Axonal bouton modeling, detection and distribution analysis for the study of neural circuit organization and plasticity

Axonal bouton modeling, detection and distribution analysis for the study of neural circuit organization and plasticity

We propose a novel method for axonal bouton modeling and automated detection in populations of labeled neurons, as well as bouton distribution analysis for the study of neural circuit organization and plasticity. Since axonal boutons are the presynaptic specializations of neural synapses, their loca...

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Bibliographic Details
Published in:2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 165 - 168
Main Authors: Hallock, C.A., Ozgunes, I., Bhagavatula, R., Rohde, G.K., Crowley, J.C., Onorato, C.E., Mavalankar, A., Chebira, A., Chuen Hwa Tan, Puschel, M., Kovacevic, J.
Format: Conference Proceeding
Language:English
Published: IEEE 01-05-2008
Subjects:
axonal bouton modeling
Biomedical engineering
bouton distribution analysis
Circuits
confocal microscopy
Data mining
Image resolution
light microscopy
Microscopy
Nerve fibers
neural circuit organization and plasticity
neuroanatomy
neuron
Neurons
Neuroscience
Neurotransmitters
Probability
two-photon microscopy
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Summary:We propose a novel method for axonal bouton modeling and automated detection in populations of labeled neurons, as well as bouton distribution analysis for the study of neural circuit organization and plasticity. Since axonal boutons are the presynaptic specializations of neural synapses, their locations can be used to determine the organization of neural circuitry, and in time-lapse studies, neural circuit dynamics. We propose simple geometric models for axonal boutons that account for variations in size, position, rotation and curvature of the axon in the vicinity of the bouton. We then use the normalized cross-correlation between the model and image data as a test statistic for bouton detection and position estimation. Thus, the problem is cast as a statistical detection problem where we can tune the algorithm parameters to maximize the probability of detection for a given probability of false alarm. For example, we can detect 81% of boutons with 9% false alarm from noisy, out of focus, images. We also present a novel method to characterize the orientation and elongation of a distribution of labeled boutons and we demonstrate its performance by applying it to a labeled data set.
ISBN:9781424420025
1424420024
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2008.4540958