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 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 Enhanced prefrontal functional–structural networks to support postural control deficits after traumatic brain injury in a pediatric population

2017 ◽  
Vol 1 (2) ◽  
pp. 116-142 ◽  
Author(s):  
Ibai Diez ◽  
David Drijkoningen ◽  
Sebastiano Stramaglia ◽  
Paolo Bonifazi ◽  
Daniele Marinazzo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cognitive Control ◽  
Postural Control ◽  
Pediatric Population ◽  
Structural Connectivity ◽  
Brain Network ◽  
Body Sway ◽  
Brain Network Reorganization ◽  
Structural Networks

Traumatic brain injury (TBI) affects structural connectivity, triggering the reorganization of structural–functional circuits in a manner that remains poorly understood. We focus here on brain network reorganization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severe TBI, comparing them to young, typically developing control participants. TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: (1) a subcortical network, including parts of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulate gyrus, and precuneus; and (2) a task-positive network, involving regions of the dorsal attention system, together with dorsolateral and ventrolateral prefrontal regions. We also found that the increased prefrontal connectivity in TBI patients was correlated with some postural control indices, such as the amount of body sway, whereby patients with worse balance increased their connectivity in frontal regions more strongly. The increased prefrontal connectivity found in TBI patients may provide the structural scaffolding for stronger cognitive control of certain behavioral functions, consistent with the observations that various motor tasks are performed less automatically following TBI and that more cognitive control is associated with such actions.

scholarly journals Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in the general population

2017 ◽  
Author(s):  
Douglas H. Schultz ◽  
Takuya Ito ◽  
Levi I. Solomyak ◽  
Richard H. Chen ◽  
Ravi D. Mill ◽  
...  
Keyword(s):  
Mental Health ◽  
Cognitive Control ◽  
Large Scale ◽  
Brain Network ◽  
Depression Symptoms ◽  
Control Network ◽  
Mental Health Symptoms ◽  
Health Symptoms ◽  
Cognitive Control Network ◽  
The Brain

ABSTRACTWe all vary in our mental health, even among people not meeting diagnostic criteria for mental illness. Understanding this individual variability may reveal factors driving the risk for mental illness, as well as factors driving sub-clinical problems that still adversely affect quality of life. To better understand the large-scale brain network mechanisms underlying this variability we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. One such system is the frontoparietal cognitive control network (FPN). Changes in FPN connectivity may impact mental health by disrupting the ability to regulate symptoms in a goal-directed manner. Here we test the hypothesis that FPN dysconnectivity relates to mental health symptoms even among individuals who do not meet formal diagnostic criteria but may exhibit meaningful symptom variation. We found that depression symptoms severity negatively correlated with between-network global connectivity (BGC) of the FPN. This suggests that decreased connectivity between the FPN and the rest of the brain is related to increased depression symptoms in the general population. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both health and disease.AUTHOR SUMMARYUnderstanding how large-scale network interactions in the brain contribute to (or serve a protective role against) mental health symptoms is an important step toward developing more effective mental health treatments. Here we test the hypothesis that cognitive control networks play an important role in mental health by being highly connected to other brain networks and able to serve as a feedback mechanism capable of regulating symptoms in a goal-directed manner. We found that the more well-connected the frontoparietal cognitive control network was to other networks in the brain the less depression symptoms were reported by participants. These results contribute to our understanding of how brain network interactions are related to mental health symptoms, even in individuals who have not been diagnosed with a disorder.

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.

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

Cognitive control of vocalizations in the primate ventrolateral-dorsomedial frontal (VLF-DMF) brain network

2017 ◽  
Vol 82 ◽  
pp. 32-44 ◽  
Author(s):  
Kep Kee Loh ◽  
Michael Petrides ◽  
William D. Hopkins ◽  
Emmanuel Procyk ◽  
Céline Amiez
Keyword(s):  
Cognitive Control ◽  
Brain Network

scholarly journals Neural systems of cognitive demand avoidance

2017 ◽  
Author(s):  
Ceyda Sayalı ◽  
David Badre
Keyword(s):  
Cognitive Control ◽  
Avoidance Behavior ◽  
Brain Network ◽  
Cognitive Effort ◽  
Network Activity ◽  
Partial Support ◽  
The Cost ◽  
Demand Selection ◽  
Control Hypothesis ◽  
Reward Network

AbstractCognitive effort is typically aversive, evident in people’s tendency to avoid cognitively demanding tasks. The ‘cost of control’ hypothesis suggests that engagement of cognitive control systems of the brain makes a task costly and the currency of that cost is a reduction in anticipated rewards. However, prior studies have relied on binary hard versus easy task subtractions to manipulate cognitive effort and so have not tested this hypothesis in “dose-response” fashion. In a sample of 50 participants, we parametrically manipulated the level of effort during fMRI scanning by systematically increasing cognitive control demands during a demand-selection paradigm over six levels. As expected, frontoparietal control network (FPN) activity increased, and reward network activity decreased, as control demands increased across tasks. However, avoidance behavior was not attributable to the change in FPN activity, lending only partial support to the cost of control hypothesis. By contrast, we unexpectedly observed that the deactivation of a task-negative brain network corresponding to the Default Mode Network (DMN) across levels of the cognitive control manipulation predicted the change in avoidance. In summary, we find partial support for the cost of control hypothesis, while highlighting the role of task-negative brain networks in modulating effort avoidance behavior.

scholarly journals Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

2021 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Zening Fu ◽  
Jingyu Liu ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Cognitive Control ◽  
Disease Progression ◽  
Brain Network ◽  
Middle Frontal Gyrus ◽  
Functional Network ◽  
Control Network ◽  
Cognitive Control Network

AbstractBackgroundAlzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.MethodsGroup independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression.ResultsProgression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.ConclusionOur results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.Impact StatementBy assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.

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