Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

Mapping Intimacies ◽

10.1101/344473 ◽

2018 ◽

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Temporal Structure ◽

Structural Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Network ◽

Dynamical Analysis ◽

Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

Download Full-text

Dynamic properties of simulated brain network models and empirical resting-state data

Network Neuroscience ◽

10.1162/netn_a_00070 ◽

2019 ◽

Vol 3 (2) ◽

pp. 405-426 ◽

Cited By ~ 3

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Structural Connectivity ◽

Simulated Data ◽

Network Models ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Whole Brain

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.

Download Full-text

Inferring multi-scale neural mechanisms with brain network modelling

10.1101/157263 ◽

2017 ◽

Author(s):

Michael Schirner ◽

Anthony Randal McIntosh ◽

Viktor K. Jirsa ◽

Gustavo Deco ◽

Petra Ritter

Keyword(s):

Resting State ◽

Brain Activity ◽

Empirical Studies ◽

Structural Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Neural Population ◽

Network Modelling ◽

Multi Scale ◽

Activity Measurements

The neurophysiological processes underlying non-invasive brain activity measurements are not well understood. Here, we developed a novel connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects’ individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) slow resting-state fMRI oscillations, (2) spatial topologies of functional connectivity networks, (3) excitation-inhibition balance, (4, 5) pulsed inhibition on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Download Full-text

Inferring multi-scale neural mechanisms with brain network modelling

eLife ◽

10.7554/elife.28927 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 49

Author(s):

Michael Schirner ◽

Anthony Randal McIntosh ◽

Viktor Jirsa ◽

Gustavo Deco ◽

Petra Ritter

Keyword(s):

Resting State ◽

Brain Activity ◽

Empirical Studies ◽

Structural Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Neural Population ◽

Network Modelling ◽

Multi Scale ◽

Activity Measurements

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Download Full-text

A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

10.1101/2021.07.07.451431 ◽

2021 ◽

Author(s):

Amrit Kashyap ◽

Sergey Plis ◽

Michael Schirner ◽

Petra Ritter ◽

Shella Keilholz

Keyword(s):

Deep Learning ◽

Initial Conditions ◽

Brain Activity ◽

Network Models ◽

Brain Network ◽

Measured Data ◽

Fmri Data ◽

Short Term ◽

Whole Brain ◽

Neural Processes

Brain Network Models (BNMs) are a family of dynamical systems that simulate whole brain activity using neural mass models to represent local activity in different brain regions that influence each other via a global structural network. Research has been interested in using these network models to explain measured whole brain activity measured via resting state functional magnetic resonance imaging (rs-fMRI). Properties computed over longer periods of simulated and measured data such as average functional connectivity (FC), have shown to be comparable with similar properties estimated from measured rs-fMRI data. While this shows that these network models have similar properties over the dynamical landscape, it is unclear how well simulated trajectories compare with empirical trajectories on a timepoint-by-timepoint basis. Previous studies have shown that BNMs are able to produce relevant features at shorter timescales, but analysis of short-term trajectories or transient dynamics as defined by synchronized predictions from BNM made at the same timescale as the collected data has not yet been conducted. Relevant neural processes exist in the time frame of measurements and are often used in task fMRI studies to understand neural responses to behavioral cues. Therefore, it is important to investigate how much of these dynamics are captured by our current brain simulations. To test the nature of BNMs short term trajectories against observed data, we utilize a deep learning technique known as Neural ODE that based on an observed sequence of fMRI measurements, estimates the initial conditions such that the BNMs simulation is synchronized to produce the closest trajectory relative to the observed data. We test to see if the parameterization of a specific well studied BNM, the Firing Rate Model, calculated by maximizing its accuracy in reproducing observed short term trajectories matches with the parameterized model that produces the best average long-term metrics. Our results show that such an agreement between parameterization using long and short simulation analysis exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity. Therefore, we conclude that there is evidence that by solving for initial conditions, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

Download Full-text

Post-stroke fatigue is linked to resting state posterior hypoactivity and prefrontal hyperactivity

10.1101/2021.03.17.21253777 ◽

2021 ◽

Author(s):

Georgia Mary Cotter ◽

Mohamed Salah Khlif ◽

Laura Bird ◽

Mark E Howard ◽

Amy Brodtmann ◽

...

Keyword(s):

Ischaemic Stroke ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Stroke Severity ◽

Stroke Survivors ◽

Whole Brain ◽

Post Stroke

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

Download Full-text

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective

10.1101/223784 ◽

2017 ◽

Cited By ~ 1

Author(s):

Matthieu Gilson

Keyword(s):

Resting State ◽

Effective Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Fmri Data ◽

Second Order Statistics ◽

Proposed Model ◽

The Many

AbstractSince the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping - determined by EC - for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data - movie viewing versus resting state - illustrates that changes in excitability and changes in brain coordination go hand in hand.

Download Full-text

Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI

10.1101/678243 ◽

2019 ◽

Cited By ~ 1

Author(s):

Matthew F. Singh ◽

Todd S. Braver ◽

Michael W. Cole ◽

ShiNung Ching

Keyword(s):

Human Brain ◽

Resting State ◽

Nonlinear Dynamical Systems ◽

Brain Activity ◽

Network Models ◽

Resting State Fmri ◽

Brain Regions ◽

Data Driven ◽

Imaging Data ◽

Individual Level

AbstractA key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

Download Full-text

Virtual connectomic datasets in dementia and Alzheimer’s Disease using whole-brain network dynamics modelling

10.1101/2020.01.18.911248 ◽

2020 ◽

Author(s):

Djouya Mohammad Arbabyazd ◽

Kelly Shen ◽

Zheng Wang ◽

Martin Hofmann-Apitius ◽

Anthony R. McIntosh ◽

...

Keyword(s):

Linear Models ◽

Discrimination Performance ◽

Network Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Original Data ◽

Brain Network ◽

Design Of Algorithms ◽

Whole Brain ◽

Machine Learning Classification

AbstractLarge neuroimaging datasets, including information about structural (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features (e.g., lack of concurrent DTI SC and resting-state fMRI FC measurements for many of the subjects).We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using the ADNI dataset for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of nonlinear brain network models, superior to simpler linear models. Furthermore, by performing machine learning classification of control and patient subjects, we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Nonlinear completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information equivalent to the one of the original data.

Download Full-text

Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

10.1101/506576 ◽

2018 ◽

Author(s):

Caroline Garcia Forlim ◽

Leonie Klock ◽

Johanna Baechle ◽

Laura Stoll ◽

Patrick Giemsa ◽

...

Keyword(s):

Resting State ◽

Local Level ◽

Resting State Fmri ◽

Brain Network ◽

Graph Analysis ◽

Resting State Functional Connectivity ◽

Whole Brain ◽

Schizophrenic Patients ◽

Brain Circuitry ◽

Behavioral Impairments

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology. In this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41 controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology. Using NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain. Our results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

Download Full-text