Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

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The physical examination on admission, as recorded in the psychiatric case notes, was assessed for 100 consecutive admissions under the age of 65. Assessment was made on the presence or absence of reference to 43 items of examination and the recording of the examinations was found to be uniformly poor. One of the authors re-examined 50 patients within 48 h of admission and the findings were then compared with the initial recorded examination. Significant unrecorded positive findings were most frequent in the neurological and locomotor systems.

The ability to deal with uncertain information is becoming increasingly important for modern database applications. Whereas a conventional (certain) object is usually represented by a vector from a multidimensional feature space, an uncertain object is represented by a multivariate probability density function (PDF). This PDF can be defined either discretely (e.g. by a histogram) or continuously in parametric form (e.g. by a Gaussian Mixture Model). For a database of uncertain objects, the users expect similar data analysis techniques as for a conventional database of certain objects. An important analysis technique for certain objects is the skyline operator which finds maximal or minimal vectors with respect to any possible attribute weighting. In this paper, we propose the concept of probabilistic skylines, an extension of the skyline operator for uncertain objects. In addition, we propose efficient and effective methods for determining the probabilistic skyline of uncertain objects which are defined by a PDF in parametric form (e.g. a Gaussian function or a Gaussian Mixture Model). To further accelerate the search, we elaborate how the computation of the probabilistic skyline can be supported by an index structure for uncertain objects. An extensive experimental evaluation demonstrates both the effectiveness and the efficiency of our technique.

Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with mixed type attributes. Most existing solutions suffer from one or both of the following drawbacks: Either they require input parameters which are difficult to estimate, or/and they do not adequately support mixed type attributes. Our technique INTEGRATE is a novel clustering approach that truly integrates the information provided by heterogeneous numerical and categorical attributes. Originating from information theory, the Minimum Description Length (MDL) principle allows a unified view on numerical and categorical information and thus naturally balances the influence of both sources of information in clustering. Moreover, supported by the MDL principle, parameter-free clustering can be performed which enhances the usability of INTEGRATE on real world data. Extensive experiments demonstrate the effectiveness of INTEGRATE in exploiting numerical and categorical information for clustering. As an efficient iterative algorithm INTEGRATE is scalable to large data sets.

SYNOPSISThe performance of 47 patients with Parkinson's disease on a battery of tests of cognition, motor function, disability and mood was compared with the performance of 47 healthy control subjects who were matched to the patients on the basis of age, sex and pre-morbid IQ. An increased prevalence of impairment over a range of cognitive functions was observed in the Parkinson's disease patients as compared with their matched controls. The differences between the Parkinson's disease patients and controls could not be accounted for by factors such as depressed mood, effects of medication or motor impairment. Our findings are discussed in relation to the methodology of previous studies in this area and to the need for a comprehensive clinico-pathological longitudinal study.