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While many neuroscience questions aim to understand the human brain, muchcurrent knowledge has been gained using animal models, which replicate genetic,structural, and connectivity aspects of the human brain. While voxel-basedanalysis (VBA) of preclinical magnetic resonance images is widely-used, athorough examination of the statistical robustness, stability, and error ratesis hindered by high computational demands of processing large arrays, and themany parameters involved. Thus, workflows are often based on intuition orexperience, while preclinical validation studies remain scarce. To increasethroughput and reproducibility of quantitative small animal brain studies, wehave developed a publicly shared, high throughput VBA pipeline in ahigh-performance computing environment, called SAMBA. The increasedcomputational efficiency allowed large multidimensional arrays to be processedin 1-3 days, a task that previously took ~1 month. To quantify the variabilityand reliability of preclinical VBA in rodent models, we propose a validationframework consisting of morphological phantoms, and four metrics. Thisaddresses several sources that impact VBA results, including registration andtemplate construction strategies. We have used this framework to inform the VBAworkflow parameters in a VBA study for a mouse model of epilepsy. We alsopresent initial efforts towards standardizing small animal neuroimaging data ina similar fashion with human neuroimaging. We conclude that verifying theaccuracy of VBA merits attention, and should be the focus of a broader effortwithin the community. The proposed framework promotes consistent qualityassurance of VBA in preclinical neuroimaging; facilitating the creation andcommunication of robust results.

Small Animal Multivariate Brain Analysis (SAMBA): A High Throughput Pipeline with a Validation Framework