Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data.
Biometrics
; 79(2): 1370-1382, 2023 06.
Article
en En
| MEDLINE
| ID: mdl-35191539
ABSTRACT
Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intracellular calcium signals. An ongoing challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time series. In this article, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a dataset from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Calcio
Tipo de estudio:
Diagnostic_studies
Límite:
Animals
Idioma:
En
Revista:
Biometrics
Año:
2023
Tipo del documento:
Article
País de afiliación:
Italia