TauRUS Tau-PET Atlas (MNI space, 1 mm)

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.<br><br>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 <i>et al.</i>, 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 <i>et al.</i>, 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 <i>et al.</i>, 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. <br>BASC next performs recursive k-means clustering on bootstrapped samples of the input data. After each clustering iteration, information about cluster membership is stored as a binarized adjacency matrix. The adjacency matrices are averaged resulting in a stability matrix representing probabilities of each pair of atoms clustering together (Figure 1). Finally, hierarchical agglomerative clustering with Ward criterion is applied to the stability matrix, resulting in the final clustering solution. The process is repeated over several clustering solutions (in this case, between 1 and 50), and the M-STEPs method (Bellec, 2013) was implemented to find the most stable clustering solutions. Briefly, M-STEPS identifies stable clustering solutions that demonstrate the best linear approximation of all solutions across a given subset. Therefore, M-STEPS identifies multiple optimal clustering solutions at different resolutions. In order to maintain relative similarity to Braak neuropathological staging (i.e. six regions-of-interest), we chose the lowest resolution solution for subsequent analysis. However, we have also provided the other higher-resolution solutions with this dataset. Note that no size constraints were imposed on clustering solutions. <br>BASC includes an option for outputting cluster “cores”, representing the portions of within-cluster peak stability for each cluster. Given that our aim was to produce covariance networks that would be generalizable across samples, we assumed that cluster cores would be more reliable than using clusters in their entirety, and therefore we used cluster cores in all subsequent analyses. Consequently, voxels were only included in a cluster when cluster probability membership exceed 0.5 (BASC default setting), eliminating unstable voxels from analysis (Bellec <i>et al.</i>, 2010; Garcia-Garcia <i>et al.</i>, 2017). This ensures voxels are only including if they fell within the same cluster around &gt; 50% of bootstrap samples. Both winner-takes-all and cluster-cores are included in this dataset.<br>RESULTS:The M-STEPS algorithm identified five-, nine- and 32-cluster solutions as optimal solutions. The five-cluster solution was selected for further analysis. The clusters were interpreted and named as follows: “1: Subcortical”, “2: Frontal”, “3: Medial/Anterior/Inferior Temporal”, “4: Temporo-parietal” and “5: Unimodal Sensory”. Cluster 3 bore resemblance to regions often involved in early tau aggregation and atrophy (Braak and Braak, 1991), while Cluster 4 also appeared similar to regions commonly associated with neurodegeneration in AD (Dickerson <i>et al.</i>, 2011). Of note, the hippocampus was largely unrepresented in any of the cluster-cores, though some voxels in the head of the hippocampus were included in Cluster 3, and a few distributed voxels were included in Cluster 1 (Subcortex). However, using a winner-takes-all clustering approach, voxels in the hippocampus were distributed between Clusters 1 and 3. <br>The clusters showed some similarity to pathological Braak stages (Break et al., 1991), and outperformed other tau-PET ROIs in describing cognitive data in a separate cohort (see Vogel et al., 2019 Human Brain Mapping). <br><br>本数据集包含一套脑区图谱，用于呈现覆盖阿尔茨海默病（Alzheimer's disease, AD）谱系个体的tau-PET信号典型空间分布模式。该脑区采用无假设、数据驱动的方法构建，旨在作为tau-PET生物标志物，用于汇总大脑内的tau-PET信号。本数据集包含完整图谱、各独立感兴趣区（region-of-interest, ROI）以及一套海马体掩膜（mask），后者分为“赢者通吃”（winner-takes-all）掩膜与簇核心（cluster-core）掩膜（详见下文）。所有图像均采用1mm分辨率的MNI空间（Montreal Neurological Institute space）格式。此外，还包含三种不同分辨率的图谱，以适配不同类型的分析需求。 **研究方法**：本研究的参与者样本来自BioFINDER队列（Hansson等，2016），共纳入123名接受[¹⁸F]AV1451-PET扫描的受试者，包括31名淀粉样蛋白阴性健康对照、24名淀粉样蛋白阳性健康对照、21名淀粉样蛋白阳性轻度认知障碍患者以及47名淀粉样蛋白阳性阿尔茨海默病痴呆患者。 采用开源无监督共识聚类算法——稳定聚类自举分析（Bootstrap Analysis of Stable Clusters, BASC），构建跨受试者的[¹⁸F]AV1451-PET协方差网络。BASC最初设计用于从静息态功能磁共振成像数据中提取多分辨率网络分区，其通过整合组内与组间稳定性矩阵的聚类结果来生成共识聚类方案（Bellec等，2010）。本研究将该算法适配至3D[¹⁸F]AV1451数据：将123例BioFINDER队列的[¹⁸F]AV1451图像沿第四维度（受试者维度）堆叠，生成单张4D图像作为输入。 BASC首先采用已报道的区域生长算法（Bellec等，2006）对数据进行降维，该算法被设置为提取空间约束的原子（即信号冗余的小区域），体积阈值设定为1000mm³。为降低计算负荷，本研究采用Desikan-Killiany图谱（Desikan-Killiany atlas）作为区域约束先验，并使用宽松的灰质掩膜对数据进行掩码处理——该掩膜包含皮层下结构，但手动移除了小脑（因小脑被用作[¹⁸F]AV1451图像的参考区域）。区域生长算法最终生成共730个原子，用于后续BASC分析。 BASC随后对输入数据的自举样本执行递归k均值聚类。每次聚类迭代后，将簇成员信息存储为二值邻接矩阵，对所有邻接矩阵取平均即可得到稳定性矩阵，该矩阵反映每一对原子共同聚类的概率（见图1）。最后，对稳定性矩阵应用基于Ward准则的层次凝聚聚类，得到最终聚类方案。本研究将聚类分辨率设置为1至50，并采用M-STEPS方法（Bellec，2013）筛选最稳定的聚类方案。简言之，M-STEPS可识别出在给定子集内对所有聚类方案具有最佳线性近似的稳定聚类结果，因此能够在不同分辨率下识别多个最优聚类方案。为了与Braak神经病理分期（即6个感兴趣区）保持相对相似性，本研究选择最低分辨率的聚类方案开展后续分析，但同时也将其他高分辨率聚类方案随本数据集一并提供。需注意，本研究未对聚类方案施加体积约束。 BASC支持输出簇“核心”（cluster-core），即每个簇内峰值稳定性最高的区域。鉴于本研究旨在生成可跨样本推广的协方差网络，我们假设簇核心相较于完整簇更为可靠，因此在所有后续分析中均采用簇核心。据此，仅当体素的簇成员概率超过0.5（BASC默认设置）时，才将其纳入对应簇，从而剔除不稳定体素（Bellec等，2010；Garcia-Garcia等，2017）。这一标准可确保仅纳入在超过50%的自举样本中均属于同一簇的体素。本数据集同时包含“赢者通吃”与簇核心两种聚类结果。 **研究结果**：M-STEPS算法筛选出5簇、9簇与32簇三种最优聚类方案，本研究选择5簇方案开展进一步分析。对各簇进行解读并命名如下：“1：皮层下”、“2：额叶”、“3：内侧/前/下颞叶”、“4：颞顶叶”以及“5：单模态感觉皮层”。第3簇与早期tau聚集和萎缩常累及的区域具有相似性（Braak与Braak，1991），而第4簇也与阿尔茨海默病中常见的神经退行性变相关区域相似（Dickerson等，2011）。值得注意的是，海马体在所有簇核心中均未得到充分体现，仅海马头部分的少量体素被纳入第3簇，另有少量散在体素被纳入第1簇（皮层下）。但采用“赢者通吃”聚类方法时，海马体的体素被分配至第1簇与第3簇中。 各聚类结果与病理Braak分期（Braak等，1991）具有一定相似性，且在独立队列的认知数据描述中表现优于其他tau-PET感兴趣区（详见Vogel等，2019《人类脑图谱》（Human Brain Mapping））。