Publications (79) View all
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Article: Combination of biomarkers: PET [18F]flutemetamol imaging and structural MRI in dementia and mild cognitive impairment.
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ABSTRACT: The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aβ accumulation and neuronal degeneration or injury. It was the aim of this study to compute hippocampus volume from MRI and a neocortical standard uptake value ratio (SUVR) from [(18)F]flutemetamol PET and investigate the performance of these biomarkers when used individually and when combined. Fully automated methods for hippocampus segmentation and for computation of neocortical SUVR were applied to MR and scans with the investigational imaging agent [(18)F]flutemetamol in a cohort comprising 27 AD patients, 25 healthy volunteers (HVs) and 20 subjects with amnestic mild cognitive impairment (MCI). Clinical follow-up was performed 2 years after the initial assessment. Hippocampus volumes showed extensive overlap between AD and HV cases while PET SUVRs showed clear group clustering. When both measures were combined, there was a relatively compact cluster of HV scans and a less compact AD cluster. MCI cases had a bimodal distribution of SUVRs. [(18)F]Flutemetamol-positive MCI subjects showed a large variability in hippocampus volumes, indicating that these subjects were in different stages of neurodegeneration. Some [(18)F]flutemetamol-negative MCI scans had hippocampus volumes that were well below the HV range. Clinical follow-up showed that 8 of 9 MCI to AD converters came from the [(18)F]flutemetamol-positive group. Combining [(18)F]flutemetamol PET with structural MRI provides additional information for categorizing disease and potentially predicting shorter time to progression from MCI to AD, but this has to be validated in larger longitudinal studies.Neurodegenerative Diseases 02/2012; 10(1-4):246-9. · 3.06 Impact Factor -
Article: Design and Application of a Generic Clinical Decision Support System for Multiscale Data.
IEEE Trans. Biomed. Engineering. 01/2012; 59:234-240. -
Article: Heterogeneous biological network visualization system: case study in context of medical image data.
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ABSTRACT: We have developed a system called megNet for integrating and visualizing heterogeneous biological data in order to enable modeling biological phenomena using a systems approach. Herein we describe megNet, including a recently developed user interface for visualizing biological networks in three dimensions and a web user interface for taking input parameters from the user, and an in-house text mining system that utilizes an existing knowledge base. We demonstrate the software with a case study in which we integrate lipidomics data acquired in-house with interaction data from external databases, and then find novel interactions that could possibly explain our previous associations between biological data and medical images. The flexibility of megNet assures that the tool can be applied in diverse applications, from target discovery in medical applications to metabolic engineering in industrial biotechnology.Advances in experimental medicine and biology 01/2012; 736:95-118. · 1.09 Impact Factor -
Article: Design and application of a generic clinical decision support system for multiscale data.
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ABSTRACT: Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patient's disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimer's disease.IEEE transactions on bio-medical engineering 01/2012; 59(1):234-40. · 2.15 Impact Factor -
Article: Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal Volumetry, Tensor-Based Morphometry and Voxel-Based Morphometry.
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ABSTRACT: MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods. To compare FTD with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy. Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups. We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55). Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.PLoS ONE 01/2012; 7(12):e52531. · 4.09 Impact Factor
