Inference of direct and multistep effective connectivities from functional connectivity of the brain and of relationships to cortical geometry

Mehta-Pandejee, Grishma; Robinson, P. A.; Henderson, James A.; Aquino, Kevin; Sarkar, Somwrita

doi:10.1016/j.jneumeth.2017.03.014

Cited by 18 publications

(11 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Effective brain connectivity can be calculated using fMRI data and structural equation modeling method. In some other papers, effective connectivity was estimated by functional connectivity with fMRI data [12,13]. But fMRI data has low time resolution.…”

Section: Introductionmentioning

confidence: 99%

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Rajabioun

Nasrabadi

Shamsollahi

et al. 2019

Biomedical Engineering / Biomedizinische Technik

View full text Add to dashboard Cite

Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

show abstract

Section: Introductionmentioning

confidence: 99%

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Rajabioun

Nasrabadi

Shamsollahi

et al. 2019

Biomedical Engineering / Biomedizinische Technik

View full text Add to dashboard Cite

show abstract

“…In contrast, functional connectivity is based on statistical relationships between the activity of neuronal populations and can be easily estimated from recorded signals. For estimating effective connectivity there are methods like Dynamic Causal Modelling, DCM [4,5], Granger causality [6] and others [7,8,9,10,11,12,13]. Only few methods to infer effective connectivity, however, can deal with large numbers of nodes (40 or more) based on zero-lag correlation only.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

“…In line with many previous definitions of EC [26,22,42,4], the model FC is generated by the interplay between the network connectivity and local dynamical variables, which must be taken care of in the estimation procedure. Note that the noise-diffusion model ignores the hemodynamic response in the gener-ation of the BOLD signals that is explicitly modeled in DCM [5,24].…”

Section: Retrospective On Neuroimaging Data Analysismentioning

confidence: 94%

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

Gilson

2017

Preprint

View full text Add to dashboard Cite

Since 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.

show abstract

Inference of direct and multistep effective connectivities from functional connectivity of the brain and of relationships to cortical geometry

Cited by 18 publications

References 58 publications

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

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

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

Contact Info

Product

Resources

About