Klinische Neurophysiologie 2012; 43 - P041
DOI: 10.1055/s-0032-1301591

A metabolic topology of cognitive ageing, Parkinson's disease and dementia

O Granert 1, R Perneczky 2, J Götz 3, H Boecker 4, AE Drzezga 5, T van Eimeren 1, P Häussermann 3
  • 1Klinik für Neurologie, UKSH, Campus Kiel, Kiel
  • 2Klinik und Poliklinik für Psychiatrie und Psychotherapie, München
  • 3Klinik für Psychiatrie und Psychotherapie des ZIP, Kiel
  • 4Radiologische Klinik, Universitätsklinik Bonn, Bonn
  • 5Klinik und Poliklinik für Nuklearmedizin der LMU München, München

Introduction: Parkinson’s disease with and without dementia (PDD and PD), dementia with Lewy-bodies (DLB), mild cognitive impairment (MCI) and Alzheimer dementia (AD) show substantial clinical and neuropathological overlap, which limits diagnostic accuracy, and even questions the concept of distinct entities [1, 2]. On the other hand, early and accurate diagnosis of dementia is fundamental for an optimal clinical treatment. Aims: Build a topological map of disease status based on patterns of regional cerebral metabolic rate of glucose (rCMRglc). Determine the expression of disease related patterns in a cohort of 112 MCI's, 104 AD's plus an additional test group (AD1) of 18 AD’s, 20 PD's, 17 PDD's and 27 DLB's as compared to 24 elderly controls. Methods: Disease specific expression values of voxel-based spatial covariance patterns are predetermined using the t-map projection method [3]. The expression vectors represent the manifestation of the disease related patterns (AD and PD related pattern, respectively) in prospective subject images. The vectors of all patients were used to build a topological map of the disease status, which can be used to localize patients and control subjects to get a rCMRglc based indication of the disease. Results: Voxel-wise group comparison of the AD and PD group with the control group exhibited typical and distinct metabolic profiles for the PD and AD groups. Projection of the individual rCMRglc maps onto these patterns provides expression values for the subjects of all groups. The visualization of these expression values on our disease specific topological map (Fig. 1) reveals a metabolic topology that reflect clinical and neuropathological findings. Conclusions: The approach provides a circumstantial grading of the disease, which may help to guide the clinician towards an earlier and more differentiated treatment. The topographic localisation provides additional and independent information to clinical indicators.

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