COBRA - COmputing BRAin signals

About the project and objectives

Most of what we know about the dynamics of the brain has been learned from measurements of electrical brain signals such as local field potentials (LFP), i.e., electrical potentials recordings inside the brain” electroencephalography (EEG), i.e., recordings of electrical potentials at the scalp, electrocorticography (ECoG), Le., potentials recorded on the cortical surface, and magnetoencephalography (MEG), i.e., recordings of magnetic fields outside the head. Despite their long history and widespread use, the proper interpretation of these brain signals in terms of the biophysical activity in underlying neurons (nerve cells) and neuronal networks is still lacking. Present-day analysis is predominantly statistical and limited to identification of phenomenological signal generators without a clear biophysical interpretation. New biophysics-based analysis methods are thus needed to take full advantage of these brain-imaging techniques.

The primary goal of the transdisciplinary project COBRA is to address this challenge by developing physics-based computational schemes, based on biologically detailed neuron models and validated against in-house experiments, for calculating the contributions from populations of cortical neurons to electric (LFP, EEG, ECoG) and magnetic (MEG) brain signals, i.e., do 'virtual brain measurements'. This COBRA scheme will then in collaboration with various collaborators, including prominent international projects such as EUs Human Brain Project and Project MindScope at the
Allen Brain Institute, be (i) used to explore how the various brain signals depend on the properties and state of the
cortical networks of the cortical neurons, (ii) compared with various types of experimental data from mice and humans, (iii) developed into a Python software package (CoBraPy) for use in large-scale brain-network simuations and analysis tools.


The Research Council of Norway


Published Aug. 13, 2018 10:21 AM - Last modified Feb. 11, 2020 9:31 AM