Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

Abstract

A brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model

“…Another key functionality of QCAlign is its ability to produce customized hierarchies via parsing through and selecting reference regions to compile into related summary regions. This is an approach also used in other studies to compensate for the difficulty in registering regions that lack anatomical boundaries 55 – 57 . Defining a standardized regional hierarchy also promotes the labeling of consistent regions of interest among laboratories.…”

Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model

“…Another key functionality of QCAlign is its ability to produce customized hierarchies, which aid in compensating for the difficulty of accurately registering small regions that lack anatomical boundaries. To combat this issue, many investigators generate lists of regions of interest (ROIs) that consist of compiled subregions [71][72][73] .…”

“…Another key functionality of QCAlign is its ability to produce customized hierarchies, which aid in compensating for the difficulty of accurately registering small regions that lack anatomical boundaries. To combat this issue, many investigators generate lists of regions of interest (ROIs) that consist of compiled subregions 71–73 . Our QCAlign tool offers the functionality to create these customized hierarchies by parsing through the 461 regions of the CCFv3 2015 and selecting subregions to compile into related summary regions.…”

Comprehensive Volumetric Analysis of Mecp2-Null Mouse Model for Rett Syndrome by T2-Weighted 3D Magnetic Resonance Imaging

“…Processing pipeline 1: The acquired structural T2-weighted images were registered to atlas coordinates ( Lein et al, 2007 ; Oh et al, 2014 ; Hikishima et al, 2017 ) using the script ‘‘antsRegistrationSyN.sh’’ in Advanced Normalization Tools (ANTs) open-source software. 1 Each brain label (575 regions in total) ( Komaki et al, 2016 ) was obtained by applying an inverse transformation based on the registration information from the atlas coordinates to the native coordinates of the individual data ( Uematsu et al, 2017 ; Takata et al, 2021 ). The individual label volume, which was automatically segmented by the ANT pipeline, was measured using the ITK-SNAP ( Yushkevich et al, 2006 ; Seki et al, 2017 ).…”