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Functional imaging markers of the MCI brain in task and at rest: detecting memory and connectivity impairments in prodromal Alzheimer's disease

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Defense Thèse de doctorat : Univ. Genève et Lausanne, 2016 - Neur. 189 - 2016/11/01
Abstract Alzheimer's disease (AD) is a major neurodegenerative disease, and currently the leading cause of dementia in the world. The application of machine learning algorithms has recently brought a novel perspective to the early identification of neurological diseases such as AD, as they can advantageously exploit multivariate information in high-dimensional data to predict patient diagnosis and ultimately prognosis at the individual level. This work aimed to develop an early functional marker for AD using task-based and resting-state (RS) functional magnetic resonance imaging (fMRI), and to build a multimodal marker for predicting future conversion to AD. We found several regions of interest in which memory task-related fMRI activity could reliably discriminate between elderly controls and patients with prodromal AD, as well as functional connectivity during RS in the whole brain and within networks. Finally, we found that imaging markers were able to accurately predict conversion to AD, while clinical data was less informative.
Keywords Alzheimer's diseaseMild cognitive impairmentFunctional magnetic resonance imagingTask-based fMRIAssociative memoryResting-state fMRIFunctional connectivityMachine learningMultimodalPartial least squares
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URN: urn:nbn:ch:unige-909342
Note Thèse en Neurosciences des universités de Genève et de Lausanne
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Project FNS: 320030-138163
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KEBETS, Valeria. Functional imaging markers of the MCI brain in task and at rest: detecting memory and connectivity impairments in prodromal Alzheimer's disease. Université de Genève. Thèse, 2016. https://archive-ouverte.unige.ch/unige:90934

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Deposited on : 2017-01-09

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