Common mechanisms of attractorless oscillatory regimes in radioengineering models of brain thalamocortical network

2021 ◽  
Vol 29 (6) ◽  
pp. 927-942
Author(s):  
Nikita Egorov ◽  
◽  
Vladimir Ponomarenko ◽  
Sofia Melnikova ◽  
Ilya Sysoev ◽  
...  
Keyword(s):  
Initial Phase ◽  
Electronic Circuit ◽  
Brain Activity ◽  
Brain Network ◽  
Connectivity Matrix ◽  
Coupling Matrix ◽  
Circuit Element ◽  
Thalamocortical System ◽  
Process Length ◽  
External Driving

This work aims to show that long transient processes in mesascale models of thalamocortical brain network can appear in very general case, in particular for different number of elements in the ensemble (different level of detalization) and different initial phase of external driving, with these regimes surviving at small variations of number and structure of couplings. Methods. Thalamocortical brain networks are modelled using electronic circuit realized using computer SPICE eluating software. FitzHugh – Nagumo analog generator is used as a single circuit element. Results. Long quasiregular and nonregular oscillation processes with stationary amplitude were shown to occur in ensembles of 14, 28 and 56 model FitzHug – Nagumo generators. The dependency of transient process length on the external driving initial phase and particular coupling matrix structure was studied. Conclusion. The proposed electronic models of thalamocortical system were proved to reproduce the pathological regimes of brain activity in similar way despite the number of elements in the circuit, connectivity matrix and initial driving phase.

Brain reactivity to humorous films is affected by insomnia

SLEEP ◽  
2021 ◽  
Author(s):  
Ernesto Sanz-Arigita ◽  
Yannick Daviaux ◽  
Marc Joliot ◽  
Bixente Dilharreguy ◽  
Jean-Arthur Micoulaud-Franchi ◽  
...  
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Cingulate Gyrus ◽  
Anterior Cingulate Gyrus ◽  
Left Caudate ◽  
Neural Reactivity ◽  
The Right

Abstract Study objectives Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. Methods We used 3T fMRI to determine neural reactivity during the presentation of standardized short, 10-40-s, humorous films in insomnia patients (n=20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n=20, 19 females, aged 26.7 +/- 7.0 years), and assessed humour ratings through a visual analogue scale (VAS). Seed-based functional connectivity was analysed for left and right amygdala networks: group-level mixed-effects analysis (FLAME; FSL) was used to compare amygdala connectivity maps between groups. Results fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in insomnia patients than in controls in several brain network clusters within the reward brain network, without humour rating differences between groups (p = 0.6). For left amygdala connectivity, cluster maxima were in the left caudate (Z=3.88), left putamen (Z=3.79) and left anterior cingulate gyrus (Z=4.11), while for right amygdala connectivity, cluster maxima were in the left caudate (Z=4.05), right insula (Z=3.83) and left anterior cingulate gyrus (Z=4.29). Cluster maxima of the right amygdala network were correlated with hyperarousal scores in insomnia patients only. Conclusions Presentation of humorous films leads to increased brain activity in the neural reward network for insomnia patients compared to controls, related to hyperarousal features in insomnia patients, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals Social and asocial learning in zebrafish are encoded by a shared brain network that is differentially modulated by local activation

2021 ◽  
Author(s):  
Julia Pinho ◽  
Vincent T. Cunliffe ◽  
Giovanni Petri ◽  
Rui Oliveira
Keyword(s):  
Social Learning ◽  
Brain Activity ◽  
Group Living ◽  
Brain Regions ◽  
Brain Network ◽  
Early Gene ◽  
Learning Performance ◽  
Social Stimulus ◽  
Visual Response ◽  
Learning Module

Group living animals can use social and asocial cues to predict the presence of a reward or a punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate, and, so far, studies investigating the neuronal basis of these two types of learning are scarce and have been restricted to primates, including humans, and rodents. Here we have used a Pavlovian fear conditioning paradigm in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early gene c-fos to map the neural circuits associated with social and asocial learning. Our results show that the learning performance is similar with social (fish image) and asocial (circle image) CSs. However, the brain regions involved in each learning type are distinct. Social learning is associated with an increased expression of c-fos in olfactory bulbs, ventral zone of ventral telencephalic area, ventral habenula and ventromedial thalamus, whereas asocial learning is associated with a decreased expression of c-fos in dorsal habenula and anterior tubercular nucleus. Using egonetworks, we further show that each learning type has an associated pattern of functional connectivity across brain regions. Moreover, a community analysis of the network data reveals four segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks: a generalized attention module, a visual response module, a social stimulus integration module and a learning module. Together, these results suggest that, although there are localized differences in brain activity between social and asocial learning, the two learning types share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.

scholarly journals Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Liang ◽  
Chunli Chen ◽  
Fali Li ◽  
Dezhong Yao ◽  
Peng Xu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Local Area ◽  
Brain Network ◽  
Fuzzy Entropy ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Functional Brain Network ◽  
Before And After

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.

scholarly journals Transient brain networks underlying interpersonal strategies during synchronized action

2020 ◽  
Author(s):  
Ole Adrian Heggli ◽  
Ivana Konvalinka ◽  
Joana Cabral ◽  
Elvira Brattico ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Human Interaction ◽  
Action Perception ◽  
Network Information ◽  
Mutual Adaptation ◽  
Underlying Mechanisms ◽  
The Brain

Abstract Interpersonal coordination is a core part of human interaction, and its underlying mechanisms have been extensively studied using social paradigms such as joint finger-tapping. Here, individual and dyadic differences have been found to yield a range of dyadic synchronization strategies, such as mutual adaptation, leading–leading, and leading–following behaviour, but the brain mechanisms that underlie these strategies remain poorly understood. To identify individual brain mechanisms underlying emergence of these minimal social interaction strategies, we contrasted EEG-recorded brain activity in two groups of musicians exhibiting the mutual adaptation and leading–leading strategies. We found that the individuals coordinating via mutual adaptation exhibited a more frequent occurrence of phase-locked activity within a transient action–perception-related brain network in the alpha range, as compared to the leading–leading group. Furthermore, we identified parietal and temporal brain regions that changed significantly in the directionality of their within-network information flow. Our results suggest that the stronger weight on extrinsic coupling observed in computational models of mutual adaptation as compared to leading–leading might be facilitated by a higher degree of action–perception network coupling in the brain.

Altered brain network centrality in patients with trigeminal neuralgia: a resting-state fMRI study

2019 ◽  
Vol 61 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Pei-Wen Zhu ◽  
You Chen ◽  
Ying-Xin Gong ◽  
Nan Jiang ◽  
Wen-Feng Liu ◽  
...  
Keyword(s):  
Trigeminal Neuralgia ◽  
Brain Activity ◽  
Characteristic Curve ◽  
Brain Regions ◽  
Brain Network ◽  
Network Activity ◽  
Degree Centrality ◽  
Healthy Controls ◽  
Postcentral Gyrus ◽  
The Right

Background Neuroimaging studies revealed that trigeminal neuralgia was related to alternations in brain anatomical function and regional function. However, the functional characteristics of network organization in the whole brain is unknown. Purpose The aim of the present study was to analyze potential functional network brain-activity changes and their relationships with clinical features in patients with trigeminal neuralgia via the voxel-wise degree centrality method. Material and Methods This study involved a total of 28 trigeminal neuralgia patients (12 men, 16 women) and 28 healthy controls matched in sex, age, and education. Spontaneous brain activity was evaluated by degree centrality. Correlation analysis was used to examine the correlations between behavioral performance and average degree centrality values in several brain regions. Results Compared with healthy controls, trigeminal neuralgia patients had significantly higher degree centrality values in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, and bilateral inferior cerebellum. Receiver operative characteristic curve analysis of each brain region confirmed excellent accuracy of the areas under the curve. There was a positive correlation between the mean degree centrality value of the right postcentral gyrus and VAS score (r = 0.885, P < 0.001). Conclusions Trigeminal neuralgia causes abnormal brain network activity in multiple brain regions, which may be related to underlying disease mechanisms.

scholarly journals Association Between Trait Empathy and Resting Brain Activity in Women With Primary Dysmenorrhea During the Pain and Pain-Free Phases

2020 ◽  
Vol 11 ◽  
Author(s):  
Wanghuan Dun ◽  
Tongtong Fan ◽  
Qiming Wang ◽  
Ke Wang ◽  
Jing Yang ◽  
...  
Keyword(s):  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Brain Network ◽  
Primary Dysmenorrhea ◽  
Pain Experience ◽  
Current State ◽  
The Neural Network ◽  
The Right ◽  
The Brain

Empathy refers to the ability to understand someone else's emotions and fluctuates with the current state in healthy individuals. However, little is known about the neural network of empathy in clinical populations at different pain states. The current study aimed to examine the effects of long-term pain on empathy-related networks and whether empathy varied at different pain states by studying primary dysmenorrhea (PDM) patients. Multivariate partial least squares was employed in 46 PDM women and 46 healthy controls (HC) during periovulatory, luteal, and menstruation phases. We identified neural networks associated with different aspects of empathy in both groups. Part of the obtained empathy-related network in PDM exhibited a similar activity compared with HC, including the right anterior insula and other regions, whereas others have an opposite activity in PDM, including the inferior frontal gyrus and right inferior parietal lobule. These results indicated an abnormal regulation to empathy in PDM. Furthermore, there was no difference in empathy association patterns in PDM between the pain and pain-free states. This study suggested that long-term pain experience may lead to an abnormal function of the brain network for empathy processing that did not vary with the pain or pain-free state across the menstrual cycle.

scholarly journals Longitudinal Spatial Patterns of Intrinsic Brain Activity and Functional Connectivity in Upper Limb Amputees Patients

2020 ◽  
Author(s):  
bingbo bao ◽  
xuyun hua ◽  
haifeng wei ◽  
pengbo luo ◽  
hongyi zhu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spatial Patterns ◽  
Brain Plasticity ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Intrinsic Connectivity ◽  
Intrinsic Brain Activity

Abstract Background: Amputation in adults is a serious condition and most patients were associated with the remapping of representations in motor and sensory brain network. Methods: The present study includes 8 healthy volunteers and 16 patients with amputation. We use resting-state fMRI to investigate the local and extent brain plasticity in patients suffering from amputation simultaneously. Both the amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) were used for the assessment of neuroplasticity in central level. Results: We described changes in spatial patterns of intrinsic brain activity and functional connectivity in amputees in the present study and we found that not only the sensory and motor cortex, but also the related brain regions involved in the functional plasticity after upper extremity deafferentation. Conclusion: Our findings showed local and extensive cortical changes in the sensorimotor and cognitive-related brain regions, which may imply the dysfunction in not only sensory and motor function, but also sensorimotor integration and motor plan. The activation and intrinsic connectivity in the brain changed a lot showed correlation with the deafferentation status.

scholarly journals A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

2021 ◽  
Author(s):  
Amrit Kashyap ◽  
Sergey Plis ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Shella Keilholz
Keyword(s):  
Deep Learning ◽  
Initial Conditions ◽  
Brain Activity ◽  
Network Models ◽  
Brain Network ◽  
Measured Data ◽  
Fmri Data ◽  
Short Term ◽  
Whole Brain ◽  
Neural Processes

Brain Network Models (BNMs) are a family of dynamical systems that simulate whole brain activity using neural mass models to represent local activity in different brain regions that influence each other via a global structural network. Research has been interested in using these network models to explain measured whole brain activity measured via resting state functional magnetic resonance imaging (rs-fMRI). Properties computed over longer periods of simulated and measured data such as average functional connectivity (FC), have shown to be comparable with similar properties estimated from measured rs-fMRI data. While this shows that these network models have similar properties over the dynamical landscape, it is unclear how well simulated trajectories compare with empirical trajectories on a timepoint-by-timepoint basis. Previous studies have shown that BNMs are able to produce relevant features at shorter timescales, but analysis of short-term trajectories or transient dynamics as defined by synchronized predictions from BNM made at the same timescale as the collected data has not yet been conducted. Relevant neural processes exist in the time frame of measurements and are often used in task fMRI studies to understand neural responses to behavioral cues. Therefore, it is important to investigate how much of these dynamics are captured by our current brain simulations. To test the nature of BNMs short term trajectories against observed data, we utilize a deep learning technique known as Neural ODE that based on an observed sequence of fMRI measurements, estimates the initial conditions such that the BNMs simulation is synchronized to produce the closest trajectory relative to the observed data. We test to see if the parameterization of a specific well studied BNM, the Firing Rate Model, calculated by maximizing its accuracy in reproducing observed short term trajectories matches with the parameterized model that produces the best average long-term metrics. Our results show that such an agreement between parameterization using long and short simulation analysis exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity. Therefore, we conclude that there is evidence that by solving for initial conditions, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

scholarly journals Spatial and temporal autocorrelation weave human brain networks

2021 ◽  
Author(s):  
Maxwell Shinn ◽  
Amber Hu ◽  
Laurel Turner ◽  
Stephanie Noble ◽  
Sophie Achard ◽  
...  
Keyword(s):  
Network Topology ◽  
Healthy Aging ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Brain Network ◽  
Temporal Autocorrelation ◽  
Activity Networks ◽  
Graph Properties ◽  
Test Retest Reliability

Correlations are a basic object of analysis across neuroscience, but multivariate patterns of correlations can be difficult to interpret. For example, correlations are fundamental to understanding timeseries derived from resting-state functional magnetic resonance imaging (rs-fMRI), a proxy of brain activity. Networks constructed from regional correlations in rs-fMRI timeseries are often interpreted as brain connectivity, yet the links between brain networks and neurobiology have until now been largely speculative. Here, we show that the topology of rs-fMRI brain networks is caused by the spatial and temporal autocorrelation of the timeseries used to construct them. Spatial and temporal autocorrelation show high test-retest reliability, and are correlated with popular measures of network topology. A generative model of spatially and temporally autocorrelated timeseries exhibits similar network topology to brain networks, and when fit to individual subjects, it captures near the reliability limit of subject and regional variation. We demonstrate why spatial and temporal autocorrelation induce network structure, and highlight their ability to link graph properties to neurobiology during healthy aging. These results offer a reductionistic account of brain network complexity, explaining characteristic patterns in brain networks using timeseries statistics.

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