scholarly journals Extracting dynamical understanding from neural-mass models of mouse cortex

2021 ◽  
Author(s):  
Pok Him Siu ◽  
Eli J Muller ◽  
Valerio Zerbi ◽  
Kevin Aquino ◽  
Ben D. Fulcher
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Neural Mass Model ◽  
Brain Dynamics ◽  
Bifurcation Diagrams ◽  
Strong Constraint ◽  
Whole Brain ◽  
Mouse Cortex ◽  
Neural Mass ◽  
Dynamical Regimes

New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain's neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain's microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes (including where all brain regions are confined to a stable fixed point) where regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.

scholarly journals Parameter inference on brain network models with unknown node dynamics and spatial heterogeneity

2021 ◽  
Author(s):  
Viktor Sip ◽  
Spase Petkoski ◽  
Meysam Hashemi ◽  
Viktor K Jirsa
Keyword(s):  
Synthetic Data ◽  
Brain Network ◽  
Training Data ◽  
Neural Mass Model ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Special Focus ◽  
Mass Model ◽  
Whole Brain ◽  
Neural Mass

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. In recent years a special focus was placed on the role of regional variance of model parameters for the emergent activity. Such analyses however depend on the properties of the employed neural mass model, which is often obtained through a series of major simplifications or analogies. Here we propose a data-driven approach where the neural mass model needs not to be specified. Building on the recent progresses in identification of dynamical systems with neural networks, we propose a method to infer from the functional data both the neural mass model representing the regional dynamics as well as the region- and subject-specific parameters, while respecting the known network structure. We demonstrate on two synthetic data sets that our method is able to recover the original model parameters, and that the trained generative model produces dynamics resembling the training data both on the regional level and on the whole-brain level. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications in understanding the changes of whole-brain dynamics during aging or in neurodegenerative diseases.

scholarly journals Mean-Field Models for EEG/MEG: From Oscillations to Waves

2021 ◽  
Author(s):  
Áine Byrne ◽  
James Ross ◽  
Rachel Nicks ◽  
Stephen Coombes
Keyword(s):  
Gap Junction ◽  
Large Scale ◽  
Mean Field ◽  
Coarse Grained ◽  
Neural Mass Model ◽  
Neuron Network ◽  
Mass Model ◽  
Mean Field Model ◽  
Neural Mass ◽  
The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

scholarly journals Directed brain connectivity identifies widespread functional network changes in Parkinson’s disease

2021 ◽  
Author(s):  
Mite Mijalkov ◽  
Giovanni Volpe ◽  
Joana B. Pereira
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
Neurodegenerative Disorder ◽  
Brain Connectivity ◽  
Brain Regions ◽  
New Method ◽  
Functional Network ◽  
Whole Brain ◽  
Functional Brain Networks

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by topological changes in large-scale functional brain networks. These networks are commonly analysed using undirected correlations between the activation signals of brain regions. However, this approach suffers from an important drawback: it assumes that brain regions get activated at the same time, despite previous evidence showing that brain activation features causality, with signals being typically generated in one region and then propagated to other ones. Thus, in order to address this limitation, in this study we developed a new method to assess whole-brain directed functional connectivity in patients with PD and healthy controls using anti-symmetric delayed correlations, which capture better this underlying causality. To test the potential of this new method, we compared it to standard connectivity analyses based on undirected correlations. Our results show that whole-brain directed connectivity identifies widespread changes in the functional networks of PD patients compared to controls, in contrast to undirected methods. These changes are characterized by increased global efficiency, clustering and transitivity as well as lower modularity. In addition, changes in the directed connectivity patterns in the precuneus, thalamus and superior frontal gyrus were associated with motor, executive and memory deficits in PD patients. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network changes occurring in PD compared to standard methods. This opens new opportunities for the analysis of brain connectivity and the development of new brain connectivity markers to track PD progression.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

scholarly journals neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

2021 ◽  
Author(s):  
Caglar Cakan ◽  
Nikola Jajcay ◽  
Klaus Obermayer
Keyword(s):  
Empirical Data ◽  
Brain Region ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain ◽  
Neural Mass ◽  
Set Up ◽  
Function Relationship ◽  
Relationship Of

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

scholarly journals Turbulent-like dynamics in the human brain

2019 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Information Transfer ◽  
Large Scale ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Large Scale Network ◽  
Maximal Sensitivity ◽  
Scale Network ◽  
Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

scholarly journals Large-scale exploration of whole-brain structural connectivity in anorexia nervosa: alterations in the connectivity of frontal and subcortical networks

2021 ◽  
Author(s):  
E. Caitlin Lloyd ◽  
Karin E. Foerde ◽  
Alexandra F. Muratore ◽  
Natalie Aw ◽  
David Semanek ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Graph Theory ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Cross Sectional ◽  
Whole Brain ◽  
Subcortical Regions

AbstractBackgroundAnorexia nervosa (AN) is characterised by disturbances in cognition and behaviour surrounding eating and weight, which may relate to the structural connectivity of the brain that supports effective information processing and transfer.MethodsDiffusion-weighted MRI data acquired from female patients with AN (n = 148) and female healthy controls (HC; n = 119), aged 12-40 years, were combined across five cross-sectional studies. Probabilistic tractography was completed, and full cortex connectomes describing streamline counts between 84 brain regions generated and harmonised. The network-based statistic tested between-group differences in connectivity strength of brain subnetworks. Whole-brain connectivity of brain regions was indexed using graph theory tools, and compared between groups using multiple linear regression. Associations between structural connectivity variables that differed between groups, and illness severity markers, were explored amongst AN patients using multiple linear regression. Statistical models included age, motion, and study as covariates.OutcomesThe network-based statistic indicated AN patients, relative to HC, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < 0.05, FWE corrected). Graph theory analyses supported reduced connectivity of subcortical regions, and greater connectivity of left occipital cortex, in patients relative to HC (p < 0.05, permutation corrected). Reduced subcortical network connectivity was associated with lower BMI among the AN group.InterpretationStructural differences in subcortical and cortical networks are present in AN, and may reflect illness mechanisms.FundingGlobal Foundation for Eating Disorders; Klarman Family Foundation; Translating Duke Health Initiative; NIMH (MH099388, MH076195, MH110445, MH105452, MH079397, MH113737).

scholarly journals Uncovering the underlying mechanisms and whole-brain dynamics of therapeutic deep brain stimulation for Parkinson's disease

2016 ◽  
Author(s):  
Victor M Saenger ◽  
Joshua Kahan ◽  
Tom Foltynie ◽  
Karl Friston ◽  
Tipu Z Aziz ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Underlying Mechanisms ◽  
Deep Brain

Deep brain stimulation (DBS) for Parkinson's disease is a highly effective treatment in controlling otherwise debilitating symptoms yet the underlying brain mechanisms are currently not well understood. We used whole-brain computational modeling to disclose the effects of DBS ON and OFF during collection of resting state fMRI in ten Parkinson's Disease patients. Specifically, we explored the local and global impact of DBS in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts the global brain dynamics of patients nearer to that of healthy people by significantly changing the bifurcation parameters in brain regions implicated in Parkinson's Disease. We also found higher communicability and coherence brain measures during DBS ON compared to DBS OFF. Finally, by modeling stimulation we identified possible novel DBS targets. These results offer important insights into the underlying effects of DBS, which may in time offer a route to more efficacious treatments.

Mapping whole-brain seizure network recruitment with optogenetic kindling

2022 ◽  
Author(s):  
James Edward Niemeyer
Keyword(s):  
Functional Imaging ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Brain Dynamics ◽  
Cell Type ◽  
Whole Brain ◽  
Common View ◽  
Cell Type Specific

Epilepsy is often labelled a network disorder, though a common view of seizures holds that they initiate in a singular onset zone before expanding contiguously outward. A recent report by Choy et al. (2021) leverages new tools to study whole-brain dynamics during epileptic seizures originating in the hippocampus. Cell-type-specific kindling and functional imaging revealed how various brain regions were recruited to seizures and uncovered a novel form of migrating seizure core.

scholarly journals Mean-field models for EEG/MEG: from oscillations to waves

2020 ◽  
Author(s):  
Á. Byrne ◽  
James Ross ◽  
Rachel Nicks ◽  
Stephen Coombes
Keyword(s):  
Gap Junction ◽  
Large Scale ◽  
Mean Field ◽  
Coarse Grained ◽  
Neural Mass Model ◽  
Neuron Network ◽  
Mass Model ◽  
Mean Field Model ◽  
Neural Mass ◽  
The Rich

AbstractNeural mass models have been actively used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomeno-logical in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit to an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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