In Alzheimer's disease (AD), recent findings suggest that amyloid-b (Ab)-pathology might start 20-30 years before first cognitive symptoms arise. To account for age as most relevant risk factor for sporadic AD, it has been hypothesized that lifespan intrinsic (i.e., ongoing) activity of hetero-modal brain areas with highest levels of functional connectivity triggers Ab-pathology. This model induces the simple question whether in older persons without any cognitive symptoms intrinsic activity of hetero-modal areas is more similar to that of symptomatic patients with AD or to that of younger healthy persons. We hypothesize that due to advanced age and therefore potential impact of pre-clinical AD, intrinsic activity of older persons resembles more that of patients than that of younger controls. We tested this hypothesis in younger (ca. 25 years) and older healthy persons (ca. 70 years) and patients with mild cognitive impairment and AD-dementia (ca. 70 years) by the use of resting-state functional magnetic resonance imaging, distinct measures of intrinsic brain activity, and different hierarchical clustering approaches. Independently of applied methods and involved areas, healthy older persons' intrinsic brain activity was consistently more alike that of patients than that of younger controls. Our result provides evidence for larger similarity in intrinsic brain activity between healthy older persons and patients with or at-risk for AD than between older and younger ones, suggesting a significant proportion of pre-clinical AD cases in the group of cognitively normal older people. The observed link of aging and AD with intrinsic brain activity supports the view that lifespan intrinsic activity may contribute critically to the pathogenesis of AD.
The collection of methods known as "data mining" offers methodological and technical solutions to deal with the analysis of medical data and the construction of models. Medical data have a special status based upon their applicability to all people; their urgency (including life-or death); and a moral obligation to be used for beneficial purposes. Due to this reality, this article addresses the special features of data mining with medical data. Specifically, we will apply a recent data mining algorithm called FuzzyPred. It performs an unsupervised learning process to obtain a set of fuzzy predicates in a normal form, specifically conjunctive (CNF) and disjunctive normal form (DNF). Experimental studies in known medical datasets shows some examples of knowledge that can be obtained by using this method. Several kind of knowledge that was obtained by FuzzyPred in these databases cannot be obtained by other popular data mining techniques.
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