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Traumatic brain injury (TBI) is a risk factor for neurodegeneration and cognitive decline, yet the underlying pathophysiologic mechanisms are incompletely understood. This gap in knowledge is in part related to the lack of analytic methods to account for cortical lesions in prior neuroimaging studies. The objective of this study was to develop a lesion detection tool and apply it to an investigation of longitudinal changes in brain structure among individuals with chronic TBI. We identified 24 individuals with chronic moderate-to-severe TBI enrolled in the Late Effects of TBI (LETBI) study who had cortical lesions detected by T1-weighted MRI at two time points. Initial MRI scans were performed more than 1-year post-injury and follow-up scans were performed 3.1 (IQR=1.7) years later. We leveraged FreeSurfer parcellations of T1-weighted MRI volumes and a recently developed super-resolution technique, SynthSR, to identify cortical lesions in this longitudinal dataset. Trained raters received the data in a randomized order and manually corrected the automated lesion segmentation, yielding a final lesion mask for each scan at each timepoint. Lesion volume significantly increased between the two time points with a median volume change of 3.2 (IQR=5.9) mL (p<0.001), and the increases significantly exceeded the possible variance in lesion volume changes due to manual tracing errors (p < 0.001). Lesion volume significantly expanded longitudinally in 23 of 24 subjects, with all FDR corrected p-values ≤ 0.02. Inter-scan duration was not associated with the magnitude of lesion growth. We also demonstrated that the semi-automated tool showed a high level of accuracy compared to "ground truth" manual lesion segmentation. Semi-automated lesion segmentation is feasible in TBI studies and creates opportunities to elucidate mechanisms of post-traumatic neurodegeneration.

Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or organ dysfunction after the acute phase of infection, termed Post-Acute Sequelae of SARS-CoV-2 (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are poorly understood. The objectives of the Researching COVID to Enhance Recovery (RECOVER) tissue pathology study (RECOVER-Pathology) are to: (1) characterize prevalence and types of organ injury/disease and pathology occurring with PASC; (2) characterize the association of pathologic findings with clinical and other characteristics; (3) define the pathophysiology and mechanisms of PASC, and possible mediation via viral persistence; and (4) establish a post-mortem tissue biobank and post-mortem brain imaging biorepository., Methods: RECOVER-Pathology is a cross-sectional study of decedents dying at least 15 days following initial SARS-CoV-2 infection. Eligible decedents must meet WHO criteria for suspected, probable, or confirmed infection and must be aged 18 years or more at the time of death. Enrollment occurs at 7 sites in four U.S. states and Washington, DC. Comprehensive autopsies are conducted according to a standardized protocol within 24 hours of death; tissue samples are sent to the PASC Biorepository for later analyses. Data on clinical history are collected from the medical records and/or next of kin. The primary study outcomes include an array of pathologic features organized by organ system. Causal inference methods will be employed to investigate associations between risk factors and pathologic outcomes., Discussion: RECOVER-Pathology is the largest autopsy study addressing PASC among US adults. Results of this study are intended to elucidate mechanisms of organ injury and disease and enhance our understanding of the pathophysiology of PASC., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Brendan Parent reports receiving a research gift from United Therapeutics. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Troxel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)