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Journal articles

Machine Learning for Neurological Disorders

Abstract : The last two decades have seen tremendous advances in our understanding of human brain structure and function, particularly at the level of systems neuroscience, where neuroimaging methods have led to better delineation of brain networks and brain modules. Brain understanding is one of the greatest challenges of our century with enormous potential impact in a number of fields, including medicine. Recent progress in the hardware side has made possible the in-vivo acquisition on top of structural/anatomical data, functional information (through emerging image modalities like functional magnetic resnonance imaging (fMRI), diffusion tensor imaging (DTI), magneto encephalogram (MEG), electro encephalogram (EEG), etc.) in a non-invasive manner depicting task-specific states of the brain. Such information can be of great interest towards understanding of neurogenerative diseases, and providing means of assessing the impact of different therapeutic strategies.
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Contributor : Matthew Blaschko Connect in order to contact the contributor
Submitted on : Sunday, April 20, 2014 - 9:18:51 PM
Last modification on : Friday, January 21, 2022 - 3:01:28 AM
Long-term archiving on: : Sunday, April 9, 2017 - 4:59:04 AM


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  • HAL Id : hal-00940262, version 1



Matthew Blaschko. Machine Learning for Neurological Disorders. Centraliens, Association des centraliens, 2014, pp.40-42. ⟨hal-00940262⟩



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