Inference of Dynamic Graph Changes for Functional Connectome

Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:3230-3240, 2020.

Abstract

Dynamic functional connectivity is an effective measure for the brain’s responses to continuous stimuli. We propose an inferential method to detect the dynamic changes of brain networks based on time-varying graphical models. Whereas most existing methods focus on testing the existence of change points, the dynamics in the brain network offer more signals in many neuroscience studies. We propose a novel method to conduct hypothesis testing on changes in dynamic brain networks. We introduce a bootstrap statistic to approximate the supreme of the high-dimensional empirical processes over dynamically changing edges. Our simulations show that this framework can capture the change points with changed connectivity. Finally, we apply our method to a brain imaging dataset under a natural audio-video stimulus and illustrate that we are able to detect temporal changes in brain networks. The functions of the identified regions are consistent with specific emotional annotations, which are closely associated with changes inferred by our method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-ji20a, title = {Inference of Dynamic Graph Changes for Functional Connectome}, author = {Ji, Dingjue and Lu, Junwei and Zhang, Yiliang and Gao, Siyuan and Zhao, Hongyu}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {3230--3240}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/ji20a/ji20a.pdf}, url = {https://proceedings.mlr.press/v108/ji20a.html}, abstract = {Dynamic functional connectivity is an effective measure for the brain’s responses to continuous stimuli. We propose an inferential method to detect the dynamic changes of brain networks based on time-varying graphical models. Whereas most existing methods focus on testing the existence of change points, the dynamics in the brain network offer more signals in many neuroscience studies. We propose a novel method to conduct hypothesis testing on changes in dynamic brain networks. We introduce a bootstrap statistic to approximate the supreme of the high-dimensional empirical processes over dynamically changing edges. Our simulations show that this framework can capture the change points with changed connectivity. Finally, we apply our method to a brain imaging dataset under a natural audio-video stimulus and illustrate that we are able to detect temporal changes in brain networks. The functions of the identified regions are consistent with specific emotional annotations, which are closely associated with changes inferred by our method. } }
Endnote
%0 Conference Paper %T Inference of Dynamic Graph Changes for Functional Connectome %A Dingjue Ji %A Junwei Lu %A Yiliang Zhang %A Siyuan Gao %A Hongyu Zhao %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-ji20a %I PMLR %P 3230--3240 %U https://proceedings.mlr.press/v108/ji20a.html %V 108 %X Dynamic functional connectivity is an effective measure for the brain’s responses to continuous stimuli. We propose an inferential method to detect the dynamic changes of brain networks based on time-varying graphical models. Whereas most existing methods focus on testing the existence of change points, the dynamics in the brain network offer more signals in many neuroscience studies. We propose a novel method to conduct hypothesis testing on changes in dynamic brain networks. We introduce a bootstrap statistic to approximate the supreme of the high-dimensional empirical processes over dynamically changing edges. Our simulations show that this framework can capture the change points with changed connectivity. Finally, we apply our method to a brain imaging dataset under a natural audio-video stimulus and illustrate that we are able to detect temporal changes in brain networks. The functions of the identified regions are consistent with specific emotional annotations, which are closely associated with changes inferred by our method.
APA
Ji, D., Lu, J., Zhang, Y., Gao, S. & Zhao, H.. (2020). Inference of Dynamic Graph Changes for Functional Connectome. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:3230-3240 Available from https://proceedings.mlr.press/v108/ji20a.html.

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