Facilitating neuronal connectivity analysis of evoked responses by exposing local activity with principal component analysis preprocessing: simulation of evoked MEG

Abstract

When connectivity analysis is carried out for event related EEG and MEG, the presence of strong spatial correlations from spontaneous activity in background may mask the local neuronal evoked activity and lead to spurious connections. In this paper, we hypothesized PCA decomposition could be used to diminish the background activity and further improve the performance of connectivity analysis in event related experiments. The idea was tested using simulation, where we found that for the 306-channel Elekta Neuromag system, the first 4 PCs represent the dominant background activity, and the source connectivity pattern after preprocessing is consistent with the true connectivity pattern designed in the simulation. Improving signal to noise of the evoked responses by discarding the first few PCs demonstrates increased coherences at major physiological frequency bands when removing the first few PCs. Furthermore, the evoked information was maintained after PCA preprocessing. In conclusion, it is demonstrated that the first few PCs represent background activity, and PCA decomposition can be employed to remove it to expose the evoked activity for the channels under investigation. Therefore, PCA can be applied as a preprocessing approach to improve neuronal connectivity analysis for event related data.

Fig.1

Averaged original simulated data, residuals after removing first 1–5 PCs, and corresponding removed PCs. `original' indicates the average without any processing. `residual1', `residual1-2', `residual1-3', `residual1-4' and `residual1-5' indicate the average after removing first 1–5 PCs, and `PC1', `PC1-2', `PC1-3', `PC1-4', and `PC1-5' present the average of the corresponding removed PCs

Fig.2

(a) Spatial distribution of MEG averages. In each sensor unit, the traces illustrate signals recorded by two orthogonal gradiometers. The selected channels with evoked response are circled. (b) GFP for original data, residuals after removing first 1–5 PCs, and corresponding removed PCs for the channels with evoked signal. `original' indicates the GFP without any processing. `residual1', `residual1-2', `residual1-3', `residual1-4' and `residual1-5' indicate the GFP after removing first 1–5 PCs, and `PC1', `PC1-2', `PC1-3', `PC1-4', and `PC1-5' stand for the GFP of corresponding removed PCs. The t-test was performed between original GFP and GFP of residuals after discarding one to five PCs, respectively, from 400ms to 700ms, when the evoked signal appears. The right column is the t-test results for the residuals, respectively. The hypothesis is that GFP of original data and residuals comes from distribution with equal means. H=0 indicates the null hypothesis (“means are equal”) cannot be rejected at the 5% significance level. H=1 indicates the null hypothesis can be rejected at 5% level. P value is the probability of observing the given result, or one more extreme, by chance if the null hypothesis is true. (c) Average coherence for selected evoked channels with PCs discarded at the physiological frequency bands in 1–4Hz (Delta band), 4–7Hz (Theta band), 7–13Hz (Alpha band), 13–30Hz (Beta band), and 30–60Hz (Gamma band), individually

Fig.3

(a) Spatial distribution of MEG averages of the residuals after removing first four PCs; (b) Spatial distribution of MEG averages of first four PCs. The selected channels with evoked response are encircled

Fig.4

The frequency spectrum for the residuals and removed PCs of the selected channels (a, b) and non-selected channels (c, d), respectively

Fig.5

Stationarity comparison of data with and without discarding the first four PCs. Before applying our method, there are 54% non-stationary channels. In contrast, after applying our procedure, all the channels present the stationarity

Fig.6

Connectivity of simulated data with and without removing first four PCs in Beta (a, b) and Gamma band (c, d), respectively