Patients inclusion
We included two patients with probable DLB: (1) a male presenting with a 5-year history of cognitive impairment (revised Addenbrooke’s Cognitive Evaluation (ACER) score 81/100) interfering with daily activities, in association with cognitive fluctuations, REM sleep behaviour disorder and parkinsonism (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III 27/132) and (2) a female presenting with a 2-year cognitive impairment (ACER 50/100) associated with cognitive fluctuations, visual hallucinations and parkinsonism (MDS-UPDRS III 13/132). Patients were recruited from specialist memory clinics in and around Cambridgeshire, the Dementias and Neurodegeneration specialty of the UK Clinical Research Network (DeNDRoN) or the Join Dementia Research (JDR) platform (www.joindementiaresearch.nihr.ac.uk). Probable DLB was defined by the 2017 consensus criteria (McKeith et al., 2017).
Both patients underwent PET imaging with 11C-UCB-J, 11C-PiB and 18F-AV1451, as well as structural magnetic resonance imaging (MRI) (Bevan-Jones et al., 2017). They were compared to ten similarly aged control subjects, who were recruited from the JDR and local registers. Healthy controls had MMSE > 26, no cognitive symptoms, unstable/significant medical history or MRI contraindications.
PET and MRI imaging acquisition and preprocessing
All radioligands were prepared at the Wolfson Brain Imaging Centre, University of Cambridge (Milicevic-Sephton et al., 2020). 11C-UCB-J PET imaging (mean injected activity: 368 MBq controls; 325 MBq DLB cases) used dynamic scanning for 90 min on a GE SIGNA PET/MR (GE Healthcare, Waukesha, USA), with attenuation correction including the use of a multi-subject atlas method (Burgos et al., 2014; Wu & Carson, 2002). For the DLB patients only, additional static 11C-PiB (550 MBq) and dynamic 18F-AV-1451 (370 MBq) PET scans were performed. Each emission image series was aligned using SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12) to ameliorate the impact of patient motion during data acquisition and then rigidly registered to the corresponding T1-weighted MRI image. The Hammersmith atlas (http://brain-development.org/brain-atlases) regions of interest (ROIs) were non-rigidly registered to each T1-weighted MRI image. Regional time-activity curves were corrected for cerebrospinal fluid partial volume using SPM12 tissue probability maps smoothed to PET spatial resolution. 11C-UCB-J non-displaceable binding potential (BPND) was determined using a basis function implementation of the simplified reference tissue model (Wu & Carson, 2002), with the reference tissue defined in the centrum semiovale (Koole et al., 2019). Regional 11C-PiB standardized uptake value ratio (SUVR) was determined using cerebellar grey matter as the reference tissue, which was also used as the reference region for 18F-AV-1451 BPND quantified using a basis function simplified reference tissue model.
MRI imaging used a 3T scanner (MAGNETOM Trio; Siemens Healthineers, Erlangen, Germany) including a magnetization-prepared rapid gradient echo (MPRAGE) T1-weighted sequence (repetition time = 2300 ms, echo time = 2.98 ms, field of view = 240 × 256 mm2, 176 slices, flip angle = 9°, isotropic 1 mm voxels). Grey matter volume was assessed by volume-based morphometry (VBM) with Computational Anatomy Toolbox 12 (CAT12) using the standard pipeline (http://www.neuro.uni-jena.de/cat) (Dahnke et al., 2013). Images were smoothed with the recommended 8 mm-full-width at half maximum Gaussian kernel and regional values were extracted from the Hammersmith atlas.
Statistical analysis
ROI-based comparison of 11C-UCB-J BPND and VBM data between the two DLB subjects (1/2 (50%) male, mean age 73 years) and 10 similarly aged controls (5/10 (50%) male, age mean ± SD 72.4 ± 3.4 years) was performed using a general linear model with repeated measure ANCOVA using age, sex and years of education as covariates. As 33 cortical and subcortical ROIs were assessed, we used a false discovery rate (FDR)-corrected p < 0.05 with Q = 0.15. Subsequently, regional Z-scores for 11C-UCB-J BPND, 11C-PiB SUVR, 18F-AV1451 BPND and VBM data were computed for the DLB subjects by comparing them to the control group. Region-by-region correlations between the different brain imaging modalities were performed for the DLB subjects using Spearman correlations.