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 Multi-scale dynamic mean field model (MDMF) relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

2021 ◽  
pp. 1-55
Author(s):  
Amit Naskar ◽  
Anirudh Vattikonda ◽  
Gustavo Deco ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Resting State ◽  
Computational Models ◽  
Field Model ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Mean Field ◽  
Clustering Coefficient ◽  
Brain Dynamics ◽  
Mean Field Model ◽  
Multi Scale

Abstract Previous computational models have related spontaneous resting-state brain activity with local excitatory−inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E-I balance governs resting state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions relate to functional brain activity is of critical importance. We propose a multi-scale dynamic mean field model (MDMF) – a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modelled as population of MDMF and are connected by realistic connection topologies estimated from Diffusion Tensor Imaging data. First, MDMF successfully predicts resting-state functionalconnectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from MDMF model.

scholarly journals Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Network ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

Abstract Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50–64 years versus older group (n = 310); age range, 65–91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local–global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

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 Whole-brain dynamics in aging: disruptions in functional connectivity and the role of the rich club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

AbstractNormal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state fMRI studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent (BOLD) signals to analyze resting-state fMRI data from 620 subjects divided into two groups (‘middle-age group’ (n=310); age range, 50-65 years vs. ‘older group’ (n=310); age range, 66-91 years). Applying the Intrinsic-Ignition Framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

scholarly journals A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Federico Calesella ◽  
Alberto Testolin ◽  
Michele De Filippo De Grazia ◽  
Marco Zorzi
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Dimensionality Reduction ◽  
Verbal Memory ◽  
Resting State ◽  
Component Analysis ◽  
Model Complexity ◽  
Stroke Patients ◽  
Prediction Ability ◽  
The Impact

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

scholarly journals Retrospective analysis of hemispheric structural network change as a function of location and size of glioma

2020 ◽  
Author(s):  
Shawn D’Souza ◽  
Lisa Hirt ◽  
David R Ormond ◽  
John A Thompson
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Temporal Lobe ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Structural Network ◽  
Tumour Location ◽  
The Impact ◽  
Tractography Data ◽  
Diffusion Tensor Imaging Tractography

Abstract Gliomas are neoplasms that arise from glial cell origin and represent the largest fraction of primary malignant brain tumours (77%). These highly infiltrative malignant cell clusters modify brain structure and function through expansion, invasion and intratumoral modification. Depending on the growth rate of the tumour, location and degree of expansion, functional reorganization may not lead to overt changes in behaviour despite significant cerebral adaptation. Studies in simulated lesion models and in patients with stroke reveal both local and distal functional disturbances, using measures of anatomical brain networks. Investigations over the last two decades have sought to use diffusion tensor imaging tractography data in the context of intracranial tumours to improve surgical planning, intraoperative functional localization, and post-operative interpretation of functional change. In this study, we used diffusion tensor imaging tractography to assess the impact of tumour location on the white matter structural network. To better understand how various lobe localized gliomas impact the topology underlying efficiency of information transfer between brain regions, we identified the major alterations in brain network connectivity patterns between the ipsilesional versus contralesional hemispheres in patients with gliomas localized to the frontal, parietal or temporal lobe. Results were indicative of altered network efficiency and the role of specific brain regions unique to different lobe localized gliomas. This work draws attention to connections and brain regions which have shared structural susceptibility in frontal, parietal and temporal lobe glioma cases. This study also provides a preliminary anatomical basis for understanding which affected white matter pathways may contribute to preoperative patient symptomology.

scholarly journals Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

2020 ◽  
Author(s):  
Kyesam Jung ◽  
Simon B. Eickhoff ◽  
Oleksandr V. Popovych
Keyword(s):  
Data Processing ◽  
Resting State ◽  
Optimal Parameter ◽  
Structural Connectivity ◽  
Model Performance ◽  
Brain Dynamics ◽  
Dynamical Modeling ◽  
Whole Brain ◽  
Brain Connectome ◽  
Network Properties

AbstractDynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

scholarly journals Attention network modulation via tRNS correlates with attention gain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Federica Contò ◽  
Grace Edwards ◽  
Sarah Tyler ◽  
Danielle Parrott ◽  
Emily Grossman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Random Noise ◽  
Cognitive Capacity ◽  
Resting State Functional Connectivity ◽  
Attention Network ◽  
Transcranial Random Noise Stimulation ◽  
The Impact ◽  
Behavioral Improvement

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

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