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Title: The Center for the Integration of Research, Teaching, and Learning: A National‐Scale Network to Prepare STEM Future Faculty
Abstract

This chapter describes a successful approach for the preparation of future faculty to implement and advance effective teaching practices for diverse learners, currently implemented at nearly 40 graduate universities in the United States and Canada.

 
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NSF-PAR ID:
10256158
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
New Directions for Teaching and Learning
Volume:
2020
Issue:
163
ISSN:
0271-0633
Page Range / eLocation ID:
p. 45-53
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Background

    A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences.

    Purpose

    To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI).

    Study Type

    Retrospective.

    Population

    Publicly available diffusion atlases, based on 60 healthy women (age 18–45 years) with normal prenatal care, from 23 and 35 weeks of gestation.

    Field Strength/Sequence

    3.0 Tesla/DTI acquired with diffusion‐weighted echo planar imaging (EPI).

    Assessment

    We performed whole‐brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small‐worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality.

    Statistical Tests

    Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. Thet‐tests were used to assess SW.P‐values were corrected using Holm‐Bonferroni method. A correctedP‐value <0.05 was considered statistically significant.

    Results

    Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32–7.37), LE (5.43%/week, 95% CI 3.63–7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes.

    Data Conclusion

    Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation.

    Level of Evidence

    3

    Technical Efficacy

    Stage 2

     
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  2. Background

    Carotid webs (CaWs) are fibromuscular projections in the internal carotid artery (ICA) that cause mild luminal narrowing (<50%), but may be causative in up to one‐third of seemingly cryptogenic strokes. Understanding hemodynamic alterations caused by CaWs is imperative to assessing stroke risk. Time‐Average Wall Shear Stress (TAWSS) and Oscillatory Shear Index (OSI) are hemodynamic parameters linked to vascular dysfunction and thrombosis.

    Purpose

    To test the hypothesis: “CaWs are associated with lower TAWSS and higher OSI than mild atherosclerosis or healthy carotid bifurcation.”

    Study Type

    Prospective study.

    Population

    A total of 35 subjects (N = 14 bifurcations with CaW, 11F, age: 49 ± 10, 10 mild atherosclerosis 6F, age: 72 ± 9, 11 healthy 9F, age: 42 ± 13).

    Field Strength/Sequence

    4D flow/STAR‐MATCH/3D TOF/3T MRI, CTA.

    Assessment

    4D Flow velocity data were analyzed in two ways: 1) 3D ROI in the ICA bulbar segment (complex flow patterns are expected) was used to quantify the regions with low TAWSS and high OSI. 2) 2D planes were placed perpendicular to the centerline of the carotid bifurcation for detailed analysis of TAWSS and OSI.

    Statistical Tests

    Independent‐samples Kruskal–Wallis‐H test with 0.05 used for statistical significance.

    Results

    The percent surface area where low TAWSS was present in the ICA bulb was 12.3 ± 8.0% (95% CI: 7.6–16.9) in CaW subjects, 1.6 ± 1.9% (95% CI: 0.2–2.9) in atherosclerosis, and 8.5 ± 7.7% (95% CI: 3.6–13.4) in healthy subjects, all differences were statistically significant (ƞ2 = 0.3 [95% CI: 0.05–0.5],P‐value CaW vs. healthy = 0.2). OSI had similar values in the CCA between groups (ƞ2 = 0.07 [95% CI: 0.0–0.2],P‐value = 0.5), but OSI was significantly higher downstream of the bifurcation in CaW subjects compared to atherosclerosis and normal subjects. OSI returned to similar values between groups 1.5 diameters distal to the bifurcation (ƞ2 = 0.03 [95% CI: 0.0–0.2],P‐value = 0.7).

    Conclusion

    Lower TAWSS and higher OSI are present in the ICA bulb in patients with CaW when compared to patients with atherosclerotic or healthy subjects.

    Evidence Level

    2

    Technical Efficacy

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    Clinical management of boys with Duchenne muscular dystrophy (DMD) relies on in‐depth understanding of cardiac involvement, but right ventricular (RV) structural and functional remodeling remains understudied.

    Purpose

    To evaluate several analysis methods and identify the most reliable one to measure RV pre‐ and postcontrast T1 (RV‐T1) and to characterize myocardial remodeling in the RV of boys with DMD.

    Study Type

    Prospective.

    Population

    Boys with DMD (N = 27) and age‐/sex‐matched healthy controls (N = 17) from two sites.

    Field Strength/Sequence

    3.0 T using balanced steady state free precession, motion‐corrected phase sensitive inversion recovery and modified Look‐Locker inversion recovery sequences.

    Assessment

    Biventricular mass (Mi), end‐diastolic volume (EDVi) and ejection fraction (EF) assessment, tricuspid annular excursion (TAE), late gadolinium enhancement (LGE), pre‐ and postcontrast myocardial T1 maps. The RV‐T1 reliability was assessed by three observers in four different RV regions of interest (ROI) using intraclass correlation (ICC).

    Statistical Tests

    The Wilcoxon rank sum test was used to compare RV‐T1 differences between DMD boys with negative LGE(−) or positive LGE(+) and healthy controls. Additionally, correlation of precontrast RV‐T1 with functional measures was performed. AP‐value <0.05 was considered statistically significant.

    Results

    A 1‐pixel thick RV circumferential ROI proved most reliable (ICC > 0.91) for assessing RV‐T1. Precontrast RV‐T1 was significantly higher in boys with DMD compared to controls. Both LGE(−) and LGE(+) boys had significantly elevated precontrast RV‐T1 compared to controls (1543 [1489–1597] msec and 1550 [1402–1699] msec vs. 1436 [1399–1473] msec, respectively). Compared to healthy controls, boys with DMD had preserved RVEF (51.8 [9.9]% vs. 54.2 [7.2]%,P = 0.31) and significantly reduced RVMi (29.8 [9.7] g vs. 48.0 [15.7] g), RVEDVi (69.8 [29.7] mL/m2vs. 89.1 [21.9] mL/m2), and TAE (22.0 [3.2] cm vs. 26.0 [4.7] cm). Significant correlations were found between precontrast RV‐T1 and RVEF (β = −0.48%/msec) and between LV‐T1 and LVEF (β = −0.51%/msec).

    Data Conclusion

    Precontrast RV‐T1 is elevated in boys with DMD compared to healthy controls and is negatively correlated with RVEF.

    Level of Evidence

    1

    Technical Efficacy

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    Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown.

    Purpose

    To test whether WMT affects PWH brain functional connectivity in resting‐state fMRI (rsfMRI).

    Study Type

    Prospective.

    Population

    A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53HIV‐seronegative controls (SN, ages 49.5 ± 1.6 years, six women).

    Field Strength/Sequence

    Axial single‐shot gradient‐echo echo‐planar imaging at 3.0 T was performed at baseline (TL1), at 1‐month (TL2), and at 6‐months (TL3), after WMT.

    Assessment

    All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training,n = 58: 28 PWH, 30 SN; nonadaptive training,n = 48: 25 PWH, 23 SN), 25 sessions over 5–8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2.

    Statistical Tests

    Two‐way analyses of variance (ANOVA) on GT metrics and two‐samplet‐tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set atP < 0.05 after false discovery rate correction.

    Results

    The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = −0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%).

    Data Conclusion

    ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH.

    Evidence Level

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    Technical Efficacy

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  5. Background

    Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized.

    Purpose

    Evaluate the generalizability of DL‐based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population.

    Study Type

    Retrospective based on prospectively acquired data.

    Population

    Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females).

    Field Strength/Sequence

    A 3‐T, quantitative double‐echo steady state (qDESS).

    Assessment

    Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)‐DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage.

    Statistical Tests

    Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank‐sum tests, root‐mean‐squared error‐coefficient‐of‐variation to quantify manual vs. automatic T2 and volume variations. Bland–Altman plots for manual vs. automatic T2 agreement. APvalue < 0.05 was considered statistically significant.

    Results

    DSCs for the qDESS‐trained model, 0.79–0.93, were higher than those for the OAI‐DESS‐trained model, 0.59–0.79. T2 and volume CCCs for the qDESS‐trained model, 0.75–0.98 and 0.47–0.95, were higher than respective CCCs for the OAI‐DESS‐trained model, 0.35–0.90 and 0.13–0.84. Bland–Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS‐trained model, ±2.4 msec and ±4.0 msec, than the OAI‐DESS‐trained model, ±4.4 msec and ±5.2 msec.

    Data Conclusion

    The qDESS‐trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.

    Evidence Level

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    Technical Efficacy

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