Universität zu Köln

Pallast, Niklas, Diedenhofen, Michael, Blaschke, Stefan, Wieters, Frederique, Wiedermann, Dirk, Hoehn, Mathias, Fink, Gereon R. ORCID: 0000-0002-8230-1856 and Aswendt, Markus ORCID: 0000-0003-1423-0934 (2019). Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri). Front. Neuroinformatics, 13. LAUSANNE: FRONTIERS MEDIA SA. ISSN 1662-5196

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Abstract

Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Pallast, Niklas UNSPECIFIED UNSPECIFIED UNSPECIFIED Diedenhofen, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Blaschke, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Wieters, Frederique UNSPECIFIED UNSPECIFIED UNSPECIFIED Wiedermann, Dirk UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoehn, Mathias UNSPECIFIED UNSPECIFIED UNSPECIFIED Fink, Gereon R. UNSPECIFIED orcid.org/0000-0002-8230-1856 UNSPECIFIED Aswendt, Markus UNSPECIFIED orcid.org/0000-0003-1423-0934 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-138198
DOI:	10.3389/fninf.2019.00042
Journal or Publication Title:	Front. Neuroinformatics
Volume:	13
Date:	2019
Publisher:	FRONTIERS MEDIA SA
Place of Publication:	LAUSANNE
ISSN:	1662-5196
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CONNECTIVITY; NOISE; OPTIMIZATION; REGISTRATION; NETWORKS; RECOVERY; STROKE; ROBUST Multiple languages Mathematical & Computational Biology; Neurosciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13819