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Cortical representations of olfactory input by trans-synaptic tracing (2010)

K. Miyamichi ; F. Amat ; F. Moussavi ; [et al.]

Nature Publishing Group (NPG)

In: Nature. 472(7342): 191-6. doi: 10.1038/nature09714.

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Publication Date: 2010-12-24

Description: In the mouse, each class of olfactory receptor neurons expressing a given odorant receptor has convergent axonal projections to two specific glomeruli in the olfactory bulb, thereby creating an odour map. However, it is unclear how this map is represented in the olfactory cortex. Here we combine rabies-virus-dependent retrograde mono-trans-synaptic labelling with genetics to control the location, number and type of 'starter' cortical neurons, from which we trace their presynaptic neurons. We find that individual cortical neurons receive input from multiple mitral cells representing broadly distributed glomeruli. Different cortical areas represent the olfactory bulb input differently. For example, the cortical amygdala preferentially receives dorsal olfactory bulb input, whereas the piriform cortex samples the whole olfactory bulb without obvious bias. These differences probably reflect different functions of these cortical areas in mediating innate odour preference or associative memory. The trans-synaptic labelling method described here should be widely applicable to mapping connections throughout the mouse nervous system.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3073090/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉 〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3073090/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Miyamichi, Kazunari -- Amat, Fernando -- Moussavi, Farshid -- Wang, Chen -- Wickersham, Ian -- Wall, Nicholas R -- Taniguchi, Hiroki -- Tasic, Bosiljka -- Huang, Z Josh -- He, Zhigang -- Callaway, Edward M -- Horowitz, Mark A -- Luo, Liqun -- R01 MH063912/MH/NIMH NIH HHS/ -- R01 NS050835/NS/NINDS NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2011 Apr 14;472(7342):191-6. doi: 10.1038/nature09714. Epub 2010 Dec 22.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉HHMI/Department of Biology, Stanford University, Stanford, California 94305, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21179085" target="_blank"〉PubMed〈/a〉

Keywords: Amygdala/anatomy & histology/cytology/physiology ; Animals ; Axons/physiology ; Bias (Epidemiology) ; Brain Mapping ; HEK293 Cells ; Humans ; Mice ; Mice, Transgenic ; *Neuroanatomical Tract-Tracing Techniques ; Odors/analysis ; Olfactory Bulb/anatomy & histology/cytology/physiology ; Olfactory Pathways/anatomy & histology/*cytology/*physiology ; Olfactory Perception/genetics/*physiology ; Olfactory Receptor Neurons/cytology/physiology ; Rabies virus/physiology ; Synapses/genetics/*metabolism

Print ISSN: 0028-0836

Electronic ISSN: 1476-4687

Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics

Published by Nature Publishing Group (NPG)

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Fast and robust optical flow for time-lapse microscopy using super-voxels (2013)

Amat, F., Myers, E. W., Keller, P. J.

Oxford University Press

In: Bioinformatics

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Publication Date: 2013-01-31

Description: Motivation: Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on 2D natural images, which are small in size and rich in edges and texture information. In contrast, 3D time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point-spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data. Results: We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10 x faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow end point error by 50% in regions with complex dynamic processes, such as cell divisions. Availability: Source code freely available in the Software section at http://janelia.org/lab/keller-lab . Contact: amatf@janelia.hhmi.org or kellerp@janelia.hhmi.org Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine