How Machine Learning is Powering Neuroimaging to Improve Brain Health
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12021_2022_Article_9572.pdf
Size
3.96 MB
Format
Adobe PDF
Checksum (MD5)
d7ee5b0afb5baf4b38e62d89cb4e601e
Author(s)
Singh, Nalini M.
Harrod, Jordan B.
Subramanian, Sandya
Robinson, Mitchell
Chang, Ken
Cetin-Karayumak, Suheyla
Dalca, Adrian V.
Eickhoff, Simon
Fox, Michael
Franke, Loraine
Date Issued
March 28, 2022
Publisher
Springer US
Citation
Singh, Nalini M., Harrod, Jordan B., Subramanian, Sandya, Robinson, Mitchell, Chang, Ken et al. 2022. "How Machine Learning is Powering Neuroimaging to Improve Brain Health."
Version
Final published version
Abstract
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Martinos Imaging Center (McGovern Institute for Brain Research at MIT)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s12021-022-09572-9
