Semantic verbal fluency brain network: delineating a physiological basis for the functional hubs using dual-echo ASL and graph theory approach

AbstractTo characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations.Author SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network. Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks. However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks). We found that SGT measures can clearly distinguish between distinct state-related brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of random-generated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

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Faculty Opinions recommendation of Patch definition in metapopulation analysis: a graph theory approach to solve the mega-patch problem.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718325966.793496160 ◽

2014 ◽

Author(s):

Frédéric Guichard

Keyword(s):

Graph Theory ◽

Theory Approach

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Effects of Cancer, Chemotherapy and Cytokines on Subjective and Objective Cognitive Functioning Among Patients with Breast Cancer

Cancers ◽

10.3390/cancers13112576 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2576

Author(s):

Vincent Chin-Hung Chen ◽

Chin-Kuo Lin ◽

Han-Pin Hsiao ◽

Bor-Show Tzang ◽

Yen-Hsuan Hsu ◽

...

Keyword(s):

Breast Cancer ◽

Cognitive Function ◽

Cognitive Abilities ◽

Verbal Fluency ◽

Control Group ◽

Control Groups ◽

Semantic Verbal Fluency ◽

Cytokine Levels ◽

Chemotherapy Patients ◽

And Control

Background: We aimed to investigate the associations of breast cancer (BC) and cancer-related chemotherapies with cytokine levels, and cognitive function. Methods: We evaluated subjective and objective cognitive function in BC patients before chemotherapy and 3~9 months after the completion of chemotherapy. Healthy volunteers without cancer were also compared as control group. Interleukins (IL) 2, 4, 5, 6, 10, 12p70, 13, 17A, 1β, IFNγ, and TNFα were measured. Associations of cancer status, chemotherapy and cytokine levels with subjective and objective cognitive impairments were analyzed using a regression model, adjusting for covariates, including IQ and psychological distress. Results: After adjustment, poorer performance in semantic verbal fluency was found in the post-chemotherapy subgroup compared to controls (p = 0.011, η2 = 0.070); whereas pre-chemotherapy patients scored higher in subjective cognitive perception. Higher IL-13 was associated with lower semantic verbal fluency in the post-chemotherapy subgroup. Higher IL-10 was associated with better perceived cognitive abilities in the pre-chemotherapy and control groups; while IL-5 and IL-13 were associated with lower perceived cognitive abilities in pre-chemotherapy and control groups. Our findings from mediation analysis further suggest that verbal fluency might be affected by cancer status, although mediated by anxiety. Conclusions: Our findings suggest that verbal fluency might be affected by cancer status, although mediated by anxiety. Different cytokines and their interactions may have different roles of neuroinflammation or neuroprotection that need further research.

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Patients with amnestic MCI Fail to Adapt Executive Control When Repeatedly Tested with Semantic Verbal Fluency Tasks

Journal of the International Neuropsychological Society ◽

10.1017/s1355617721000849 ◽

2021 ◽

pp. 1-8

Author(s):

Johannes Tröger ◽

Hali Lindsay ◽

Mario Mina ◽

Nicklas Linz ◽

Stefan Klöppel ◽

...

Keyword(s):

Executive Control ◽

Verbal Fluency ◽

Fixed Time ◽

Semantic Space ◽

Semantic Knowledge ◽

Semantic Verbal Fluency ◽

Amnestic Mci ◽

Retrieval Processes ◽

Theoretical View ◽

First Time

Abstract Objective: Semantic verbal fluency (SVF) tasks require individuals to name items from a specified category within a fixed time. An impaired SVF performance is well documented in patients with amnestic Mild Cognitive Impairment (aMCI). The two leading theoretical views suggest either loss of semantic knowledge or impaired executive control to be responsible. Method: We assessed SVF 3 times on 2 consecutive days in 29 healthy controls (HC) and 29 patients with aMCI with the aim to answer the question which of the two views holds true. Results: When doing the task for the first time, patients with aMCI produced fewer and more common words with a shorter mean response latency. When tested repeatedly, only healthy volunteers increased performance. Likewise, only the performance of HC indicated two distinct retrieval processes: a prompt retrieval of readily available items at the beginning of the task and an active search through semantic space towards the end. With repeated assessment, the pool of readily available items became larger in HC, but not patients with aMCI. Conclusion: The production of fewer and more common words in aMCI points to a smaller search set and supports the loss of semantic knowledge view. The failure to improve performance as well as the lack of distinct retrieval processes point to an additional impairment in executive control. Our data did not clearly favour one theoretical view over the other, but rather indicates that the impairment of patients with aMCI in SVF is due to a combination of both.

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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

Brain Sciences ◽

10.3390/brainsci11010118 ◽

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.

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