Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data, neurophysiological models and human connectome project data

Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Tu, Tao; Kochalka, John; Cai, Weidong; Menon, Vinod

doi:10.1016/j.jneumeth.2016.03.010

Cited by 21 publications

(23 citation statements)

References 72 publications

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“…Also, for data of sufficient length with a fairly good signal-to-noise ratio, the estimation of the connectivity is possible even when only a part of the network is observed. To allow comparison of our new method with other known methods for network inference [31,32,33], we applied it to the NetSim dataset provided by [34]. For details on the result of this, please see Fig 10 in the supporting information.…”

Section: Ornstein-uhlenbeck Processes As Model For Bold Signalsmentioning

confidence: 99%

From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals

et al. 2018

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Knowing brain connectivity is of great importance both in basic research and for clinical applications. We are proposing a method to infer directed connectivity from zero-lag covariances of neuronal activity recorded at multiple sites. This allows us to identify causal relations that are reflected in neuronal population activity. To derive our strategy, we assume a generic linear model of interacting continuous variables, the components of which represent the activity of local neuronal populations. The suggested method for inferring connectivity from recorded signals exploits the fact that the covariance matrix derived from the observed activity contains information about the existence, the direction and the sign of connections. Assuming a sparsely coupled network, we disambiguate the underlying causal structure via L1-minimization, which is known to prefer sparse solutions. In general, this method is suited to infer effective connectivity from resting state data of various types. We show that our method is applicable over a broad range of structural parameters regarding network size and connection probability of the network. We also explored parameters affecting its activity dynamics, like the eigenvalue spectrum. Also, based on the simulation of suitable Ornstein-Uhlenbeck processes to model BOLD dynamics, we show that with our method it is possible to estimate directed connectivity from zero-lag covariances derived from such signals. In this study, we consider measurement noise and unobserved nodes as additional confounding factors. Furthermore, we investigate the amount of data required for a reliable estimate. Additionally, we apply the proposed method on full-brain resting-state fast fMRI datasets. The resulting network exhibits a tendency for close-by areas being connected as well as inter-hemispheric connections between corresponding areas. In addition, we found that a surprisingly large fraction of more than one third of all identified connections were of inhibitory nature.

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Section: Ornstein-uhlenbeck Processes As Model For Bold Signalsmentioning

confidence: 99%

From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals

et al. 2018

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“…CDN in this paper is presented as a purely data-driven method. Other data-driven methods for effective connectivity were proposed in the literature, including latent-space GCA (David et al, 2008; Wheelock et al, 2014; Grant et al, 2015) and state-space multivariate dynamical systems (Ryali et al, 2011, 2016a,b). In order to extend these data-driven methods, it is possible to constrain the network structures and input nodes based on prior knowledge or integrating with other sources of data.…”

Section: Discussionmentioning

confidence: 99%

“…Together, Equations (5) and (6) can be interpreted as a latent space model in statistics. Our use of a two-equation model is similar to MDS (Ryali et al, 2011, 2016a,b). However, there is an important difference.…”

Section: Methodsmentioning

confidence: 99%

“…Though GCA is usually employed to analyze BOLD signals, recent extensions (David et al, 2008; Wheelock et al, 2014; Grant et al, 2015) enable applications of GCA in the latent neuronal space. A closely related extension of GCA in the latent space is state-space multivariate dynamical systems (MDS) (Ryali et al, 2011, 2016a,b). Dynamic causal modeling (DCM), being the more sophisticated modeling approach, utilizes ordinary differential equation (ODE) models for the neuronal dynamics and hemodynamic response, and it is thus an important tool for understanding the biophysical (or neuronal) connections in the brain.…”

Section: Introductionmentioning

confidence: 99%

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A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

2019

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Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks.

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“…There is a recent interest in estimating effective networks from multiple subjects while accommodating the heterogeneity of the group (Ryali et al, 2016 ; Gates and Molenaar, 2012 ; Smith, 2012 ). Specifically, the IMaGES algorithm (Ryali et al, 2016 ) estimates one generalized network from a group by assuming all subjects are homogeneous, and the GIMME algorithm (Gates and Molenaar, 2012 ) can further refine the estimate for each individual subject from the general information estimated from the whole group.…”

Section: Methods Prediction Correlationmentioning

confidence: 99%

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

Spreng

Doerschuk

2017

Front. Neurosci.

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Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

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Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data, neurophysiological models and human connectome project data

Cited by 21 publications

References 72 publications

From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals

From correlation to causation: Estimating effective connectivity from zero-lag covariances of brain signals

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

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