Effective connectivity measuring of ERP signals in recognition memory process by Generalized Partial Directed Coherence

2016 ◽  
Author(s):  
M.J. Darvishi ◽  
A.M. Nasrabadi ◽  
T. Curran
Keyword(s):  
Recognition Memory ◽  
Effective Connectivity ◽  
Memory Process ◽  
Partial Directed Coherence ◽  
Generalized Partial Directed Coherence

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

2019 ◽  
Author(s):  
Mohammad Javad Darvishi Bayazi ◽  
Ali Motie Nasrabadi ◽  
Tim Curran
Keyword(s):  
Recognition Memory ◽  
Memory Retrieval ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Human Cognition ◽  
Connectivity Pattern ◽  
Memory Process ◽  
Retrieval Task ◽  
New Words ◽  
Time Frequency

AbstractVarious processes occur in memory retrieval in recognition memory and it is necessary to investigate memory brain function. Most of the research in past decades have focused on particular brain region function, but the interaction between these has a major role in human cognition. In this study, we used the memory retrieval task to investigate the underlying mechanism of recognition memory. The connectivity between brain regions is estimated from scalp electroencephalography signals that were recorded from twenty-three healthy subject participated in recognition memory task to correctly classify old/new words. Multivariate autoregressive models (MVAR) are used for the determination of Granger causality to estimate the effective connectivity in the time-frequency domain. We use GPDC and dDTF methods because they have almost resolved the previous problems in estimations. Results show that brain regions in the old condition have greater global connectivity in the theta and gamma band compared to the new words retrieval. Connectivity within and between the brain’s hemisphere may be related to correct rejection. The left frontal has a crucial role in recollection. theta and gamma specific connectivity pattern between temporal, parietal and frontal cortex may disclose the retrieval mechanism. old/new comparison resulted in the different patterns of network connection. These results and other evidence emphasize the role of frequency of causal network interactions in the memory process.

The Effects of Increasing Semantic-Associate List Length on the Deese–Roediger–McDermott False Recognition Memory: Dual False-Memory Process in Retrieval from Sub- and Supraspan Lists

2017 ◽  
Vol 70 (10) ◽  
pp. 2076-2093 ◽  
Author(s):  
Jerwen Jou ◽  
Mario L. Arredondo ◽  
Cheng Li ◽  
Eric E. Escamilla ◽  
Richard Zuniga
Keyword(s):  
Reaction Time ◽  
Recognition Memory ◽  
False Memory ◽  
List Length ◽  
Correct Rejection ◽  
Memory Process ◽  
Retrieval Process ◽  
Critical Lure ◽  
Set Size ◽  
Sternberg Paradigm

In this study, the number of semantic associates in Deese–Roediger–McDermott (DRM) lists was varied from 4 to 14 in a modified Sternberg paradigm. The false alarm (FA) and correct rejection (CR) reaction time (RT)/memory-set size (MSS) functions of critical lures showed a cross-over interaction at approximately MSS 7, suggesting a reversal of the relative dominance between these two responses to the critical lure at this point and also indicating the location of the boundary between the sub- and supraspan MSS. For the subspan lists, FA to critical lures was slower than CR, suggesting a slow, strategic mechanism driving the false memory. Conversely, for the supraspan lists, critical lure FA was faster than its CR, suggesting a spontaneous mechanism driving the false memory. Results of two experiments showed that an automatic, fast, and a slow, controlled process could be error-prone or error-corrective, depending on the length of the DRM memory list. Thus there is a dual retrieval process in false memory as in true memory. The findings can be explained by both the activation/monitoring and the fuzzy-trace theories.

scholarly journals Dynamic connectivity analyses of intracranial EEG during recognition memory reveal various large-scale functional networks

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Jaroslav Hlinka ◽  
Elodie Despouy ◽  
Luc Valton ◽  
Marie Denuelle ◽  
...  
Keyword(s):  
Recognition Memory ◽  
Large Scale ◽  
Visual Recognition ◽  
Effective Connectivity ◽  
Memory Task ◽  
Brain Regions ◽  
Neural Basis ◽  
Intracranial Eeg ◽  
Large Scale Networks ◽  
The Right

Recognition memory is the ability to recognize previously encountered events, objects, or people. It is characterized by its robustness and rapidness. Even this relatively simple ability requires the coordinated activity of a surprisingly large number of brain regions. These spatially distributed, but functionally linked regions are interconnected into large-scale networks. Understanding memory requires an examination of the involvement of these networks and the interactions between different regions while memory processes unfold. However, little is known about the dynamical organization of large-scale networks during the early phases of recognition memory. We recorded intracranial EEG, which affords high temporal and spatial resolution, while epileptic subjects performed a visual recognition memory task. We analyzed dynamic functional and effective connectivity as well as network properties. Various networks were identified, each with its specific characteristics regarding information flow (feedforward or feedback), dynamics, topology, and stability. The first network mainly involved the right visual ventral stream and bilateral frontal regions. It was characterized by early predominant feedforward activity, modular topology, and high stability. It was followed by the involvement of a second network, mainly in the left hemisphere, but notably also involving the right hippocampus, characterized by later feedback activity, integrated topology, and lower stability. The transition between networks was associated with a change in network topology. Overall, these results confirm that several large-scale brain networks, each with specific properties and temporal manifestation, are involved during recognition memory. Ultimately, understanding how the brain dynamically faces rapid changes in cognitive demand is vital to our comprehension of the neural basis of cognition.

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