Cobrawap: A pipeline for the analysis of wave activity at different brain states

Plenary talk at "WP2 Meeting: Networks underlying consciousness and cognition" held in Barcelona, Spain, from 19 to 21 June, 2023.Progetto EBRAINS-Italy IR00011, CUP B51E2200015006,Missione 4 - Istruzione e Ricerca, Componente 2, Azione 3.1.1 Funded by EU

Hierarchical Optimal Sampling (HOS): a tool for managing and manipulating wide-field imaging datasets

Powerful recording techniques and increasing anatomical knowledge provided by atlases allow studying the brain at a new level with unprecedented spatial resolution as that achieved with wide-field calcium imaging data. One of the the main issues related to the increase of the spatial resolution is to find the optimal condition which allows to obtain an appropriate signal-to-noise ratio for each channel, relative to the phenomenon of interest. Hierarchical Optimal Sampling (HOS) provides a data-driven inhomogeneous gridding of the field of view with the optimal spatial resolution that preserves and emphasizes the majority of the information from the high number of native signal sources

Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

Recent enhancements in neuroscience, like the development of new and powerful recording techniques of the brain activity combined with the increasing anatomical knowledge provided by atlases and the growing understanding of neuromodulation principles, allow studying the brain at a whole new level, paving the way to the creation of extremely detailed effective network models directly from observed data. Leveraging the advantages of this integrated approach, we propose a method to infer models capable of reproducing the complex spatio-temporal dynamics of the slow waves observed in the experimental recordings of the cortical hemisphere of a mouse under anesthesia. To reliably claim the good match between data and simulations, we implemented a versatile ensemble of analysis tools, applicable to both experimental and simulated data and capable to identify and quantify the spatio-temporal propagation of waves across the cortex. In order to reproduce the observed slow wave dynamics, we introduced an inference procedure composed of two steps: the inner and the outer loop. In the inner loop, the parameters of a mean-field model are optimized by likelihood maximization, exploiting the anatomical knowledge to define connectivity priors. The outer loop explores "external" parameters, seeking for an optimal match between the simulation outcome and the data, relying on observables (speed, directions, and frequency of the waves) apt for the characterization of cortical slow waves; the outer loop includes a periodic neuro-modulation for better reproduction of the experimental recordings. We show that our model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the biological network. Also, the proposed method allows to infer which are the relevant modifications of parameters when the brain state changes, e.g. according to anesthesia levels

Towards an EBRAINS service for brain wave analysis: Cobrawap. Poster presented at CNS2023 - Leipzig

The current variety of data from neuronal recordings, collected with heterogeneous experimental techniques and setups, poses the challenge to consistently compare data across experiments, species, and spatio-temporal scales, and to provide standardized measures/observables for model validation and calibration. We developed Cobrawap (Collaborative Brain Wave Analysis Pipeline) to achieve these goals in the context of brain wave analysis. Aiming at facilitating the usage of this tool for an extended community of users, we are pointing at providing Cobrawap as a service accessible through the EBRAINS web portal, leveraging computational resources from High-Performance Computing (HPC) platforms belonging to the FENIX-ICEI federation

Towards an EBRAINS service for brain wave analysis: Cobrawap

The current variety of data from neuronal recordings, collected with heterogeneous experimental techniques and setups, poses the challenge to consistently compare data across experiments, species, and spatio-temporal scales, and to provide standardized metrics for model validation and calibration. In the context of brain wave analysis, Cobrawap (Collaborative Brain Wave Analysis Pipeline) is a successful tool, built to achieve these goals. Aiming at a wider diffusion and at facilitating the usage of this tool for an extended community of users, we are pointing at providing Cobrawap as a service accessible through the EBRAINS web portal, leveraging computational resources from High-Performance Computing (HPC) platforms belonging to the FENIX-ICEI federation