Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer’s disease continuum

Abstract

Objective. The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due to Alzheimer’s disease (AD). Approach. Magnitude squared coherence (MSCOH), imaginary part of coherence (iCOH), lagged coherence (lagCOH), amplitude envelope correlation (AEC), synchronization likelihood (SL), phase lag index (PLI), phase locking value (PLV), and corrected imaginary PLV (ciPLV) were applied to: (i) synthetic signals generated with a Kuramoto-based model of several coupled oscillators; and (ii) a resting-state EEG database of real recordings from 51 cognitively healthy controls, 51 mild cognitive impairment (MCI) subjects, 51 mild AD (ADmil) patients, 50 moderate AD (ADmod) patients, and 50 severe AD (ADsev) patients. Main results. Our results using synthetic signals showed that PLI was the least affected parameter by spurious influences in a simulated volume conduction environment. Results using real EEG recordings showed that spontaneous activity of MCI patients is characterized by a significant coupling increase in the band. As dementia progresses, this increase in the band became more pronounced, and a significant widespread decrease in band appeared at the last stage of dementia. Significance. Our results revealed that the estimation of functional EEG connectivity using PLI could reduce the bias introduced by the spurious influence of volume conduction, and it could increase the insight into the underlying brain dynamics at different stages of the AD continuum.

Differences between encoding and retrieval failure in mild cognitive impairment: results from quantitative electroencephalography and magnetic resonance volumetry

“…The signi cant differences for iCoh were found in the lower frequency bands involving parietal-frontal connections [41]. Phase based measures have also reported increases in the theta band connectivity [42]. Similar to previous studies, signi cant differences in iCoh between EF and RF were found in the theta and beta2 band in our study.…”

“…ROIs with remaining all other 67 ROIs. We have estimated the functional connectivity at eight frequency bands (delta[1][2][3][4], theta[4][5][6][7][8], alpha1[8][9][10], alpha2[10][11][12], beta1[12][13][14][15], beta2[15][16][17][18][19][20], beta3[20][21][22][23][24][25][26][27][28][29][30], and gamma[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].2.3 ...…”

Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction

“…The simulated data results were consistent with the real-data used in this paper where the best results also were obtained for positive . The better performance can be of particular importance for such real-world applications like electroencephalography (EEG) and magnetoencephalography in which the volume conduction effect can cause IC [ 18 ]. There are also other frameworks like compensated transfer entropy [ 31 ], which tries to improve the estimation of the TE in the presence of IC.…”

“…In other words, it can occur due to fast sharing information [ 31 ]. For example, in neuro-physiological time series like EEG, the recorded electrical activity at each electrode located at the scalp, is considered to be a mixture of the source generators because the sources pass through the volume conductor [ 18 ]. The volume conduction can be considered as the zero lag coupling which may lead to detection of false directed dependency by the NUE algorithms.…”

“…As an example, assume that we are interested in measuring TE between processes which, for example, represent sensor measurement data from different regions of the brain, e.g., multi-channel electroencephalography (EEG) data. The recorded EEG data are spatially auto-correlated due to the phenomenon known as the volume conduction effect in neuro-physiological time series [ 18 ]. The spatial auto-correlation in such data can lead to overestimate in the estimated TE and eventually lead to false positives detection of TE.…”