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

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.

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

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.

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

2019 ◽  
Vol 3 (2) ◽  
pp. 405-426 ◽  
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.

scholarly journals GP.04 Network connectivity following a single unprovoked seizure using 7 Tesla resting-state fMRI

2017 ◽  
Vol 44 (S2) ◽  
pp. S8-S8
Author(s):  
KM Ikeda ◽  
SM Mirsattari ◽  
AR Khan ◽  
I Johnsrude ◽  
JG Burneo ◽  
...  
Keyword(s):  
Functional Mri ◽  
Resting State ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Clustering Coefficient ◽  
7 Tesla ◽  
Unprovoked Seizure ◽  
Whole Brain ◽  
Network Function

Background: Predicting epilepsy following a first seizure is difficult. Network abnormalities are observed in patients with epilepsy using resting-state functional MRI (rs-fMRI), which worsen with duration of epilepsy. We use rs-fMRI to identify network abnormalities in patients after a first seizure that can be used as a biomarker to predict development of epilepsy. Methods: Patients after a single, unprovoked seizure and age/sex matched healthy controls underwent 7 Tesla structural and resting-state functional MRI. Data were analyzed using graph theory measures. Patients were followed for development of epilepsy. Results: Nine patients and nine control subjects were analyzed. There were no differences in baseline characteristics. No patients developed epilepsy (average follow-up 3 months). No differences between groups occurred on a whole-brain network level. At a 20% threshold, significant differences occurred in the default mode network (DMN). Patients demonstrated an increased local efficiency (p=0.02) and clustering coefficient (p=0.04), and decreased path length (p=0.02) and betweenness centrality (p=0.02). Conclusions: No whole-brain network changes occur after a single unprovoked seizure. No patient has developed epilepsy suggesting this group does not have network alterations after a single seizure. In the DMN, the alterations noted indicate increased segregation of network function.

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

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.

scholarly journals Electrocorticography and stereo EEG provide distinct measures of brain connectivity: Implications for network models

2020 ◽  
Author(s):  
John M. Bernabei ◽  
T. Campbell Arnold ◽  
Preya Shah ◽  
Andrew Revell ◽  
Ian Z. Ong ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Network Models ◽  
Brain Network ◽  
Seizure Freedom ◽  
Epileptogenic Zone ◽  
Depth Electrodes ◽  
Brain Network Connectivity ◽  
Patients With Epilepsy

AbstractBrain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by center, region and country, from cortical grid & strip electrodes, to purely stereotactic depth electrodes, to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocortiography (ECoG) and stereoelectroencephalography (SEEG) in a matched cohort of patients who underwent epilepsy surgery. We show that ECoG and SEEG map broad network structure differently, and demonstrate substantial disparity in the ability of node strength to localize the epileptogenic zone in SEEG compared to ECoG. We demonstrate that eliminating white matter contacts and reducing network nodes to anatomical regions of interest rather than individual contacts improves the ability of these models to distinguish between epileptogenic and non-epileptogenic regions in SEEG. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analyzing both ECoG and SEEG recordings in the same cohort. Finally, we share all code and data in this study, aiming for our findings to accelerate research in brain network connectivity in epilepsy and beyond.

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

2017 ◽  
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.

scholarly journals Multiframe Evolving Dynamic Functional Connectivity (EVOdFNCs): A Method for Constructing and Investigating Functional Brain Motifs

2021 ◽  
Author(s):  
Robyn L. Miller ◽  
Victor M Vergara ◽  
Godfrey D Pearlson ◽  
Vince D Calhoun
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Functional Network ◽  
High Dimensional ◽  
Time Varying ◽  
Markov Modeling ◽  
Group Level ◽  
Brain Disorder ◽  
Whole Brain ◽  
Functional Network Connectivity

The study of brain network connectivity as a time-varying property began relatively recently and to date has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus 'lifts' to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting a more dynamic characterization is needed to help illuminate such a complex brain disorder.

scholarly journals Whole Brain Polarity Regime Dynamics are Significantly Disrupted in Schizophrenia and Correlate Strongly with Network Connectivity Measures

2019 ◽  
Author(s):  
Robyn L. Miller ◽  
Godfrey Pearlson ◽  
Vince D. Calhoun
Keyword(s):  
Statistical Significance ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Blood Oxygen Level Dependent ◽  
Functional Network ◽  
Blood Oxygen Level ◽  
Whole Brain ◽  
Time Resolved ◽  
Clinical Disorder ◽  
Functional Network Connectivity

AbstractFrom a large clinical blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as “polarization.” These are whole-brain states in which the voxelwise-normalized BOLD (vnBOLD) activation of a large proportion of voxels is simultaneously either very high or very low. The presence of such states during a resting-state fMRI (rs-fMRI) scan is significantly anti-correlated with diagnosed schizophrenia, significantly anti-correlated with connectivity between subcortical networks and auditory, visual and sensorimotor networks and also significantly anti-correlated with contemporaneous occupancy of transient functional network connectivity states featuring broad disconnectivity or strong inhibitory connections between the default mode and other networks. Conversely, the presence of highly polarized vn-BOLD states is significantly correlated with connectivity strength between auditory, visual and sensorimotor networks and with contemporaneous occupancy of transient whole-brain patterns of strongly modularized network connectivity and diffuse hyperconnectivity. Despite their consistency with well-documented effects of schizophrenia on static and time-varying functional network connectivity, the observed relationships between polarization and network connectivity are with very few exceptions unmediated by schizophrenia diagnosis. We also find that the spatial distribution of voxels most likely to contribute to the highly polarized states (polarity participation maps (PPMs)) differs with a high degree of statistical significance between schizophrenia patients and healthy controls. Finally, we report evidence suggesting the process by which the most polarized states are achieved, i.e. the ways that strongly polarized voxel regions extend, merge and recede also differs significantly between patient and control populations. Many differences observed between patients and controls are echoed within the patient population itself in the effect patterns of positive symptomology (e.g. hallucinations, delusions, grandiosity and other positive symptoms of schizophrenia). Our findings highlight a particular whole-brain spatiotemporal BOLD activation phenomenon that differs markedly between healthy subjects and schizophrenia patients, one that also strongly informs time-resolved network connectivity patterns that are associated with this serious clinical disorder.

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