A quantitative analytic pipeline for evaluating neuronal activities by high-throughput synaptic vesicle imaging

NeuroImage

Volumn 62, Issue 3, 2012, Pages 2040-2054

  Fan, Jing ^a,b   Xia, Xiaofeng ^a   Li, Ying ^a   Dy, Jennifer G ^b   Wong, Stephen T C ^a  

^a WEILL CORNELL MEDICAL COLLEGE   (United States)

^b Northeastern University   (United States)

Author keywords

Detection and quantification; High throughput image screening; Neurodegenerative disease; Neuron activity; Synaptic vesicle

Indexed keywords

ACCURACY; ALGORITHM; ANIMAL CELL; ARTICLE; AUTOMATION; BRAIN NERVE CELL; EMBRYO; HIGH THROUGHPUT SCREENING; HIPPOCAMPUS; IMAGE PROCESSING; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MOUSE; NEUROIMAGING; NOISE REDUCTION; NONHUMAN; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SYNAPSE VESICLE;

ALGORITHMS; ANIMALS; BRAIN; CELLS, CULTURED; DIAGNOSTIC IMAGING; HIGH-THROUGHPUT SCREENING ASSAYS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; MICE, INBRED C57BL; NEURONS; SYNAPTIC VESICLES;

EID: 84863704632     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.06.020     Document Type: Article

References (33)

1. Abramov E., Dolev I., et al. Amyloid-beta as a positive endogenous regulator of release probability at hippocampal synapses. Nat. Neurosci. 2009, 12(12):1567-1576.
2. Besl P.J., McKay N.D. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992, 239-256.
3. Betz W.J., Bewick G.S. Optical analysis of synaptic vesicle recycling at the frog neuromuscular junction. Science 1992, 255(5041):200-203.
4. Boland M.V., Markey M.K., et al. Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 1998, 33(3):366-375.
5. Bordes A., Ertekin S., et al. Fast kernel classifiers with online and active learning. J. Mach. Learn. Res. 2005, 6:1579-1619.
6. Chang C.C., Lin C.J. LIBSVM: A Library for Support Vector Machines 2001.
7. Cheung Z.H., Fu A.K., et al. Synaptic roles of Cdk5: implications in higher cognitive functions and neurodegenerative diseases. Neuron 2006, 50(1):13-18.
8. Cochilla A.J., Angleson J.K., et al. Monitoring secretory membrane with FM1-43 fluorescence. Annu. Rev. Neurosci. 1999, 22:1-10.
9. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20(3):273-297.
10. Dejnozkova E., Dokladal P. Modelling of overlapping circular objects based on level set approach. Image Anal. Recognit. 2004, 416-423.
11. Dreosti E., Lagnado L. Optical reporters of synaptic activity in neural circuits. Exp. Physiol. 2011, 96(1):4-12.
12. Esposito G., Fernandes A.C., et al. Synaptic vesicle trafficking and Parkinson's disease. Dev. Neurobiol. 2012, 72(1):134-144.
13. Fan J., Zhou X., et al. An automated pipeline for dendrite spine detection and tracking of 3D optical microscopy neuron images of in vivo mouse models. Neuroinformatics 2009, 7(2):113-130.
14. Feichtinger H.G., Strohmer T. Gabor analysis and algorithms: Theory and applications 1998, Birkhauser, Basel.
15. Gaffield M.A., Betz W.J. Imaging synaptic vesicle exocytosis and endocytosis with FM dyes. Nat. Protoc. 2006, 1(6):2916-2921.
16. Gonzalez R.C., Woods R.E. Digital Image Processing 2008, Pearson/Prentice Hall, Upper Saddle River, NJ.
17. Hann M.M., Oprea T.I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol. 2004, 8(3):255-263.
18. Jung C., Kim C., et al. Unsupervised segmentation of overlapped nuclei using Bayesian classification. IEEE Trans. Biomed. Eng. 2010, 57(12):2825-2832.
19. Li X., Lebonvallet S., et al. An improved level set method for automatically volume measure: application in tumor tracking from MRI images. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007, 2007:808-811.
20. Li C., Kao C.Y., et al. Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 2008, 17(10):1940-1949.
21. Ma W.Y., Manjunath B.S. Texture features and learning similarity. Proc. IEEE Conf. CVPR 1996, 425-430.
22. Moretti P., Levenson J.M., et al. Learning and memory and synaptic plasticity are impaired in a mouse model of Rett syndrome. J. Neurosci. 2006, 26(1):319-327.
23. Rakotomamonjy A. Variable selection using svm based criteria. J. Mach. Learn. Res. 2003, 3:1357-1370.
24. Rizzoli S.O., Betz W.J. The structural organization of the readily releasable pool of synaptic vesicles. Science 2004, 303(5666):2037-2039.
25. Selkoe D.J. Alzheimer's disease is a synaptic failure. Science 2002, 298(5594):789-791.
26. Sudhof T.C. The synaptic vesicle cycle. Annu. Rev. Neurosci. 2004, 27:509-547.
27. Wang M., Zhou X., et al. Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 2008, 24(1):94-101.
28. Wang J., Zhou X., et al. An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling. J. Biomed. Inform. 2009, 42(1):32-40.
29. Yan P., Zhou X., et al. Automatic segmentation of high-throughput RNAi fluorescent cellular images. IEEE Trans. Inf. Technol. Biomed. 2008, 12(1):109-117.
30. Zeng Q., Miao Y., et al. Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation. Opt. Eng. 2009, 48:027201.
31. Zhang B., Fadili J.M., et al. Wavelets, ridgelets, and curvelets for Poisson noise removal. IEEE Trans. Image Process. 2008, 17(7):1093-1108.
32. Zhou X., Li F., et al. A novel cell segmentation method and cell phase identification using Markov model. IEEE Trans. Inf. Technol. Biomed. 2009, 13(2):152-157.
33. Zoghbi H.Y. Postnatal neurodevelopmental disorders: meeting at the synapse?. Science 2003, 302(5646):826-830.