Structural connectivity to reconstruct brain activation and effective connectivity between brain regions

Belaoucha, Brahim; Papadopoulo, Théodore

doi:10.1088/1741-2552/ab8b2b

Cited by 9 publications

(10 citation statements)

References 67 publications

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“…On the other hand, if two connections contribute equally to the squared error, the connection that is least penalized will be favored. Finally, the wgLASSO approach presented here is flexible, and can easily accommodate other types of prior information, as as from diffusion tensor imaging (Zhu et al (2013), Belaoucha and Papadopoulo (2020)) or electrical stimulation tract tracing (Rocchi et al (2021)).…”

Section: Discussionmentioning

confidence: 99%

Improved high-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Nagle

Gerrelts²,

Krause

et al. 2022

Preprint

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Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model and is motivated by the potential of MVAR models and the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as fMRI, into MVAR model estimation using a weighted group LASSO regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al. (2022) while resulting in models that are both more parsimonious and have higher fidelity to the ground truth. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from iEEG data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach will allow accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.

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Section: Discussionmentioning

confidence: 99%

Improved high-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Nagle

Gerrelts²,

Krause

et al. 2022

Preprint

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“…The source space was parcellated into 68 neuroanatomical regions of interest according to the Desikan-Killiany atlas [19]. Cortical activity accounting for connections in both space and time is modeled using a multivariate autoregressive (MAR) models as proposed in [7,8] to constrain the sources' dynamics for inverse problems. Thus, we model source-level brain activity with the following MAR model as a function of both local and long-range connections:…”

Section: Simulated and Real Datamentioning

confidence: 99%

“…While the structural connectivity was exploited in several approaches [5,6], temporal dynamics of the data was not considered. Very few methods include connections supported by dMRI as a prior structural information such as [7,8] where source intensities are modeled with multivariate autoregressive models whose elements are constrained by dMRI-derived anatomical connections, or [9,10] that are based on a probabilistic technique called maximum entropy on the mean (MEM), which explicitly use delays inferred from dMRI. However, MEM approaches suffer from high computational complexity and can be highly sensitive to the initialization of the reference distribution representing the prior knowledge of the current distribution [11].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Transmission Delays Supported By Diffusion Mri In Meg Source Reconstruction

Kojcic¹,

Papadopoulo²,

Deriche³

et al. 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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White matter fibers transfer the information between brain regions with delays that are measurable with magnetoencephalography and electroencephalography (M/EEG). In the context of regularizing the dynamics of M/EEG and recovering electrical activity of the brain from M/EEG measurements, this article proposes a graph representation-based framework to solve the M/EEG inverse problem, where prior information about transmission delays supported by diffusion MRI (dMRI) are included to enforce temporal smoothness. Results of the reconstruction of brain activity from simulated MEG measurements are compared to MNE, LORETA and CGS methods and we show that our approach improves MEG source localization when compared to these three state-of-theart approaches. In addition, we show preliminary qualitative results of the proposed reconstruction method on real MEG data for a sensory-motor task.

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“…The AR model has been used on several occasions and in different ways. For example they allow to model the interactions between the different zones of the brain during cognitive tasks [1], to extract information from signals [2] or detect state changes [3]. Finally, autoregressive models are sometimes used in conjunction with machine learning methods for performing classification tasks [4].…”

Section: Introductionmentioning

confidence: 99%

Classification of BCI-EEG based on augmented covariance matrix

Carrara¹,

Papadopoulo²

2023

Preprint

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Objective: Electroencephalography signals are recorded as a multidimensional dataset. We propose a new framework based on the augmented covariance extracted from an autoregressive model to improve motor imagery classification. Methods: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a symmetric positive definite matrix: the augmented covariance matrix. The state-of the art for classifying covariance matrices is based on Riemannian Geometry. A fairly natural idea is therefore to extend the standard approach using these augmented covariance matrices. The methodology for creating the augmented covariance matrix shows a natural connection with the delay embedding theorem proposed by Takens for dynamical systems. Such an embedding method is based on the knowledge of two parameters: the delay and the embedding dimension, respectively related to the lag and the order of the autoregressive model. This approach provides new methods to compute the hyper-parameters in addition to standard grid search. Results: The augmented covariance matrix performed noticeably better than any state-of-the-art methods. We will test our approach on several datasets and several subjects using the MOABB framework, using both within-session and cross-session evaluation. Conclusion:The improvement in results is due to the fact that the augmented covariance matrix incorporates not only spatial but also temporal information, incorporating nonlinear components of the signal through an embedding procedure, which allows the leveraging of dynamical systems algorithms. Significance: These results extend the concepts and the results of the Riemannian distance based classification algorithm.

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Structural connectivity to reconstruct brain activation and effective connectivity between brain regions

Cited by 9 publications

References 67 publications

Improved high-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Improved high-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Incorporating Transmission Delays Supported By Diffusion Mri In Meg Source Reconstruction

Classification of BCI-EEG based on augmented covariance matrix

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