Shahed University

Frequency-specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators

Mohammad Javad Darvishi Bayazi | Ali Motie Nasrabadi | Chad Dubé

Date :  2021/03/15
Publish in :    Medical and Biological Engineering and Computing
Link :
Keywords :Recognition memory, Time-frequency effective connectivity, Generalized partial directed coherence, Direct directed transfer function, Multivariate autoregressive models

Abstract :
Human memory retrieval is one of the brain’s most important, and least understood cognitive mechanisms. Traditionally, research on this aspect of memory has focused on the contributions of particular brain regions to recognition responses, but the interaction between regions may be of even greater importance to a full understanding. In this study, we examined patterns of network connectivity during retrieval in a recognition memory task. We estimated connectivity between brain regions from electroencephalographic signals recorded from twenty healthy subjects. A multivariate autoregressive model (MVAR) was used to determine the Granger causality to estimate the effective connectivity in the time-frequency domain. We used GPDC and dDTF methods because they have almost resolved the previous volume conduction and bivariate problems faced by previous estimation methods. Results show enhanced global connectivity in the theta and gamma bands on target trials relative to lure trials. Connectivity within and between the brain’s hemispheres may be related to correct rejection. The left frontal signature appears to have a crucial role in recollection. Theta- and gamma-specific connectivity patterns between temporal, parietal, and frontal cortex may disclose the retrieval mechanism. Old/new comparison resulted in different patterns of network connection. These results and other evidence emphasize the role of frequency-specific causal network interactions in the memory retrieval process.