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.

In this work, we propose bag of adversarial features (BAF) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRI) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI 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. Unlike most of previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MR images. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-word approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex matched healthy controls), and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.

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).

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.

BACKGROUND:While the cognitive sequelae of traumatic brain injury (TBI) are well known, emotional impairments after TBI are suboptimally characterized. Lack of awareness of emotional difficulties can make self-report unreliable. However, individuals with TBI demonstrate involuntary changes in heart rate variability which may enable objective quantification of emotional dysfunction. METHODS:Sixteen subjects with chronic TBI and 10 age-matched controls were tested on an emotional function battery during which they watched a series of film clips normed to elicit specific positively and negatively valenced emotions: amusement, sexual amusement, sadness, fear and disgust. Subjective responses to the emotional stimuli were also obtained. Additionally, surface electrodes measured cardiac and respiratory signals to compute heart rate variability (HRV), from which measures of parasympathetic activity, the respiratory frequency area (RFA) and sympathetic activity, the low frequency area (LFA), of the HRV frequency spectrum were derived. The Neurobehavioral Rating Scale-Revised (NRS-R) and the King-Devick (KD) test were administered to assess neurobehavioral dysfunction. RESULTS:The two groups showed no differences in subjective ratings of emotional intensity. Subjects with TBI showed significantly decreased sympathetic activity when viewing amusing stimuli and significantly increased sympathetic activity when viewing sad stimuli compared to controls. Most of the subjects did not show agitation, anxiety, depression, blunted affect, emotional withdrawal, decreased motivation or mental fatiguability on the NRS-R. However, 13/16 subjects with TBI demonstrated attention difficulty on the NRS-R which was positively correlated with the increased sympathetic activity during sad stimuli. Both attention difficulty and abnormal autonomic responses to sad stimuli were correlated with the timing on the KD test, which reflected difficulty with visual attention shifting. CONCLUSIONS:The HRV spectrum may be useful to identify subclinical emotional dysfunction in individuals with TBI. Attention difficulites, specifically impairment in visual attention shifting, may contribute to abnormal reactivity to sad stimuli that may be detected and potentially treated to improve emotional function.