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. 2024 Jun 5;15(1):4803.
doi: 10.1038/s41467-024-49205-0.

Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer's disease

Sadhana Ravikumar  1 , Amanda E Denning  2 , Sydney Lim  2 , Eunice Chung  2 , Niyousha Sadeghpour  2 , Ranjit Ittyerah  2 , Laura E M Wisse  3 , Sandhitsu R Das  4 , Long Xie  2 , John L Robinson  4 , Theresa Schuck  5 , Edward B Lee  5 , John A Detre  4 , M Dylan Tisdall  2 , Karthik Prabhakaran  4 , Gabor Mizsei  2 , Maria Mercedes Iñiguez de Onzono Martin  6 , Maria Del Mar Arroyo Jiménez  6 , Monica Mũnoz  6 , Maria Del Pilar Marcos Rabal  6 , Sandra Cebada Sánchez  6 , José Carlos Delgado González  6 , Carlos de la Rosa Prieto  6 , David J Irwin  4 , David A Wolk  4 , Ricardo Insausti  6 , Paul A Yushkevich  7
Affiliations

Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer's disease

Sadhana Ravikumar et al. Nat Commun. .

Abstract

Our current understanding of the spread and neurodegenerative effects of tau neurofibrillary tangles (NFTs) within the medial temporal lobe (MTL) during the early stages of Alzheimer's Disease (AD) is limited by the presence of confounding non-AD pathologies and the two-dimensional (2-D) nature of conventional histology studies. Here, we combine ex vivo MRI and serial histological imaging from 25 human MTL specimens to present a detailed, 3-D characterization of quantitative NFT burden measures in the space of a high-resolution, ex vivo atlas with cytoarchitecturally-defined subregion labels, that can be used to inform future in vivo neuroimaging studies. Average maps show a clear anterior to poster gradient in NFT distribution and a precise, spatial pattern with highest levels of NFTs found not just within the transentorhinal region but also the cornu ammonis (CA1) subfield. Additionally, we identify granular MTL regions where measures of neurodegeneration are likely to be linked to NFTs specifically, and thus potentially more sensitive as early AD biomarkers.

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Conflict of interest statement

D.A.W. has served as a paid consultant to Eli Lilly, GE Healthcare, and Qynapse. He serves on a DSMB for Functional Neuromodulation and is a site investigator for a clinical trial sponsored by Biogen. All of this is outside of this work. L.X. received personal consulting fees from Galileo CDS, Inc., outside of this work, and has become an employee of Siemens Healthineers since May 2022. The current study was started during his employment at the University of Pennsylvania and is outside of his work at Siemens. S.R.D. received consultation fees from Rancho Biosciences and Nia Therapeutics, outside of this work. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Computational atlas of the medial temporal lobe (MTL) constructed from ex vivo MRI scans of 55 donor specimens and serial histology in 17 specimens.
Four coronal sections are shown ordered from anterior (ant) to posterior (post), indicated as I, II, III, and IV, as well as a sagittal cross-section and 3-D reconstructions of the MTL and SRLM surfaces. For each cross-sectional view, the “average” MRI is shown with and without the histology-derived, consensus MTL subregion segmentation. The subregion labeling includes the subdivisions of the ERC (in shades of purple), BA35 (in shades of blue), BA36 (in shades of pink), and area TF (in shades of green). (med medial, lat lateral, sup superior, inf inferior, PrSPaS pre/parasubiculum, S subiculum, CA cornu ammonis, DG dentate gyrus, SRLM stratum radiatum lacunosum molecular, PP perforant pathway, ERC entorhinal cortex, BA Brodmann Area, RSC retrosplenial cortex).
Fig. 2
Fig. 2. Characterization of histology-derived quantitative tau neurofibrillary tangle (NFT) burden maps in the space of a 3-D ex vivo anatomical atlas.
A Average and summary frequency maps of NFT burden in the space of the ex vivo MRI atlas of the medial temporal lobe (MTL). Maps are computed separately for specimens with a low B score (B0 or B1, which corresponds to Braak stages 0–II; n = 11) and a high B score (B2 or B3, which corresponds to Braak stages III–VI). For each subgroup, four coronal and one sagittal cross-sectional view of the average NFT burden map, and four frequency maps are visualized. The frequency maps at each voxel describe the fraction of cases for which the NFT burden at that voxel was above a given threshold. Thresholds were chosen based on the analysis conducted by Yushkevich et al. (2021) and correspond to different levels of pathological burden (> 1.0 for `severe’; > 0.5 for `moderate’; > 0.25 for `mild’; > 0.1 for `rare’). The top row shows the corresponding cross-sections of the consensus histology-based MTL subregion segmentation. For simplicity, we combine the presubiculum and parasubiculum labels, and the subdivisions of the ERC, BA35, and BA36. B Box plots comparing the distribution of mean NFT burden within each subregion between patients with a low and high B score. Using the two-sided t test, significant increases in NFT burden are observed in all subregions except the subiculum, where it nearly reaches significance (p < 0.05). C Box plot showing the NFT burden in MTL subregions normalized to BA35 NFT burden (dashed blue line). Subregions are sorted in order of decreasing mean NFT burden relative to BA35, going from top to bottom. Box plots in (B) and (C) show the median as the middle box line, first quartile (Q1) and third quartiles (Q3) as box edges (denoting the interquartile range, IQR), whiskers as the minima/maxima and outliers based on thresholds < Q1 − 1.5(IQR) or > Q3 + 1.5(IQR). Sample sizes are provided in Supplementary Table 2. Source data for 2B) and (C) are provided as a Source Data file. (S subiculum, PrS-PaS Pre/Parasubiculum, SRLM stratum radiatum lacunosum molecular PP perforant pathway, CA cornu ammonis, DG dentate gyrus, HATA hippocampal amygdala transition area, ERC entorhinal cortex, BA Brodmann area).
Fig. 3
Fig. 3. Relationship between mean cortical thickness and mean quantitative NFT burden computed within the same MTL subregion.
The scatter plots illustrate the regional relationship between cortical thickness and NFT burden measured within the same subregion for each of the 14 MTL subregions. Each plot also includes the Spearman’s rank correlation calculated between mean cortical thickness and mean NFT burden within the same subregion. The two exceptions are SRLM and PP since these ROIs do not directly accumulate NFT pathology. SRLM and PP thickness are correlated with mean NFT burden in CA1 and the ERC region respectively. Significant negative associations are bolded (one-sided, uncorrected p < 0.05). The asterisk is used to indicate ROIs where the model including both age and mean NFT burden is significant. Sample sizes are provided in Supplementary Table 2. Source data are provided as a Source Data file. (S subiculum, PrS-PaS: Pre/Parasubiculum, SRLM stratum radiatum lacunosum molecular, PP perforant pathway, CA cornu ammonis, DG dentate gyrus, ERC entorhinal cortex, BA Brodmann area).
Fig. 4
Fig. 4. Association between pointwise medial temporal lobe thickness and ipsilateral, quantitative NFT burden measures.
The t-statistic maps show the correlation between pointwise cortical thickness and the mean NFT burden computed within different anatomical subregions (n = 25). The NFT burden measures are computed based on the 3-D quantitative NFT density maps. The analyses are arranged in the order in which the anatomical subregions used are affected during the AD process, from early to late, based on the results shown in Fig. 2C. The t-statistic maps reveal increasingly strengthened associations with NFT burden measures derived from regions affected during the later stage of AD. Clusters were defined based on an empirical threshold (uncorrected p < 0.01) and permutation testing with the Freedman & Lane method (1000 iterations) was used to assign each potential cluster a one-sided, corrected p-value. To account for multiple comparisons, the analysis uses cluster-level family-wise error rate correction. The clusters outlined in black indicate regions where a significant correlation was observed after correction for multiple hypothesis testing (corrected p < 0.05). No covariates were included in this model. (S subiculum, PrS-PaS Pre/Parasubiculum, SRLM = stratum radiatum lacunosum molecular, PP perforant pathway, CA cornu ammonis, DG dentate gyrus, ERC entorhinal cortex, BA Brodmann area).
Fig. 5
Fig. 5. Association between pointwise medial temporal lobe (MTL) thickness and contralateral, semi-quantitative neuropathology measures.
The t-statistic maps show the correlation between cortical thickness and the semi-quantitative neuropathology ratings based on tissue samples obtained from the MTL contralateral to the thickness measures (n = 47). Each row specifies the variable of interest with the covariates used in the analysis in parentheses. The first two rows show the association between thickness and tau burden, with and without correction for co-pathologies. Rows 3 and 4 show the patterns of correlation obtained between pointwise thickness and TDP-43 and α-synuclein pathology respectively. Clusters were defined based on an empirical threshold (uncorrected p < 0.01) and permutation testing with the Freedman & Lane method (1000 iterations) was used to assign each potential cluster a one-sided, corrected p-value. To account for multiple comparisons, the analysis uses cluster-level family-wise error rate correction. The clusters outlined in black indicate regions where a significant correlation was observed after correction for multiple hypothesis testing (corrected p < 0.05). No covariates were included in this model. (S subiculum, PrS-PaS: Pre/Parasubiculum, SRLM stratum radiatum lacunosum molecular, PP: perforant pathway, CA cornu ammonis, DG dentate gyrus, ERC entorhinal cortex, BA Brodmann area).
Fig. 6
Fig. 6. 3-D distribution of neurofibrillary tangle pathology within the entorhinal cortex.
A Coronal cross-sectional view of the medial temporal lobe atlas at different levels, ordered from anterior to posterior, showing the histology-based consensus labeling of the entorhinal subfields. Note that since the anterior border of the atlas only starts at the hippocampal head, we are partially missing the anterior extents of the entorhinal subfields Eo, ER, and EMI. B Box and whisker plots showing the distribution of mean NFT burden across the different entorhinal subfields, computed using all cases and separately for cases with low (B0/1; n = 11) and high (B2/3; n = 14) B scores respectively. These plots are shown together with corresponding surface heat maps which highlight the distribution of NFT burden across the entorhinal subfields ranked in order of highest to lowest mean NFT burden. C Scatter plots showing the relationship between mean cortical thickness and mean NFT burden computed within each subfield. Each plot also includes the Spearman’s rank correlation. Significant negative associations are bolded (one-sided, uncorrected p < 0.05). Sample sizes are provided in Supplementary Table 3. Source data for 6(B) and (C) are provided as a Source Data file. (Eo: olfactory; EMI: medial intermediate; EI: intermediate; ELr: lateral rostral; ELc: lateral caudal; EC: caudal; ECL: caudal limiting).
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