%0 Generic %1 brown2016machine %A Brown, Colin J %A Hamarneh, Ghassan %D 2016 %K brain connectome cordis correlation disruption fmri functional image images learning machine nalab review survey %T Machine Learning on Human Connectome Data from MRI %U http://arxiv.org/abs/1611.08699 %X Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these technologies has enabled faster and better diagnoses and treatments of neurological disorders and a deeper understanding of the human brain. Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning. The purpose of this work is to review the literature on the topic of applying machine learning models to MRI-based connectome data. This field is growing rapidly and now encompasses a large body of research. To summarize the research done to date, we provide a comparative, structured summary of 77 relevant works, tabulated according to different criteria, that represent the majority of the literature on this topic. (We also published a living version of this table online at http://connectomelearning.cs.sfu.ca that the community can continue to contribute to.) After giving an overview of how connectomes are constructed from dMRI and fMRI data, we discuss the variety of machine learning tasks that have been explored with connectome data. We then compare the advantages and drawbacks of different machine learning approaches that have been employed, discussing different feature selection and feature extraction schemes, as well as the learning models and regularization penalties themselves. Throughout this discussion, we focus particularly on how the methods are adapted to the unique nature of graphical connectome data. Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research.

@misc{brown2016machine, abstract = {Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these technologies has enabled faster and better diagnoses and treatments of neurological disorders and a deeper understanding of the human brain. Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning. The purpose of this work is to review the literature on the topic of applying machine learning models to MRI-based connectome data. This field is growing rapidly and now encompasses a large body of research. To summarize the research done to date, we provide a comparative, structured summary of 77 relevant works, tabulated according to different criteria, that represent the majority of the literature on this topic. (We also published a living version of this table online at http://connectomelearning.cs.sfu.ca that the community can continue to contribute to.) After giving an overview of how connectomes are constructed from dMRI and fMRI data, we discuss the variety of machine learning tasks that have been explored with connectome data. We then compare the advantages and drawbacks of different machine learning approaches that have been employed, discussing different feature selection and feature extraction schemes, as well as the learning models and regularization penalties themselves. Throughout this discussion, we focus particularly on how the methods are adapted to the unique nature of graphical connectome data. Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research.}, added-at = {2020-01-06T16:15:41.000+0100}, author = {Brown, Colin J and Hamarneh, Ghassan}, biburl = {https://www.bibsonomy.org/bibtex/2f1677d8c7d5b2bb5d2592d9d4189003d/becker}, description = {[1611.08699] Machine Learning on Human Connectome Data from MRI}, interhash = {8fbc131f51ee8e73f652ff7c86429b93}, intrahash = {f1677d8c7d5b2bb5d2592d9d4189003d}, keywords = {brain connectome cordis correlation disruption fmri functional image images learning machine nalab review survey}, note = {cite arxiv:1611.08699Comment: 51 pages, 6 figures. To be submitted to a journal}, timestamp = {2020-01-06T16:15:41.000+0100}, title = {Machine Learning on Human Connectome Data from MRI}, url = {http://arxiv.org/abs/1611.08699}, year = 2016 }