An analysis of functional connectivity across timescales

Abstract

Event Abstract Back to Event An analysis of functional connectivity across timescales Methods for estimating functional or effective connectivity between neural signals are becoming increasingly popular. However, different techniques record neural signals at different rates. While spike data is often recorded with a temporal resolution in the sub-millisecond range, other signals like fMRI are typically recorded with much lower temporal resolution. Here examine under what conditions connectivity inferred from slow timescales matches that inferred from fast timescales. Using multi-electrode spike recordings from motor cortex we smooth, down-sample, and estimate the effective connectivity between the underlying signals using pair-wise Granger causality. We find that fast time-scale connectivity is robust to low-pass filtering down to ~1Hz and down-sampling to ~2Hz. Estimates are also fairly robust to fixed-SNR Gaussian noise. These results suggest that there is a large space in which connectivity estimates are relatively independent of temporal filtering and sampling rates. Pooling results from connectivity studies using relatively fast signals, such as intrinsic signal imaging and spike data into joint estimates, thus seems reasonable. It also speaks to the question to how fast imaging data should be acquired to allow for inference of connectivity between individual neurons; such devices should have at least a temporal resolution of about 2Hz. Averaging over populations of neurons may allow for the inference of functional connectivity from very slow signals. This may explain the success of connectivity estimates from fMRI signals using very low temporal resolution. To examine why connectivity should be robust to filtering we fit spike data to two models: an Ising model (maximal entropy, Schneideman et al., 2006) and a generalized linear model (GLM, Pillow et al., 2008). The Ising model assumes that spikes are generated through a Bernoulli process with pair-wise interactions between neurons. The GLM, on the other-hand, assumes that the firing rate of each neuron depends on a history of spikes from the observed neurons. Both models estimate connectivity between neurons, but the GLM takes into account possible delays and variations in the strength of connectivity over fast time-scales. Using these two models we simulate spike data and perform the smoothing, down-sampling analysis above. Connectivity from the Ising model simulations was not robust to smoothing and down-sampling. GLM simulations (200ms history) were slightly more robust but not to the extent of the original data. These results suggest that slow time-scale connectivity, while correlated with fast time-scale connectivity, is not caused by fast time-scale connectivity. One possibility may be that common-input affects connectivity across both fast and slow timescales. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). An analysis of functional connectivity across timescales. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.102 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Feb 2009; Published Online: 02 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.