
F. Shi, D. Shen, P.-T. Yap, Y. Fan, J.-Z. Cheng, H. An, L.L. Wald, G. Gerig, J.H. Gilmore, W. Lin. In Human Brain Mapping HBM, Vol. 32, No. 3, Note: ePub 5 Aug 2010, pp. 382--396. March, 2011.
PubMed ID: 20690143
Multi-scale Series Contextual Model for Image Parsing|
SCI Technical Report, M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen. No. UUSCI-2011-004, SCI Institute, University of Utah, 2011.
Exploring the Retinal Connectome|
J.R. Anderson, B.W. Jones, C.B. Watt, M.V. Shaw, J.-H. Yang, D. DeMill, J.S. Lauritzen, Y. Lin, K.D. Rapp, D. Mastronarde, P. Koshevoy, B. Grimm, T. Tasdizen, R.T. Whitaker, R.E. Marc. In Molecular Vision, Vol. 17, pp. 355--379. 2011.
PubMed ID: 21311605
Purpose: A connectome is a comprehensive description of synaptic connectivity for a neural domain. Our goal was to produce a connectome data set for the inner plexiform layer of the mammalian retina. This paper describes our first retinal connectome, validates the method, and provides key initial findings.
Methods: We acquired and assembled a 16.5 terabyte connectome data set RC1 for the rabbit retina at .2 nm resolution using automated transmission electron microscope imaging, automated mosaicking, and automated volume registration. RC1 represents a column of tissue 0.25 mm in diameter, spanning the inner nuclear, inner plexiform, and ganglion cell layers. To enhance ultrastructural tracing, we included molecular markers for 4-aminobutyrate (GABA), glutamate, glycine, taurine, glutamine, and the in vivo activity marker, 1-amino-4-guanidobutane. This enabled us to distinguish GABAergic and glycinergic amacrine cells; to identify ON bipolar cells coupled to glycinergic cells; and to discriminate different kinds of bipolar, amacrine, and ganglion cells based on their molecular signatures and activity. The data set was explored and annotated with Viking, our multiuser navigation tool. Annotations were exported to additional applications to render cells, visualize network graphs, and query the database.
Results: Exploration of RC1 showed that the 2 nm resolution readily recapitulated well known connections and revealed several new features of retinal organization: (1) The well known AII amacrine cell pathway displayed more complexity than previously reported, with no less than 17 distinct signaling modes, including ribbon synapse inputs from OFF bipolar cells, wide-field ON cone bipolar cells and rod bipolar cells, and extensive input from cone-pathway amacrine cells. (2) The axons of most cone bipolar cells formed a distinct signal integration compartment, with ON cone bipolar cell axonal synapses targeting diverse cell types. Both ON and OFF bipolar cells receive axonal veto synapses. (3) Chains of conventional synapses were very common, with intercalated glycinergic-GABAergic chains and very long chains associated with starburst amacrine cells. Glycinergic amacrine cells clearly play a major role in ON-OFF crossover inhibition. (4) Molecular and excitation mapping clearly segregates ultrastructurally defined bipolar cell groups into different response clusters. (5) Finally, low-resolution electron or optical imaging cannot reliably map synaptic connections by process geometry, as adjacency without synaptic contact is abundant in the retina. Only direct visualization of synapses and gap junctions suffices.
Conclusions: Connectome assembly and analysis using conventional transmission electron microscopy is now practical for network discovery. Our surveys of volume RC1 demonstrate that previously studied systems such as the AII amacrine cell network involve more network motifs than previously known. The AII network, primarily considered a scotopic pathway, clearly derives ribbon synapse input from photopic ON and OFF cone bipolar cell networks and extensive photopic GABAergic amacrine cell inputs. Further, bipolar cells show extensive inputs and outputs along their axons, similar to multistratified nonmammalian bipolar cells. Physiologic evidence of significant ON-OFF channel crossover is strongly supported by our anatomic data, showing alternating glycine-to-GABA paths. Long chains of amacrine cell networks likely arise from homocellular GABAergic synapses between starburst amacrine cells. Deeper analysis of RC1 offers the opportunity for more complete descriptions of specific networks.
Keywords: neuroscience, retina, vision, blindness, visus, crcns
Trace Driven Registration of Neuron Confocal Microscopy Stacks|
L. Hogrebe, A. Paiva, E. Jurrus, C. Christensen, M. Bridge, J.R. Korenberg, T. Tasdizen. In IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1345--1348. 2011.
The Viking Viewer: Scalable Multiuser Annotation and Summarization of Large Volume Datasets|
J.R. Anderson, B.C. Grimm, S. Mohammed, B.W. Jones, T. Tasdizen, J. Spaltenstein, P. Koshevoy, R.T. Whitaker, R.E. Marc. In Journal of Microscopy, Vol. 241, No. 1, pp. 13--28. 2010.
Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI|
S.K. Iyer, E. DiBella, T. Tasdizen. In IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, pp. 704--707. 2010.
Reconstruction of 3D Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Nonlocal Means|
G. Adluru, T. Tasdizen, M.C. Schabel, E.V.R. DiBella. In Journal of Magnetic Resonance Imaging, Vol. 32, pp. 1217--1227. 2010.
Improving Undersampled MRI Reconstruction Using Non-Local Means|
G. Adluru, T. Tasdizen, R. Whitaker, E. DiBella. In Proceedings of the 2010 International Conference on Pattern Recognition, pp. 4000--4003. 2010.
Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images|
F. Jiao, J.M. Phillips, J.G. Stinstra, J. Kueger, R. Varma, E. Hsu, J. Korenberg, C.R. Johnson. In Proceedings of the 5th international conference on Medical imaging and augmented reality (MIAR), Beijing, China, Springer-Verlag, Berlin, Heidelberg pp. 179--190. September, 2010.
POCS-enhanced correction of motion artifacts in parallel MRI|
A.A. Samsonov, J.V. Velikina, Y.K. Jung, E.G. Kholmovski, C.R. Johnson, W.F. Block. In Magnetic Resonance in Medicine, Vol. 63, No. 4, pp. 1104--1110. May, 2010.
Brain volumes in psychotic youth with schizophrenia and mood disorders|
M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. In Journal of Psychiatry and Neuroscience, Vol. 35, No. 4, pp. 229--236. July, 2010.
PubMed ID: 20569649
Deficits in gray matter volume in psychotic youth with schizophrenia-spectrum disorders are not evident in psychotic youth with mood disorders|
M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. In J Psychiatry Neurosci, July, 2010.
|Detection of Neuron Membranes in Electron Microscopy Images Using a Serial Neural Network Architecture,
E. Jurrus, A.R.C. Paiva, S. Watanabe, J.R. Anderson, B.W. Jones, R.T. Whitaker, E.M. Jorgensen, R.E. Marc, T. Tasdizen. In Medical Image Analysis, Vol. 14, No. 6, pp. 770--783. 2010.
PubMed ID: 20598935