Using an ex vivo MRI atlas of the human medial temporal lobe to characterize the 3D distribution of neurofibrillary tangle burden across histologically defined subregions
Authors: Ravikumar Sadhana , Denning Amanda E , Lim Sydney A , Chung Eunice , Ittyerah Ranjit , Wisse Laura EM , Xie Long , Das Sandhitsu R. , Robinson John L. , Schuck Theresa , Grossman Murray , Lee Eddie B , Tisdall Dylan M , Prabhakaran Karthik , Detre John A. , Mizsei Gabor , Artacho‐Perula Emilio , Martin Maria Mercedes Iniguez de Onzono , Mar Arroyo Jimenez Maria , Munoz Monica , Romero Francisco Javier Molina , del Pilar Marcos Rabal Maria , Sanchez Sandra Cebada , Gonzalez Jose Carlos Delgado , Rosa Prieto Carlos , Parada Marta Corcoles , Irwin David J. , Trojanowski John Q. , Wolk David A. , Insausti Ricardo , Yushkevich Paul A. 
Published: December 2023

Background

Accumulation of tau neurofibrillary tangles (NFT) in the medial temporal lobe (MTL) is an early pathological change associated with neurodegeneration in Alzheimer’s Disease (AD). Current understanding of the spread of NFTs within the MTL is based on 2‐D histological sections sampled at sparse locations. Here, using human MTL specimens from donors with diagnoses spanning the AD continuum, we examine 3‐D quantitative maps of NFT burden derived from serial histology leveraging a high‐resolution, ex vivo MRI atlas, thus enabling more granular analyses.

Method

We combined ex vivo MRI scans (0.2×0.2×0.2mm3, 9.4T) of 55 MTL specimens using a customized registration approach to construct a 3‐D atlas (Ravikumar et al., Acta Neuropathol Comm, 2021). MTL subregions in the atlas were labelled based on cytoarchitecture using serial Nissl histology (n = 17) (Fig.1). For 25 specimens with a primary diagnosis of AD or primary age‐related tauopathy (78.6±10.9 years; 16M/9F), quantitative NFT burden maps were derived from serial anti‐tau immunohistochemistry histology and registered to the atlas for group‐level analysis (Yushkevich et al., Brain, 2021). For quantitative comparisons, we computed the mean NFT burden and cortical thickness within each subregion.

Result

Fig.2A shows average and summary frequency maps of NFT burden, computed separately for specimens with a low Braak stage (Braak0‐II; n = 11) and high Braak stage (BraakIV‐VI), that reveal a clear anterior‐to‐posterior gradient in NFT burden. At low Braak stages, we observe high levels of NFT burden in cornu ammonis (CA)1, entorhinal cortex (EC), and Brodmann area 35 (BA35;≈transentorhinal region), particularly in the outer cortical layers. CA1 and EC also show higher NFT burden relative to BA35 (Fig.2C). At high Braak stages, overall NFT burden is visibly higher and more widespread, with significant increases found across almost all subregions (Fig.2B). Moreover, significant correlations between NFT burden and cortical thinning were found in the perforant pathway (PP; molecular layer of the subiculum), dentate gyrus, EC and Areas TE, TF and TH (Fig.3).

Conclusion

Building on earlier work, we provide a detailed characterization of the distribution of NFT burden in the MTL. Our findings of specific subregional involvement of early NFT pathology can help inform the development of improved in vivo neuroimaging biomarkers