Tau protein neurofibrillary tangles are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimer’s disease and related dementias. Our knowledge of the pattern of neurofibrillary tangle progression in the human brain, critical to the development of imaging biomarkers and interpretation of in vivo imaging studies in Alzheimer’s disease, is based on conventional two-dimensional histology studies that only sample the brain sparsely. To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe specimens (age 75.3 ± 11.4 years, 45 to 93) were used to construct three-dimensional quantitative maps of neurofibrillary tangle burden in the medial temporal lobe at individual and group levels. Group-level maps were obtained in the space of an in vivo brain template, and neurofibrillary tangle was measured in specific anatomical regions defined in this template. Three-dimensional maps of neurofibrillary tangle burden reveal significant variation along the anterior-posterior axis. While early neurofibrillary tangle pathology is thought to be confined to the transentorhinal region, we find similar levels of burden in this region and other medial temporal lobe subregions, including amygdala, temporopolar cortex, and subiculum/cornu Ammonis 1 hippocampal subfields. Overall, the three-dimensional maps of neurofibrillary tangle burden presented here provide more complete information about the distribution of this neurodegenerative pathology in the region of the cortex where it first emerges in Alzheimer’s disease, and may help inform the field about the patterns of pathology spread, as well as support development and validation of neuroimaging biomarkers.

Tau neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s disease (AD). To elucidate patterns of structural change in the MTL specifically associated with tau pathology, we compared high-resolution ex vivo MRI scans of human postmortem MTL specimens with histology-based pathological assessments of the MTL. MTL specimens were obtained from twenty-nine brain donors, including patients with AD, other dementias, and individuals with no known history of neurological disease. Ex vivo MRI scans were combined using a customized groupwise diffeomorphic registration approach to construct a 3D probabilistic atlas that captures the anatomical variability of the MTL. Using serial histology imaging in eleven specimens, we labelled the MTL subregions in the atlas based on cytoarchitecture. Leveraging the atlas and neuropathological ratings of tau and TAR DNA-binding protein 43 (TDP-43) pathology severity, morphometric analysis was performed to correlate regional MTL thickness with the severity of tau pathology, after correcting for age and TDP-43 pathology. We found significant correlations between tau pathology and thickness in the entorhinal cortex (ERC) and stratum radiatum lacunosum moleculare (SRLM). When focusing on cases with low levels of TDP-43 pathology, we found strong associations between tau pathology and thickness in the ERC, SRLM and the subiculum/cornu ammonis 1 (CA1) subfields of the hippocampus, consistent with early Braak stages.

Graphical abstract

The medial temporal lobe (MTL) is a nidus for neurodegenerative pathologies and therefore an important region in which to study polypathology. We investigated associations between neurodegenerative pathologies and the thickness of different MTL subregions measured using high-resolution post-mortem MRI. Tau, TAR DNA-binding protein 43 (TDP-43), amyloid-β and α-synuclein pathology were rated on a scale of 0 (absent)—3 (severe) in the hippocampus and entorhinal cortex (ERC) of 58 individuals with and without neurodegenerative diseases (median age 75.0 years, 60.3% male). Thickness measurements in ERC, Brodmann Area (BA) 35 and 36, parahippocampal cortex, subiculum, cornu ammonis (CA)1 and the stratum radiatum lacunosum moleculare (SRLM) were derived from 0.2 × 0.2 × 0.2 mm3 post-mortem MRI scans of excised MTL specimens from the contralateral hemisphere using a semi-automated approach. Spearman’s rank correlations were performed between neurodegenerative pathologies and thickness, correcting for age, sex and hemisphere, including all four proteinopathies in the model. We found significant associations of (1) TDP-43 with thickness in all subregions (r =  − 0.27 to r =  − 0.46), and (2) tau with BA35 (r =  − 0.31) and SRLM thickness (r =  − 0.33). In amyloid-β and TDP-43 negative cases, we found strong significant associations of tau with ERC (r =  − 0.40), BA35 (r =  − 0.55), subiculum (r =  − 0.42) and CA1 thickness (r =  − 0.47). This unique dataset shows widespread MTL atrophy in relation to TDP-43 pathology and atrophy in regions affected early in Braak stageing and tau pathology. Moreover, the strong association of tau with thickness in early Braak regions in the absence of amyloid-β suggests a role of Primary Age-Related Tauopathy in neurodegeneration.

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BackgroundEx vivo magnetic resonance imaging (MRI) enables detailed characterization of neuroanatomy (Augustinack et al. 2013), such as hippocampal subfields in the medial temporal lobe (MTL) (Yushkevich et al. 2021, Ravikumar et al. 2021). However, automated cortical segmentation methods in ex vivo MRI are not well developed due to limited data availability and heterogeneity in scanners and acquisition. Here, we investigate a deep learning framework to parcellate the cortical mantle, compute thickness and link them with neuropathology ratings across 16 cortical regions in 7 Tesla MRIs of 38 ex vivo brain specimens spanning Alzheimer Disease and Related Dementias.MethodA deep learning method, nnU‐Net (Isensee et al. 2021), was trained on manually segmented 3D image patches (Figure 1C) to obtain automated cortical segmentations across 38 subjects (Table 1). We identified 16 landmarks (Figure 1A) for localized quantitative signatures of cortical morphometry and used the pipeline in Wisse et al. 2021 to measure local thickness (Figure 1B). Associations were computed between cortical thickness from manual and automated segmentations via Pearson’s correlation and average fixed‐raters Intra‐class Correlation Coefficient (ICC) for 16 locations (Figure 3). We also correlated thickness from both automated and manual segmentations with neuropathological ratings of tau and neuronal loss in corresponding contralateral regions and global Braak staging (Figures 4 and 5).ResultFigure 2 depicts cortical mantle segmentation across brain hemispheres. Figure 3 shows good agreement between ground truth and automated thickness, with 15 regions with significant associations (p<0.05) and 8 regions having r>0.6. We observe high ICC scores with 9 regions where ICC>0.7, confirming that automated segmentations accurately measure thickness. Figure 4 shows significant correlations between thickness and Tau ratings for Brodmann Area 35 (BA35) and midfrontal regions and trends between neuronal loss and thickness in entorhinal cortex (ERC), anterior temporal pole and anterior insula. Figure 5 shows significant correlations between thickness and Braak staging in ventrolateral temporal cortex and ERC, with trends in other regions.ConclusionOur automated ex vivo neuroimaging framework accurately segments the cortical mantle, provides thickness measurements that concur with user‐supervised thickness and links morphometry with underlying neurodegeneration, thus suggesting the strengths of ex vivo MRI.