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.

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.

Altered Effective Connectivity during a Processing Speed Task in Individuals with Multiple Sclerosis

2016 ◽  
Vol 22 (2) ◽  
pp. 216-224 ◽  
Author(s):  
E. Dobryakova ◽  
S.L. Costa ◽  
G.R. Wylie ◽  
J. DeLuca ◽  
H.M. Genova
Keyword(s):  
Multiple Sclerosis ◽  
Processing Speed ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Connectivity Pattern ◽  
Control Group ◽  
Dynamic Representation ◽  
Speed Task ◽  
Healthy Participants

AbstractObjectives: Processing speed impairment is the most prevalent cognitive deficit in individuals with multiple sclerosis (MS). However, the neural mechanisms associated with processing speed remain under debate. The current investigation provides a dynamic representation of the functioning of the brain network involved in processing speed by examining effective connectivity pattern during a processing speed task in healthy adults and in MS individuals with and without processing speed impairment. Methods: Group assignment (processing speed impaired vs. intact) was based on participants’ performance on the Symbol Digit Modalities test (Parmenter, Testa, Schretlen, Weinstock-Guttman, & Benedict, 2010). First, brain regions involved in the processing speed task were determined in healthy participants. Time series from these functional regions of interest of each group of participants were then subjected to the effective connectivity analysis (Independent Multiple-Sample Greedy Equivalence Search and Linear, Non-Gaussian Orientation, Fixed Structure algorithms) that showed causal influences of one region on another during task performance. Results: The connectivity pattern of the processing speed impaired group was significantly different from the connectivity pattern of the processing speed intact group and of the healthy control group. Differences in the strength of common connections were also observed. Conclusions: Effective connectivity results reveal that MS individuals with processing speed impairment not only have connections that differ from healthy participants and MS individuals without processing speed impairment, but also have increased strengths of connections. (JINS, 2016, 22, 216–224)

scholarly journals Distinctive BOLD Connectivity Patterns in the Schizophrenic Brain: Machine-learning based comparison between various connectivity measures

2016 ◽  
Author(s):  
Hio-Been Han
Keyword(s):  
Functional Connectivity ◽  
Memory Task ◽  
Similarity Measures ◽  
Low Frequency ◽  
Pattern Discrimination ◽  
Brain Regions ◽  
Connectivity Pattern ◽  
Time Frequency ◽  
Connectivity Patterns ◽  
Network Properties

AbstractRecent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenic brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered wirings in the schizophrenic brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenic brain relative to healthy brain with various similarity measures in the time-frequency domain, while participants are performing the working memory task in the MRI scanner. To assess the distinctiveness of the connectivity pattern, discrimination performances of the pattern classifier machine trained with each similarity measure were compared. Interestingly, the classifier machine trained by time-lagging patterns of low frequency fluctuation (LFF) produced higher classifying sensitivity than the machines trained by other measures. Also, the classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that there are unobserved but considerable features in the functional connectivity pattern of schizophrenic brain which traditional emphasis on correlation analysis does not capture.

scholarly journals Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

2017 ◽  
Author(s):  
Matthieu Gilson ◽  
Gustavo Deco ◽  
Karl Friston ◽  
Patric Hagmann ◽  
Dante Mantini ◽  
...  
Keyword(s):  
Effective Connectivity ◽  
Functional Integration ◽  
Brain Regions ◽  
Connectivity Pattern ◽  
Model Parameters ◽  
Auditory Information ◽  
Brain Areas ◽  
Brain Hemispheres ◽  
Dynamic Coordination ◽  
The Brain

AbstractOur behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. How-ever, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

scholarly journals Resting-State Fluctuations Underlie Free and Creative Verbal Behaviors in the Human Brain

2020 ◽  
Vol 31 (1) ◽  
pp. 213-232
Author(s):  
Rotem Broday-Dvir ◽  
Rafael Malach
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Language Production ◽  
Inferior Frontal Gyrus ◽  
Related Result ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Verbal Response ◽  
Human Cognition ◽  
Time Frequency

Abstract Resting-state fluctuations are ubiquitous and widely studied phenomena of the human brain, yet we are largely in the dark regarding their function in human cognition. Here we examined the hypothesis that resting-state fluctuations underlie the generation of free and creative human behaviors. In our experiment, participants were asked to perform three voluntary verbal tasks: a verbal fluency task, a verbal creativity task, and a divergent thinking task, during functional magnetic resonance imaging scanning. Blood oxygenation level dependent (BOLD)-activity during these tasks was contrasted with a control- deterministic verbal task, in which the behavior was fully determined by external stimuli. Our results reveal that all voluntary verbal-generation responses displayed a gradual anticipatory buildup that preceded the deterministic control-related responses. Critically, the time–frequency dynamics of these anticipatory buildups were significantly correlated with resting-state fluctuations’ dynamics. These correlations were not a general BOLD-related or verbal-response related result, as they were not found during the externally determined verbal control condition. Furthermore, they were located in brain regions known to be involved in language production, specifically the left inferior frontal gyrus. These results suggest a common function of resting-state fluctuations as the neural mechanism underlying the generation of free and creative behaviors in the human cortex.

scholarly journals Analysis of the Human Connectome Data Supports the Notion of A “Common Model of Cognition” for Human and Human-Like Intelligence Across Domains

2019 ◽  
Author(s):  
Andrea Stocco ◽  
Catherine Sibert ◽  
Zoe Steine-Hanson ◽  
Natalie Koh ◽  
John E. Laird ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Causal Modeling ◽  
Human Cognition ◽  
Functional Architecture ◽  
Hub And Spoke ◽  
Cognitive Domains ◽  
Fmri Analysis

AbstractThe Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. The results suggest that a common, general architecture underpins human cognition across multiple cognitive domains, from the overall functional architecture of the human brain to higher-level thought processes.

scholarly journals Encoding Processes during Retrieval Tasks

2001 ◽  
Vol 13 (3) ◽  
pp. 406-415 ◽  
Author(s):  
Randy L. Buckner ◽  
Mark E. Wheeler ◽  
Margaret A. Sheridan
Keyword(s):  
Episodic Memory ◽  
Memory Retrieval ◽  
Memory Encoding ◽  
Imaging Data ◽  
Repeated Testing ◽  
Retrieval Task ◽  
Partial Explanation ◽  
New Words ◽  
Episodic Encoding ◽  
Incidental Encoding

Episodic memory encoding is pervasive across many kinds of task and often arises as a secondary processing effect in tasks that do not require intentional memorization. To illustrate the pervasive nature of information processing that leads to epeisodic encoding, a form of incidental encoding was explored based on the “Testing” phenomenon: The incidental-encoding task was an episodic memory retrieval task. Behavioral data showed that performing a memory retrieval task was as effedctive as intentional instructions at promoting episodic encoding. During fMRI imaging, subjedcts veiewed old and new words adn indicated whether they remembered them. Relevant to encoding, the fate of the new words was examined using a second, surprise test of recognition after the imaging session, fMRI analysis of those new words that were later remembered revealed greater activity in left frontal regions than those that were later forgotten-the same pattern of results as previously observed for traditional incidental and intentional episodic encoding tasks. This finding may offer a partial explanation for why repeated testing improves memory performance. Furthermore, the observation of correlates of episodic memory encoding during retrieval tasks challenges some interpretations that aris from direct comparisons between: encoding tasks and “retrieval tasks” in imaging data. Encoding processes and their neural correlates may arise in many tasks, even those nominally labeled as retrieval tasks by the experimenter.

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