Browsing by Author "Newman, Sharlene"

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    Collegiate athlete brain data for white matter mapping and network neuroscience
    (Nature Portfolio, 2021) Caron, Bradley; Stuck, Ricardo; McPherson, Brent; Bullock, Daniel; Kitchell, Lindsey; Faskowitz, Joshua; Kellar, Derek; Cheng, Hu; Newman, Sharlene; Port, Nicholas; Pestilli, Franco; Indiana University Bloomington; University of Alabama Tuscaloosa; University of Texas Austin; Johns Hopkins University; Johns Hopkins University Applied Physics Laboratory
    We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.
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    Denoising diffusion weighted imaging data using convolutional neural networks
    (PLOS, 2022) Cheng, Hu; Vinci-Booher, Sophia; Wang, Jian; Caron, Bradley; Wen, Qiuting; Newman, Sharlene; Pestilli, Franco; Indiana University Bloomington; Vanderbilt University; Shandong Normal University; University of Alabama Tuscaloosa; University of Texas Austin
    Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
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    An exploratory study of a hands-on naloxone training for rural clinicians and staff
    (Wiley, 2023) Cody, Shameka L.; Hines, Cheryl B.; Glenn, Christina J.; Sharp-Marbury, Rochelle; Newman, Sharlene; University of Alabama Tuscaloosa
    IntroductionSince the COVID-19 pandemic, an increase in fentanyl-combined drugs has led to a surge in opioid overdose deaths in the United States. Higher opioid overdose mortality rates are problematic in rural communities, and there are few prevention, treatment, and recovery resources for individuals experiencing opioid use disorder. MethodThis exploratory project aimed to investigate a hands-on naloxone training for rural clinicians and staff. Rural clinicians and staff at two behavioral health centers were recruited to participate in a 30-min lecture and 30-min hands-on intranasal naloxone training using a low-fidelity mannequin. A pre-post opioid knowledge questionnaire, rubric based on the Substance Abuse and Mental Health Services Administration toolkit, and investigator-generated survey were used to evaluate opioid knowledge and response, demonstration of intranasal naloxone administration, and participants' perceptions of the training. Enrollment characteristics were summarized using descriptive statistics and paired t-tests were used to assess mean differences. ResultsOf the nine participants in the project, seven (87.5%) were female and six (75.0%) were Black. Four participants assumed a therapist role, attained a MS or MA degree, and had 5 or more years of experience working in healthcare. The total mean rubric score for all participants was 96.0 (SD = 8.8). No significant pre-post mean differences among opioid knowledge, overdose risk, and overdose response categories were found, all p > 0.05. However, post-intervention mean scores were slightly higher in all categories except overdose risk. Most participants (77.8%) responded that they felt comfortable handling an opioid situation and teaching the training to community members. Open-ended responses indicated that participants liked the demonstrations, examples used, hands-on nature of the training, and the presentation materials. ConclusionA hands-on naloxone training is beneficial for training rural clinicians and staff to respond to opioid overdose. This training may be a promising solution to reduce response time between recognition of opioid symptoms and administration of the life-saving medication, naloxone. Future studies should examine the efficacy of this training in larger samples with the inclusion of rural interdisciplinary teams, trusted community leaders, and family and friends of those impacted by opioid use disorder. Clinical relevanceThis innovative hands-on naloxone training is designed for rural clinicians and residents who are most likely to witness individuals experiencing opioid toxicity. The primary goal is to reduce response time between recognition of signs and symptoms and administration of the life-saving medication, Naloxone.