The perception of pain is characterized by its tremendous intra- and interindividual variability. Different individuals perceive the very same painful event largely differently. Here, we aimed to predict the individual pain sensitivity from brain activity. We repeatedly applied identical painful stimuli to healthy human subjects and recorded brain activity by using electroencephalography (EEG). We applied a multivariate pattern analysis to the time-frequency transformed single-trial EEG responses. Our results show that a classifier trained on a group of healthy individuals can predict another individual's pain sensitivity with an accuracy of 83%. Classification accuracy depended on pain-evoked responses at about 8 Hz and pain-induced gamma oscillations at about 80 Hz. These results reveal that the temporal-spectral pattern of pain-related neuronal responses provides valuable information about the perception of pain. Beyond, our approach may help to establish an objective neuronal marker of pain sensitivity which can potentially be recorded from a single EEG electrode.

During the last few years, Graphics Processing Units (GPU) have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks such as rendering of 3D scenarios but can also be used for general numeric and symbolic computation tasks such as simulation and optimization. As major advantage, GPUs provide extremely high parallelism (with several hundred simple programmable processors) combined with a high bandwidth in memory transfer at low cost. In this paper, we propose several algorithms for computationally expensive data mining tasks like similarity search and clustering which are designed for the highly parallel environment of a GPU. We define a multidimensional index structure which is particularly suited to support similarity queries under the restricted programming model of a GPU, and define a similarity join method. Moreover, we define highly parallel algorithms for density-based and partitioning clustering. In an extensive experimental evaluation, we demonstrate the superiority of our algorithms running on GPU over their conventional counterparts in CPU.

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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.