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Multi-scale and multimodal imaging biomarkers for the early detection of Alzheimer’s disease

Authors
  • Hett, Kilian
Publication Date
Jan 25, 2019
Source
HAL-UPMC
Keywords
Language
English
License
Unknown
External links

Abstract

Alzheimer’s disease (AD) is the most common dementia leading to a neurodegenerative process and causing mental dysfunctions. According to the world health organization, the number of patients having AD will double in 20 years. Neuroimaging studies performed on AD patients revealed that structural brain alterations are advanced when the diagnosis is established. Indeed, the clinical symptoms of AD are preceded by brain changes. This stresses the need to develop new biomarkers to detect the first stages of the disease. The development of such biomarkers can make easier the design of clinical trials and therefore accelerate the development of new therapies. Over the past decades, the improvement of magnetic resonance imaging (MRI) has led to the development of new imaging biomarkers. Such biomarkers demonstrated their relevance for computer-aided diagnosis but have shown limited performances for AD prognosis. Recently, advanced biomarkers were proposed toimprove computer-aided prognosis. Among them, patch-based grading methods demonstrated competitive results to detect subtle modifications at the earliest stages of AD. Such methods have shown their ability to predict AD several years before the conversion to dementia. For these reasons, we have had a particular interest in patch-based grading methods. First, we studied patch-based grading methods for different anatomical scales (i.e., whole brain, hippocampus, and hippocampal subfields). We adapted patch-based grading method to different MRI modalities (i.e., anatomical MRI and diffusion-weighted MRI) and developed an adaptive fusion scheme. Then, we showed that patch comparisons are improved with the use of multi-directional derivative features. Finally, we proposed a new method based on a graph modeling that enables to combine information from inter-subjects’ similarities and intra-subjects’ variability. The conducted experiments demonstrate that our proposed method enable an improvement of AD detection and prediction.

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