scholarly journals Centralized and distributed cognitive task processing in the human connectome

2019 ◽  
Vol 3 (2) ◽  
pp. 455-474 ◽  
Author(s):  
Enrico Amico ◽  
Alex Arenas ◽  
Joaquín Goñi
Keyword(s):  
Cognitive Processing ◽  
Resting State ◽  
Large Scale ◽  
Cognitive Task ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Cognitive Changes ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Jensen Shannon Divergence

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks.

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.

Candidate Mechanisms of Spontaneous Cognition as Revealed by Dementia Syndromes

2018 ◽  
Author(s):  
Claire O'Callaghan ◽  
Muireann Irish
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Human Experience ◽  
Comprehensive Overview ◽  
Cognitive Changes ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Disease Specific

The capacity to engage in spontaneous self-generated thought is fundamental to the human experience, yet surprisingly little is known regarding the neurocognitive mechanisms that support this complex ability. Dementia syndromes offer a unique opportunity to study how the breakdown of large-scale functional brain networks impacts spontaneous cognition. Indeed, many of the characteristic cognitive changes in dementia reflect the breakdown of foundational processes essential for discrete aspects of self-generated thought. This chapter discusses how disease-specific alterations in memory-based/construction and mentalizing processes likely disrupt specific aspects of spontaneous, self-generated thought. In doing so, it provides a comprehensive overview of the neurocognitive architecture of spontaneous cognition, paying specific attention to how this sophisticated endeavor is compromised in dementia.

scholarly journals Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques

2021 ◽  
Vol 15 ◽  
Author(s):  
Ramon Casanova ◽  
Robert G. Lyday ◽  
Mohsen Bahrami ◽  
Jonathan H. Burdette ◽  
Sean L. Simpson ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Resting State ◽  
Brain Networks ◽  
Memory Task ◽  
Fmri Data ◽  
High Dimensional ◽  
Functional Brain ◽  
Learning Techniques ◽  
Human Connectome Project ◽  
Functional Brain Networks

Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning.Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics.Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly.Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.

scholarly journals The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

2021 ◽  
Author(s):  
Hamish Deery ◽  
Rober Di Paolo ◽  
Chris Moran ◽  
Gary F. Egan ◽  
Sharna Jamadar
Keyword(s):  
Older Adults ◽  
Age Differences ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Adult Brain ◽  
Functional Brain ◽  
Adult Lifespan ◽  
Age Related ◽  
Functional Brain Networks

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2,938 records, 144 studies were included. Results on 11 network measures were summarised and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function. Higher-order networks reliably showed age differences, whereas basic processing and control networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for increased hemispheric lateralisation at rest, reduced BOLD signal variability within-subjects and altered patterns and speed of dynamic connectivity. Research on connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Emerging neuroimaging technologies: Toward future personalized diagnostics, prognosis, targeted intervention, and ethical challenges

2017 ◽  
Author(s):  
Urs Ribary ◽  
Alex L. MacKay ◽  
Alexander Rauscher ◽  
Christine M. Tipper ◽  
Deborah E. Giaschi ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Targeted Intervention ◽  
Ethical Challenges ◽  
Electrochemical System ◽  
Common Goal ◽  
Functional Brain Networks ◽  
The Common ◽  
Dynamic Connectivity

The human brain is a fine-tuned and balanced structural, functional, and dynamic electrochemical system. Any alterations, from slight slowing of partial brain networks to severe disruptions in structural, functional, and dynamic connectivity across local and large-scale brain networks will result in slight to severe changes in cognitive ability, awareness, and consciousness. Using future noninvasive technologies, the common goal is to relate typical or atypical resting-state, sensory-motor functions, cognition, and consciousness to underlying typical or altered quantified brain structure, biochemistry, pathways, functional brain networks, and connectivity. This will pose enormous ethical challenges of quantitative diagnostic and prognostic strategies in future neurologic and psychiatric clinical practice.

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.

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