Faculty Bibliography

BACKGROUND:Traumatic brain injury (TBI) often results in chronic impairments to cognitive function, and these may be related to disrupted functional connectivity (FC) of the brain at rest. OBJECTIVE:To investigate changes in default mode network (DMN) FC in adults with chronic TBI following 40 hours of auditory processing speed training. METHODS:Eleven adults with chronic TBI underwent 40-hours of auditory processing speed training over 13-weeks and seven adults with chronic TBI were assigned to a non-intervention control group. For all participants, resting-state FC and cognitive and self-reported function were measured at baseline and at a follow-up visit 13-weeks later. RESULTS:No significant group differences in cognitive function or resting-state FC were observed at baseline. Following training, the intervention group demonstrated objective and subjective improvements on cognitive measures with moderate-to-large effect sizes. Repeated measures ANCOVAs revealed significant (p <  0.001) group×time interactions, suggesting training-related changes in DMN FC, and semipartial correlations demonstrated that these were associated with changes in cognitive functioning. CONCLUSIONS:Changes in the FC between the DMN and other resting-state networks involved in the maintenance and manipulation of internal information, attention, and sensorimotor functioning may be facilitated through consistent participation in plasticity-based auditory processing speed training in adults with chronic TBI.

AIMS/OBJECTIVE:Gulf War Illness (GWI) is a prevalent and disabling condition characterized by persistent physical symptoms. Clinical practice guidelines recommend self-management to reduce the disability from GWI. This study evaluated which GWI self-management strategies patients currently utilize and view as most effective and ineffective. MATERIALS AND METHODS/METHODS:Data were collected from 267 Veterans during the baseline assessment of a randomized clinical trial for GWI. Respondents answered 3 open-ended questions regarding which self-management strategies they use, view as effective, and view as ineffective. Response themes were coded, and code frequencies were analyzed. KEY FINDINGS/RESULTS:Response frequencies varied across questions (in-use: n = 578; effective: n = 470; ineffective: n = 297). Healthcare use was the most commonly used management strategy (38.6% of 578), followed by lifestyle changes (28.5% of 578), positive coping (13% of 578), and avoidance (13.7% of 578). When asked about effective strategies, healthcare use (25.9% of 470), lifestyle change (35.7% of 470), and positive coping (17.4% of 470) were identified. Avoidance was frequently identified as ineffective (20.2% of 297 codes), as was invalidating experiences (14.1% of 297) and negative coping (10.4% of 297). SIGNIFICANCE/CONCLUSIONS:Patients with GWI use a variety of self-management strategies, many of which are consistent with clinical practice guidelines for treating GWI, including lifestyle change and non-pharmacological strategies. This suggests opportunities for providers to encourage effective self-management approaches that patients want to use.

BACKGROUND:Adults with chronic traumatic brain injury (TBI) may experience long-term deficits in multiple cognitive domains. Higher-order functions, such as verbal memory, are impacted by deficits in the ability to acquire verbal information. OBJECTIVE:This study investigated the effects of a neuroplasticity-based computerized cognitive remediation program for auditory information processing in adults with a chronic TBI. METHODS:Forty-eight adults with TBI were randomly assigned to an intervention or control group. Both groups underwent a neuropsychological assessment at baseline and post-training. The Intervention group received 40 one-hour cognitive training sessions with the Brain Fitness Program. RESULTS:The intervention group improved in performance on measures of the Woodcock-Johnson-III Understanding Directions subtest and Trail Making Test Part-A. They also reported improvement on the cognitive domain of the Cognitive Self-Report Questionnaire. CONCLUSIONS:The present study demonstrated that a neuroplasticity-based computerized cognitive remediation program may improve objective and subjective cognitive function in adults with TBI several years post-injury.

BACKGROUND:Medically unexplained syndromes (MUS) are both prevalent and disabling. While illness beliefs and behaviors are thought to maintain MUS-related disability, little is known about which specific behavioral responses to MUS are related to disability or the way in which beliefs and behaviors interact to impact functioning. The purpose of the present study was to examine the relationship between illness beliefs and disability among patients with MUS, and assess the extent to which behaviors mediate this relationship. METHODS:The study examined data from the baseline assessment of a multi-site randomized controlled trial (RCT). Participants were 248 veterans with MUS. Illness beliefs, behavioral responses to illness, and disability were assessed through self-report questionnaire. Data were analyzed using mediation analysis. RESULTS:Threat-related beliefs predicted greater disability through decreased activity and increased practical support seeking. Protective beliefs predicted less disability through reductions in all-or-nothing behavior and limiting behavior. CONCLUSIONS:These outcomes suggest that all-or-nothing behavior, limiting behavior, and practical support seeking are important in the perpetuation of disability among those with MUS. This has implications for improving MUS treatment by highlighting potential treatment targets. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov Identifier: NCT02161133.

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.