Elastic source selection for in vivo imaging of neuronal ensembles

Advances in microscopy and biochemistry now allow investigators to image the calcium dynamics of hundreds to thousands of neurons in awake behaving animals. However, as speed and resolution of such techniques rapidly increase, so do the dimension and complexity of the data collected. ICA has been wi...

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Bibliographic Details
Main Authors: Grosenick, L., Anderson, T., Smith, S.J.
Format: Conference Proceeding
Language:English
Subjects:
Animals
Biochemistry
Calcium
Calcium Imaging
ICA
In vivo
Independent component analysis
Microscopy
Neuroimaging
Neurons
Optical filters
Optical imaging
Sparse matrices
Sparse Regression
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Summary:Advances in microscopy and biochemistry now allow investigators to image the calcium dynamics of hundreds to thousands of neurons in awake behaving animals. However, as speed and resolution of such techniques rapidly increase, so do the dimension and complexity of the data collected. ICA has been widely employed to reveal independent non-Gaussian sources underlying large data sets consisting of mixed sources. We apply a recently developed sparse regression method, the Elastic Net (ENET), to the columns of the mixing matrix of a independent component analysis (ICA) procedure. This regression method automatically selects only those columns of the mixing matrix relevant to a dependent variable of interest. Further, because ICA is a linear operator, we can easily project the ldquorelevance filteredrdquo data back into the native data space for interpretation. We demonstrate the utility of this method on 3D calcium imaging data collected from the optic tectum of an awake behaving larval zebrafish watching a prey-like stimulus.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2009.5193292