Apathy has been reported to occur after subthalamic nucleus stimulation, a treatment of motor complications in advanced Parkinson's disease. We carried out a prospective study of the occurrence of apathy and associated symptoms, predictors and mechanisms in the year following subthalamic stimulation. Dopamine agonist drugs were discontinued immediately after surgery and levodopa was markedly reduced within 2 weeks. Apathy and depression were assessed monthly, using the Starkstein apathy scale and the Beck Depression Inventory. Dopamine agonists were re-introduced if patients developed apathy or depression. Preoperative non-motor fluctuations were evaluated using the Ardouin Scale. Depression, apathy and anxiety were evaluated both on and off levodopa. Analysis of predictors of apathy was performed using a Cox proportional hazard model. Twelve patients who developed apathy and a control group of 13 patients who did not underwent [11C]-raclopride positron emission tomography scanning before and after oral intake of methylphenidate. In 63 patients with Parkinson's disease treated with subthalamic stimulation, dopaminergic treatment was decreased by 82% after surgery. Apathy occurred after a mean of 4.7 (3.3-8.2) months in 34 patients and was reversible in half of these by the 12-month follow-up. Seventeen patients developed transient depression after 5.7 (4.7-9.3) months and these fell into the apathy group with one single exception. At baseline, fluctuations in depression, apathy and anxiety scores were greater in the group with apathy. Fluctuations in apathy, depression and anxiety ratings during a baseline levodopa challenge were also significant predictors of postoperative apathy in univariate analysis, but not motor and cognitive states or the level of reduction of dopaminergic medication. The multivariate model identified non-motor fluctuations in everyday life and anxiety score during the baseline levodopa challenge as two independent significant predictors of postoperative apathy. Without methylphenidate, [11C]-raclopride binding potential values were greater in apathetic patients bilaterally in the orbitofrontal, dorsolateral prefrontal, posterior cingulate and temporal cortices, left striatum and right amygdala, reflecting greater dopamine D2/D3 receptor density and/or reduced synaptic dopamine level in these areas. The variations of [11C]-raclopride binding potential values induced by methylphenidate were greater in non-apathetic patients in the left orbitofrontal cortex, dorsolateral prefrontal cortex, thalamus and internal globus pallidus and bilaterally in the anterior and posterior cingulate cortices, consistent with a more important capacity to release dopamine. Non-motor fluctuations are related to mesolimbic dopaminergic denervation. Apathy, depression and anxiety can occur after surgery as a delayed dopamine withdrawal syndrome. A varying extent of mesolimbic dopaminergic denervation and differences in dopaminergic treatment largely determine mood, anxiety and motivation in patients with Parkinson...

Despite intense interests in developing blood measurements of Alzheimer’s disease (AD), the progress has been confounded by limited sensitivity and poor correlation to brain pathology. Here, we present a dedicated analytical platform for measuring different populations of circulating amyloid β (Aβ) proteins – exosome-bound vs. unbound – directly from blood. The technology, termed a mplified p lasmonic ex osome (APEX), leverages in situ enzymatic conversion of localized optical deposits and double-layered plasmonic nanostructures to enable sensitive, multiplexed population analysis. It demonstrates superior sensitivity (~200 exosomes), and enables diverse target co-localization in exosomes. Employing the platform, we find that prefibrillar Aβ aggregates preferentially bind with exosomes. We thus define a population of Aβ as exosome-bound (Aβ42+ CD63+) and measure its abundance directly from AD and control blood samples. As compared to the unbound or total circulating Aβ, the exosome-bound Aβ measurement could better reflect PET imaging of brain amyloid plaques and differentiate various clinical groups.

In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems.

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In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BP). On static [F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [F]MPPF, most regional errors on BP ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This work demonstrates that inaccuracies in attenuation maps can induce bias in dynamic brain PET studies. Multi-atlas attenuation correction with MaxProb enables quantification on hybrid PET-MR scanners, eschewing the need for CT.

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