scholarly journals Unilateral thalamic glioma disrupts large-scale functional architecture of human brain during resting state

2019 ◽  
Vol Volume 15 ◽  
pp. 947-956
Author(s):  
Sirui Li ◽  
Lei Gao ◽  
Ying Liu ◽  
Yawen Ao ◽  
Haibo Xu
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Large Scale ◽  
Functional Architecture ◽  
Thalamic Glioma

scholarly journals Opposing Changes in the Functional Architecture of Large-Scale Networks in Bipolar Mania and Depression

2020 ◽  
Vol 46 (4) ◽  
pp. 971-980
Author(s):  
Daniel Russo ◽  
Matteo Martino ◽  
Paola Magioncalda ◽  
Matilde Inglese ◽  
Mario Amore ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Depressive Symptomatology ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Functional Architecture ◽  
Sensorimotor Network ◽  
Large Scale Networks ◽  
Intrinsic Brain Activity ◽  
Affective Dimensions

Abstract Objective Manic and depressive phases of bipolar disorder (BD) show opposite symptoms in psychomotor, thought, and affective dimensions. Neuronally, these may depend on distinct patterns of alterations in the functional architecture of brain intrinsic activity. Therefore, the study aimed to characterize the spatial and temporal changes of resting-state activity in mania and depression, by investigating the regional homogeneity (ReHo) and degree of centrality (DC), in different frequency bands. Methods Using resting-state functional magnetic resonance imaging (fMRI), voxel-wise ReHo and DC were calculated—in the standard frequency band (SFB: 0.01–0.10 Hz), as well as in Slow5 (0.01–0.027 Hz) and Slow4 (0.027–0.073 Hz)—and compared between manic (n = 36), depressed (n = 43), euthymic (n = 29) patients, and healthy controls (n = 112). Finally, clinical correlations were investigated. Results Mania was mainly characterized by decreased ReHo and DC in Slow4 in the medial prefrontal cortex (as part of the default-mode network [DMN]), which in turn correlated with manic symptomatology. Conversely, depression was mainly characterized by decreased ReHo in SFB in the primary sensory-motor cortex (as part of the sensorimotor network [SMN]), which in turn correlated with depressive symptomatology. Conclusions Our data show a functional reconfiguration of the spatiotemporal structure of intrinsic brain activity to occur in BD. Mania might be characterized by a predominance of sensorimotor over associative networks, possibly driven by a deficit of the DMN (reflecting in internal thought deficit). Conversely, depression might be characterized by a predominance of associative over sensorimotor networks, possibly driven by a deficit of the SMN (reflecting in psychomotor inhibition).

scholarly journals Changes in electrophysiological static and dynamic human brain functional architecture from childhood to late adulthood

2020 ◽  
Author(s):  
N Coquelet ◽  
V Wens ◽  
A Mary ◽  
M Niesen ◽  
D Puttaert ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Development ◽  
Resting State ◽  
Healthy Aging ◽  
Functional Integration ◽  
Functional Architecture ◽  
Late Adulthood ◽  
Functional Brain ◽  
Network Activation ◽  
Age Related

AbstractThis magnetoencephalography study aimed at characterizing age-related changes in resting-state functional brain organization from mid-childhood to late adulthood. We investigated neuromagnetic brain activity at rest in 105 participants divided into three age groups: children (6–9 years), young adults (18–34 years) and healthy elders (53–78 years). The effects of age on static resting-state functional integration were assessed using band-limited power envelope correlation, whereas those on transient functional dynamics were disclosed using hidden Markov modeling of power envelope activity. Brain development from childhood to adulthood came with (i) a strengthening of functional integration within and between resting-state networks and (ii) an increased temporal stability of transient (100–300 ms lifetime) and recurrent states of network activation or deactivation mainly encompassing lateral or medial associative neocortical areas. Healthy aging was characterized by decreased static resting-state functional integration and dynamical stability within the visual network. These results based on electrophysiological measurements free of neurovascular biases suggest that functional brain integration mainly evolves during brain development, with limited changes in healthy aging. These novel electrophysiological insights into human brain functional architecture across the lifespan pave the way for future clinical studies investigating how brain disorders affect brain development or healthy aging.

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 The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core

2016 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Viktor K. Jirsa ◽  
Petra Ritter
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Spectral Characteristics ◽  
Network Models ◽  
Brain Network ◽  
Network Modelling ◽  
Novel Approach ◽  
Underlying Mechanisms

AbstractIn the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into brain network models we construct personalized brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain. Personalized brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of brain function.

scholarly journals Changes in electrophysiological static and dynamic human brain functional architecture from childhood to late adulthood

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
N. Coquelet ◽  
V. Wens ◽  
A. Mary ◽  
M. Niesen ◽  
D. Puttaert ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Development ◽  
Resting State ◽  
Healthy Aging ◽  
Functional Integration ◽  
Functional Architecture ◽  
Late Adulthood ◽  
Functional Brain ◽  
Network Activation ◽  
Age Related

Abstract This magnetoencephalography study aimed at characterizing age-related changes in resting-state functional brain organization from mid-childhood to late adulthood. We investigated neuromagnetic brain activity at rest in 105 participants divided into three age groups: children (6–9 years), young adults (18–34 years) and healthy elders (53–78 years). The effects of age on static resting-state functional brain integration were assessed using band-limited power envelope correlation, whereas those on transient functional brain dynamics were disclosed using hidden Markov modeling of power envelope activity. Brain development from childhood to adulthood came with (1) a strengthening of functional integration within and between resting-state networks and (2) an increased temporal stability of transient (100–300 ms lifetime) and recurrent states of network activation or deactivation mainly encompassing lateral or medial associative neocortical areas. Healthy aging was characterized by decreased static resting-state functional integration and dynamic stability within the primary visual network. These results based on electrophysiological measurements free of neurovascular biases suggest that functional brain integration mainly evolves during brain development, with limited changes in healthy aging. These novel electrophysiological insights into human brain functional architecture across the lifespan pave the way for future clinical studies investigating how brain disorders affect brain development or healthy aging.

scholarly journals Analysis of the Human Connectome Data Supports the Notion of A “Common Model of Cognition” for Human and Human-Like Intelligence Across Domains

2019 ◽  
Author(s):  
Andrea Stocco ◽  
Catherine Sibert ◽  
Zoe Steine-Hanson ◽  
Natalie Koh ◽  
John E. Laird ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Causal Modeling ◽  
Human Cognition ◽  
Functional Architecture ◽  
Hub And Spoke ◽  
Cognitive Domains ◽  
Fmri Analysis

AbstractThe Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. The results suggest that a common, general architecture underpins human cognition across multiple cognitive domains, from the overall functional architecture of the human brain to higher-level thought processes.

scholarly journals Discovering dynamic functional networks in the human neonatal brain with electric source imaging

2019 ◽  
Author(s):  
Steve Mehrkanoon
Keyword(s):  
Human Brain ◽  
Limbic System ◽  
Resting State ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Functional Networks ◽  
Source Imaging ◽  
Functional Brain Network ◽  
Macro Scale

AbstractWhen the human brain manifests the birth of organised communication among local and large-scale neuronal populations activity remains undescribed. We report, in resting-state EEG source-estimates of 100 infants at term age, the existence of macro-scale dynamic functional connectivity, which have rich topological organisations, distinct spectral fingerprints and scale-invariance temporal dynamics. These functional networks encompass the default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor and visual-limbic system confined in the delta and low-alpha frequency intervals (1-8 Hz). The temporal dynamics of these networks not only are nested within much slower timescale (¡ 0.1 Hz) but also correlated in a hierarchical leading-following organisation. We show that the anatomically constrained richly organised spatial topologies, spectral contents and temporal fluctuations of resting-state networks reflect an established intrinsic dynamic functional connectome in the human brain at term age. The graph theoretical analysis of the spatial architectures of the networks revealed small-world topology and distinct rich-club organisations of interconnected cortical hubs that exhibit rich synchronous dynamics at multiple timescales. The approach opens new avenues to advance our understanding about the early configuration organisation of dynamic networks in the human brain and offers a novel monitoring platform to investigate functional brain network development in sick preterm infants.

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