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 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 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 Improving Social Interactive Learning through Dual Brain Stimulation

2019 ◽  
Author(s):  
Yafeng Pan ◽  
Giacomo Novembre ◽  
Bei Song ◽  
Yi Zhu ◽  
Yi Hu
Keyword(s):  
Social Behavior ◽  
Social Learning ◽  
Brain Stimulation ◽  
Information Transfer ◽  
Brain Research ◽  
Interactive Learning ◽  
Body Movement ◽  
Learning Task ◽  
Brain Regions ◽  
Learning Performance

AbstractSocial interactive learning denotes the ability to acquire new information from a conspecific – a prerequisite for cultural evolution and survival. As inspired by recent neurophysiological research, here we tested whether social interactive learning can be augmented by exogenously synchronizing oscillatory brain activity across an instructor and a learner engaged in a naturalistic song-learning task. We used a dual brain stimulation protocol entailing the trans-cranial delivery of synchronized electric currents in two individuals simultaneously. When we stimulated inferior frontal brain regions, with 6 Hz alternating currents being in-phase between the instructor and the learner, the dyad exhibited spontaneous and synchronized body movement. Remarkably, this stimulation also led to enhanced learning performance. A mediation analysis further disclosed that interpersonal movement synchrony acted as a partial mediator of the effect of dual brain stimulation on learning performance, i.e. possibly facilitating the effect of dual brain stimulation on learning. Our results provide a causal demonstration that inter-brain synchrony is a sufficient condition to improve real-time information transfer between pairs of individuals.SignificanceThe study of social behavior, including but not limited to social learning, is undergoing a paradigm shift moving from single- to multi-person brain research. Yet, nearly all evidence in this area is purely correlational: inter-dependencies between brains’ signals are used to predict success in social behavior. For instance, inter-brain synchrony has been shown to be associated with successful communication, cooperation, and joint attention. Here we took a radically different approach. We stimulated two brains simultaneously, hence manipulating inter-brain synchrony, and measured the resulting effect upon behavior in the context of a social learning task. We report that frequency- and phase-specific dual brain stimulation can lead to the emergence of spontaneous synchronized body movement between an instructor and a learner. Remarkably, this can also augment learning performance.

scholarly journals DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training

2020 ◽  
Author(s):  
Xiaodan Xing ◽  
Qingfeng Li ◽  
Mengya Yuan ◽  
Hao Wei ◽  
Zhong Xue ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Disease Classification ◽  
Sliding Windows ◽  
Convolutional Networks ◽  
Gender And Age ◽  
The Brain ◽  
Normal Controls

Abstract Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally, the nodes of which are brain regions and the edges are interregional connectivity. Thus, in this study, we proposed novel graph convolutional networks (GCNs) to extract efficient disease-related features from FC matrices. Considering the time-dependent nature of brain activity, we computed dynamic FC matrices with sliding windows and implemented a graph convolution–based LSTM (long short–term memory) layer to process dynamic graphs. Moreover, the demographics of patients were also used as additional outputs to guide the classification. In this paper, we proposed to utilize the demographic information as extra outputs and to share parameters among three networks predicting subject status, gender, and age, which serve as assistant tasks. We tested the performance of the proposed architecture in ADNI II dataset to classify Alzheimer’s disease patients from normal controls. The classification accuracy, sensitivity, and specificity reach 90.0%, 91.7%, and 88.6%, respectively, on ADNI II dataset.

scholarly journals Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

scholarly journals Altered brain network centrality in patients with adult comitant exotropia strabismus: A resting-state fMRI study

2017 ◽  
Vol 46 (1) ◽  
pp. 392-402 ◽  
Author(s):  
Gang Tan ◽  
Zeng-Renqing Dan ◽  
Ying Zhang ◽  
Xin Huang ◽  
Yu-Lin Zhong ◽  
...  
Keyword(s):  
Brain Activity ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Brain Network ◽  
Network Activity ◽  
Ocular Motility ◽  
Behavioral Performance ◽  
Parietal Lobule ◽  
The Right ◽  
The Relationship

Objective To investigate the underlying functional network brain-activity changes in patients with adult comitant exotropia strabismus (CES) and the relationship with clinical features using the voxel-wise degree centrality (DC) method. Methods A total of 30 patients with CES (17 men, 13 women), and 30 healthy controls (HCs; 17 men, 13 women) matched in age, sex, and education level participated in the study. DC was used to evaluate spontaneous brain activity. Receiver operating characteristic (ROC) curve analysis was conducted to distinguish CESs from HCs. The relationship between mean DC values in various brain regions and behavioral performance was examined with correlation analysis. Results Compared with HCs, CES patients exhibited decreased DC values in the right cerebellum posterior lobe, right inferior frontal gyrus, right middle frontal gyrus and right superior parietal lobule/primary somatosensory cortex (S1), and increased DC values in the right superior temporal gyrus, bilateral anterior cingulate, right superior temporal gyrus, and left inferior parietal lobule. However, there was no correlation between mean DC values and behavioral performance in any brain regions. Conclusions Adult comitant exotropia strabismus is associated with abnormal brain network activity in various brain regions, possibly reflecting the pathological mechanisms of ocular motility disorders in CES.

scholarly journals Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms

2019 ◽  
Author(s):  
Jessica S. Flannery ◽  
Michael C. Riedel ◽  
Katherine L. Bottenhorn ◽  
Ranjita Poudel ◽  
Taylor Salo ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Reward Processing ◽  
Network Activity ◽  
Reward Learning ◽  
Learning Theories ◽  
Behavior Profiles

ABSTRACTReward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we utilized emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution which represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

scholarly journals Dynamic properties of simulated brain network models and empirical resting-state data

2019 ◽  
Vol 3 (2) ◽  
pp. 405-426 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Simulated Data ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain

Brain network models (BNMs) have become a promising theoretical framework for 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 obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. 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 explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions 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 GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Bi ◽  
Zhixun Liu ◽  
Yao He ◽  
Xiangguo Zhao ◽  
Yongjiao Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Euclidean Space ◽  
Brain Regions ◽  
Brain Network ◽  
Graph Representation ◽  
Learning Performance ◽  
Graph Classification ◽  
Learning Methods ◽  
Brain Images ◽  
Graph Learning

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.

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