# Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository

## TL;DR

This paper presents the first morphometry analysis of a postmortem brain repository, combining neuroimaging and neuropathology data to study dementia-related brain changes.

## Contribution

The paper introduces new image processing pipelines and deep learning tools for postmortem MRI analysis.

## Key findings

- Deep Learning algorithms can almost perfectly separate brain tissues from formalin buffered solution using structural MRI sequences.
- Regional brain volumes can be estimated despite postmortem challenges like tissue fixation and resolution issues.
- The analysis successfully detects atrophy patterns linked to healthy aging and dementia.

## Abstract

•We conduct the first morphometry study of the South Texas ADRC postmortem repository.•We compare neuroimaging findings with the neuropathology examinations available for this cohort.•Our analysis demonstrates the robustness of our new image processing pipelines.•We successfully detect the atrophy induced by healthy aging and dementia.•We hope that our new Deep Learning tools and pipelines will be useful in the future.

We conduct the first morphometry study of the South Texas ADRC postmortem repository.

We compare neuroimaging findings with the neuropathology examinations available for this cohort.

Our analysis demonstrates the robustness of our new image processing pipelines.

We successfully detect the atrophy induced by healthy aging and dementia.

We hope that our new Deep Learning tools and pipelines will be useful in the future.

Histopathology provides critical insights into the neurological processes inducing neurodegenerative diseases and their impact on the brain, but brain banks combining histology and neuroimaging data are difficult to create. As part of an ongoing global effort to establish new brain banks providing both high-quality neuroimaging scans and detailed histopathology examinations, the South Texas Alzheimer’s Disease Re- search Center postmortem repository was recently created with the specific purpose of studying comorbid dementias. As the repository is reaching a milestone of two hundred brain donations and a hundred curated MRI sessions are ready for processing, robust statistical analyses can now be conducted. In this work, we report the very first morphometry analysis conducted with this new data set. We describe the processing pipelines that were specifically developed to exploit the available MRI sequences, and we explain how we addressed several postmortem neuroimaging challenges, such as the separation of brain tissues from fixative fluids, the need for updated brain atlases, and the tissue contrast changes induced by brain fixation. In general, our results establish that a combination of structural MRI sequences can provide enough informa- tion for state-of-the-art Deep Learning algorithms to almost perfectly separate brain tissues from a formalin buffered solution. Regional brain volumes are challenging to measure in postmortem scans, but robust estimates sensitive to sex differences and age trends, reflecting clinical diagnosis, neuropathology findings, and the shrinkage induced by tissue fixation can be obtained. We hope that the new processing methods developed in this work, such as the lightweight Deep Networks we used to identify the formalin signal in multimodal MRI scans and the MRI synthesis tools we used to fix our anisotropic resolution brain scans, will inspire other research teams working with postmortem MRI scans.