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FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis

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Albrecht,  Franziska
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Ballarini,  Tommaso
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Neumann,  Jane
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany;
Department of Medical Engineering and Biotechnology, University of Applied Science, Jena, Germany;

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Schroeter,  Matthias L.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic of Cognitive Neurology, University of Leipzig, Germany;

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Zitation

Albrecht, F., Ballarini, T., Neumann, J., & Schroeter, M. L. (2018). FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis. NeuroImage: Clinical, 21: 101594. doi:10.1016/j.nicl.2018.11.004.


Zitierlink: https://hdl.handle.net/21.11116/0000-0002-AF20-5
Zusammenfassung
Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy.

We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis.

Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD.

Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.