Faculty Bibliography

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.

We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (MTBI) using multi-shell diffusion MRI, deriving compartment-specific WM tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of WM changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with MTBI within 30 days of injury and twenty-one age- and sex-matched controls were imaged on a 3T MR scanner. Multi-shell diffusion acquisition was performed with 5 b-values (250 - 2500 s/mm²) along 6 - 60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrate focally lower intra-axonal diffusivity (D_axon) in MTBI patients in the splenium of the corpus callosum (sCC) (p < 0.05, FWE-corrected). The Area Under the Curve (AUC)-value for was 0.76 with low sensitivity of 46.9%, but 100% specificity. These results indicate that D_axon may be a useful imaging biomarker highly specific for MTBI-related WM injury. The observed decrease in D_axon suggests restriction of the diffusion along the axons occurring shortly after injury.

Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.