The acquisition, analysis and representation of experimental data describing both anatomical and functional information of in-vitro neuronal networks allow to investigate fine network interactions and dynamics at the basis of the brain processing.
Aim of this project is to provide computational methods for a joint structural and functional analysis of neuronal networks, by integrating multimodal data sources. More specifically, our datasets consist of fluorescence microscopy images, representing network structure, and electrophysiological signals of the neuronal functional activity, acquired with a high resolution Multi Electrodes Array (MEA) technology.
The novelty and complexity of such data modalities require advanced approaches for the analysis of both network morphology and functional mechanisms. Particularly, the computational methods we have been focusing on mainly involve: shape-based segmentation algorithms (e.g. Hough Transform) for the detection of neuronal nuclei, probabilistic models for the segmentation of neuronal connectivity (e.g. particle filters) and, more generally, advanced pattern recognition techniques.
We strongly believe that innovative computational methods could have a significant impact on the investigation of the functional aspects of large-scale brain processing.
- S. Ullo, A. Del Bue, A. Maccione, L. Berdondini, V. Murino
"A Joint Structural and Functional Analysis of In-Vitro Neuronal Networks"
19th IEEE International Conference on Image Processing (ICIP), 2012, Orlando, USA
- A. Maccione, S. Ullo, A. Simi, D. Sona, A. Del Bue, V. Murino, L. Berdondini
"Structural and functional identification of sub-networks in dissociated neuronal cultures: an automated multimodal analysis combining high density MEA and fluorescence imaging"
8th International Meeting on Substrate-Integrated Microelectrode Arrays (MEA), 2012, Reutlingen, Germany.
Datasets and results: