Included is an atlas of brain regions representing the typical spatial patterns of tau-PET signal distribution across individuals spanning the Alzheimer's disease spectrum. The regions were created using hypothesis-free, data-driven methods, and are designed to be tau-PET biomarkers used for summarizing tau-PET signal in the brain. A full atlas is included, as well as each ROI separately, and a set of masks of the hippocampus. These are divided into winner-takes-all and cluster-core masks (see below). All images are in MNI space at 1 mm resolution. In addition, atlases at three different resolutions are included to support different types of analyses.
METHODS: The participant sample included 123 individuals with [18F]AV1451-PET from the BioFINDER cohort (Hansson et al., 2016), including 31 amyloid-negative healthy controls, 24 amyloid+ healthy controls, 21 amyloid-positive patients with mild cognitive impairment, and 47 amyloid-positive patients with Alzheimer's disease dementia. Cross-subject [18F]AV1451-PET covariance networks were derived using an open-source unsupervised consensus-clustering algorithm called Bootstrap Analysis of Stable Clusters (BASC). BASC was originally designed to extract multi-resolution network parcellations from resting-state functional MRI data, where it builds consensus between clustering solutions across within- and between-subject stability matrices (Bellec et al., 2010). The algorithm was adapted to 3D [18F]AV1451 data by stacking all 123 BioFINDER [18F]AV1451 images along a fourth (subject) dimension, creating a single 4D image to be submitted as input. BASC first reduces the dimensions of the data with a previously described region-growing algorithm (Bellec et al., 2006), which was set to extract spatially constrained atoms (small regions of redundant signal) with a size threshold of 1000mm3. In order to reduce computational demands, the Desikan-Killainy atlas (Desikan et al., 2006) was used as a prior for region constraint, and the data was masked with a liberal gray matter mask, which included the subcortex but had the cerebellum manually removed (since this was used as the reference region for [18F]AV1451 images). The region-growing algorithm resulted in a total of 730 atoms, which were included in the BASC algorithm.
BASC next performs recursive k-means clustering on bootstrapped samples of the input data. After each c...
METHODS: The participant sample included 123 individuals with [18F]AV1451-PET from the BioFINDER cohort (Hansson et al., 2016), including 31 amyloid-negative healthy controls, 24 amyloid+ healthy controls, 21 amyloid-positive patients with mild cognitive impairment, and 47 amyloid-positive patients with Alzheimer's disease dementia. Cross-subject [18F]AV1451-PET covariance networks were derived using an open-source unsupervised consensus-clustering algorithm called Bootstrap Analysis of Stable Clusters (BASC). BASC was originally designed to extract multi-resolution network parcellations from resting-state functional MRI data, where it builds consensus between clustering solutions across within- and between-subject stability matrices (Bellec et al., 2010). The algorithm was adapted to 3D [18F]AV1451 data by stacking all 123 BioFINDER [18F]AV1451 images along a fourth (subject) dimension, creating a single 4D image to be submitted as input. BASC first reduces the dimensions of the data with a previously described region-growing algorithm (Bellec et al., 2006), which was set to extract spatially constrained atoms (small regions of redundant signal) with a size threshold of 1000mm3. In order to reduce computational demands, the Desikan-Killainy atlas (Desikan et al., 2006) was used as a prior for region constraint, and the data was masked with a liberal gray matter mask, which included the subcortex but had the cerebellum manually removed (since this was used as the reference region for [18F]AV1451 images). The region-growing algorithm resulted in a total of 730 atoms, which were included in the BASC algorithm.
BASC next performs recursive k-means clustering on bootstrapped samples of the input data. After each c...