Towards a comprehensive 3D mapping of tau progression in early Alzheimer’s disease

Characterizing the progression of Alzheimer’s disease pathology is critical for early diagnosis and assessment of response to treatment. Accumulation of tau neurofibrillary tangles (NFTs) is one of the pathological hallmarks of Alzheimer’s disease and highly related to neurodegeneration and cognitive decline. Histological studies over the past 30 years have demonstrated the characteristic spread of NFTs in ageing and Alzheimer’s disease. The transentorhinal region of the medial temporal lobe (MTL) is thought to be one of the first sites of tau accumulation, along with the locus coeruleus and dorsal raphe nucleus. Tau then spreads from the MTL to limbic and isocortical areas. Although these histological studies have led to significant advances in biomarker and drug development, they are intrinsically limited and may not actually reveal a complete picture of the underlying NFT progression because of limited sampling of the brain; ‘the sheer size of the cerebral cortex, the enormous amount of tissue to be processed, and the complexity of the many regions to be analyzed have virtually precluded a fully comprehensive neuropathological characterization of Alzheimer’s disease’. In this issue of Brain, Yushkevich and colleagues perform the most comprehensive characterization of NFT burden to date, using the latest advances in neuroimaging, machine learning, and histological reconstruction to densely quantify 3D maps of NFT burden in the MTL of 18 individuals. In their study, the authors made use of brain samples from two different research centres, from donors aged 45 to 93 with no known neurological disease (n = 12) or who were previously subjects in a dementia imaging study (n = 6). While one hemisphere was reserved for diagnostic pathology, tissue from the other underwent a series of imaging and cutting steps to ensure accurate alignment could be maintained or recovered to automatically reconstruct histological slides in 3D. This included overnight MRI at 9.4 T to acquire 200-lm resolution anatomical images, lower resolution 7 T MRI to correct distortions, sectioning using a 3D printed mould and cutting guide of the tissue designed from the 7 T scan, and frozen sectioning into 50-lm sections with blockface photographs after each cut. Sections were stained for neuronal cell bodies (Nissl, every 0.5 mm) and phosphorylated tau (AT8, every 1 mm), then digitized at 20 resolution. Since manually annotating tau pathologies across the entire set of slides would be infeasible, the authors developed a custom web-based slide annotation tool to crowdsource over 11000 example annotations from 12 raters. They then used these annotations to train an artificial neural network in a weakly supervised approach, whereby pixel-wise segmentation is performed using image-level labels, in order to automatically annotate all the slides. The authors employed the WildCat algorithm, which consists of a fully convolutional neural network with a ResNet-101 architecture followed by a multilevel transfer layer and spatial pooling, to classify histology image patches into tangle versus non-tangle classes. These segmentations were transformed to a reconstructed 3D space through a series of largely automated image processing and registration steps that align the MRI volumes, blockface images, and tissue (including torn fragments) from histology slides. Tau burden heat maps were generated for each subject and also registered to a standard in vivo MRI template, where MTL regions of interest along with whole-brain regions of interest were used to provide summary measures. An illustration of the main imaging, histological processing, reconstruction, and quantification stages is shown in Fig. 1A. The authors found that the transentorhinal cortex was consistently an area of high tangle burden. However, unlike the classical observations suggesting that early stage tangles are confined to this region, this work demonstrated similar and sometimes greater burden in surrounding MTL structures, including the amygdala, anterior temporal lobe, and anterior portions of the CA1 and subiculum hippocampal subfields. The semi-supervised WildCat approach was able to classify NFT burden with a high degree of accuracy, with especially strong performance discriminating between the lowest classes of NFT burden. This demonstrates the potential for mapping the earliest stages of tau pathology. Greater NFT burden in anterior structures over posterior, or an anterior-posterior gradient, in the MTL was also generally observed; seemingly contrary to expectations given that progression in later stages of typical Alzheimer’s disease dementia follows a posterior trajectory. It should be noted however that the majority of cases included in the study did not present with high amyloid-b burden, which may have limited the representation of more Alzheimer’s disease-typical progression patterns of tau. These findings by Yushkevich et al., when considering the evidence that the subjects were at an early stage of tau deposition, may suggest that early-stage progression has a more nuanced pattern than previously described, with a greater effect on anterior temporal regions. Anterior and posterior MTL have differential involvement in brain networks subserving memory, with an anterior temporal system involved in object memory, and a posterior medial system involved in scene and spatial memory. Recent imaging studies have shown that in the earliest stage of preclinical