Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data.

Alard Roebroeck, Anil K. Seth, Pedro Valdes-Sosa
Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series, PMLR 12:65-94, 2011.

Abstract

This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspective. Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS) tradition and dynamical systems are treated in a state space modeling framework. The nature of the fMRI signal is reviewed with emphasis on the involved neuronal, physiological and physical processes and their modeling as dynamical systems. In this context, two streams of development in modeling causal brain connectivity using fMRI are discussed: time series approaches to causality in a discrete time tradition and dynamic systems and control theory approaches in a continuous time tradition. This review closes with discussion of ongoing work and future perspectives on the integration of the two approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v12-roebroeck11, title = {Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data.}, author = {Roebroeck, Alard and Seth, Anil K. and Valdes-Sosa, Pedro}, booktitle = {Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series}, pages = {65--94}, year = {2011}, editor = {Popescu, Florin and Guyon, Isabelle}, volume = {12}, series = {Proceedings of Machine Learning Research}, address = {Vancouver, Canada}, month = {10 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v12/roebroeck11/roebroeck11.pdf}, url = {https://proceedings.mlr.press/v12/roebroeck11.html}, abstract = {This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspective. Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS) tradition and dynamical systems are treated in a state space modeling framework. The nature of the fMRI signal is reviewed with emphasis on the involved neuronal, physiological and physical processes and their modeling as dynamical systems. In this context, two streams of development in modeling causal brain connectivity using fMRI are discussed: time series approaches to causality in a discrete time tradition and dynamic systems and control theory approaches in a continuous time tradition. This review closes with discussion of ongoing work and future perspectives on the integration of the two approaches.} }
Endnote
%0 Conference Paper %T Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data. %A Alard Roebroeck %A Anil K. Seth %A Pedro Valdes-Sosa %B Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series %C Proceedings of Machine Learning Research %D 2011 %E Florin Popescu %E Isabelle Guyon %F pmlr-v12-roebroeck11 %I PMLR %P 65--94 %U https://proceedings.mlr.press/v12/roebroeck11.html %V 12 %X This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspective. Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS) tradition and dynamical systems are treated in a state space modeling framework. The nature of the fMRI signal is reviewed with emphasis on the involved neuronal, physiological and physical processes and their modeling as dynamical systems. In this context, two streams of development in modeling causal brain connectivity using fMRI are discussed: time series approaches to causality in a discrete time tradition and dynamic systems and control theory approaches in a continuous time tradition. This review closes with discussion of ongoing work and future perspectives on the integration of the two approaches.
RIS
TY - CPAPER TI - Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data. AU - Alard Roebroeck AU - Anil K. Seth AU - Pedro Valdes-Sosa BT - Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series DA - 2011/03/03 ED - Florin Popescu ED - Isabelle Guyon ID - pmlr-v12-roebroeck11 PB - PMLR DP - Proceedings of Machine Learning Research VL - 12 SP - 65 EP - 94 L1 - http://proceedings.mlr.press/v12/roebroeck11/roebroeck11.pdf UR - https://proceedings.mlr.press/v12/roebroeck11.html AB - This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspective. Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS) tradition and dynamical systems are treated in a state space modeling framework. The nature of the fMRI signal is reviewed with emphasis on the involved neuronal, physiological and physical processes and their modeling as dynamical systems. In this context, two streams of development in modeling causal brain connectivity using fMRI are discussed: time series approaches to causality in a discrete time tradition and dynamic systems and control theory approaches in a continuous time tradition. This review closes with discussion of ongoing work and future perspectives on the integration of the two approaches. ER -
APA
Roebroeck, A., Seth, A.K. & Valdes-Sosa, P.. (2011). Causal Time Series Analysis of Functional Magnetic Resonance Imaging Data.. Proceedings of the Neural Information Processing Systems Mini-Symposium on Causality in Time Series, in Proceedings of Machine Learning Research 12:65-94 Available from https://proceedings.mlr.press/v12/roebroeck11.html.

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