TY - GEN
T1 - Axonal bouton modeling, detection and distribution analysis for the study of neural circuit organization and plasticity
AU - Hallock, Christina A.
AU - Özgüneş, Inci
AU - Bhagavatula, Ramamurthy
AU - Rohde, Gustavo K.
AU - Crowley, Justin C.
AU - Onorato, Christina E.
AU - Mavalankar, Abhay
AU - Chebira, Amina
AU - Chuen, Hwa Tan
AU - Püschel, Markus
AU - Kovačević, Jelena
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Axonal bouton modeling
KW - Bouton distribution analysis
KW - Confocal microscopy
KW - Light microscopy
KW - Neural circuit organization and plasticity
KW - Neuroanatomy
KW - Neuron
KW - Two-photon microscopy
UR - http://www.scopus.com/inward/record.url?scp=51049115135&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51049115135&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2008.4540958
DO - 10.1109/ISBI.2008.4540958
M3 - Conference contribution
AN - SCOPUS:51049115135
SN - 9781424420032
T3 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
SP - 165
EP - 168
BT - 2008 5th IEEE International Symposium on Biomedical Imaging
T2 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Y2 - 14 May 2008 through 17 May 2008
ER -