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000910705 1001_ $$0P:(DE-HGF)0$$aSingh, Nalini M.$$b0
000910705 245__ $$aHow Machine Learning is Powering Neuroimaging to Improve Brain Health
000910705 260__ $$aNew York, NY$$bSpringer$$c2022
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000910705 520__ $$aThis 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.
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000910705 7001_ $$0P:(DE-HGF)0$$aHarrod, Jordan B.$$b1
000910705 7001_ $$0P:(DE-HGF)0$$aSubramanian, Sandya$$b2
000910705 7001_ $$0P:(DE-HGF)0$$aRobinson, Mitchell$$b3
000910705 7001_ $$0P:(DE-HGF)0$$aChang, Ken$$b4
000910705 7001_ $$0P:(DE-HGF)0$$aCetin-Karayumak, Suheyla$$b5
000910705 7001_ $$0P:(DE-HGF)0$$aDalca, Adrian Vasile$$b6
000910705 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b7$$ufzj
000910705 7001_ $$0P:(DE-HGF)0$$aFox, Michael$$b8
000910705 7001_ $$0P:(DE-HGF)0$$aFranke, Loraine$$b9
000910705 7001_ $$0P:(DE-HGF)0$$aGolland, Polina$$b10
000910705 7001_ $$0P:(DE-HGF)0$$aHaehn, Daniel$$b11
000910705 7001_ $$0P:(DE-HGF)0$$aIglesias, Juan Eugenio$$b12
000910705 7001_ $$0P:(DE-HGF)0$$aO’Donnell, Lauren J.$$b13
000910705 7001_ $$0P:(DE-HGF)0$$aOu, Yangming$$b14
000910705 7001_ $$0P:(DE-HGF)0$$aRathi, Yogesh$$b15
000910705 7001_ $$0P:(DE-HGF)0$$aSiddiqi, Shan H.$$b16
000910705 7001_ $$0P:(DE-HGF)0$$aSun, Haoqi$$b17
000910705 7001_ $$0P:(DE-HGF)0$$aWestover, M. Brandon$$b18
000910705 7001_ $$0P:(DE-HGF)0$$aWhitfield-Gabrieli, Susan$$b19
000910705 7001_ $$0P:(DE-HGF)0$$aGollub, Randy L.$$b20$$eCorresponding author
000910705 773__ $$0PERI:(DE-600)2099780-2$$a10.1007/s12021-022-09572-9$$gVol. 20, no. 4, p. 943 - 964$$n4$$p943 - 964$$tNeuroinformatics$$v20$$x1539-2791$$y2022
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000910705 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Massachusetts General Hospital, Boston$$b20
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