TSPO expression was dynamically upregulated during epileptogenesis, persisted in the chronic phase and correlated with microglia activation rather than reactive astrocytes. TSPO expression was correlating with spontaneous seizures and its high expression during the latent phase might possibly suggest being an important switching point in disease ontogenesis which could be further investigated by PET imaging.

Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18 F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-β (Aβ) burden was quantified using the 18 F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18 F-FDG SUVR. CSF measures included Aβ 1–42 , Aβ 1–40 , T-tau, P-tau 181 , and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [ p < 0.001] and + 0.37 [ p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = −0.28 [ p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18 F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.

An overview of the theory of 4D image reconstruction for emission tomography is given along with a review of the current state of the art, covering both positron emission tomography and single photon emission computed tomography (SPECT). By viewing 4D image reconstruction as a matter of either linear or non-linear parameter estimation for a set of spatiotemporal functions chosen to approximately represent the radiotracer distribution, the areas of so-called 'fully 4D' image reconstruction and 'direct kinetic parameter estimation' are unified within a common framework. Many choices of linear and non-linear parameterization of these functions are considered (including the important case where the parameters have direct biological meaning), along with a review of the algorithms which are able to estimate these often non-linear parameters from emission tomography data. The other crucial components to image reconstruction (the objective function, the system model and the raw data format) are also covered, but in less detail due to the relatively straightforward extension from their corresponding components in conventional 3D image reconstruction. The key unifying concept is that maximum likelihood or maximum a posteriori (MAP) estimation of either linear or non-linear model parameters can be achieved in image space after carrying out a conventional expectation maximization (EM) update of the dynamic image series, using a Kullback-Leibler distance metric (comparing the modeled image values with the EM image values), to optimize the desired parameters. For MAP, an image-space penalty for regularization purposes is required. The benefits of 4D and direct reconstruction reported in the literature are reviewed, and furthermore demonstrated with simple simulation examples. It is clear that the future of reconstructing dynamic or functional emission tomography images, which often exhibit high levels of spatially correlated noise, should ideally exploit these 4D approaches.

Synaptic pathology is associated with several brain disorders, thus positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A) using the radioligand [11C]UCB-J may provide a tool to measure synaptic alterations. Given the pivotal role of mouse models in understanding neuropsychiatric and neurodegenerative disorders, this study aims to validate and characterize [11C]UCB-J in mice. We performed a blocking study to verify the specificity of the radiotracer to SV2A, examined kinetic models using an image-derived input function (IDIF) for quantification of the radiotracer, and investigated the in vivo metabolism. Regional TACs during baseline showed rapid uptake of [11C]UCB-J into the brain. Pretreatment with levetiracetam confirmed target engagement in a dose-dependent manner. VT (IDIF) values estimated with one- and two-tissue compartmental models (1TCM and 2TCM) were highly comparable (r=0.999, p < 0.0001), with 1TCM performing better than 2TCM for K1 (IDIF). A scan duration of 60 min was sufficient for reliable VT (IDIF) and K1 (IDIF) estimations. In vivo metabolism of [11C]UCB-J was relatively rapid, with a parent fraction of 22.5 ± 4.2% at 15 min p.i. In conclusion, our findings show that [11C]UCB-J selectively binds to SV2A with optimal kinetics in the mouse representing a promising tool to noninvasively quantify synaptic density in comparative or therapeutic studies in neuropsychiatric and neurodegenerative disorder models.

Although the STRATIFY satisfactorily predicted the fall risk of patients admitted to general medical and surgical wards and patients younger than 75, it failed to predict the fall risk of patients admitted to geriatric wards and patients aged 75 and older (particularly those aged 75-84).