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- W4292965897 abstract "Model-based Bayesian inference from high-content data obtained on live specimens is a burgeoning field with demonstrated applications to neuroscience. In parallel, computer vision methods for extracting the calcium signaling information from imaging data have advanced in application to neuronal physiology. Here, we are describing in detail a method we have recently developed to study calcium dynamics in astrocytes, which combines computer vision with model-based Bayesian learning to deduce the most likely molecular kinetic parameters underlying the observed calcium activity. As reported in the companion experimental study, this method allowed us to identify the key molecular changes downstream of a multi-gene deletion modeling the human 22q11.2 deletion syndrome, the most common human microdeletion and the genetic factor with the highest penetrance for schizophrenia. Here, the methodological details are laid out, from our imaging approach to our adaptation of the MIN1PIPE algorithm previously developed for neurons, and to our adaptation of the VBA-CaBBI algorithm previously developed primarily for brain functional imaging data and calcium in neurons. The entire imaging-computer vision-Bayesian learning pipeline is suited for further applications to glial cells and adaptable to other cell types exhibiting complex calcium dynamics." @default.
- W4292965897 created "2022-08-24" @default.
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- W4292965897 date "2022-01-01" @default.
- W4292965897 modified "2023-09-27" @default.
- W4292965897 title "Bayesian inference of molecular kinetic parameters from astrocyte calcium imaging data" @default.
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- W4292965897 doi "https://doi.org/10.1016/j.mex.2022.101825" @default.
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