Multimodal neuroimaging on neurodegenerative disorders using the hybrid PET/MRI technique

Simultaneous evaluation of intrinsic functional connectivity and local metabolic activity using a hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) offers a new in-vivo tool to provide both better understanding of the pathogenesis and diagnostics of neurodegenerative disorders. In the current thesis, first steps into this direction have been realized with respect to Alzheimer’s disease and frontotemporal lobar degeneration. Particularly, I have studied changes in multimodal properties of different brain regions in two different projects for these disorders: In project one, the relationship between local glucose metabolism, functional connectivity, and grey matter volume of the hippocampus and precuneus has been studied in 40 patients with Alzheimer’s disease-dementia, 21 patients with mild cognitive impairment and 26 healthy subjects. 18F-fluorodesoxyglucose-PET was used to measure local glucose metabolism, resting-state functional MRI and seed-based functional connectivity analysis to measure intrinsic functional connectivity, and structural MRI and voxel-based morphometry to estimate regional brain volumes. Group comparisons were corrected for the effects of atrophy, partial volume effect, age, and gender. Our findings demonstrated that in patients, intrinsic connectivity between hippocampus and precuneus was reduced. Patients’ metabolism was progressively reduced in the precuneus, while it was unchanged in the hippocampus. Critically, for patients with Alzheimer’s disease-dementia hippocampal metabolism was negatively correlated with connectivity between hippocampus and precuneus. Results provide the first evidence for an inverse relationship between intra-hippocampal metabolism and reduced extra-hippocampal intrinsic connectivity in Alzheimer’s disease. Data are consistent with the hippocampus disconnection hypothesis of Alzheimer’s disease. For the second project, it has been demonstrated that each neurodegenerative disorder affects preferentially specific brain networks and our hypothesis was that the network degeneration hypothesis of neurodegenerative diseases can help to separate individual patients with Alzheimer’s disease and 3 main subtypes frontotemporal lobar degeneration, namely, semantic dementia, progressive non-ﬂuent aphasia, and behavioral variant frontotemporal dementia. Forty patients with simultaneous resting-state functional MRI, structural MRI, and 18F-fluorodesoxyglucose were assessed in the hybrid PET/MRI scanner. Afterwards, we measured voxel-wise degree centrality values as a surrogate for intrinsic functional connectivity, voxel-wise regional glucose metabolism values as a surrogate for effective neuronal activity and voxel-wise voxel-based morphometry values as a surrogate for regional gray matter volume within the main cores of four intrinsic networks that are primarily affected by those four diseases based on the network degeneration hypothesis. Subsequently, support vector machine was applied to classify each patient from others by leave-one-out cross-validation. The classification accuracy findings for separation of neurodegenerative disorders were 77.5% for Alzheimer’s disease vs others, 97.5% for semantic dementia vs others, 87.5% for progressive non-ﬂuent aphasia vs others and 82.5% for behavioral variant frontotemporal dementia vs others. Our data suggest that combination of intrinsic functional connectivity, glucose metabolism, and grey matter volume based on network degeneration hypothesis can serve as a valid biomarker to separate patients with distinct neurodegenerative disorders.