Discrimination Veiled As Diversity: The Use Of Social Science To Undermine The Law

Fifty years after racially-based segregation was outlawed in Brown v. Board of Education, segregation continues to occur not as a result of legal mandates, but as a result of socioeconomic and racial composition of neighborhoods in which a school may be contained. Chief Justice Earl Warren, writing for a unanimous Court, declared “[t]o separate [minorities] from others of similar age and qualifications solely because of their race generates a feeling of inferiority as to their status in the community that may affect their hearts and minds in a way unlikely ever to be undone.” Even though the Supreme Court was clear in holding that race cannot be a factor in primary and secondary school assignments, some school districts continue to defy that ruling

Food insecurity and subsequent weight gain in women

Objective: Cross-sectional data indicate that a relationship between household food insecurity and overweight exists among women in the USA. Cross-sectional data cannot determine if food insecurity leads to overweight as some have hypothesised. The purpose of the present study was to examine the relationship of food insecurity with subsequent weight gain in women using data from the Panel Study of Income Dynamics (PSID). Design, setting and subjects:Panel data from the 1999 and 2001 PSID, a nationally representative sample of households, were analysed using multivariate regression procedures. Results: Average weight gain among all women (n=5595) was 1.1 kg on average over the two years. There were no significant differences in the percentages of women who gained a clinically significant amount (2.3kg) by food insecurity status. Overweight women who were on a weight-gain trajectory during the 2-year period gained less if they were food-insecure. This relationship was not observed among healthy-weight or obese women. Conclusions: Overall, food insecurity does not appear to be strongly associated with subsequent weight gain in women

Dynamics of the human structural connectome underlying working memory training

Brain region-specific changes have been demonstrated with a variety of cognitive training interventions. The effect of cognitive training on brain subnetworks in humans, however, remains largely unknown, with studies limited to functional networks. Here, we used a well-established working memory training program and state-of-the art neuroimaging methods in 40 healthy adults (21 females, mean age 26.5 years). Near and far-transfer training effects were assessed using computerized working memory and executive function tasks. Adaptive working memory training led to improvement on (non)trained working memory tasks and generalization to tasks of reasoning and inhibition. Graph theoretical analysis of the structural (white matter) network connectivity (“connectome”) revealed increased global integration within a frontoparietal attention network following adaptive working memory training compared with the nonadaptive group. Furthermore, the impact on the outcome of graph theoretical analyses of different white matter metrics to infer “connection strength” was evaluated. Increased efficiency of the frontoparietal network was best captured when using connection strengths derived from MR metrics that are thought to be more sensitive to differences in myelination (putatively indexed by the [quantitative] longitudinal relaxation rate, R1) than previously used diffusion MRI metrics (fractional anisotropy or fiber-tracking recovered streamlines). Our findings emphasize the critical role of specific microstructural markers in providing important hints toward the mechanisms underpinning training-induced plasticity that may drive working memory improvement in clinical populations

Longitudinal data on cortical thickness before and after working memory training

The data and supplementary information provided in this article relate to our research article “Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training.” [1]. We provide cortical thickness and subcortical volume data derived from parieto-frontal cortical regions and the basal ganglia with the FreeSurfer longitudinal analyses stream (http://surfer.nmr.mgh.harvard.edu [2]) before and after working memory training, “Cogmed and Cogmed Working Memory Training” [3]. This article also provides supplementary information to the research article, i.e., within-group comparisons between baseline and outcome cortical thickness and subcortical volume measures, between-group tests of performance changes in cognitive benchmark tests (www.cambridgebrainsciences.com[4]), correlation analyses between performance changes in benchmark tests and training-related structural changes, correlation analyses between the time spent training and structural changes, a scatterplot of the relationship between cortical thickness measures derived from the occipital lobe as control region and the chronological order of the MRI sessions to assess potential scanner drift effects and a post-hoc vertex-wise whole brain analysis with FreeSurfer Qdec (https://surfer.nmr.mgh.harvard.edu/fswiki/Qdec [5])

Student Loans, Financial Stress, and College Student Retention

This study examined a sample of 2,475 undergraduate students to determine the influence of financial stress, debt loads, and financial counseling on retention rates. Results indicate, among other findings, that financial stress contributes to an increased likelihood of discontinuing college. Self-reported student loan debt contributes to an increased likelihood of discontinuing college, although students with the highest amount of university-reported student loan debt have a decreased likelihood of discontinuing college one year later as compared to students with no student loan debt. Interestingly, in this study students who sought financial counseling were more likely to discontinue college within the next year. Although this contradicts prior studies that have shown that students experience less financial stress immediately after meeting with a peer counselor and for two months later, it is suggested that the timing of the counseling may be an important factor. Implications for practice include early intervention for students who are self funding their education, who are under high financial stress, or have a perception of high student loan debt. At the campus level, financial aid professionals should collaborate with personal finance researchers to better understand how financial stress and student debt relate to retention

Improving the reliability of network metrics in structural brain networks by integrating different network weighting strategies into a single graph

Structural brain networks estimated from diffusion MRI (dMRI) via tractography have been widely studied in healthy controls and in patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS) can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost full-weighted. Here, we scanned 5 healthy participants 5 times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROIs) from the AAL template. The edges were weighted according to nine different methods.We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an integrated weighted structural brain network (ISWBN). Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of : a) intra-class correlation coefficient (ICC) of well-known network metrics, both node-wise and per network level; and b) the recognition accuracy of each subject over the rest of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject; after first applying our proposed topological filtering scheme. Based on a threshold that the network-level ICC should be > 0.90, our findings revealed six out of nine NWS lead to unreliable results at the network-level, while all nine NWS were unreliable at the node-level. In comparison, our proposed ISWBN performed as well as the best-performing individual NWS at the network-level, and the ICC was higher compared to all individual NWS at the node-level. Importantly, both network- and node-wise ICCs of network metrics derived from the topologically filtered ISBWN(ISWBNTF), were further improved compared to non-filtered ISWBN. Finally, in the recognition accuracy tests, we assigned each single ISWBNTF to the correct subject. Overall, these findings suggest that the proposed methodology results in improved characterisation of genuine between-subject differences in connectivit

Why diffusion tensor MRI does well only some of the time: Variance and covariance of white matter tissue microstructure attributes in the living human brain

Fundamental to increasing our understanding of the role of white matter microstructure in normal/abnormal function in the living human is the development of MR-based metrics that provide increased specificity to distinct attributes of the white matter (e.g., local fibre architecture, axon morphology, and myelin content). In recent years, different approaches have been developed to enhance this specificity, and the Tractometry framework was introduced to combine the resulting multi-parametric data for a comprehensive assessment of white matter properties. The present work exploits that framework to characterise the statistical properties, specifically the variance and covariance, of these advanced microstructural indices across the major white matter pathways, with the aim of giving clear indications on the preferred metric(s) given the specific research question. A cohort of healthy subjects was scanned with a protocol that combined multi-component relaxometry with conventional and advanced diffusion MRI acquisitions to build the first comprehensive MRI atlas of white matter microstructure. The mean and standard deviation of the different metrics were analysed in order to understand how they vary across different brain regions/individuals and the correlation between them. Characterising the fibre architectural complexity (in terms of number of fibre populations in a voxel) provides clear insights into correlation/lack of correlation between the different metrics and explains why DT-MRI is a good model for white matter only some of the time. The study also identifies the metrics that account for the largest inter-subject variability and reports the minimal sample size required to detect differences in means, showing that, on the other hand, conventional DT-MRI indices might still be the safest choice in many contexts

Food Insecurity and Assistance on Campus: A Survey of the Student Body

According to recent studies, food insecurity affects from 34%-59% of college students. This will continue to be an issue as tuition increases and more low-income and first-generation students enter universities and colleges. Nearly 52% of college students live at, or near, the poverty level, compared to a national poverty rate of 14.5%. This leaves many undergraduate and graduate students with challenging decisions around meeting their basic housing, nutritional, and educational expenses. To assess food insecurity at Kansas State University (KSU), a random sample of undergraduate and graduate students was surveyed. Findings include a high rate of food insecurity (44.3%) among respondents. This measure was calculated by summing the affirmative responses to the USDA short-form food security questions in the survey. This means that during a 7-month period during the 2016 to 2017 academic year, 44.3% of respondents experienced at least two of the following: 1) didn’t have enough food to last and didn’t have money to buy more, 2) couldn’t afford to eat balanced meals, 3) cut the size of or skipped meals, 4) ate less than they felt they should because they didn’t have enough money, or, 5) were hungry and didn’t eat. This finding is consistent with other studies that report food insecurity rates between 34% and 59% at U.S. universities and community colleges. Fifty-seven percent of respondents were generally aware that food insecurity is a significant problem on college campuses. A majority of respondents (63%) reported that they knew students besides themselves who, currently or sometime during the academic year, had problems with food insecurity or hunger. Yet food assistance (e.g., food pantries) and SNAP are seldom used and responses regarding the use of an on-campus food pantry were mixed. Despite this mixed response, over 2,000 students had used the campus food pantry within the one-year period between opening in 2017 to 2018 (Bishop 2018)