Web Application for Quantification of Traumatic Brain Injury-Induced Cortical Lesions in Adult Mice

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Abstract

Disabilities resulting from traumatic brain injury (TBI) strongly correlate with the cytoarchitectonic part of the brain damaged, lesion area, and type of lesion. We developed a Web application to estimate the location of the lesion on mouse cerebral cortex caused by TBI induced by lateral fluid-percussion injury. The application unfolds user-determined TBI lesion measurements, e.g., from histologic sections to a reference template, and estimates the total lesion area, including the percentage of cortex damaged in different cytoarchitectural cortical regions. The resulting lesion can be visualized on a two-dimensional map of mouse cerebral cortex. The application also visualizes the development of the lesion over time when measurements from multiple time points are available. The web application was validated by comparing its performance to the manual method. The total area of the cortical lesion was similar between the manual (9.19 ± 0.66 mm2, range 4.25–14.93 mm2) and the automated analysis (9.27 ± 0.66 mm2, range 4.50–15.10 mm2) (p = 0.938). The results of the manual and automated analyses were strongly correlated (r = 0.999, p < 0.0001, Pearson correlation). The lesion localized in the same cytoarchitectonic regions when the unfolded map from the automated method was superimposed onto the map obtained using the manual method. The Web application-automated method is faster than the manual method in generating unfolded cortical lesion maps. The accuracy of the presented automated method in determining the anteroposterior level and outlining the lesion is equal to or greater than that of the manual method. Our application provides a novel tool for accurately quantifying and visualizing TBI lesions on mouse cerebral cortex.

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APA

Ciszek, R., Andrade, P., Tapiala, J., Pitkänen, A., & Ndode-Ekane, X. E. (2020). Web Application for Quantification of Traumatic Brain Injury-Induced Cortical Lesions in Adult Mice. Neuroinformatics, 18(2), 307–317. https://doi.org/10.1007/s12021-019-09444-9

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