Faculty Bibliography

BACKGROUND AND PURPOSE/OBJECTIVE:Working memory impairment is one of the most troubling and persistent symptoms after mild traumatic brain injury (MTBI). Here we investigate how working memory deficits relate to detectable WM microstructural injuries to discover robust biomarkers that allow early identification of patients with MTBI at the highest risk of working memory impairment. MATERIALS AND METHODS/METHODS:Multi-shell diffusion MR imaging was performed on a 3T scanner with 5 b-values. Diffusion metrics of fractional anisotropy, diffusivity and kurtosis (mean, radial, axial), and WM tract integrity were calculated. Auditory-verbal working memory was assessed using the Wechsler Adult Intelligence Scale, 4th ed, subtests: 1) Digit Span including Forward, Backward, and Sequencing; and 2) Letter-Number Sequencing. We studied 19 patients with MTBI within 4 weeks of injury and 20 healthy controls. Tract-Based Spatial Statistics and ROI analyses were performed to reveal possible correlations between diffusion metrics and working memory performance, with age and sex as covariates. RESULTS:= .04), mainly present in the right superior longitudinal fasciculus, which was not observed in healthy controls. Patients with MTBI also appeared to lose the normal associations typically seen in fractional anisotropy and axonal water fraction with Letter-Number Sequencing. Tract-Based Spatial Statistics results also support our findings. CONCLUSIONS:Differences between patients with MTBI and healthy controls with regard to the relationship between microstructure measures and working memory performance may relate to known axonal perturbations occurring after injury.

PURPOSE/OBJECTIVE/OBJECTIVE:Racial/ethnic minorities and other vulnerable social groups experience health care disparities. There is a lack of research exploring how time to acute rehabilitation admission is impacted by race/ethnicity and other marginalizing systemic vulnerabilities. The purpose of this study is to investigate whether race/ethnicity and other sociodemographic vulnerabilities impact expediency of acute rehabilitation admission following traumatic brain injury (TBI). Research Method/Design: This study is a secondary analysis of an existing dataset of 111 patients admitted for acute TBI rehabilitation at an urban public hospital. Patient groups were defined by race/ethnicity (People of color or White) and vulnerable group status (high or low vulnerable group membership [VGM]). RESULTS:White patients are admitted to acute TBI rehabilitation significantly faster than people of color. After taking vulnerabilities into account, high VGM people of color experience the most severe injuries and take the longest to receive acute TBI rehabilitation. Despite small differences in injury severity, low VGM people of color take longer to be admitted to acute TBI rehabilitation than White patients. High VGM White patients have less severe injuries yet take longer to be admitted to acute rehabilitation than low VGM White patients. Finally, notable differences exist between White patients and patients of color on rater-based injury severity scales that are discordant with severity as measured by more objective markers. CONCLUSIONS/IMPLICATIONS/CONCLUSIONS:Overall, findings indicate that sociodemographic factors including race/ethnicity and systemic vulnerabilities impact injury severity and time to acute TBI rehabilitation admission. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

OBJECTIVE:To analyze the complex relationship between various social indicators that contribute to socioeconomic status and healthcare barriers. DESIGN/METHODS:Cluster analysis of historical patient data obtained from inpatient visits. SETTING/METHODS:Setting: Inpatient rehabilitation unit in a large, urban university hospital PARTICIPANTS: Adult patients receiving acute inpatient care, predominantly for closed head injury. INTERVENTIONS/METHODS:Not applicable MAIN OUTCOME MEASURES: We examined the membership of TBI patients in various "vulnerable group" (VG) clusters (e.g., homeless, unemployed, racial/ethnic minority) and characterized the rehabilitation outcomes of the patients (e.g., duration of stay, changes in Functional Independence Measure [FIM] scores between admission to inpatient stay and discharge). RESULTS:Analysis revealed four major clusters (i.e., Clusters A-D) separated by VG memberships, with distinct durations of stay and FIM gains during their stay. Cluster B, the largest cluster and also consisting of mostly racial/ethnic minorities, had the shortest duration of hospital stay and one of the lowest FIM improvements among the four clusters despite higher FIM scores at admission. In cluster C, also consisting of mostly ethnic minorities with multiple SES vulnerabilities, patients were characterized by low cognitive FIM scores at admission and the longest duration of stay, and they showed good improvement in FIM scores. CONCLUSIONS:Application of clustering techniques to inpatient data identified distinct clusters of patients who may experience differences in their rehabilitation outcome due to their membership in various "at-risk" groups. Results identified patients (i.e., cluster B, with minority patients and Cluster D, with elderly patients) who attain below-average gains in brain injury rehabilitation. Results also suggested that systemic (e.g., duration of stay) or clinical service improvements (e.g., staff's language skills, ability to offer substance abuse therapy, provide appropriate referrals or liaise with intensive social work services or plan subacute rehabilitation phase) could be beneficial for acute settings. Stronger recruitment, training and retention initiatives for bilingual and multiethnic professionals may also be considered to optimize gains from acute inpatient rehabilitation following traumatic brain injury.

Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.