scholarly journals Brain network reconfiguration for narrative and argumentative thought

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yangwen Xu ◽  
Lorenzo Vignali ◽  
Olivier Collignon ◽  
Davide Crepaldi ◽  
Roberto Bottini
Keyword(s):  
Control System ◽  
Cognitive Functioning ◽  
Neural System ◽  
Brain Network ◽  
Network Reconfiguration ◽  
Intraparietal Sulcus ◽  
Neural Responses ◽  
Language System ◽  
Mental Operations ◽  
Argumentative Texts

AbstractOur brain constructs reality through narrative and argumentative thought. Some hypotheses argue that these two modes of cognitive functioning are irreducible, reflecting distinct mental operations underlain by separate neural bases; Others ascribe both to a unitary neural system dedicated to long-timescale information. We addressed this question by employing inter-subject measures to investigate the stimulus-induced neural responses when participants were listening to narrative and argumentative texts during fMRI. We found that following both kinds of texts enhanced functional couplings within the frontoparietal control system. However, while a narrative specifically implicated the default mode system, an argument specifically induced synchronization between the intraparietal sulcus in the frontoparietal control system and multiple perisylvian areas in the language system. Our findings reconcile the two hypotheses by revealing commonalities and differences between the narrative and the argumentative brain networks, showing how diverse mental activities arise from the segregation and integration of the existing brain systems.

scholarly journals Brain Network Reconfiguration for Narrative and Argumentative Thought

2020 ◽  
Author(s):  
Yangwen Xu ◽  
Lorenzo Vignali ◽  
Olivier Collignon ◽  
Davide Crepaldi ◽  
Roberto Bottini
Keyword(s):  
Control System ◽  
Cognitive Functioning ◽  
Neural System ◽  
Brain Network ◽  
Network Reconfiguration ◽  
Intraparietal Sulcus ◽  
Neural Responses ◽  
Language System ◽  
Mental Operations ◽  
Argumentative Texts

AbstractOur brain constructs reality through narrative and argumentative thought. Some hypotheses argue that these two modes of cognitive functioning are irreducible, reflecting distinct mental operations underlain by separate neural bases; Others ascribe both to a unitary neural system dedicated to long-timescale information. We addressed this question by employing inter-subject measures to investigate the stimulus-induced neural responses when participants were listening to narrative and argumentative texts during fMRI. We found that following both kinds of texts enhanced functional couplings within the frontoparietal control system. However, while a narrative specifically implicated the default mode system, an argument specifically induced synchronization between the intraparietal sulcus in the frontoparietal control system and multiple perisylvian areas in the language system. Our findings reconcile the two hypotheses by revealing commonalities and differences between the narrative and the argumentative brain networks, showing how diverse mental activities arise from the segregation and integration of the existing brain systems.

Brain network reconfiguration for language and domain-general cognitive control in bilinguals

NeuroImage ◽  
2019 ◽  
Vol 199 ◽  
pp. 454-465 ◽  
Author(s):  
Junjie Wu ◽  
Jing Yang ◽  
Mo Chen ◽  
Shuhua Li ◽  
Zhaoqi Zhang ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Brain Network ◽  
Network Reconfiguration

scholarly journals JUSTIFICATION OF PARAMETERS OF THE CONTROL SYSTEM OF ELECTROMECHANICAL DEBALANCE VIBRODRIVE OF VIBRATION MACHINES ON THE BASIS OF ARTIFICIAL NEURAL NETWORK

2021 ◽  
pp. 52-63
Author(s):  
Leonid Yaroshenko ◽  
Roman Chubyk ◽  
Iryna Derevenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Mechanical System ◽  
Predictive Model ◽  
Control Method ◽  
Resonant Mode ◽  
Neural System ◽  
Artificial Neural ◽  
Vibration Parameters

The article analyzes and proposes an approach to the construction of a control system for electromechanical debalance vibrodrive for vibration machines based on an artificial neural network. As a result of the analysis of various methods of managing dynamic objects it is concluded that the most appropriate and perfect for this type of machine is neurocontrol method of predictive model neurocontrol, which allows to expand the functionality of these vibrating machines and significantly save energy for vibratory drive of their oscillations. A direct neuro-emulator is used to predict the future behavior of the oscillating mechanical system of the vibration technological machines and to calculate errors. An important feature of the predictive neurocontrol model in the proposed method of controlling the operation of vibrating technological machines using an artificial neural system is that there is no neurocontroller that needs to be trained, its place is taken by the optimization algorithm. Applying the proposed method of controlling operation of adaptive vibration technology machines using artificial neural network will optimize the electromechanical control of debalanced vibration drive of vibrating machines and provide optimal resonant modes of its operation (which is energy efficient) in all technological modes of vibrating operation. The technical and economic characteristics of this control method are further improved due to the fact that the proposed control method uses the technology of predictive model neurocontrol and as a result is constantly calculated (forecasted) several cycles in advance and determines the best strategy to control the frequency of forced cyclic vibration. As a result, the mechanical system of vibration machines spends less time in non-resonant mode. This method of control also minimizes the duration of transients when changing the load mass of the working body vibration or changing the mode of vibration parameters and the parameters of their technological process.

scholarly journals Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence

2021 ◽  
Author(s):  
Jonas Alexander Thiele ◽  
Joshua Faskowitz ◽  
Olaf Sporns ◽  
Kirsten Hilger
Keyword(s):  
Cognitive Ability ◽  
Network Architecture ◽  
Brain Networks ◽  
Brain Network ◽  
Task Demands ◽  
Human Intelligence ◽  
Network Reconfiguration ◽  
General Intelligence ◽  
Functional Brain ◽  
Functional Brain Networks

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different tasks as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (N = 138, N = 184). Our findings suggest that the intrinsic network architecture of individuals with higher general intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, reflect the neural equivalent of the behavioral positive manifold, i.e., the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

scholarly journals Functional brain connectivity phenotypes for schizophrenia drug discovery

2015 ◽  
Vol 29 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Neil Dawson ◽  
Brian J Morris ◽  
Judith A Pratt
Keyword(s):  
Drug Discovery ◽  
Brain Connectivity ◽  
Neural System ◽  
Brain Dysfunction ◽  
Brain Network ◽  
Attrition Rate ◽  
Rodent Models ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Electrophysiological Studies

While our knowledge of the pathophysiology of schizophrenia has increased dramatically, this has not translated into the development of new and improved drugs to treat this disorder. Human brain imaging and electrophysiological studies have provided dramatic new insight into the mechanisms of brain dysfunction in the disease, with a swathe of recent studies highlighting the differences in functional brain network and neural system connectivity present in the disorder. Only recently has the value of applying these approaches in preclinical rodent models relevant to the disorder started to be recognised. Here we highlight recent findings of altered functional brain connectivity in preclinical rodent models and consider their relevance to those alterations seen in the brains of schizophrenia patients. Furthermore, we highlight the potential translational value of using the paradigm of functional brain connectivity phenotypes in the context of preclinical schizophrenia drug discovery, as a means both to understand the mechanisms of brain dysfunction in the disorder and to reduce the current high attrition rate in schizophrenia drug discovery.

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

2018 ◽  
Vol 32 (2) ◽  
pp. 304-314 ◽  
Author(s):  
Fali Li ◽  
Chanlin Yi ◽  
Limeng Song ◽  
Yuanling Jiang ◽  
Wenjing Peng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Motor Imagery ◽  
Large Scale ◽  
Brain Network ◽  
Network Reconfiguration ◽  
Scalp Eeg ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Detection of functional brain network reconfiguration during task-driven cognitive states

NeuroImage ◽  
2016 ◽  
Vol 142 ◽  
pp. 198-210 ◽  
Author(s):  
Qawi K. Telesford ◽  
Mary-Ellen Lynall ◽  
Jean Vettel ◽  
Michael B. Miller ◽  
Scott T. Grafton ◽  
...  
Keyword(s):  
Brain Network ◽  
Network Reconfiguration ◽  
Functional Brain ◽  
Cognitive States ◽  
Functional Brain Network

scholarly journals A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Juntao Xue ◽  
Feiyue Ren ◽  
Xinlin Sun ◽  
Miaomiao Yin ◽  
Jialing Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Neural System ◽  
Brain Network ◽  
Eeg Signals ◽  
Stroke Patients ◽  
Common Spatial Pattern ◽  
Convolutional Network ◽  
The Public ◽  
Learning Framework

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject’s intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

scholarly journals Increased functional connectivity of the intraparietal sulcus underlies the attenuation of numerosity estimations for self-generated words

2020 ◽  
Author(s):  
Giedre Stripeikyte ◽  
Michael Pereira ◽  
Giulio Rognini ◽  
Jevita Potheegadoo ◽  
Olaf Blanke ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Functions ◽  
Supplementary Motor Area ◽  
Superior Temporal Gyrus ◽  
Intraparietal Sulcus ◽  
Neural Responses ◽  
Passive Condition ◽  
Sensorimotor Processing ◽  
Active Condition ◽  
The Right

ABSTRACTPrevious studies have shown that self-generated stimuli in auditory, visual, and somatosensory domains are attenuated, producing decreased behavioral and neural responses compared to the same stimuli that are externally generated. Yet, whether such attenuation also occurs for higher-level cognitive functions beyond sensorimotor processing remains unknown. In this study, we assessed whether cognitive functions such as numerosity estimations are subject to attenuation. We designed a task allowing the controlled comparison of numerosity estimations for self (active condition) and externally (passive condition) generated words. Our behavioral results showed a larger underestimation of self-compared to externally-generated words, suggesting that numerosity estimations for self-generated words are attenuated. Moreover, the linear relationship between the reported and actual number of words was stronger for self-generated words, although the ability to track errors about numerosity estimations was similar across conditions. Neuroimaging results revealed that numerosity underestimation involved increased functional connectivity between the right intraparietal sulcus and an extended network (bilateral supplementary motor area, left inferior parietal lobule and left superior temporal gyrus) when estimating the number of self vs. externally generated words. We interpret our results in light of two models of attenuation and discuss their perceptual versus cognitive origins.

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