DOI: 10.5061/dryad.msbcc2g0n
title: Processed FDG-PET data from: A computational model of neurodegeneration in Alzheimer’s disease

description: Disruption of mental functions in Alzheimer’s disease (AD) and related
 disorders is accompanied by selective degeneration of brain regions. These
 regions comprise large-scale ensembles of cells organized into systems for
 mental functioning, however the relationship between clinical symptoms of
 dementia, patterns of neurodegeneration, and functional systems is not
 clear.  We developed a model of the association between dementia
 symptoms and degenerative brain anatomy using F18-fluorodeoxyglucose (FDG)
 PET and dimensionality reduction techniques patients with AD. This data
 and code package contains preprocessed FDG-PET images from 423 subjects
 across the Alzheimer’s disease spectrum and the MATLAB code to produce
 eigenbrains from this data.
description: <strong>Participants:</strong> All
 participants or their designee provided written consent with approval of
 the Mayo Clinic Foundation and Olmsted Medical Center Institutional Review
 boards. All participants in the Mayo Clinic Rochester Alzheimer’s Disease
 Research Center (ADRC) and the Mayo Clinic Study of Aging (MCSA) that met
 our inclusion criteria were included in this study. The Mayo Clinic
 Rochester ADRC is a longitudinal cohort study that enrolls subjects from
 the clinical practice at Mayo Clinic in Rochester, MN.
 1 The MCSA is a population-based study of cognitive aging among
 Olmsted County, MN residents 2. Enrolled participants
 are adjudicated to be clinically normal or cognitively impaired by a
 consensus panel consisting of study coordinators, neuropsychologists, and
 behavioral neurologists. Methods for defining clinically unimpaired, mild
 cognitive impairment and dementia in both studies conform to standards in
 the field 3-5. MCSA study participants receive renumeration of USD 100 as
 part of study participation. Both the MCSA and the ADRC studies offer
 assistance with ground transportation cost associated with study
 participation and USD 50 for participation in PET scanning portions of the
 study.  Inclusion criteria for this study consisted of
 1) a CDR global score greater than zero, 2) presence of amyloid plaques,
 defined as amyloid-PET standard uptake value ratio (SUVR) greater than
 1.5, and 3) had high quality MRI, amyloid-PET, and FDG-PET data available
 for analysis. A higher more conservative SUVR cut point was used for
 defining amyloid-PET positivity to avoid false positives 1. 
 <strong>Structural Magnetic Resonance
 Imaging:</strong> MRI was performed on one of
 three compatible 3T systems from the same vendor (General Electric,
 Waukesha, WI, USA).1 A 3D magnetization prepared rapid acquisition
 gradient echo (MPRAGE) structural imaging sequence developed for the
 Alzheimer's Disease Neuroimaging Initiative (ADNI) study was acquired
 6. All images were acquired using an 8-channel phased array head coil.
 Post-processing to correct for gradient distortion correction and
 processing has been validated in multiple studies, shown to give
 consistent stable results in ADNI data, and geometric fidelity after
 correction is independent of scanner 7,8. Parameters were: TR/TE/T1,
 2300/3/900 msec; flip angle 8°, 26 cm field of view (FOV); 256 × 256
 in-plane matrix with a phase FOV of .94, and slice thickness of 1.2 mm.
 These MPRAGE parameters have been held invariant since approximately 2008.
 This structural MRI was used for preprocessing PET data. MRI data is not
 included with this dataset.  <strong>PET
 Acquisition and Preprocessing: </strong> The
 amyloid-PET imaging was performed with C-11 Pittsburgh Compound B 9 and
 FDG-PET with F-18 fluorodeoxyglucose. PET images were acquired using 1 of
 2 PET/CT scanners (DRX; GE Healthcare). A computed tomography scan was
 obtained for attenuation correction. These images were usually acquired on
 the same day with 1 hour between amyloid-PET and FDG-PET acquisitions.
 Subjects were prepared for FDG-PET in a dimly lit room, with minimal
 auditory stimulation.   Amyloid-PET images consisted of four 5-min dynamic
 frames from 40 to 60 min after injection. FDG-PET consisted of four 2-min
 dynamic frames acquired from 30 to 38 min after injection. PET sinograms
 were iteratively reconstructed into a 256 mm FOV. The pixel size was 1.0
 mm and the slice thickness 3.3 mm. Standard corrections were
 applied. The global amyloid-PET SUVRs were calculated
 as previously described 10. The amyloid-PET images are not incuded with
 this dataset. The FDG-PET image volumes of each subject were coregistered
 to the subject’s own T1-weighted MRI scan, using a 6 degree-of-freedom
 affine registration with mutual information cost function. Each MRI scan
 was then spatially normalized to an older adult template space 11 using a
 unified segmentation and normalization algorithm 12 with transforms
 applied to co-registered FDG-PET images. These spatially normalized images
 were then intensity normalized to the pons and spatially smoothed with a 6
 mm full-width half-maximum Gaussian kernel. 
 <strong>Between-subject variability projection and
 reduction: </strong> The unsupervised machine
 learning framework, Between-subject variability Projection and Reduction
 (BPR), was designed to capture pathophysiologic information present in
 between-subject variability in a disease parameter of interest. The
 singular value decomposition (SVD) at the heart of the data reduction
 portion of the algorithm is widely used and interpretable, but other
 methods could be used depending on the framing of the problem at hand. The
 goals of this framework also motivate data preprocessing decisions that
 focus on between-subject variance within the class being studied rather
 than variance in the observed modality under investigation or variance
 relative to classes not being studied. This algorithm conceptualizes
 multivariate medical data from an individual as representing a particular
 parameterization of a (patho)physiological process of interest and uses
 within-class individual differences in this parametrization to define a
 high dimensional parameter space that contains a smaller dimensional
 subspace manifold that describes common features of the disease generating
 processes of interest. This lower dimensional subspace can be isolated in
 many ways, but ideally the dimensionality reduction technique used would
 retain interpretability in order to promote understanding of the
 pathophysiology of interest and be able to meaningfully place new subjects
 into the learned subspace and make interpretable predictions about
 clinical variables of interest. In the present study,
 we assume that macroscale glucose uptake patterns in cognitively impaired
 individuals with amyloid plaque deposits represent a parameterization of
 macroscale AD pathophysiology. We then isolated the between-subject
 variability of interest to this study from these preprocessed FDG-PET
 scans in the following way. The preprocessed FDG-PET images are
 three-dimensional arrays of voxel intensities that correspond to SUVR
 values in a standard template space. Taking only the voxel intensities
 that fall within the set of voxels that have a greater than 15%
 probability of being gray matter in template space, this three-dimensional
 array can be reduced to a one-dimensional vector. To isolate subject
 effects, each element is non-parametrically standardized by the median and
 interquartile range for that element across subjects. Subject-wise
 centering of each image is then performed. This can then be used to
 represent the individual differences of interest in the brain images
 between each image pair, or between subject variance, by calculating the
 subject-wise covariance  matrix. This high-dimensional
 projection of individual differences can be represented as an
 eigendecomposition, using the singular-value decomposition, such that the
 eigenvectors of subject-wise covariance determine the linear combination
 of the M set of FDG-PET images that produce image space eigenvectors, or
 eigenbrains given that they can be ordered into a three-dimensional
 configuration corresponding to the original brain images, as previously
 described for the eigenfaces facial recognition algorithm for
 two-dimensional facial recognition 13. This algorithm demonstrates how
 individual differences in multivariate patterns in brain images can be
 mapped back into the original image space in the form of a compact
 lower-dimensional basis-set of eigenbrains (EBs). This allows for a highly
 interpretable understanding of the parameterization of a disease process
 affecting the individuals included in the analysis. The first 10 EBs
 explained 51% of the variance in the dataset. 
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