Bridging the gap in connectomic studies: A particle filtering framework for estimating structural connectivity at network scale.
Med Image Anal
; 21(1): 1-14, 2015 Apr.
Article
en En
| MEDLINE
| ID: mdl-25576426
ABSTRACT
The ultimate goal of neuroscience is understanding the brain at a functional level. This requires the investigation of the structural connectivity at multiple scales from the single-neuron micro-connectomics to the brain-region macro-connectomics. In this work, we address the study of connectomics at the intermediate mesoscale, introducing a probabilistic approach capable of reconstructing complex topologies of large neuronal networks. Suitable directional features are designed to model the local neuritic architecture and a feature-based particle filtering framework is proposed which allows the spatial tracking of neurites on microscopy images. The experimental results on cultures of increasing complexity, grown on High-Density Micro Electrode Arrays, show good stability and performance as compared to ground truth annotations drawn by domain experts. We also show how the method can be used to dissect the structural connectivity of inhibitory and excitatory subnetworks opening new perspectives towards the investigation of functional interactions among multiple cellular populations.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Modelos Estadísticos
/
Conectoma
/
Hipocampo
/
Modelos Neurológicos
/
Red Nerviosa
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
Idioma:
En
Revista:
Med Image Anal
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2015
Tipo del documento:
Article