Transfer Learning on fMRI Datasets

Hejia Zhang, Po-Hsuan Chen, Peter Ramadge
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:595-603, 2018.

Abstract

We explore transferring learning between fMRI datasets. A method is introduced to improve prediction accuracy on a primary fMRI dataset by jointly learning a model using other secondary fMRI datasets. We assume the secondary datasets are directly or indirectly linked to the primary dataset through sets of partially shared subjects. This method is particularly useful when the primary dataset is small. Using six fMRI datasets linked by various subsets of shared subjects, we show that the method yields improved performance in various predictive tasks. Our tests are performed on a variety of regions of interest in the brain and across various stimuli.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-zhang18b, title = {Transfer Learning on fMRI Datasets}, author = {Zhang, Hejia and Chen, Po-Hsuan and Ramadge, Peter}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {595--603}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/zhang18b/zhang18b.pdf}, url = {https://proceedings.mlr.press/v84/zhang18b.html}, abstract = {We explore transferring learning between fMRI datasets. A method is introduced to improve prediction accuracy on a primary fMRI dataset by jointly learning a model using other secondary fMRI datasets. We assume the secondary datasets are directly or indirectly linked to the primary dataset through sets of partially shared subjects. This method is particularly useful when the primary dataset is small. Using six fMRI datasets linked by various subsets of shared subjects, we show that the method yields improved performance in various predictive tasks. Our tests are performed on a variety of regions of interest in the brain and across various stimuli.} }
Endnote
%0 Conference Paper %T Transfer Learning on fMRI Datasets %A Hejia Zhang %A Po-Hsuan Chen %A Peter Ramadge %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-zhang18b %I PMLR %P 595--603 %U https://proceedings.mlr.press/v84/zhang18b.html %V 84 %X We explore transferring learning between fMRI datasets. A method is introduced to improve prediction accuracy on a primary fMRI dataset by jointly learning a model using other secondary fMRI datasets. We assume the secondary datasets are directly or indirectly linked to the primary dataset through sets of partially shared subjects. This method is particularly useful when the primary dataset is small. Using six fMRI datasets linked by various subsets of shared subjects, we show that the method yields improved performance in various predictive tasks. Our tests are performed on a variety of regions of interest in the brain and across various stimuli.
APA
Zhang, H., Chen, P. & Ramadge, P.. (2018). Transfer Learning on fMRI Datasets. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:595-603 Available from https://proceedings.mlr.press/v84/zhang18b.html.

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