#### Challenges to Preprocessing of Multi-Site TBI MRI Data  
Automated brain extraction is a critical step in MRI preprocessing where TBI-related challenges manifest. Brain extraction, or skull stripping, refers to the separation of brain tissue from non-brain components, such as the skull, scalp, and dura mater, and is often one of the first and most critical preprocessing steps in MRI analysis. While manual brain extraction is often more accurate than automated brain extraction for TBI MRI data due to the limitations of contemporary automated methods, manual extractions are susceptible to human error and are plagued with inter-rater reliability issues. Moreover, manual methods are far more logistically complex and labor-intensive, taking from weeks to months to accomplish what an automated method can in seconds. A pivotal development in the domain of brain extraction has been the automated Brain Extraction Tool (BET) from the FMRIB Software Library (FSL) [17; 18]. BET utilizes a deformable model guided by Bayesian priors to generate an outline of the brain's surface. In the healthy brain, this strategy is overwhelmingly effective and has made the tool very popular, with over 8000+ studies citing it to date [19]. While BET performs extractions effectively in the healthy brain, its reliance on Bayesian priors severely reduces its accuracy in brains with pathology (see Figure 1c). This is because Bayesian priors rely on predefined assumptions of what a \"typical\" brain should look like. In brains with TBI, these assumptions are often broken, ultimately resulting in inaccurate brain extractions [20]. Inaccurate brain extraction is a critical misstep in MRI analyses. The precision of brain extraction directly influences the accuracy of all volume-based brain analyses. However, because brain extraction is one of the first steps in MRI preprocessing pipelines, inaccurate brain extraction is also detrimental to the accuracy of subsequent preprocessing steps, such as tissue segmentation and registration. Thus, poor-quality brain extraction prompts a cascade of errors that fundamentally compromise the validity of findings with these data, obscuring true insights into brain anatomy and function and potentially leading to misinterpretations of neural substrates underlying cognitive processes and disease mechanisms. Several methods that improve brain extraction for patient data have been proposed. These techniques aim to overcome the shortcomings of conventional automated methods in extracting the brain. ROBEX from [PERSON] et al. (2018), SPECTRE from [PERSON] et al. (2018), BEaST from [PERSON] et al. (2019), optiBET from [PERSON] et al. (2019), and MONSTR from ([PERSON] et al., 2019) have shown improved performance in TBI data compared to conventional techniques like BET. Of these, optiBET is particularly notable for its outstanding performance in 74 human MRIs with TBI--outperforming machine learning-based algorithms such as ROBEX ([PERSON] et al., 2014). OptiBET is a brain extraction script optimized for brains with pathology that, in addition to its robust testing on TBI data, also stands out from other extraction tools in being entirely composed of freely available and near-universally employed neuroimaging software. These strengths have made OptiBET a widely adopted tool for TBI brain extraction. To perform brain extraction, the optiBET script first employs BET to make the initial brain outline estimate, then it performs linear and non-linear registration to the MNI template space, and finally, an MNI brain mask is back-projected to the original subject space to mask out non-brain tissue from the original image ([PERSON] et al., 2014). While optiBET demonstrates significantly improved performance in TBI brain extractions, it is unreliable, requiring extensive manual correction in some cases--shown in Figure 1d.