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

• Published in: Medical & biological engineering & computing Vol. 59; no. 3; pp. 575 - 588
• Main Authors: Darvishi Bayazi, Mohammad Javad, Motie Nasrabadi, Ali, Dubé, Chad
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021; Springer Nature B.V
• Subjects: Autoregressive models; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Bivariate analysis; Brain; Cerebral hemispheres; Cognitive ability; Computer Applications; Cortex (frontal); Cortex (parietal); Cortex (temporal); EEG; Event-related potentials; Hemispheres; Human Physiology; Imaging; Memory; Memory tasks; Mental task performance; Neural networks; Original Article; Radiology; Retrieval; Recognition memory; Direct directed transfer function; Time-frequency effective connectivity; Generalized partial directed coherence; Multivariate autoregressive models
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-020-02304-8

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. Graphical abstract a Schematic of processing workflow which is consists of pre-processing, sliding-window AMVAR modeling, connectivity estimation, and validation and group network analysis. b Co-registration between Geodesic Sensor Net. and 10–20 system, the arrows mention eight regions of interest (Left, Anterior, Inferior (LAI) and Right, Anterior, Inferior (RAI) and Left, Anterior, Superior (LAS) and Right, Anterior, Superior (RAS) and Left, Posterior, Inferior (LPI) and Right, Posterior, Inferior (RPI) and Left, Posterior, Superior (LPS) and Right, Posterior, Superior (RPS))