Understanding pathologies and proposing effective treatment strategies can be heavily influenced by the possibility of assessing tissue characteristics in vivo with quantitative metrics that are reliable and pathologically meaningful, and can be compared across subjects and between time points. Such metrics can be used to inform hypothesis-driven studies on the functional mechanisms supporting the healthy and diseased brain. They can also be used in selecting patient groups in clinical trials and as in vivo biomarkers for monitoring outcomes. Technological advances and powerful computing have contributed to the emergence of the “big data” theory, which allows such metrics to be studied at population level, together with a whole range of other quantitative information from clinical and neuropsychological tests, and fed into algorithms that transform this wealth of information into something personalized and meaningful at single-patient level

The challenge of in vivo tissue characterization, connectivity and big data

GANDINI, CLAUDIA
2016-01-01

Abstract

Understanding pathologies and proposing effective treatment strategies can be heavily influenced by the possibility of assessing tissue characteristics in vivo with quantitative metrics that are reliable and pathologically meaningful, and can be compared across subjects and between time points. Such metrics can be used to inform hypothesis-driven studies on the functional mechanisms supporting the healthy and diseased brain. They can also be used in selecting patient groups in clinical trials and as in vivo biomarkers for monitoring outcomes. Technological advances and powerful computing have contributed to the emergence of the “big data” theory, which allows such metrics to be studied at population level, together with a whole range of other quantitative information from clinical and neuropsychological tests, and fed into algorithms that transform this wealth of information into something personalized and meaningful at single-patient level
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1182766
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