scholarly journals Abnormal dynamic resting-state brain network organization in auditory verbal hallucination

2020 ◽  
Vol 225 (8) ◽  
pp. 2315-2330
Author(s):  
Haiyang Geng ◽  
Pengfei Xu ◽  
Iris E. Sommer ◽  
Yue-Jia Luo ◽  
André Aleman ◽  
...  
Keyword(s):  
Brain Networks ◽  
Brain Network ◽  
Lower Probability ◽  
Brain State ◽  
Dynamic Interactions ◽  
Auditory Verbal Hallucinations ◽  
Auditory Verbal Hallucination ◽  
History Of ◽  
Dynamic Connectivity ◽  
Language Network

Abstract Auditory-verbal hallucinations (AVH) are a key symptom of schizophrenia. Recent neuroimaging studies examining dynamic functional connectivity suggest that disrupted dynamic interactions between brain networks characterize complex symptoms in mental illness including schizophrenia. Studying dynamic connectivity may be especially relevant for hallucinations, given their fluctuating phenomenology. Indeed, it remains unknown whether AVH in schizophrenia are directly related to altered dynamic connectivity within and between key brain networks involved in auditory perception and language, emotion processing, and top-down control. In this study, we used dynamic connectivity approaches including sliding window and k-means to examine dynamic interactions among brain networks in schizophrenia patients with and without a recent history of AVH. Dynamic brain network analysis revealed that patients with AVH spent less time in a ‘network-antagonistic’ brain state where the default mode network (DMN) and the language network were anti-correlated, and had lower probability to switch into this brain state. Moreover, patients with AVH showed a lower connectivity within the language network and the auditory network, and lower connectivity was observed between the executive control and the language networks in certain dynamic states. Our study provides the first neuroimaging evidence of altered dynamic brain networks for understanding neural mechanisms of AVH in schizophrenia. The findings may inform and further strengthen cognitive models of AVH that aid the development of new coping strategies for patients.

scholarly journals A novel dynamic brain network in arousal for brain states and emotion analysis

2021 ◽  
Vol 18 (6) ◽  
pp. 7440-7463
Author(s):  
Yunyuan Gao ◽  
◽  
Zhen Cao ◽  
Jia Liu ◽  
Jianhai Zhang ◽  
...  
Keyword(s):  
Parietal Lobe ◽  
Brain Networks ◽  
Brain Network ◽  
Brain State ◽  
High Arousal ◽  
Emotion Analysis ◽  
Significant Difference ◽  
Brain States ◽  
The Brain ◽  
Channel Norm

<abstract> <sec><title>Background</title><p>Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals.</p> </sec> <sec><title>New Method</title><p>Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain.</p> </sec> <sec><title>Results</title><p>The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal.</p> </sec> <sec><title>Comparison with Existing Method(s)</title><p>Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis.</p> </sec> <sec><title>Conclusions</title><p>The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.</p> </sec> </abstract>

Interacting Brain Networks Underlying Creative Cognition and Artistic Performance

2018 ◽  
Author(s):  
Roger E. Beaty ◽  
Rex E. Jung
Keyword(s):  
Brain Networks ◽  
Region Of Interest ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Potential Interaction ◽  
Significant Advance ◽  
Dynamic Interactions ◽  
Creative Cognition ◽  
Brain Network Connectivity

Cognitive neuroscience research has begun to address the potential interaction of brain networks supporting creativity by employing new methods in brain network science. Network methods offer a significant advance compared to individual region of interest studies due to their ability to account for the complex and dynamic interactions among discrete brain regions. As this chapter demonstrates, several recent studies have reported a remarkably similar pattern of brain network connectivity across a range of creative tasks and domains. In general, such work suggests that creative thought may involve dynamic interactions, primarily between the default and control networks, providing key insights into the roles of spontaneous and controlled processes in creative cognition. The chapter summarizes this emerging body of research and proposes a framework designed to account for the joint influence of controlled and spontaneous thought processes in creativity.

scholarly journals Auditory Verbal Hallucination-Specific Functional Brain Networks in Schizophrenia: A Study Based on Graph Theory

2021 ◽  
Author(s):  
Weiliang Yang ◽  
Yan li ◽  
Haiyan Cao ◽  
Wen Qin ◽  
Yongying Cheng ◽  
...  
Keyword(s):  
Graph Theory ◽  
Clinical Features ◽  
Brain Networks ◽  
Brain Network ◽  
Local Network ◽  
Topological Properties ◽  
Network Efficiency ◽  
Functional Brain ◽  
Auditory Verbal Hallucinations ◽  
Functional Brain Networks

Abstract Background Although mounting previous studies have characterized auditory verbal hallucinations (AVH) related brain network abnormalities in the patients with schizophrenia, AVH related brain network alterations based on graph theory was rarely reported. In addition, the relationship between the features of AVH related brain networks based on graph theory and clinical features of schizophrenia patients with AVH is unclear. Our study to explore associations among network metrics, and clinical features in schizophrenia patients with AVH. Method Thirty-one schizophrenia patients without AVH, 17 patients with AVH, and 31 healthy controls were examined by functional magnetic resonance imaging. Graph theory method was performed to analyze the topological properties of functional network in three groups. Results Our results showed that schizophrenia patients with AVH displayed decreased local network efficiency, clustering coefficients, and nodal efficiency of the right dorsolateral prefrontal cortex. Local network efficiency was positively correlated with AVH characteristics. Conclusion The topological properties of brain functional networks are disrupted in schizophrenia patients with AVH, suggesting a role of functional brain networks in the pathogenesis of AVH.

scholarly journals Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

2020 ◽  
Author(s):  
Danielle L. Kurtin ◽  
Ines R. Violante ◽  
Karl Zimmerman ◽  
Robert Leech ◽  
Adam Hampshire ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Brain Networks ◽  
Brain Network ◽  
Network Activity ◽  
Brain State ◽  
State Dependent ◽  
Cathodal Tdcs ◽  
The Right

AbstractBackgroundTranscranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure.ObjectivesIn this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity.MethodsWe applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n=22) and TBI participants (n=34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure.ResultsWe find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional – in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region.ConclusionsOur results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the factors in isolation.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

Flexible and Modular Brain Network Dynamics Characterize Flow Experiences During Media Use: A Functional Magnetic Resonance Imaging Study

2021 ◽  
Author(s):  
Richard Huskey ◽  
Justin Robert Keene ◽  
Shelby Wilcox ◽  
Xuanjun (Jason) Gong ◽  
Robyn Adams ◽  
...  
Keyword(s):  
Task Difficulty ◽  
Media Use ◽  
Imaging Study ◽  
Well Being ◽  
Brain Network ◽  
Theoretical Inquiry ◽  
History Of ◽  
Individual Ability ◽  
High Control ◽  
Flow Experiences

Abstract Flow is thought to occur when both task difficulty and individual ability are high. Flow experiences are highly rewarding and are associated with well-being. Importantly, media use can be a source of flow. Communication scholars have a long history of theoretical inquiry into how flow biases media selection, how different media content results in flow, and how flow influences media processing and effects. However, the neurobiological basis of flow during media use is not well understood, limiting our explanatory capacity to specify how media contribute to flow or well-being. Here, we show that flow is associated with a flexible and modular brain-network topology, which may offer an explanation for why flow is simultaneously perceived as high-control and effortless, even when the task difficulty is high. Our study tests core predictions derived from synchronization theory, and our results provide qualified support for the theory while also suggesting important theoretical updates.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Decline ◽  
Path Length ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Subjective Cognitive Decline ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

scholarly journals Psychological resilience correlates with EEG source-space brain network flexibility

2019 ◽  
Vol 3 (2) ◽  
pp. 539-550 ◽  
Author(s):  
Véronique Paban ◽  
Julien Modolo ◽  
Ahmad Mheich ◽  
Mahmoud Hassan
Keyword(s):  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Personality Testing ◽  
Functional Networks ◽  
Psychological Resilience ◽  
Full Spectrum ◽  
Sliding Windows ◽  
Resilience Scale ◽  
Network Flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

scholarly journals Individualized non-invasive brain stimulation engages the subgenual anterior cingulate and amygdala

2018 ◽  
Author(s):  
Desmond J Oathes ◽  
Jared Zimmerman ◽  
Romain Duprat ◽  
Seda Cavdaroglu ◽  
Morgan Scully ◽  
...  
Keyword(s):  
Brain Stimulation ◽  
Brain Networks ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Clinical Effects ◽  
Brain Response ◽  
Subcortical Structures ◽  
Non Invasive ◽  
Individual Participant ◽  
Communication Flow

Brain stimulation is used clinically to treat a variety of neurological and psychiatric conditions. The mechanisms of the clinical effects of these brain-based therapies are presumably dependent on their effects on brain networks. It has been hypothesized that using individualized brain network maps is an optimal strategy for defining network boundaries and topologies. Traditional non-invasive imaging can determine correlations between structural or functional time series. However, they cannot easily establish hierarchies in communication flow as done in non-human animals using invasive methods. In the present study, we interleaved functional MRI recordings with non-invasive transcranial magnetic stimulation in the attempt to map causal communication between the prefrontal cortex and two subcortical structures thought to contribute to affective dysregulation: the subgenual anterior cingulate cortex (sgACC) and the amygdala. In both cases, we found evidence that these brain areas were engaged when TMS was applied to prefrontal sites determined from each participant's previous fMRI scan. Specifically, after transforming individual participant images to within-scan quantiles of evoked TMS response, we modeled the average quantile response within a given region across stimulation sites and individuals to demonstrate that the targets were differentially influenced by TMS. Furthermore, we found that the sgACC distributed brain network, estimated in a separate cohort, was engaged in response to sgACC focused TMS and was partially separable from the proximal default mode network response. The amygdala, but not its distributed network, responded to TMS. Our findings indicate that individual targeting and brain response measurements usefully capture causal circuit mapping to the sgACC and amygdala in humans, setting the stage for approaches to non-invasively modulate subcortical nodes of distributed brain networks in clinical interventions and mechanistic human neuroscience studies.

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