scholarly journals The diversity and multiplexity of edge communities within and between brain systems

2020 ◽  
Author(s):  
Youngheun Jo ◽  
Farnaz Zamani Esfahlani ◽  
Joshua Faskowitz ◽  
Evgeny J. Chumin ◽  
Olaf Sporns ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Individual Differences ◽  
Human Brain ◽  
Cognitive Processes ◽  
Brain Networks ◽  
Brain Regions ◽  
System Level ◽  
Future Research ◽  
Behavior Development ◽  
Using Data

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions’ “edge community profiles”, we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions’ edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain’s system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

scholarly journals Masked features of task states found in individual brain networks

2021 ◽  
Author(s):  
Alexis Porter ◽  
Ashley M. Nielsen ◽  
Caterina Gratton
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Cognitive States ◽  
Single Person ◽  
Flexible Integration ◽  
Brain States ◽  
Using Data

Completing complex tasks requires flexible integration of functions across brain regions. While studies have shown that functional networks are altered across tasks, recent work highlights that brain networks exhibit substantial individual differences. Here we asked whether individual differences are important for predicting brain network interactions across cognitive states. We trained classifiers to decode state using data from single person "precision" fMRI datasets across 5 diverse cognitive states. Classifiers were then tested on either independent sessions from the same person or new individuals. Classifiers were able to decode task states in both the same and new participants above chance. However, classification performance was significantly higher within a person, a pattern consistent across model types, datasets, tasks, and feature subsets. This suggests that individualized approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individualized approaches have potential to deepen our understanding of brain interactions during complex cognition.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

scholarly journals Conditioned fear reactions are associated with gray matter density but not cortical microstructure

2020 ◽  
Author(s):  
Christoph Fraenz ◽  
Dorothea Metzen ◽  
Christian J. Merz ◽  
Helene Selpien ◽  
Nikolai Axmacher ◽  
...  
Keyword(s):  
Gray Matter ◽  
Conditioned Fear ◽  
Brain Networks ◽  
Brain Regions ◽  
Matter Density ◽  
Fear Learning ◽  
Future Research ◽  
Fear Reaction ◽  
Gray Matter Density ◽  
Diffusion Weighted Images

AbstractResearch has shown that fear acquisition, in reaction to potentially harmful stimuli or situations, is characterized by pronounced interindividual differences. It is likely that such differences are evoked by variability in the macro- and microstructural properties of brain regions involved in the processing of threat or safety signals from the environment. Indeed, previous studies have shown that the strength of conditioned fear reactions is associated with the cortical thickness or volume of various brain regions. However, respective studies were exclusively targeted at single brain regions instead of whole brain networks. Here, we tested 60 young and healthy individuals in a differential fear conditioning paradigm while they underwent fMRI scanning. In addition, we acquired T1-weighted and multi-shell diffusion-weighted images prior to testing. We used task-based fMRI data to define global brain networks which exhibited increased BOLD responses towards CS+ or CS- presentations, respectively. From these networks, we obtained mean values of gray matter density, neurite density, and neurite orientation dispersion. We found that mean gray matter density averaged across the CS+ network was significantly correlated with the strength of conditioned fear reactions quantified via skin conductance response. Measures of neurite architecture were not associated with conditioned fear reaction in any of the two networks. Our results extend previous findings on the relationship between brain morphometry and fear learning. Most importantly, our study is the first to introduce neurite imaging to fear learning research and discusses how its implementation can be improved in future research.

scholarly journals Regional Specialization and Coordination Within the Network for Perceiving and Knowing About Others

2019 ◽  
Author(s):  
Aidas Aglinskas ◽  
Scott L Fairhall
Keyword(s):  
Cognitive Processes ◽  
Imaging Study ◽  
Superior Temporal Sulcus ◽  
Brain Regions ◽  
Knowledge Network ◽  
System Level ◽  
Angular Gyrus ◽  
Semantic Retrieval ◽  
Temporoparietal Junction ◽  
Cognitive Domains

Abstract Seeing familiar faces prompts the recall of diverse kinds of person-related knowledge. How this information is encoded within the well-characterized face-/person-selective network remains an outstanding question. In this functional magnetic resonance imaging study, participants rated famous faces in 10 tasks covering 5 domains of person knowledge (social, episodic, semantic, physical, and nominal). Comparing different cognitive domains enabled us to 1) test the relative roles of brain regions in specific cognitive processes and 2) apply a multivariate network-level representational similarity analysis (NetRSA) to gain insight into underlying system-level organization. Comparing across cognitive domains revealed the importance of multiple domains in most regions, the importance of social over nominal knowledge in the anterior temporal lobe, and the functional subdivision of the temporoparietal junction into perceptual superior temporal sulcus and knowledge-related angular gyrus. NetRSA revealed a strong divide between regions implicated in ``default-mode” cognition and the fronto-lateral elements that coordinated more with ``core” perceptual components (fusiform/occipital face areas and posterior superior temporal sulcus). NetRSA also revealed a taxonomy of cognitive processes, with semantic retrieval being more similar to episodic than nominal knowledge. Collectively, these results illustrate the importance of coordinated activity of the person knowledge network in the instantiation of the diverse cognitive capacities of this system.

scholarly journals Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

2019 ◽  
Vol 30 (3) ◽  
pp. 1087-1102
Author(s):  
Shi Gu ◽  
Cedric Huchuan Xia ◽  
Rastko Ciric ◽  
Tyler M Moore ◽  
Ruben C Gur ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Function ◽  
Brain Networks ◽  
Brain Regions ◽  
Imaging Data ◽  
Modular Architecture ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Networks ◽  
Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

scholarly journals Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242985
Author(s):  
Howard Muchen Hsu ◽  
Zai-Fu Yao ◽  
Kai Hwang ◽  
Shulan Hsieh
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Motor Inhibition ◽  
Attention Network ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Dorsal Attention Network ◽  
Functional Brain Networks

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

scholarly journals EEG activity during balance platform test in humans

2019 ◽  
pp. 132-136 ◽  
Author(s):  
Vladimir Khorev ◽  
Artem Badarin ◽  
Vladimir Antipov ◽  
Vladimir Maksimenko ◽  
Semen Kurkin
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Brain Activity ◽  
Body Posture ◽  
Future Research ◽  
Eeg Activity ◽  
Common Features ◽  
Computer Interfaces ◽  
New Directions ◽  
Brain States

In order to analyze different human brain states related to perception and maintaining of body posture, we implemented an experiment with a balance platform. It is known the cerebral cortex regulates subcortical postural centers to maintain upright balance and posture and balance demands. However, the cortical mechanisms that support standing balance remain elusive. In this work, we present an EEG-based analysis during execution of balance responses with distinct postural demands. The results suggest the existence of common features in the EEG structure associated with distinct activity during balance maintaining. This may give new directions for future research in the field of brain activity, and for the development of brain-computer interfaces.

scholarly journals Patterning and Plasticity of the Cerebral Cortex

Science ◽  
2005 ◽  
Vol 310 (5749) ◽  
pp. 805-810 ◽  
Author(s):  
Mriganka Sur ◽  
John L. R. Rubenstein
Keyword(s):  
Cerebral Cortex ◽  
Human Brain ◽  
Brain Regions ◽  
Cortical Areas ◽  
Processing Networks

The cerebral cortex of the human brain is a sheet of about 10 billion neurons divided into discrete subdivisions or areas that process particular aspects of sensation, movement, and cognition. Recent evidence has begun to transform our understanding of how cortical areas form, make specific connections with other brain regions, develop unique processing networks, and adapt to changes in inputs.

scholarly journals Spatiotemporal functional interactivity among large-scale brain networks

2020 ◽  
Author(s):  
Nan Xu ◽  
Peter C. Doerschuk ◽  
Shella D. Keilholz ◽  
R. Nathan Spreng
Keyword(s):  
Human Brain ◽  
Short Duration ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Spatial Direction ◽  
Macro Scale ◽  
High Degree

AbstractThe macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting-state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N=200) we applied prediction correlation techniques to four resting state fMRI runs (total TRs = 4800). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region’s role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.

scholarly journals word2brain

2018 ◽  
Author(s):  
Abraham Nunes
Keyword(s):  
Meta Analysis ◽  
Brain Networks ◽  
Semantic Space ◽  
Brain Regions ◽  
Imaging Data ◽  
Brain Functions ◽  
Semantic Concepts ◽  
Local Correlations ◽  
Brain Imaging Data ◽  
Using Data

AbstractMapping brain functions to their underlying neural substrates is a central goal of cognitive neuroscience. Functional magnetic resonance imaging (fMRI) has proven indispensable in this endeavour. Recently, there has been growing interest in tackling this problem by mapping semantic concepts onto brain regions using repositories of images and text from the neuroimaging literature. However, no study has thus far approached this problem using (dense) vector representations of words. Using data from the Neurosynth database, we sought to develop a model that could (A) capture local correlations between words in text, as well as topics, (B) capture representation of distributed brain networks in relation to word embeddings, and (C) generate synthetic images given word inputs. We show that jointly embedding words and brain imaging data on a vector space can yield semantic representations that sensibly relate concepts across biological, psychological, and observational levels of analysis. Moreover, our proposed model makes no assumption about spatial orientation of fMRI voxels, which allows for embedding of distributed brain networks onto the semantic space. We demonstrate this capability by generating synthetic brain activation vectors from word inputs. Our model has the potential to advance neuroimaging meta-analysis as well as contextual word-embedding methods more broadly.

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