scholarly journals Fibromyalgia Detection Based on EEG Connectivity Patterns

2021 ◽  
Vol 10 (15) ◽  
pp. 3277
Author(s):  
Ramón Martín-Brufau ◽  
Manuel Nombela Gómez ◽  
Leyre Sanchez-Sanchez-Rojas ◽  
Cristina Nombela
Keyword(s):  
Clinical Features ◽  
Resting State ◽  
Roc Analysis ◽  
Brain Activity ◽  
Brain Structures ◽  
Source Analysis ◽  
Bioelectrical Activity ◽  
Connectivity Patterns ◽  
Eeg Connectivity

Objective: The identification of a complementary test to confirm the diagnosis of FM. The diagnosis of fibromyalgia (FM) is based on clinical features, but there is still no consensus, so patients and clinicians might benefit from such a test. Recent findings showed that pain lies in neuronal bases (pain matrices) and, in the long term, chronic pain modifies the activity and dynamics of brain structures. Our hypothesis is that patients with FM present lower levels of brain activity and therefore less connectivity than controls. Methods: We registered the resting state EEG of 23 patients with FM and compared them with 23 control subjects’ resting state recordings from the PhysioBank database. We measured frequency, amplitude, and functional connectivity, and conducted source localization (sLORETA). ROC analysis was performed on the resulting data. Results: We found significant differences in brain bioelectrical activity at rest in all analyzed bands between patients and controls, except for Delta. Subsequent source analysis provided connectivity values that depicted a distinct profile, with high discriminative capacity (between 91.3–100%) between the two groups. Conclusions: Patients with FM show a distinct neurophysiological pattern that fits with the clinical features of the disease.

Resting State Brain Activity During Long-Term Dry Immersion

2018 ◽  
Vol 89 (7) ◽  
pp. 642-647 ◽  
Author(s):  
Ivan E. Lazarev ◽  
Elena S. Tomilovskaya ◽  
Inesa B. Kozlovskaya
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Dry Immersion

scholarly journals Brain Synchrony in Competition and Collaboration During Multiuser Neurofeedback-Based Gaming

2021 ◽  
Vol 2 ◽  
Author(s):  
Ioana Susnoschi Luca ◽  
Finda Dwi Putri ◽  
Hao Ding ◽  
Aleksandra Vuckovič
Keyword(s):  
Social Exchange ◽  
Resting State ◽  
Winning Strategy ◽  
Brain Structures ◽  
Physical World ◽  
Similar Degree ◽  
Connectivity Patterns ◽  
Close Relationship ◽  
The Difference ◽  
Rules Of The Game

EEG hyperscanning during multiuser gaming offers opportunities to study brain characteristics of social interaction under various paradigms. In this study, we aimed to characterize neural signatures and phase-based functional connectivity patterns of gaming strategies during collaborative and competitive alpha neurofeedback games. Twenty pairs of participants with no close relationship took part in three sessions of collaborative or competitive multiuser neurofeedback (NF), with identical graphical user interface, using Relative Alpha (RA) power as a control signal. Collaborating dyads had to keep their RA within 5% of each other for the team to be awarded a point, while members of competitive dyads scored points if their RA was 10% above their opponent's. Interbrain synchrony existed only during gaming but not during baseline in either collaborative or competitive gaming. Spectral analysis and interbrain connectivity showed that in collaborative gaming, players with higher resting state alpha content were more active in regulating their RA to match those of their partner. Moreover, interconnectivity was the strongest between homologous brain structures of the dyad in theta and alpha bands, indicating a similar degree of planning and social exchange. Competitive gaming emphasized the difference between participants who were able to relax and, in this way, maintain RA, and those who had an unsuccessful approach. Analysis of interbrain connections shows engagement of frontal areas in losers, but not in winners, indicating the formers' attempt to mentalise and apply strategies that might be suitable for conventional gaming, but inappropriate for the alpha neurofeedback-based game. We show that in gaming based on multiplayer non-verbalized NF, the winning strategy is dependent on the rules of the game and on the behavior of the opponent. Mental strategies that characterize successful gaming in the physical world might not be adequate for NF-based gaming.

Chinese Classical Dancers Have Improved Spontaneous Activity in Visual Brain Areas

2021 ◽  
pp. 1-7
Author(s):  
Rou Wen ◽  
Lijuan Hou ◽  
Jilong Shi ◽  
Mi Zhang
Keyword(s):  
Resting State ◽  
Professional Training ◽  
Brain Activity ◽  
Brain Areas ◽  
Classical Dance ◽  
Functional Changes ◽  
Dance Training ◽  
Significant Difference ◽  
Professional Dancers

Abstract. Resting-state functional magnetic resonance imaging (fMRI) studies demonstrate that long-term exercise or dance training may cause changes in brain structure and function. However, the changes of neurofunction in the long-term practitioners of Chinese classical dance are still unclear. The purpose of the study is to explore the neurofunctional alterations associated with long-term Chinese classical dance training. Thirty female college students were selected, 15 students majoring in Chinese classical dance (average training years = 9.73 ± 1.75 years) and 15 education-matched non-dancer students with no previous experience of regular dance training. In this cross-sectional design, the resting-state fMRI data were acquired only once to observe the structural and functional changes of the brain. Compared with non-dancers, professional dancers had no significant difference in the total volume of whole brain, gray matter, white matter, and cerebrospinal fluid. While in professional dancers, we found increased amplitude of low-frequency fluctuation (ALFF) in the left superior occipital gyrus, right Cuneus, and left calcarine fissure and surrounding cortex (Calcarine); increased fractional ALFF and regional homogeneity in the right Calcarine, indicating the increase of spontaneous brain activity in these brain areas. Since these brain areas are related to visual cognitive function, the results suggest that long-term Chinese classical dance training is associated with increased spontaneous regional brain activity in the visual areas. This may be closely related to the specific characteristics of Chinese classical dance and long-term professional training.

scholarly journals A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ali Yener Mutlu ◽  
Edward Bernat ◽  
Selin Aviyente
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Dynamic Network ◽  
Principal Component ◽  
Brain Network ◽  
Event Related Potential ◽  
Time Varying ◽  
Error Related Negativity ◽  
Connectivity Patterns

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

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