Search

Purpose: We hypothesized that severe tau burden in brain regions involved in direct or indirect pathways of the basal ganglia correlate with more severe striatal dopamine deficiency in four-repeat (4R) tauopathies. Therefore, we correlated [18F]PI-2620 tau-positron-emission-tomography (PET) imaging with [123I]-Ioflupane single-photon-emission-computed tomography (SPECT) for dopamine transporter (DaT) availability. Methods: Thirty-eight patients with clinically diagnosed 4R-tauopathies (21 male; 69.0 ± 8.5 years) and 15 patients with clinically diagnosed α-synucleinopathies (8 male; 66.1 ± 10.3 years) who underwent [18F]PI-2620 tau-PET and DaT-SPECT imaging with a time gap of 3 ± 5 months were evaluated. Regional Tau-PET signals and DaT availability as well as their principal components were correlated in patients with 4R-tauopathies and α-synucleinopathies. Both biomarkers and the residuals of their association were correlated with clinical severity scores in 4R-tauopathies. Results: In patients with 4R-tauopathies, [18F]PI-2620 binding in basal ganglia and midbrain regions was negatively associated with striatal DaT availability (i.e. globus pallidus internus and putamen (β = − 0.464, p = 0.006, Durbin-Watson statistics = 1.824) in a multiple regression model. Contrarily, [18F]PI-2620 binding in the dentate nucleus showed no significant regression factor with DaT availability in the striatum (β = 0.078, p = 0.662, Durbin-Watson statistics = 1.686). Patients with α-synucleinopathies did not indicate any regional associations between [18F]PI-2620-binding and DaT availability. Higher DaT-SPECT binding relative to tau burden was associated with better clinical performance (β = − 0.522, p = 0.011, Durbin-Watson statistics = 2.663) in patients with 4R-tauopathies. Conclusion: Tau burden in brain regions involved in dopaminergic pathways is associated with aggravated dopaminergic dysfunction in patients with clinically diagnosed primary tauopathies. The ability to sustain dopamine transmission despite tau accumulation may preserve motor function. [ABSTRACT FROM AUTHOR]

Objectives : Asymmetric disease characteristics on neuroimaging are common in structural and functional imaging of neurodegenerative diseases, particularly in Alzheimer's disease (AD). However, a standardized clinical evaluation of asymmetric neuronal degeneration and its impact on clinical findings has only sporadically been investigated for F-18-fluorodeoxyglucose positron emission tomography (F-18-FDG-PET). This study aimed to evaluate the impact of lateralized neuronal degeneration on the detection of AD by detailed clinical testing. Furthermore, we compared associations between clinical evaluation and lateralized neuronal degeneration between FDG-PET hypometabolism and hippocampal atrophy. Finally, we investigated if specific subtests show associations with lateralized neuronal degeneration. Methods : One-hundred and forty-six patients with a clinical diagnosis of AD (age 71 ± 8) were investigated by FDG-PET and the "Consortium to Establish a Registry for Alzheimer's disease" (CERAD) test battery. For assessment of neuronal degeneration, FDG-PET hypometabolism in brain regions typically affected in AD were graded by visual (3D-surface projections) and semiquantitative analysis. Asymmetry of the hippocampus (left-right) in magnetic resonance tomography (MRI) was rated visually by the Scheltens scale. Measures of asymmetry were calculated to quantify lateralized neuronal degeneration and asymmetry scores were subsequently correlated with CERAD. Results : Asymmetry with left-dominant neuronal degeneration to FDG-PET was an independent predictor of cognitive impairment (visual: β = −0.288, p < 0.001; semiquantitative: β = −0.451, p < 0.001) when controlled for age, gender, years of education and total burden of neuronal degeneration, whereas hippocampal asymmetry to MRI was not (β = −0.034; p = 0.731). Direct comparison of CERAD-PET associations in cases with right- and left-lateralized neuronal degeneration estimated a detection gap of 2.7 years for right-lateralized cases. Left-hemispheric neuronal degeneration was significantly associated with the total CERAD score and multiple subscores, whereas only MMSE (semiquantitative: β = 0.429, p < 0.001) and constructional praxis (semiquantitative: β = 0.292, p = 0.008) showed significant associations with right-hemispheric neuronal degeneration. Conclusions : Asymmetry of deteriorated cerebral glucose metabolism has a significant impact on the coupling between neuronal degeneration and cognitive function. Right dominant neuronal degeneration shows a delayed detection by global CERAD testing and requires evaluation of specific subdomains of cognitive testing. [ABSTRACT FROM AUTHOR]

• Population-based metabolic connectome determined by PET. • Connectivity information used for statistical classification of single subjects. • Connectivity-based classification superior to univariate ML classifier. • Metabolic connectome as potential PET image biomarker. Metabolic connectivity patterns on the basis of [18F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [18F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes). [ABSTRACT FROM AUTHOR]