How Machine Learning is Powering Neuroimaging to Improve Brain Health
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 presente...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer US
2022
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| Online Access: | https://hdl.handle.net/1721.1/141633 |
| _version_ | 1826199349454962688 |
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| author | Singh, Nalini M. Harrod, Jordan B. Subramanian, Sandya Robinson, Mitchell Chang, Ken Cetin-Karayumak, Suheyla Dalca, Adrian V. Eickhoff, Simon Fox, Michael Franke, Loraine Golland, Polina Haehn, Daniel |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Singh, Nalini M. Harrod, Jordan B. Subramanian, Sandya Robinson, Mitchell Chang, Ken Cetin-Karayumak, Suheyla Dalca, Adrian V. Eickhoff, Simon Fox, Michael Franke, Loraine Golland, Polina Haehn, Daniel |
| author_sort | Singh, Nalini M. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:18:46Z |
| format | Article |
| id | mit-1721.1/141633 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:18:46Z |
| publishDate | 2022 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1416332023-07-28T20:35:38Z How Machine Learning is Powering Neuroimaging to Improve Brain Health Singh, Nalini M. Harrod, Jordan B. Subramanian, Sandya Robinson, Mitchell Chang, Ken Cetin-Karayumak, Suheyla Dalca, Adrian V. Eickhoff, Simon Fox, Michael Franke, Loraine Golland, Polina Haehn, Daniel 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 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. 2022-04-04T13:00:53Z 2022-04-04T13:00:53Z 2022-03-28 2022-04-03T03:13:21Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/141633 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." PUBLISHER_CC en https://doi.org/10.1007/s12021-022-09572-9 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer US Springer US |
| spellingShingle | Singh, Nalini M. Harrod, Jordan B. Subramanian, Sandya Robinson, Mitchell Chang, Ken Cetin-Karayumak, Suheyla Dalca, Adrian V. Eickhoff, Simon Fox, Michael Franke, Loraine Golland, Polina Haehn, Daniel How Machine Learning is Powering Neuroimaging to Improve Brain Health |
| title | How Machine Learning is Powering Neuroimaging to Improve Brain Health |
| title_full | How Machine Learning is Powering Neuroimaging to Improve Brain Health |
| title_fullStr | How Machine Learning is Powering Neuroimaging to Improve Brain Health |
| title_full_unstemmed | How Machine Learning is Powering Neuroimaging to Improve Brain Health |
| title_short | How Machine Learning is Powering Neuroimaging to Improve Brain Health |
| title_sort | how machine learning is powering neuroimaging to improve brain health |
| url | https://hdl.handle.net/1721.1/141633 |
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