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Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers∗This work was supported by the Bernstein Computational Program of the German Federal Ministry of Education and Research (01GQ1001C, 01GQ0851, GRK 1589/1), the European Regional Development Fund of the European Union (10153458 and 10153460), and Philips Research.∗, 1
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BACKGROUNDAlzheimer's disease (AD) is an age‐dependent neurodegenerative disease hallmarked by the accumulation of extracellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tau tangles that emerge decades before symptom onset.1 This preclinical period is followed by a prodromal stage during which a diagnosis of amnestic mild cognitive impairment (MCI) indicates high probability of conversion to dementia within several years.2 Because AD has a complex, multifactorial etiology with genetic and modifiable risk factors, diagnostic accuracy in preclinical periods is limited, which poses a particular challenge for clinical trial enrollment in which pre‐screening precision is critical to minimize cost and subject burden. Thus, there is an outstanding need to develop tools that can identify individuals with a high probability of converting to AD for timely diagnosis and streamlined clinical trial screening.Cerebrospinal fluid (CSF) or positron emission tomography (PET) measures of amyloid and tau pose obstacles to routine clinical use due to their cost, invasiveness, and radiation exposure. Inexpensive, non‐invasive, and widely available approaches to quantify personalized AD risk will improve clinicians’ ability to select patients with the greatest potential for therapeutic benefit, and to guide clinical trial enrichment to minimize trial cost and patient burden. To this end, accurate longitudinal prediction of
Alzheimer s & Dementia – Wiley
Published: Nov 1, 2023
Keywords: Alzheimer's disease; amyloid; genetics; magnetic resonance imaging; memory; mild cognitive impairment; multimodal prediction; tau
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