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1. Loss of consciousness, but not etiology, predicts better working memory performance years after concussion

   https://doi.org/10.18053/jctres.05.2020S4.003  

   Hector Arciniega, Alexandrea Kilgore-Gomez ( January 2020 , Journal of Clinical and Translational Research)

   null (Ed.)

   Background: Patients with uncomplicated cases of concussion are thought to fully recover within several months as symptoms resolve. However, at the group level, undergraduates reporting a history of concussion (mean: 4.14 years post-injury) show lasting deficits in visual working memory performance. To clarify what predicts long-term visual working memory outcomes given heterogeneous performance across group members, we investigated factors surrounding the injury, including gender, number of mild traumatic brain injuries, time since mild traumatic brain injury (mTBI), loss of consciousness (LOC) (yes, no), and mTBI etiology (non-sport, team sport, high impact sport, and individual sport). We also collected low-density resting state electroencephalogram to test whether spectral power was correlated with performance. Aim: The purpose of this study was to identify predictors for poor visual working memory outcomes in current undergraduates with a history of concussion. Methods: Participants provided a brief history of their injury and symptoms. Participants also completed an experimental visual working memory task. Finally, low-density resting-state electroencephalogram was collected. Results: The key observation was that LOC at the time of injury predicted superior visual working memory years later. In contrast, visual working memory performance was not predicted by other factors, including etiology, high impact sports, or electroencephalogram spectral power. Conclusions: Visual working memory deficits are apparent at the group level in current undergraduates with a history of concussion. LOC at the time of concussion predicts less impaired visual working memory performance, whereas no significant links were associated with other factors. One interpretation is that after LOC, patients are more likely to seek medical advice than without LOC. Relevance for patients: Concussion is a head injury associated with future cognitive changes in some people. Concussion should be taken seriously, and medical treatment sought whenever a head injury occurs. 

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2. Impaired visual working memory and reduced connectivity in undergraduates with a history of mild traumatic brain injury

   https://doi.org/10.1038/s41598-021-80995-1  

   Arciniega, Hector ; Shires, Jorja ; Furlong, Sarah ; Kilgore-Gomez, Alexandrea ; Cerreta, Adelle ; Murray, Nicholas G. ; Berryhill, Marian E. ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract Mild traumatic brain injury (mTBI), or concussion, accounts for 85% of all TBIs. Yet survivors anticipate full cognitive recovery within several months of injury, if not sooner, dependent upon the specific outcome/measure. Recovery is variable and deficits in executive function, e.g., working memory (WM) can persist years post-mTBI. We tested whether cognitive deficits persist in otherwise healthy undergraduates, as a conservative indicator for mTBI survivors at large. We collected WM performance (change detection, n-back tasks) using various stimuli (shapes, locations, letters; aurally presented numbers and letters), and wide-ranging cognitive assessments (e.g., RBANS). We replicated the observation of a general visual WM deficit, with preserved auditory WM. Surprisingly, visual WM deficits were equivalent in participants with a history of mTBI (mean 4.3 years post-injury) and in undergraduates with recent sports-related mTBI (mean 17 days post-injury). In seeking the underlying mechanism of these behavioral deficits, we collected resting state fMRI (rsfMRI) and EEG (rsEEG). RsfMRI revealed significantly reduced connectivity within WM-relevant networks (default mode, central executive, dorsal attention, salience), whereas rsEEG identified no differences (modularity, global efficiency, local efficiency). In summary, otherwise healthy current undergraduates with a history of mTBI present behavioral deficits with evidence of persistent disconnection long after full recovery is expected. 

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3. Effective connectivity in the default mode network after paediatric traumatic brain injury

   https://doi.org/10.1111/ejn.15546  

   Vaughn, Kelly A. ; DeMaster, Dana ; Kook, Jeong Hwan ; Vannucci, Marina ; Ewing‐Cobbs, Linda ( December 2021 , European Journal of Neuroscience)

   Children who experience a traumatic brain injury (TBI) are at elevated risk for a range of negative cognitive and neuropsychological outcomes. Identifying which children are at greatest risk for negative outcomes can be difficult due to the heterogeneity of TBI. To address this barrier, the current study applied a novel method of characterizing brain connectivity networks, Bayesian multi‐subject vector autoregressive modelling (BVAR‐connect), which used white matter integrity as priors to evaluate effective connectivity—the time‐dependent relationship in functional magnetic resonance imaging (fMRI) activity between two brain regions—within the default mode network (DMN). In a prospective longitudinal study, children ages 8–15 years with mild to severe TBI underwent diffusion tensor imaging and resting state fMRI 7 weeks after injury; post‐concussion and anxiety symptoms were assessed 7 months after injury. The goals of this study were to (1) characterize differences in positive effective connectivity of resting‐state DMN circuitry between healthy controls and children with TBI, (2) determine if severity of TBI was associated with differences in DMN connectivity and (3) evaluate whether patterns of DMN effective connectivity predicted persistent post‐concussion symptoms and anxiety. Healthy controls had unique positive connectivity that mostly emerged from the inferior temporal lobes. In contrast, children with TBI had unique effective connectivity among orbitofrontal and parietal regions. These positive orbitofrontal‐parietal DMN effective connectivity patterns also differed by TBI severity and were associated with persisting behavioural outcomes. Effective connectivity may be a sensitive neuroimaging marker of TBI severity as well as a predictor of chronic post‐concussion symptoms and anxiety.

    

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4. Exploring the Evolution of Engineering Students™ Feelings of Inclusion in Their College and the Broader Scientific Community

   Melissa Lynn Morris, University of ; Joseph Dygert, West Virginia ; Robin A.M Hensel, West Virginia ( July 2021 , 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference)

   null (Ed.)

   This complete research paper discusses how students’ feelings of inclusion change throughout their undergraduate career. Student responses acquired through focus groups and one-on-one interviews were examined to determine how included the students felt in their engineering college and also the broader scientific community. A small group of non-calculus ready engineering students enrolled in a large land grant institution in the Mid-Atlantic region consented to participate in the study. The student cohort participated in an NSF S-STEM funded program aimed at fostering a sense of inclusion in engineering by implementing a curriculum focused on cohort formation, career exploration, and professional development. The AcES, consisting of a weeklong pre-fall bridge experience, two common courses, and a variety of co-curricular activities, has been operating for eight years. Students who receive S-STEM funded scholarships participate in three focus groups and two one-on-one interviews each semester throughout their undergraduate studies. Student responses from the one-on-one interviews and focus groups conducted from 2017-2020 were examined with qualitative coding methods. Questions examined in this work include: 1) Did the engineering in history course help make you feel like you belong in engineering at WVU and that you are included in engineering at WVU?, 2) Do you feel part of the group when working on projects in your engineering courses?, 3) Do you consider yourself a member of the scientific and engineering community here at WVU? Why or why not, and 4) Do you consider yourself a member of the broader scientific and engineering community? During the exploratory coding phase three codes were established to represent the degree of inclusion felt by students: Edge of Inclusion, Slight Inclusion, and Feelings of Inclusion. Edge of Inclusion was characterized by student responses such as “almost there but not totally”, “just starting to be”, and “no, well maybe a bit” while student responses such as “yes, but only a little” and “in some classes or situations” were recognized as Slight Inclusion. Examples of student responses such as “yes, I do feel part of it”, “absolutely, since I’ve…”, and “I would consider myself part of . . .” were classified with the code Feelings of Inclusion. Since the sample size was limited by scholarship funding, statistically significant results weren’t obtainable, but clear themes emerged that can be used to influence engineering curricula and serve as justification for an expanded study. Participating in an internship emerged as a major contributor to students feeling included in the broader scientific community. Interestingly, a decrease in the average degree of inclusion occurred after the students’ first semester, prior to increasing in later semesters. It is hypothesized that the emphasis on cohort formation, career exploration, and planned co-curricular activities during the first semester in the AcES program bolstered the initial feelings of inclusion. A student’s feeling of inclusion is known to be a contributing factor in retention. The findings of this research indicate that internships should not only be strongly encouraged, but university resources should be invested in helping students be prepared for, apply to, and obtain internships. The researchers suggest the study be expanded beyond the AcES program to examine a broader sample and greater number of students. 

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5. PoseASL: An RGBD Dataset of American Sign Language

   https://doi.org/10.17910/b7.1279  

   Huenerfauth, Matt ( January 2021 , Databrary)

   The PoseASL dataset consists of color and depth videos collected from ASL signers at the Linguistic and Assistive Technologies Laboratory under the direction of Matt Huenerfauth, as part of a collaborative research project with researchers at the Rochester Institute of Technology, Boston University, and the University of Pennsylvania. Access: After becoming an authorized user of Databrary, please contact Matt Huenerfauth if you have difficulty accessing this volume. We have collected a new dataset consisting of color and depth videos of fluent American Sign Language signers performing sequences ASL signs and sentences. Given interest among sign-recognition and other computer-vision researchers in red-green-blue-depth (RBGD) video, we release this dataset for use by the research community. In addition to the video files, we share depth data files from a Kinect v2 sensor, as well as additional motion-tracking files produced through post-processing of this data. Organization of the Dataset: The dataset is organized into sub-folders, with codenames such as "P01" or "P16" etc. These codenames refer to specific human signers who were recorded in this dataset. Please note that there was no participant P11 nor P14; those numbers were accidentally skipped during the process of making appointments to collect video stimuli. Task: During the recording session, the participant was met by a member of our research team who was a native ASL signer. No other individuals were present during the data collection session. After signing the informed consent and video release document, participants responded to a demographic questionnaire. Next, the data-collection session consisted of English word stimuli and cartoon videos. The recording session began with showing participants stimuli consisting of slides that displayed English word and photos of items, and participants were asked to produce the sign for each (PDF included in materials subfolder). Next, participants viewed three videos of short animated cartoons, which they were asked to recount in ASL: - Canary Row, Warner Brothers Merrie Melodies 1950 (the 7-minute video divided into seven parts) - Mr. Koumal Flies Like a Bird, Studio Animovaneho Filmu 1969 - Mr. Koumal Battles his Conscience, Studio Animovaneho Filmu 1971 The word list and cartoons were selected as they are identical to the stimuli used in the collection of the Nicaraguan Sign Language video corpora - see: Senghas, A. (1995). Children’s Contribution to the Birth of Nicaraguan Sign Language. Doctoral dissertation, Department of Brain and Cognitive Sciences, MIT. Demographics: All 14 of our participants were fluent ASL signers. As screening, we asked our participants: Did you use ASL at home growing up, or did you attend a school as a very young child where you used ASL? All the participants responded affirmatively to this question. A total of 14 DHH participants were recruited on the Rochester Institute of Technology campus. Participants included 7 men and 7 women, aged 21 to 35 (median = 23.5). All of our participants reported that they began using ASL when they were 5 years old or younger, with 8 reporting ASL use since birth, and 3 others reporting ASL use since age 18 months. Filetypes: *.avi, *_dep.bin: The PoseASL dataset has been captured by using a Kinect 2.0 RGBD camera. The output of this camera system includes multiple channels which include RGB, depth, skeleton joints (25 joints for every video frame), and HD face (1,347 points). The video resolution produced in 1920 x 1080 pixels for the RGB channel and 512 x 424 pixels for the depth channels respectively. Due to limitations in the acceptable filetypes for sharing on Databrary, it was not permitted to share binary *_dep.bin files directly produced by the Kinect v2 camera system on the Databrary platform. If your research requires the original binary *_dep.bin files, then please contact Matt Huenerfauth. *_face.txt, *_HDface.txt, *_skl.txt: To make it easier for future researchers to make use of this dataset, we have also performed some post-processing of the Kinect data. To extract the skeleton coordinates of the RGB videos, we used the Openpose system, which is capable of detecting body, hand, facial, and foot keypoints of multiple people on single images in real time. The output of Openpose includes estimation of 70 keypoints for the face including eyes, eyebrows, nose, mouth and face contour. The software also estimates 21 keypoints for each of the hands (Simon et al, 2017), including 3 keypoints for each finger, as shown in Figure 2. Additionally, there are 25 keypoints estimated for the body pose (and feet) (Cao et al, 2017; Wei et al, 2016). Reporting Bugs or Errors: Please contact Matt Huenerfauth to report any bugs or errors that you identify in the corpus. We appreciate your help in improving the quality of the corpus over time by identifying any errors. Acknowledgement: This material is based upon work supported by the National Science Foundation under award 1749376: "Collaborative Research: Multimethod Investigation of Articulatory and Perceptual Constraints on Natural Language Evolution." 

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