Acta Neuropathologica

[18F]AV-1451 tau-PET uptake does correlate with quantitatively measured 4R-tau burden in autopsy confirmed corticobasal degeneration

¥Keith A. Josephs, MD, MST, MSc; ‡Jennifer L. Whitwell, PhD; †Pawel Tacik, PhD;

¥Joseph R. Duffy, PhD; πMatthew L. Senjem, MS; ≠Nirubol Tosakulwong, BS;

‡Clifford R. Jack, Jr MD; ‡Val Lowe, MD; †Dennis W. Dickson, MD; †Melissa E. Murray, PhD

Departments of ¥Neurology, ‡Radiology, πInformation Technology and ≠Health Sciences Research, Mayo Clinic, Rochester MN and †Department of †Neuroscience, Mayo Clinic, Jacksonville Florida

ADDITIONAL METHODS

Image acquisition

Tau-PET imaging was performed using the [18F]AV-1451 ligand and a 690 XT PET/CT scanner (GE Healthcare, Milwaukee, Wisconsin) operating in 3D mode. An intravenous bolus injection of approximately 370MBq (10mCi) of [18F]AV-1451 was administered, followed by a 20 minute PET acquisition performed 80 minutes after injection. Emission data was reconstructed into a 256x256 matrix with a 30-cm FOV (Pixel size=1.0mm, slice thickness=1.96mm).

The FDG-PET and PiB-PET scans were also acquired using a PET/CT scanner (GE Healthcare, Milwaukee, Wisconsin) operating in 3D mode. For FDG-PET, the patient was injected with FDG (average, 459 MBq; range, 367-576 MBq) in a dimly lit room with minimal auditory stimulation. After a 30-minute uptake period an 8-minute FDG scan was performed consisting of four 2-minute dynamic frames following a low dose CT transmission scan. For PiB-PET, the patient were injected with PiB (average, 614 MBq; range, 414-695 MBq) and after a 40-60 minute uptake period a 20 minute PiB scan was obtained consisting of four 5-minute dynamic frames following a low dose CT transmission scan. Standard corrections were applied. Individual frames of the FDG and PiB dynamic series were realigned if motion was detected and then a mean image was created. Emission data were reconstructed into a 256x256 matrix with a 30-cm FOV. The image thickness was 3.75-mm.

Our patient also underwent a 3T MRI protocol that included a 3D magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence (TR/TE/TI, 2300/3/900 ms; flip angle 8°, 26-cm FOV; 256 x 256 in-plane matrix with a phase FOV of 0.94, slice thickness=1.2mm, in-plane resolution =1mm).

All imaging sequences were performed within two days of each other.

Image analysis

[18F]AV-1451 and FDG-PET images were co-registered to the patients MPRAGE using 6-degree-of-freedom (DOF) rigid body registration. A modified version of the automated anatomical labeling (AAL) atlas [4] was transformed into the native space of the MPRAGE. Median [18F]AV-1451 uptake and median FDG-PET uptake were calculated for the following 10 regions-of-interest (ROIs): middle frontal, supplementary motor area, primary motor, Broca’s area, orbitofrontal cortex, inferior parietal, superior temporal, parahippocampal gyrus, visual cortex and striatum. [18F]AV-1451 uptake was calculated in both the grey and white matter, while FDG-PET was calculated in grey matter, using segmentations created in Statistical Parametric Mapping (SPM5) (www.fil.ion.ucl.ac.uk/spm). To create standard uptake ratios (SUVRs), tau-PET uptake in each ROI was divided by uptake in cerebellar crus grey matter, and FDG-PET uptake was divided by uptake in pons.

The modified version of the AAL atlas was also used to calculate grey matter volumes from the MPRAGE scan for the same 10 ROIs. In order to be able to perform correlations across grey matter volumes from regions of differing size, the grey matter volumes were converted into z scores representing the difference from a group of 10 cognitively normal male controls that were the same age as the CBD patient. All grey matter volumes were divided by total intracranial volume before being converted to z scores.

Rates of grey matter atrophy for each ROI were calculated between the MPRAGE scans performed 9 and 22 months before death using an in-house developed version of tensor-based morphometry using symmetric normalization (TBM-SyN)[1,2] and Advanced Normalization Tools (ANTs) software (http://picsl.upenn.edu/software/ants), and SPM5 (www.fil.ion.ucl.ac.uk/spm) software. Prior to computing SyN deformations, the serial scans were co-registered using a 9DOF linear registration to their common mean. Next, an in-house developed implementation of differential bias correction was run in order to remove the intensity inhomogeneity bias between the subjects series of images, in a manner similar to that of [3]. Next, ANTs software was used to compute a SyN deformation between the 9DOF registered and differential bias corrected scan-pair. The SyN algorithm was used to compute a nonlinear deformation required to transform the later image to the earlier image for each pair of scans, producing an image of the log of the Jacobian determinants for each. These log Jacobian images were then scaled by the interscan interval in years, thus producing annualized log Jacobian images. The SyN deformation was applied to warp the late image to the early image, and the warped late image was then averaged with the early image to form a softmean image in the space of the early image. We then applied unified segmentation, with a custom elderly template [5] to the softmean image. The inverse spatial normalization parameters from the unified segmentation step were used to propagate the AAL atlas from the custom template space, into subject space. Atlas labels denoting grey matter regions-of-interest that were defined on the softmean image were then applied to the annualized log Jacobian image to get the mean annualized log Jacobian values within each cortical region. These values were then averaged across the two computation directions (after inverting the backwards direction).

References

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