A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Pruttiakaravanich, Anupon; Songsiri, Jitkomut

doi:10.4186/ej.2016.20.3.207

“…It is of great importance in neuroscience to study the direction of network connections among regions of interest (ROI) or neural nodes. Common methods of exploring directional connectivity include dynamic causal modeling (DCM), Granger causality analysis (GC), directed transfer function (DTF), partial directed coherence (PDC) that can be applied to several brain modalities such as EEG, MEG, fMRI; see a recent review in [HAVS + 19] and detailed mathematical description of connectivity in [PS16]. In our scope, we limit ourselves to EEG analysis due to the equipment economy compared to other brain acquisitions.…”

Section: Introductionmentioning

confidence: 99%

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

Manomaisaowapak

¹

,

Nartkulpat

²

,

Songsiri

³

2022

IEEE Trans. Neural Netw. Learning Syst.

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This paper considers a problem of estimating brain effective connectivity from EEG signals using a Granger causality (GC) concept characterized on state-space models. We propose a state-space model for explaining coupled dynamics of the source and EEG signals where EEG is a linear combination of sources according to the characteristics of volume conduction. Our formulation has a sparsity prior on the source output matrix that can further classify active and inactive sources. The scheme is comprised of two main steps: model estimation and model inference to estimate brain connectivity. The model estimation consists of performing a subspace identification and the active source selection based on a group-norm regularized least-squares. The model inference relies on the concept of state-space GC that requires solving a discrete-time Riccati equation for the covariance of estimation error. We verify the performance on simulated data sets that represent realistic human brain activities under several conditions including percentages of active sources, a number of EEG electrodes and the location of active sources. The performance of estimating brain networks is compared with a two-stage approach using source reconstruction algorithms and VAR-based Granger analysis. Our method achieved better performances than the two-stage approach under the assumptions that the true source dynamics are sparse and generated from state-space models. The method is applied to a real EEG SSVEP data set and we found that the temporal lobe played a role of a mediator of connections between temporal and occipital areas, which agreed with findings in previous studies.

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“…It is of great importance in neuroscience to study the direction of network connections among regions of interest (ROI) or neural nodes. Common methods of exploring directional connectivity include dynamic causal modeling (DCM), Granger causality analysis (GC), directed transfer function (DTF), partial directed coherence (PDC) that can be applied to several brain modalities such as EEG, MEG, fMRI; see a recent review in [HAVS + 19] and detailed mathematical description of connectivity in [PS16]. In our scope, we limit ourselves to EEG analysis due to the equipment economy compared to other brain acquisitions.…”

Section: Introductionmentioning

confidence: 99%

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

Manomaisaowapak

¹

,

Nartkulpat

²

,

Songsiri

³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper considers a problem of estimating brain effective connectivity from EEG signals using a Granger causality (GC) concept characterized on state-space models. We propose a state-space model for explaining coupled dynamics of the source and EEG signals where EEG is a linear combination of sources according to the characteristics of volume conduction. Our formulation has a sparsity prior on the source output matrix that can further classify active and inactive sources. The scheme is comprised of two main steps: model estimation and model inference to estimate brain connectivity. The model estimation consists of performing a subspace identification and the active source selection based on a group-norm regularized least-squares. The model inference relies on the concept of state-space GC that requires solving a discrete-time Riccati equation for the covariance of estimation error. We verify the performance on simulated data sets that represent realistic human brain activities under several conditions including percentages of active sources, a number of EEG electrodes and the location of active sources. The performance of estimating brain networks is compared with a two-stage approach using source reconstruction algorithms and VAR-based Granger analysis. Our method achieved better performances than the two-stage approach under the assumptions that the true source dynamics are sparse and generated from state-space models. The method is applied to a real EEG SSVEP data set and we found that the temporal lobe played a role of a mediator of connections between temporal and occipital areas, which agreed with findings in previous studies.

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Brain functional network modeling and analysis based on fMRI: a systematic review

Wang

¹

,

Xin

²

,

Yao

³

et al. 2020

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In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as smallworld, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions. Keywords Brain functional networks Á Complex network Á Topological properties Á Neurological and psychiatric diseases Abbreviations AAL Anatomical automatic labeling BOLD Blood oxygenation level dependent CAD Computer aided diagnosis EEG Electroencephalogram fMRI Functional magnetic resonance imaging ICA Independent component analysis MEG Magnetoencephalography PCA Principal component analysis ROI Region of interest SVD Singular value decomposition SVM Support vector machine SPM statistical parametric mapping toolkit TSCI Traumatic complete spinal cord injury. WHO World Health Organization

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A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Cited by 3 publications

References 87 publications

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

Brain functional network modeling and analysis based on fMRI: a systematic review

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