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Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis via inclusion of lesioned tissue labels. Moreover, our protocol allows for analysis of the refinement process. Though tedious, the methods reported in this work are necessary to create reliable data for effective training of future machine-learning based lesion segmentation methods in TBI patients and subsequent PTE analyses.

IntroductionCOVID-19 vaccine inequities have been widespread across California, the United States, and globally. As COVID-19 vaccine inequities have not been fully understood in the youth population, it is vital to determine possible factors that drive inequities to enable actionable change that promotes vaccine equity among vulnerable minor populations.MethodsThe present study used the social vulnerability index (SVI) and daily vaccination numbers within the age groups of 12–17, 5–11, and under 5 years old across all 58 California counties to model the growth velocity and the anticipated maximum proportion of population vaccinated.ResultsOverall, highly vulnerable counties, when compared to low and moderately vulnerable counties, experienced a lower vaccination rate in the 12–17 and 5–11 year-old age groups. For age groups 5–11 and under 5 years old, highly vulnerable counties are expected to achieve a lower overall total proportion of residents vaccinated. In highly vulnerable counties in terms of socioeconomic status and household composition and disability, the 12–17 and 5–11 year-old age groups experienced lower vaccination rates. Additionally, in the 12–17 age group, high vulnerability counties are expected to achieve a higher proportion of residents vaccinated compared to less vulnerable counterparts.DiscussionThese findings elucidate shortcomings in vaccine uptake in certain pediatric populations across California and may help guide health policies and future allocation of vaccines, with special emphasis placed on vulnerable populations, especially with respect to socioeconomic status and household composition and disability.