scholarly journals Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains

2021 ◽  
Vol 15 ◽  
Author(s):  
Xinchun Zhou ◽  
Ningning Ma ◽  
Benseng Song ◽  
Zhixi Wu ◽  
Guangyao Liu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Clustering Coefficient ◽  
Critical Dynamics ◽  
Network Organization ◽  
Whole Brain ◽  
Optimal Organization ◽  
Human Brains

The optimal organization for functional segregation and integration in brain is made evident by the “small-world” feature of functional connectivity (FC) networks and is further supported by the loss of this feature that has been described in many types of brain disease. However, it remains unknown how such optimally organized FC networks arise from the brain's structural constrains. On the other hand, an emerging literature suggests that brain function may be supported by critical neural dynamics, which is believed to facilitate information processing in brain. Though previous investigations have shown that the critical dynamics plays an important role in understanding the relation between whole brain structural connectivity and functional connectivity, it is not clear if the critical dynamics could be responsible for the optimal FC network configuration in human brains. Here, we show that the long-range temporal correlations (LRTCs) in the resting state fMRI blood-oxygen-level-dependent (BOLD) signals are significantly correlated with the topological matrices of the FC brain network. Using structure-dynamics-function modeling approach that incorporates diffusion tensor imaging (DTI) data and simple cellular automata dynamics, we showed that the critical dynamics could optimize the whole brain FC network organization by, e.g., maximizing the clustering coefficient while minimizing the characteristic path length. We also demonstrated with a more detailed excitation-inhibition neuronal network model that loss of local excitation-inhibition (E/I) balance causes failure of critical dynamics, therefore disrupting the optimal FC network organization. The results highlighted the crucial role of the critical dynamics in forming an optimal organization of FC networks in the brain and have potential application to the understanding and modeling of abnormal FC configurations in neuropsychiatric disorders.

scholarly journals Multiscale dynamic mean field model to relate resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis in health and disease

2018 ◽  
Author(s):  
Amit Naskar ◽  
Anirudh Vattikonda ◽  
Gustavo Deco ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Diffusion Tensor ◽  
Mean Field ◽  
Clustering Coefficient ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Health And Disease ◽  
The Impact ◽  
Tractography Data

AbstractPrevious neuro-computational studies have established the connection of spontaneous resting-state brain activity with “large-scale” neuronal ensembles using dynamic mean field approach and showed the impact of local excitatory−inhibitory (E−I) balance in sculpting dynamical patterns. Here, we argue that whole brain models that link multiple scales of physiological organization namely brain metabolism that governs synaptic concentrations of gamma-aminobutyric acid (GABA) and glutamate on one hand and neural field dynamics that operate on the macroscopic scale. The multiscale dynamic mean field (MDMF) model captures the synaptic gating dynamics over a cortical macrocolumn as a function of neurotransmitter kinetics. Multiple MDMF units were placed in brain locations guided by an anatomical parcellation and connected by tractography data from diffusion tensor imaging. The resulting whole-brain model generates the resting-state functional connectivity and also reveal that optimal configurations of glutamate and GABA captures the dynamic working point of the brain, that is the state of maximum metsatability as observed in BOLD signals. To demonstrate test-retest reliability we validate the observation that healthy resting brain dynamics is governed by optimal glutamate-GABA configurations using two different brain parcellations for model set-up. Furthermore, graph theoretical measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) on the functional connectivity generated from healthy and pathological brain network studies could be explained by the MDMF model. In conclusion, the MDMF model could relate the various scales of observations from neurotransmitter concentrations to dynamics of synaptic gating to whole-brain resting-state network topology in health and disease.

scholarly journals Intrinsic Functional Connectivity Networks in Healthy Elderly Subjects: A Multiparametric Approach with Structural Connectivity Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Martin Gorges ◽  
Hans-Peter Müller ◽  
Albert C. Ludolph ◽  
Volker Rasche ◽  
Jan Kassubek
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Elderly Subjects ◽  
Single Subject ◽  
Group Level ◽  
Intrinsic Functional Connectivity ◽  
Healthy Elderly ◽  
Healthy Elderly Subjects

Intrinsic functional connectivity magnetic resonance imaging (iFCMRI) provides an encouraging approach for mapping large-scale intrinsic connectivity networks (ICNs) in the “resting” brain. Structural connections as measured by diffusion tensor imaging (DTI) are a major constraint on the identified ICNs. This study aimed at the combined investigation of ten well-defined ICNs in healthy elderly subjects at single subject level as well as at the group level, together with the underlying structural connectivity. IFCMRI and DTI data were acquired in twelve subjects (68 ± 7 years) at a 3T scanner and were studied using thetensor imaging and fiber trackingsoftware package. The seed-based iFCMRI analysis approach was comprehensively performed with DTI analysis, following standardized procedures including an 8-step processing of iFCMRI data. Our findings demonstrated robust ICNs at the single subject level and conclusive brain maps at the group level in the healthy elderly sample, supported by the complementary fiber tractography. The findings demonstrated here provide a methodological framework for future comparisons of pathological (e.g., neurodegenerative) conditions with healthy controls on the basis of multiparametric functional connectivity mapping.

scholarly journals Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

2021 ◽  
Author(s):  
David Pascucci ◽  
Maria Rubega ◽  
Joan Rue-Queralt ◽  
Sebastien Tourbier ◽  
Patric Hagmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Meta Analysis ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Noise Perturbation ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Anatomical Priors ◽  
And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

scholarly journals Comparing the temporal relationship of structural and functional connectivity changes in different adult human brain networks: a single-case study

2018 ◽  
Vol 3 ◽  
pp. 50 ◽  
Author(s):  
Takamitsu Watanabe ◽  
Geraint Rees
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Single Case ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Time Lags ◽  
Single Case Study ◽  
Adult Human

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

scholarly journals Regional, not global, functional connectivity contributes to isolated focal dystonia

Neurology ◽  
2020 ◽  
Vol 95 (16) ◽  
pp. e2246-e2258 ◽  
Author(s):  
Scott A. Norris ◽  
Aimee E. Morris ◽  
Meghan C. Campbell ◽  
Morvarid Karimi ◽  
Babatunde Adeyemo ◽  
...  
Keyword(s):  
Quality Control ◽  
Functional Connectivity ◽  
Sensorimotor Cortex ◽  
Large Scale ◽  
Focal Dystonia ◽  
Whole Brain ◽  
Correlation Matrices ◽  
Body Regions ◽  
And Control ◽  
Rigorous Quality Control

ObjectiveTo test the hypothesis that there is shared regional or global functional connectivity dysfunction in a large cohort of patients with isolated focal dystonia affecting different body regions compared to control participants. In this case-control study, we obtained resting-state MRI scans (three or four 7.3-minute runs) with eyes closed in participants with focal dystonia (cranial [17], cervical [13], laryngeal [18], or limb [10]) and age- and sex-matched controls.MethodsRigorous preprocessing for all analyses was performed to minimize effect of head motion during scan acquisition (dystonia n = 58, control n = 47 analyzed). We assessed regional functional connectivity by computing a seed-correlation map between putamen, pallidum, and sensorimotor cortex and all brain voxels. We assessed significant group differences on a cluster-wise basis. In a separate analysis, we applied 300 seed regions across the cortex, cerebellum, basal ganglia, and thalamus to comprehensively sample the whole brain. We obtained participant whole-brain correlation matrices by computing the correlation between seed average time courses for each seed pair. Weighted object-oriented data analysis assessed group-level whole-brain differences.ResultsParticipants with focal dystonia had decreased functional connectivity at the regional level, within the striatum and between lateral primary sensorimotor cortex and ventral intraparietal area, whereas whole-brain correlation matrices did not differ between focal dystonia and control groups. Rigorous quality control measures eliminated spurious large-scale functional connectivity differences between groups.ConclusionRegional functional connectivity differences, not global network level dysfunction, contributes to common pathophysiologic mechanisms in isolated focal dystonia. Rigorous quality control eliminated spurious large-scale network differences between patients with focal dystonia and control participants.

scholarly journals δ-Oscillation Correlates of Anesthesia-induced Unconsciousness in Large-scale Brain Networks of Human Infants

2019 ◽  
Vol 131 (6) ◽  
pp. 1239-1253 ◽  
Author(s):  
Ioannis Pappas ◽  
Laura Cornelissen ◽  
David K. Menon ◽  
Charles B. Berde ◽  
Emmanuel A. Stamatakis
Keyword(s):  
Functional Connectivity ◽  
General Anesthesia ◽  
Information Transfer ◽  
Large Scale ◽  
Aminobutyric Acid ◽  
Brain State ◽  
Whole Brain ◽  
Efficient System ◽  
Median Difference ◽  
Infant Brain

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional brain connectivity studies can provide important information about changes in brain-state dynamics during general anesthesia. In adults, γ-aminobutyric acid–mediated agents disrupt integration of information from local to the whole-brain scale. Beginning around 3 to 4 months postnatal age, γ-aminobutyric acid–mediated anesthetics such as sevoflurane generate α-electroencephalography oscillations. In previous studies of sevoflurane-anesthetized infants 0 to 3.9 months of age, α-oscillations were absent, and power spectra did not distinguish between anesthetized and emergence from anesthesia conditions. Few studies detailing functional connectivity during general anesthesia in infants exist. This study’s aim was to identify changes in functional connectivity of the infant brain during anesthesia. Methods A retrospective cohort study was performed using multichannel electroencephalograph recordings of 20 infants aged 0 to 3.9 months old who underwent sevoflurane anesthesia for elective surgery. Whole-brain functional connectivity was evaluated during maintenance of a surgical state of anesthesia and during emergence from anesthesia. Functional connectivity was represented as networks, and network efficiency indices (including complexity and modularity) were computed at the sensor and source levels. Results Sevoflurane decreased functional connectivity at the δ-frequency (1 to 4 Hz) in infants 0 to 3.9 months old when comparing anesthesia with emergence. At the sensor level, complexity decreased during anesthesia, showing less whole-brain integration with prominent alterations in the connectivity of frontal and parietal sensors (median difference, 0.0293; 95% CI, −0.0016 to 0.0397). At the source level, similar results were observed (median difference, 0.0201; 95% CI, −0.0025 to 0.0482) with prominent alterations in the connectivity between default-mode and frontoparietal regions. Anesthesia resulted in fragmented modules as modularity increased at the sensor (median difference, 0.0562; 95% CI, 0.0048 to 0.1298) and source (median difference, 0.0548; 95% CI, −0.0040 to 0.1074) levels. Conclusions Sevoflurane is associated with decreased capacity for efficient information transfer in the infant brain. Such findings strengthen the hypothesis that conscious processing relies on an efficient system of integrated information transfer across the whole brain.

scholarly journals Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Zhang ◽  
Rosa Cortese ◽  
Nicola De Stefano ◽  
Antonio Giorgio
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Diffusion Kurtosis Imaging ◽  
High Angular Resolution ◽  
Diffusion Spectrum Imaging ◽  
Analysis Methods ◽  
Scan Time

Cognitive impairment (CI) occurs in 43 to 70% of multiple sclerosis (MS) patients at both early and later disease stages. Cognitive domains typically involved in MS include attention, information processing speed, memory, and executive control. The growing use of advanced magnetic resonance imaging (MRI) techniques is furthering our understanding on the altered structural connectivity (SC) and functional connectivity (FC) substrates of CI in MS. Regarding SC, different diffusion tensor imaging (DTI) measures (e.g., fractional anisotropy, diffusivities) along tractography-derived white matter (WM) tracts showed relevance toward CI. Novel diffusion MRI techniques, including diffusion kurtosis imaging, diffusion spectrum imaging, high angular resolution diffusion imaging, and neurite orientation dispersion and density imaging, showed more pathological specificity compared to the traditional DTI but require longer scan time and mathematical complexities for their interpretation. As for FC, task-based functional MRI (fMRI) has been traditionally used in MS to brain mapping the neural activity during various cognitive tasks. Analysis methods of resting fMRI (seed-based, independent component analysis, graph analysis) have been applied to uncover the functional substrates of CI in MS by revealing adaptive or maladaptive mechanisms of functional reorganization. The relevance for CI in MS of SC–FC relationships, reflecting common pathogenic mechanisms in WM and gray matter, has been recently explored by novel MRI analysis methods. This review summarizes recent advances on MRI techniques of SC and FC and their potential to provide a deeper understanding of the pathological substrates of CI in MS.

scholarly journals Functional brain network reconfiguration during learning in a dynamic environment

2019 ◽  
Author(s):  
Chang-Hao Kao ◽  
Ankit N. Khambhati ◽  
Danielle S. Bassett ◽  
Matthew R. Nassar ◽  
Joseph T. McGuire ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Functional Integration ◽  
Dynamic Environment ◽  
Brain Network ◽  
Uncertain Environments ◽  
Whole Brain ◽  
Functional Brain Network ◽  
Brain Pattern ◽  
Brain Functional Connectivity

AbstractWhen learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

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