Abstract
Current understanding of the synaptic organization of the brain depends to a large extent on knowledge about the synaptic inputs to the neurons. Indeed, the dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by thin protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition. This paper presents a new method that facilitates the analysis of the 3D structure of spine insertions in dendrites, providing insight on spine distribution patterns. This method is based both on the implementation of straightening and unrolling transformations to move the analysis process to a planar, unfolded arrangement, and on the design of DISPINE, an interactive environment that supports the visual analysis of 3D patterns.
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Notes
The dendrite’s sheath medial axis is a simplified representation that is manually created; it is actually a polyline whose vertices are markers which store the tangent unit vector.
For example, the morphology, number and density of dendritic spines are altered in many brain diseases and under several conditions such as malnutrition, alcohol or toxin exposure (Fiala et al. 2002).
References
Arellano, J. I., Benavides-Piccione, R., Defelipe, J., & Yuste, R. (2007). Ultrastructure of dendritic spies: Correlation between synaptic and spine morphologies. Frontiers in Neuroscience, 1, 131–143.
Ballesteros-Yáñez, I., Benavides-Piccione, R., Bourgeois, J. P., Changeux, J. P., & DeFelipe, J. (2010). Alterations of cortical pyramidal neurons in mice lacking high-affinity nicotinic receptors. Proceedings of the National Academy of Sciences of the United States of America, 107(25), 11,567–11,572. http://www.biomedsearch.com/nih/Alterations-cortical-pyramidal-neurons-in/20534523.html.
Bitplane (2011). Imaris. Web. http://www.bitplane.com/go/products/imaris.
Borgefors, G., Nyström, I., & Baja, G. S. D. (1999). Computing skeletons in three dimensions. Pattern Recognition, 32(7), 1225–1236. doi:10.1016/S0031-3203(98)00082-X.
DeFelipe, J. (2010). From the connectome to the synaptome: An epic love story. Science, 330(6008), 1198–1201. doi:10.1126/science.1193378.
Defelipe, J., & Fariñas, I. (1992). The pyramidal neuron of the cerebral cortex: Morphological and chemical characteristics of the synaptic inputs. Progress in Neurobiology, 39(6), 563–607. doi:10.1016/0301-0082(92)90015-7.
Deutsch, D. (2010). The paradox of pitch circularity. Acoustics Today, 6(3), 8–14. doi:10.1121/1.3488670. http://link.aip.org/link/?ATC/6/8/1
Donohue, D. E., & Ascoli, G. A. (2011). Automated reconstruction of neuronal morphology: An overview. Brain Research Reviews, 67(1–2), 94–102.
Eick, S. G., & Karr, A. F. (2002). Visual scalability. Journal of Computational and Graphical Statistics, 11(1), 22–43.
Elston, G. N., & Rosa, M. G. (1997). The occipitoparietal pathway of the macaque monkey: Comparison of pyramidal cell morphology in layer III of functionally related cortical visual areas. Cerebral Cortex, 7(5), 432–452. http://www.biomedsearch.com/nih/occipitoparietal-pathway-macaque-monkey-comparison/9261573.html.
Elston, G. N., Benavides-Piccione, R., & DeFelipe, J. (2001). The pyramidal cell in cognition: A comparative study in human and monkey. Journal of Neuroscience, 21(17), RC163(1–5). http://view.ncbi.nlm.nih.gov/pubmed/11511694.
Fiala, J. C., Spacek, J., & Harris, K. M. (2002). Dendritic spine pathology: Cause or consequence of neurological disorders? Brain Research Reviews, 39(1), 29–54. http://view.ncbi.nlm.nih.gov/pubmed/12086707.
Fuchs, R., & Hauser, H. (2009). Visualization of multi-variate scientific data. Computer Graphics Forum, 28(6), 1670–1690.
Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1995). Design patterns: Elements of reusable object-oriented software. Reading, MA: Addison-Wesley Professional.
Kasai, H., Fukuda, M., Watanabe, S., Hayashi-Takagi, A., & Noguchi, J. (2010). Structural dynamics of dendritic spines in memory and cognition. Trends in Neurosciences, 33(3), 121–129. http://www.ncbi.nlm.nih.gov/pubmed/20138375.
Lakshmi, J. K., & Punithavalli, M. (2009). A survey on skeletons in digital image processing. In 2009 international conference on digital image processing (pp. 260–269). doi:10.1109/ICDIP.2009.21.
Mansur, D. L., Blattner, M. M., & Joy, K. I. (1985). Sound graphs: A numerical data analysis method for the blind. Journal of Medical Systems, 9(3), 163–174 (1985). doi:10.1007/BF00996201.
Meijering, E. (2010). Neuron tracing in perspective. Cytometry. Part A, 77(7), 693–704. doi:10.1002/cyto.a.20895.
Morales, J., Benavides-Piccione, R., Dar, M., Rodríguez, A., Bielza, C., Fernaud, I., et al. (2012). Dendritic spine organization on the human cerebral cortex (p. 18, under review).
O’Brien, J., & Unwin, N. (2006). Organization of spines on the dendrites of purkinje cells. Proceedings of the National Academy of Sciences of the United States of America, 103(5), 1575–80. http://www.biomedsearch.com/nih/Organization-spines-dendrites-Purkinje-cells/16423897.html.
Portera-Cailliau, C., Pan, D. T., & Yuste, R. (2003). Activity-regulated dynamic behavior of early dendritic protrusions: Evidence for different types of dendritic filopodia. Journal of Neuroscience, 23(18), 7129–7142. http://www.jneurosci.org/cgi/content/abstract/23/18/7129.
Rising, L., & Janoff, N. S. (2000). The Scrum software development process for small teams. IEEE Software, 17(4), 26–32 (2000). doi:10.1109/52.854065.
Semwogerere, D., & Weeks, E. (2005). Confocal microscopy. In G. Wnek, & G. Bowlin (Eds.), Encylopedia of biomaterials and biomedical engineering. New York: Taylor & Francis.
Spruston, N. (2008). Pyramidal neurons: Dendritic structure and synaptic integration. Nature Reviews Neuroscience, 9(3), 206–221. http://www.ncbi.nlm.nih.gov/pubmed/18270515.
Yuste, R. (2010). Dendritic spines. Cambridge, MA: MIT Press.
Acknowledgements
This work was supported by grants from the following entities: Centre for Networked Biomedical Research into Neurodegenerative Diseases (CIBERNED, CB06/05/0066) and the Spanish Ministry of Education, Science and Innovation (grants BFU2006-13395; SAF2009-09394 to Javier DeFelipe; TIN2010-21289 and the Cajal Blue Brain Project, Spanish partner of the Blue Brain Project initiative from EPFL).
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Morales, J., Benavides-Piccione, R., Rodríguez, A. et al. Three-Dimensional Analysis of Spiny Dendrites Using Straightening and Unrolling Transforms. Neuroinform 10, 391–407 (2012). https://doi.org/10.1007/s12021-012-9153-2
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DOI: https://doi.org/10.1007/s12021-012-9153-2