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Neuroinformatics. 2019 Jul;17(3):451-472. doi: 10.1007/s12021-018-9410-0.

Small Animal Multivariate Brain Analysis (SAMBA) - a High Throughput Pipeline with a Validation Framework.

Author information

1
Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.
2
Department of Biomedical Engineering, Duke University Medical Center, 3302, Durham, NC, 27710, USA.
3
Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, 27710, USA.
4
Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, 27599, USA.
5
Department of Neurobiology, Duke University Medical Center, Durham, NC, 27710, USA.
6
Department of Neurology, Duke University Medical Center, Durham, NC, 27710, USA.
7
Biogen, Cambridge, MA, 02142, USA.
8
Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA. alexandra.badea@duke.edu.
9
Department of Biomedical Engineering, Duke University Medical Center, 3302, Durham, NC, 27710, USA. alexandra.badea@duke.edu.

Abstract

While many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved therein. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To increase throughput and reproducibility of quantitative small animal brain studies, we have developed a publicly shared, high throughput VBA pipeline in a high-performance computing environment, called SAMBA. The increased computational efficiency allowed large multidimensional arrays to be processed in 1-3 days-a task that previously took ~1 month. To quantify the variability and reliability of preclinical VBA in rodent models, we propose a validation framework consisting of morphological phantoms, and four metrics. This addresses several sources that impact VBA results, including registration and template construction strategies. We have used this framework to inform the VBA workflow parameters in a VBA study for a mouse model of epilepsy. We also present initial efforts towards standardizing small animal neuroimaging data in a similar fashion with human neuroimaging. We conclude that verifying the accuracy of VBA merits attention, and should be the focus of a broader effort within the community. The proposed framework promotes consistent quality assurance of VBA in preclinical neuroimaging, thus facilitating the creation and communication of robust results.

KEYWORDS:

MR-DTI; Parallel computing; Pipeline; Simulated atrophy; Validation methods; Voxel-based analysis

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