A humán agyi aktivitás hálózatelemzési modellezése – Humán agyi hálózatok

Összefoglalás. A hálózatkutatás idegtudományi alkalmazása áttörő eredményt hozott a humán kogníció és a neurális rendszerek közötti kapcsolat megértésében. Jelen tanulmány célja a neurális hálózatok néhány kutatási területét mutatja be a laborunkban végzett vizsgálatok eredményein keresztül. Bemutatjuk az agyi aktivitás mérésének és az agyi területek közötti kommunikációs hálózatok modellezésének technikáját. Majd kiemelünk két kutatási terület: 1) az agyi hálózatok életkori változásainak vizsgálatát, ami választ ad arra, hogy hogyan öregszik az emberi agy; 2) az emberi agyak közötti hálózat modelljének vizsgálatát, amely a hatékony emberi kommunikáció idegrendszeri mechanizmusait próbálja feltárni. Tárgyaljuk a humán kommunikációra képes mesterséges intelligencia fejlesztésének lehetőségét is. Végül kitérünk az agyi hálózatok kutatásának biztonságpolitikai vonatkozásaira. Summary. The human brain consists of 100 billion neurons connected by about 100 trillion synapses, which are hierarchically organized in different scales in anatomical space and time. Thus, it sounds reasonable to assume that the brain is the most complex network known to man. Network science applications in neuroscience are aimed to understand how human feeling, thought and behavior could emerge from this biological system of the brain. The present review focuses on the recent results and the future of network neuroscience. The following topics will be discussed: Modeling the network of communication among brain areas. Neural activity can be recorded with high temporal precision using electroencephalography (EEG). Communication strength between brain regions then might be estimated by calculating mathematical synchronization indices between source localized EEG time series. Finally, graph theoretical models can describe the relationship between system elements (i.e. efficiency of communication or centrality of an element). How does the brain age? While for a newborn the high plasticity of the brain provides the foundation of cognitive development, cognition declines with advanced age due to so far largely unknown neural mechanisms. In one of our studies, we demonstrated that there is a correlation between the anatomical development of the brain (at prenatal age) and its network topology. Specifically, the more developed the baby’s brain, the more functionally specialized/modular it was. In another study we found that in older adults, when compared to young adults, connectivity within modules of their brain network is decreased, with an associated decline in their short-term memory capacity. Moreover, Mild Cognitive Impairment patients (early stage of Alzheimer) were characterized with a significantly lower level of connectivity between their brain modules than the healthy elderly. Human communication via shared network of brain activity. In another study we recorded the brain activity of a speaker and multiple listeners. We investigated the brain network similarity across listeners and between the speaker and listeners. We found that brain activity was significantly correlated among listeners, providing evidence for the fact that the same content is processed via similar neural computations within different brains. The data also suggested that the more the brain activity synchronizes the more the mental state of the individuals overlap. We also found significantly synchronized brain activity between speaker and listeners. Specifically 1) listeners’ brain activity within the speech processing cortices was synchronized to speaker’s brain activity with a time lag, indicating that listeners’ speech comprehension processes replicated the speaker’s speech production processes; and 2) listeners’ frontal cortical activity was synchronized to speaker’s later brain activity, that is, listeners preceded the speaker, indicating that speech content is predicted by the listeners based on the context. Future challenges. Future research could target artificial intelligence development that is capable of human-like communication. To achieve this, the simultaneous recording of brain activity from listener and speaker is needed together with efficiency of the communication. These data could be then modelled via AI to detect biomarkers of communication efficiency. In general, neurotechnology has been rapidly developing within and outside of research and in clinical fields thus it is time for re-conceptualizing the corresponding human right law in order to avoid unwanted consequences of technological applications.

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

Cerebral Cortex ◽

10.1093/cercor/bhaa292 ◽

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.

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RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

10.36106/paripex/9308196 ◽

2020 ◽

pp. 1-2

Author(s):

Zhang- sensen

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Brain Network ◽

Classification Model ◽

Comparative Experiment ◽

Machine Learning Methods ◽

Healthy Elderly ◽

Healthy Elderly People ◽

The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

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Spontaneous perception: a framework for task-free, self-paced perception

Neuroscience of Consciousness ◽

10.1093/nc/niab016 ◽

2021 ◽

Vol 2021 (2) ◽

Author(s):

Shira Baror ◽

Biyu J He

Keyword(s):

Time Course ◽

Visual Experience ◽

Brain Activity ◽

Human Perception ◽

Future Research ◽

Task Constraints ◽

Spontaneous Brain Activity ◽

The Brain ◽

Coarse To Fine ◽

Organizing Principles

Abstract Flipping through social media feeds, viewing exhibitions in a museum, or walking through the botanical gardens, people consistently choose to engage with and disengage from visual content. Yet, in most laboratory settings, the visual stimuli, their presentation duration, and the task at hand are all controlled by the researcher. Such settings largely overlook the spontaneous nature of human visual experience, in which perception takes place independently from specific task constraints and its time course is determined by the observer as a self-governing agent. Currently, much remains unknown about how spontaneous perceptual experiences unfold in the brain. Are all perceptual categories extracted during spontaneous perception? Does spontaneous perception inherently involve volition? Is spontaneous perception segmented into discrete episodes? How do different neural networks interact over time during spontaneous perception? These questions are imperative to understand our conscious visual experience in daily life. In this article we propose a framework for spontaneous perception. We first define spontaneous perception as a task-free and self-paced experience. We propose that spontaneous perception is guided by four organizing principles that grant it temporal and spatial structures. These principles include coarse-to-fine processing, continuity and segmentation, agency and volition, and associative processing. We provide key suggestions illustrating how these principles may interact with one another in guiding the multifaceted experience of spontaneous perception. We point to testable predictions derived from this framework, including (but not limited to) the roles of the default-mode network and slow cortical potentials in underlying spontaneous perception. We conclude by suggesting several outstanding questions for future research, extending the relevance of this framework to consciousness and spontaneous brain activity. In conclusion, the spontaneous perception framework proposed herein integrates components in human perception and cognition, which have been traditionally studied in isolation, and opens the door to understand how visual perception unfolds in its most natural context.

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Transient brain networks underlying interpersonal strategies during synchronized action

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsaa056 ◽

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.

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

Frontiers in Psychiatry ◽

10.3389/fpsyt.2020.608928 ◽

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.

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Neurophysiological network dynamics of pitch change detection

10.1101/2020.10.07.328450 ◽

2020 ◽

Author(s):

Soheila Samiee ◽

Dominique Vuvan ◽

Esther Florin ◽

Philippe Albouy ◽

Isabelle Peretz ◽

...

Keyword(s):

Motor Cortex ◽

Auditory Cortex ◽

Change Detection ◽

Brain Activity ◽

Inferior Frontal Gyrus ◽

Brain Network ◽

Slow Phase ◽

Speech Comprehension ◽

Pitch Change ◽

Phase Amplitude

AbstractThe detection of pitch changes is crucial to sound localization, music appreciation and speech comprehension, yet the brain network oscillatory dynamics involved remain unclear. We used time-resolved cortical imaging in a pitch change detection task. Tone sequences were presented to both typical listeners and participants affected with congenital amusia, as a model of altered pitch change perception.Our data show that tone sequences entrained slow (2-4 Hz) oscillations in the auditory cortex and inferior frontal gyrus, at the pace of tone presentations. Inter-regional signaling at this slow pace was directed from auditory cortex towards the inferior frontal gyrus and motor cortex. Bursts of faster (15-35Hz) oscillations were also generated in these regions, with directed influence from the motor cortex. These faster components occurred precisely at the expected latencies of each tone in a sequence, yielding a form of local phase-amplitude coupling with slower concurrent activity. The intensity of this coupling peaked dynamically at the moment of anticipated pitch changes.We clarify the mechanistic relevance of these observations in relation to behavior as, by task design, typical listeners outperformed amusic participants. Compared to typical listeners, inter-regional slow signaling toward motor and inferior frontal cortices was depressed in amusia. Also, the auditory cortex of amusic participants over-expressed tonic, fast-slow phase-amplitude coupling, pointing at a possible misalignment between stimulus encoding and internal predictive signaling. Our study provides novel insight into the functional architecture of polyrhythmic brain activity in auditory perception and emphasizes active, network processes involving the motor system in sensory integration.

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Altered Functional Connectivity in Children with ADHD Revealed by Scalp EEG: An ERP Study

Neural Plasticity ◽

10.1155/2021/6615384 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Chunli Chen ◽

Huan Yang ◽

Yasong Du ◽

Guangzhi Zhai ◽

Hesheng Xiong ◽

...

Keyword(s):

Cognitive Processing ◽

Early Stage ◽

Brain Activity ◽

Brain Networks ◽

Brain Network ◽

Event Related Potential ◽

Children With Adhd ◽

Scalp Eeg ◽

Adhd Children ◽

The One

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD.

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Sensory-modality independent activation of the brain network for language

10.1101/714998 ◽

2019 ◽

Author(s):

Sophie Arana ◽

André Marquand ◽

Annika Hultén ◽

Peter Hagoort ◽

Jan-Mathijs Schoffelen

Keyword(s):

Language Processing ◽

Sentence Processing ◽

Human Subjects ◽

Brain Activity ◽

Activity Patterns ◽

Sensory Modality ◽

Brain Network ◽

Large Network ◽

Individual Subject ◽

The Brain

AbstractThe meaning of a sentence can be understood, whether presented in written or spoken form. Therefore it is highly probable that brain processes supporting language comprehension are at least partly independent of sensory modality. To identify where and when in the brain language processing is independent of sensory modality, we directly compared neuromagnetic brain signals of 200 human subjects (102 males) either reading or listening to sentences. We used multiset canonical correlation analysis to align individual subject data in a way that boosts those aspects of the signal that are common to all, allowing us to capture word-by-word signal variations, consistent across subjects and at a fine temporal scale. Quantifying this consistency in activation across both reading and listening tasks revealed a mostly left hemispheric cortical network. Areas showing consistent activity patterns include not only areas previously implicated in higher-level language processing, such as left prefrontal, superior & middle temporal areas and anterior temporal lobe, but also parts of the control-network as well as subcentral and more posterior temporal-parietal areas. Activity in this supramodal sentence processing network starts in temporal areas and rapidly spreads to the other regions involved. The findings do not only indicate the involvement of a large network of brain areas in supramodal language processing, but also indicate that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects.

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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

10.1101/2020.03.26.009795 ◽

2020 ◽

Author(s):

Paul Triebkorn ◽

Joelle Zimmermann ◽

Leon Stefanovski ◽

Dipanjan Roy ◽

Ana Solodkin ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Function ◽

Large Scale ◽

Brain Activity ◽

Brain Network ◽

Model Parameters ◽

Network Modelling ◽

Neural Populations ◽

Global Coupling ◽

The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

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Positron Emission Tomography of Auditory Sensation in Deaf Patients and Patients with Cochlear Implants

Annals of Otology Rhinology & Laryngology ◽

10.1177/000348949310201011 ◽

1993 ◽

Vol 102 (10) ◽

pp. 797-801 ◽

Cited By ~ 33

Author(s):

Juichi Ito ◽

Yasushi Iwasaki ◽

Junji Sakakibara ◽

Yoshiharu Yonekura

Keyword(s):

Auditory Cortex ◽

Brain Activity ◽

Emission Computed Tomography ◽

Speech Comprehension ◽

Metabolic Rates ◽

Quantitative Measurements ◽

Positron Emission ◽

Before And After ◽

Auditory Cortices ◽

The Brain

The present study investigated the function of the auditory cortices in severely hearing-impaired or deaf patients and cochlear implant patients before and after auditory stimulation. Positron emission computed tomography (PET), which can detect brain activity by providing quantitative measurements of the metabolic rates of oxygen and glucose, was used. In patients with residual hearing, the activity of the auditory cortex measured by PET was almost normal. Among the totally deaf patients, the longer the duration of deafness, the lower the brain activity in the auditory cortex measured by PET. Patients who had been deaf for a long period showed remarkably reduced metabolic rates in the auditory cortices. However, following implantation of the cochlear device, the metabolic activity returned to nearnormal levels. These findings suggest that activation of the speech comprehension mechanism of the higher brain system can be initiated by sound signals from the implant devices.

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