Nonlinear brain connectivity from neurons to networks: quantification, sources and localization

Since the first studies in functional connectivity, Pearson’s correlation has been the primary tool to determine relatedness between the activity of different brain locations. Over the years, concern over the information neglected by correlation pushed toward using different measures accounting for non-linearity. However, some studies suggest that, at the typical observation scale, a linear description of the brain captures a vast majority of the information. Therefore, we measured the fraction of information that would be lost using a linear description and which regions would be affected the most. We considered fMRI, EEG, iEEG, and single unit spikes to assess how the observation scale impacts the amount of non-linearity. We observe that the information loss is reduced for modalities with large temporal or spatial averaging (fMRI and EEG) and gains relevance on more fine descriptions of the activity (iEEG and single unit spikes). We conclude that for most human applications, Pearson’s correlation coefficient adequately describes pairwise interactions in time series from current recording techniques.