Voxel based analysis (VBA) of preclinical magnetic resonance images is widely used in neuroscience. Recent studies have highlighted the need for robust statistical analysis in neuroimaging , stability, and controlling error rates ; but a thorough examination of such effects is hindered by computational demands. Workflow optimization is often based on intuition and experience; while validation studies are scarce. While the main goal of many neuroscience questions is to understand the human brain, much of the current knowledge has been gained from small animal models, which replicate many genetic, structural, and connectivity aspects of the human brain. To increase throughput and reproducibility of  small animal brain studies, we have developed a publicly shared  VBA pipeline in a high-performance computing environment, called SAMBA. Increasing computational efficiency allowed us to produce VBA results in 1-3 days for large multidimensional arrays which previously took 1 month. To address the need for quantitative VBA validation we propose a  framework consisting of morphological phantoms, and four quantitative metrics. We address several factors that impact VBA results, including registration parameters, and template construction strategies. Due to shorter processing times, we could explore multiple parameter sets, and VBA stability. We have used this framework to guide the selection of spatial registration parameters in a VBA study for a mouse model of epilepsy. We  conclude that verifying the accuracy of VBA merits attention, and should be the focus of a broader effort within the community . By significantly reducing processing times, we were able to do a thorough evaluation of the variability and reliability of VBA results . The proposed framework promotes consistent quality assurance of VBA in preclinical neuroimaging; facilitating the creation and communication of robust results.
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. 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; facilitating the creation and communication of robust results.
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Voxel based
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
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widely used in neuroscience. Recent studies have highlighted the need for robust statistical analysis in neuroimaging
widely-used, a thorough examination of the statistical robustness
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controlling error rates ; but a thorough examination of such effects
error rates
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computational demands. Workflow optimization is
high computational demands of processing large arrays, and the many parameters involved. Thus, workflows are
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and experience; while validation studies are scarce. While the main goal of many neuroscience questions is to understand the human brain, much of the current knowledge has been gained from small animal models, which replicate many genetic, structural, and connectivity aspects of the human brain.
or experience, while preclinical validation studies remain scarce.
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quantitative
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, high throughput
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Increasing
The increased
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us to produce VBA results
large multidimensional arrays to be processed
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for large multidimensional arrays which previously took
, a task that previously took ~
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address the need for quantitative VBA validation
quantify the variability and reliability of preclinical VBA in rodent models,
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validation
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quantitative metrics. We address several factors
metrics. This addresses several sources
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parameters,
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Due to shorter processing times, we could explore multiple parameter sets, and VBA stability.
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guide the selection of spatial registration
inform the VBA workflow
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also present initial efforts towards standardizing small animal neuroimaging data in a similar fashion with human neuroimaging. We
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. By significantly reducing processing times, we were able to do a thorough evaluation of the variability and reliability of VBA results