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With the increasing success of mapping brain networks and availability of multiple MR- and PET-based connectivity measures, the need for novel methodologies to unravel the structure and function of the brain at multiple spatial and temporal scales is emerging. Therefore, in this work, we used hybrid PET-MR data of healthy volunteers (n = 67) to identify multiplex core nodes in the human brain. First, monoplex networks of structural, functional and metabolic connectivity were constructed, and consequently combined into a multiplex SC-FC-MC network by linking the same nodes categorically across layers. Taking into account the multiplex nature using a tensorial approach, we identified a set of core nodes in this multiple network based on a combination of eigentensor centrality and overlapping degree. We introduced a coreness coefficient, which mitigates the effect of modeling parameters to obtain robust results. The proposed methodology was applied onto young and elderly healthy volunteers, where differences observed in the monoplex networks persisted in the multiplex as well. The multiplex core showed a decreased contribution to the default mode and salience network, while an increased contribution to the dorsal attention and somatosensory network was observed in the elderly population. Moreover, a clear distinction in eigentensor centrality was found between young and elderly healthy volunteers.

We investigated the glucose metabolism in an adeno-associated viral vector based alpha-synuclein rat model for Parkinson's disease (PD) using longitudinal 18F-FDG PET imaging, which resulted in an improved characterization of this animal model. We generated a PD specific pattern (PDSP) based on a multivariate classification approach to differentiate between a PD and control group at a late disease stage, where the neurodegeneration is considered nearly complete. In particular, we applied a principal component analysis prior to classification by a support vector machine (SVM). Moreover, by using a SVM for regression to predict corresponding motor scores, a PD motor pattern (PDMP) was derived as well. The PDSP mainly corresponds to the PDMP and overlaps to a large extent with the human pattern. We were able to quantify disease expression at previous time points by projecting onto the PDSP and PDMP. While a univariate analysis indicated metabolic changes which did not persist through time, both PDSP and PDMP were able to differentiate significantly (p-value

Importance During a time with the potential for novel treatment strategies, early detection of disease manifestations at an individual level in presymptomatic carriers of a hexanucleotide repeat expansion in the C9orf72 gene (preSxC9) is becoming increasingly relevant. Objectives To evaluate changes in glucose metabolism before symptom onset of amyotrophic lateral sclerosis or frontotemporal dementia in preSxC9 using simultaneous fluorine 18–labeled fluorodeoxyglucose ([18F]FDG positron emission tomographic (PET) and magnetic resonance imaging as well as the mutation’s association with clinical and fluid biomarkers. Design, Setting, and Participants A prospective, case-control study enrolled 46 participants from November 30, 2015, until December 11, 2018. The study was conducted at the neuromuscular reference center of the University Hospitals Leuven, Leuven, Belgium. Main Outcomes and Measures Neuroimaging data were spatially normalized and analyzed at the voxel level at a height threshold of P

PURPOSE: A [11C]UCB-J blocking study was performed in healthy volunteers to validate simplified, non-invasive measures for quantifying presynaptic SV2A expression using subcortical white matter as reference tissue. METHODS: Ninety minutes dynamic [11C]UCB-J PET scanning with arterial blood sampling was performed in 10 healthy volunteers (8 M/2F; age 27.6 ± 10.0 yrs), before and after administration of a novel chemical entity with selective affinity for SV2A. The centrum semi-ovale (SO) was validated as reference region by comparing baseline and post treatment distribution volume (VT). Using SO as reference tissue, Binding Potential (BPSO) using a Simplified Reference Tissue Model (SRTM, down to 60 min acquisition) and Standardized Uptake Value Ratios (60-90 min post injection - SUVRSO,60-90min) were compared with regional distribution volume ratios (DVR). Next, SV2A occupancy values based on SRTM BPSO and SUVRSO,60-90min were compared to occupancy estimates using regional VT values and a Lassen plot. RESULTS: After pretreatment, regional VT values were reduced significantly except for SO. Highly significant correlations were found between DVR, SRTM BPSO and SUVRSO,60-90min. Compared to DVR, baseline SRTM BPSO showed a small bias (≤ 6.1%) with lower precision for shorter acquisition times, while SUVRSO,60-90min showed 3.5% bias with similar precision. Differences between SV2A occupancy values based on SUVRSO,60-90min and occupancy estimates using VT and a Lassen plot were small but significant, while negligible bias was found for SRTM based occupancy estimates (at least 70 min acquisition). CONCLUSION: This [11C]UCB-J blocking study validated SO as a suitable reference region for non-invasive quantification of SV2A availability and drug occupancy in the human brain. Accurate quantification can be achieved by using either SUVRSO,60-90min with a 60-90 min PET acquisition or SRTM BPSOwith at least 70 min dynamic PET acquisition. ispartof: EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING vol:46 issue:2 pages:396-406 ispartof: location:Germany status: published

We investigated the glucose metabolism in an adeno-associated viral vector based alpha-synuclein rat model for Parkinson’s disease (PD) using longitudinal 18F-FDG PET imaging, which resulted in an improved characterization of this animal model. We generated a PD specific pattern (PDSP) based on a multivariate classification approach to differentiate between a PD and control group at a late disease stage, where the neurodegeneration is considered nearly complete. In particular, we applied a principal component analysis prior to classification by a support vector machine (SVM). Moreover, by using a SVM for regression to predict corresponding motor scores, a PD motor pattern (PDMP) was derived as well. The PDSP mainly corresponds to the PDMP and overlaps to a large extent with the human pattern. We were able to quantify disease expression at previous time points by projecting onto the PDSP and PDMP. While a univariate analysis indicated metabolic changes which did not persist through time, both PDSP and PDMP were able to differentiate significantly (p-value