scholarly journals Brain Networks Detectable by fMRI during On-Line Self Report of Hallucinations in Schizophrenia

2021 ◽  
Author(s):  
Karanvir Singh Gill ◽  
Chantal Percival ◽  
Meighen Roes ◽  
Leo Arreaza ◽  
Abhijit Chinchani ◽  
...  
Keyword(s):  
Brain Networks ◽  
Speech Sound ◽  
Brain Network ◽  
Multidimensional Analysis ◽  
Self Report ◽  
Analysis Technique ◽  
Response Network ◽  
On Line ◽  
Speech Events ◽  
Sound Files

An analysis of an internationally shared functional magnetic resonance imaging (fMRI) data involving healthy participants and schizophrenia patients extracted brain networks involved in listening to radio speech and capture hallucination experiences. A multidimensional analysis technique demonstrated that for radio-speech sound files, a brain network matching known auditory perception networks emerged, and importantly, displayed speech-duration-dependent hemodynamic responses (HDRs), confirming fMRI detection of these speech events. In the hallucination-capture data, although a sensorimotor (response) network emerged, it did not show hallucination-duration-dependent HDRs. We conclude that although fMRI retrieved the brain network involved in generating the motor responses indicating the start and end of an experienced hallucination, the hallucination event itself was not detected. Previous reports on brain networks detected by fMRI during hallucination capture is reviewed in this context.

scholarly journals Fast reconfiguration of high-frequency brain networks in response to surprising changes in auditory input

2012 ◽  
Vol 107 (5) ◽  
pp. 1421-1430 ◽  
Author(s):  
Ruth M. Nicol ◽  
Sandra C. Chapman ◽  
Petra E. Vértes ◽  
Pradeep J. Nathan ◽  
Marie L. Smith ◽  
...  
Keyword(s):  
High Frequency ◽  
Brain Networks ◽  
Small World ◽  
Brain Network ◽  
Auditory Presentation ◽  
Long Distance ◽  
Sensory Environment ◽  
Response Network ◽  
Rapid Changes ◽  
Measure Synchronization

How do human brain networks react to dynamic changes in the sensory environment? We measured rapid changes in brain network organization in response to brief, discrete, salient auditory stimuli. We estimated network topology and distance parameters in the immediate central response period, <1 s following auditory presentation of standard tones interspersed with occasional deviant tones in a mismatch-negativity (MMN) paradigm, using magnetoencephalography (MEG) to measure synchronization of high-frequency (gamma band; 33–64 Hz) oscillations in healthy volunteers. We found that global small-world parameters of the networks were conserved between the standard and deviant stimuli. However, surprising or unexpected auditory changes were associated with local changes in clustering of connections between temporal and frontal cortical areas and with increased interlobar, long-distance synchronization during the 120- to 250-ms epoch (coinciding with the MMN-evoked response). Network analysis of human MEG data can resolve fast local topological reconfiguration and more long-range synchronization of high-frequency networks as a systems-level representation of the brain's immediate response to salient stimuli in the dynamically changing sensory environment.

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.

Faculty/staff perceptions of a free campus fitness facility

2014 ◽  
Vol 7 (3) ◽  
pp. 156-170 ◽  
Author(s):  
Theresa C. Brown ◽  
Jennifer Volberding ◽  
Timothy Baghurst ◽  
John Sellers
Keyword(s):  
Motivational Climate ◽  
Self Report ◽  
Staff Perceptions ◽  
Content Type ◽  
Fitness Facility ◽  
Barriers To Exercise ◽  
On Line ◽  
Southern University ◽  
Practical Implications ◽  
Support Facility

Purpose – The purpose of this paper is to determine the reason for faculty and staff (N=657; 35 percent males; M age=45.20) at a large Southern university, for either using or not using the free fitness facilities on campus. Design/methodology/approach – Participants identified themselves as either current (n=306), former (n=213), or never-users (n=138) of the facilities, and completed an on-line self-report qualitative questionnaire asking them to describe their reasons for using or not using the campus fitness facilities. Findings – Thematic coding revealed that motives fell into three broad categories for all user types: personal (i.e. cost, location, social support), facility-specific (i.e. quality and amount of equipment, class variety, hours of operation), and motivational climate (i.e. feeling valued, welcomed, best effort was emphasized). Current users highlighted positive aspects of each category whereas former and never users described each category as a barrier to their exercise routines. Practical implications – The identified themes offer campus administration specific suggestions to entice more non-users and former-users to exercise in the fitness facilities available on campus. Originality/value – While researchers have considered barriers to exercise in past studies, the barriers identified were not specific to fitness facilities. The current work not only examines individuals’ reasons for choosing or not choosing a campus fitness facility for their exercise, but also compares the perspectives of former- and never-users to current-users.

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 Collaborative Learning and Co-Author Students in Online Higher Education

2018 ◽  
pp. 440-456
Author(s):  
Ana Nobre ◽  
Vasco Nobre
Keyword(s):  
Higher Education ◽  
Content Analysis ◽  
Collaborative Learning ◽  
Data Collection ◽  
Learning Activity ◽  
Online Higher Education ◽  
Analysis Technique ◽  
On Line ◽  
Per Se ◽  
Individual Dimension

The technologies themselves cannot be analyzed as instruments per se, nor can they be exhausted in their relation with science. There is a social and even an individual dimension that affects our own way of relating to society. It is in open education that we have been developing our educational practices. This chapter presents a collaborative learning activity, the curricular unit Materiais e Recursos para eLearning, part of an on-line Master in Pedagogy of eLearning, Universidade Aberta, Portugal. In the present work, the authors dedicate their attention to co-learning and co-research, as processes that help to exemplify some situations, the a-REAeduca. The data collection was supported essentially by the content analysis technique.

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.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Percolation Analysis of Brain Structural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shu Guo ◽  
Xiaoqi Chen ◽  
Yimeng Liu ◽  
Rui Kang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Failure Modes ◽  
Critical Infrastructure ◽  
Network Reliability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Features ◽  
Brain Network ◽  
Structure Design ◽  
Reliability Design ◽  
The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

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