Measures Of Segregation Research Articles

CHASM: A configurational measure of socio-spatial residential segregation

CHASM: A configurational measure of socio-spatial residential segregation

Gender occupational segregation: a new approach to quantifying the effect of educational segregation

ABSTRACT This article analyses the impact of educational segregation on the gender gap in the probability of working in a gendered or gender-balanced occupation, which is our measure of occupational segregation. Specifically, our methodology allows quantifying two different effects of education on this gap: one due to girls and boys choosing different educational options (presorting), and the other due to men and women with the same education entering the same or different occupations (postsorting). Using a sample of university graduates from Spain, a country with a highly segregated labour market, we conclude that the gender composition of education and occupation are strongly related. However, the greater presence of women in female-dominated jobs is not only due to them choosing female-dominated degrees, but also due to many women from gender-balanced, and even some from male-dominated programmes, working in female-dominated occupations. Thus, reducing educational segregation does not seem to be enough to fight against occupational segregation. It is also necessary to guarantee that men and women enjoy the same opportunities in the labour market, removing discrimination practices, and to ensure that both, workers and employers, do not make decisions based on gender role expectations or stereotypes.

Exploring Urban Socio-Spatial Disparities in Liverpool, Manchester, and Newcastle upon Tyne Metropolitan Areas

In recent decades, rising social disparities in European cities have structured new forms of urban inequalities. Employing an innovative analysis approach based on statistical, geographical, and spatial methods, this paper aims to explore the social geography of population groups in Liverpool, Manchester, and Newcastle upon Tyne by examining how the spatial distribution of socio-economic groups intersects with urban settlements of the resident foreign population. The study addresses methodological challenges in segregation measurement while shedding light on existing spatial patterns of vulnerable groups and emerging trajectories of residential segregation at the metropolitan scale, utilising the most recent UK Census data.

A matter of measurement? Segregation and all-cause mortality in Sweden’s three metropolitan regions

Abstract Background Residential segregation can be operationalized and measured in many ways, along distinct dimensions. There are multiple mechanisms through which segregation and health mutually affect each other. Consequently, contextual health effects of segregation may vary depending on how segregation is measured. Methods We used longitudinal Swedish register data to compare associations between local measures of residential segregation (migrant density, migrant isolation, and the mutual information index) and all-cause mortality among the adult migrant and native-born populations in Sweden’s three largest metropolitan regions between 2004-2016. Results Among migrants, modest positive mortality gradients were observed across measures of migrant density and isolation, where a higher level of segregation was associated with higher risks of all-cause mortality (Q5 RR ranged from 1.08-1.20 across cities and measures). However, these effects were largely attenuated by individual-level socioeconomic factors, and reversed when further controlling for neighborhood economic deprivation (Q5 RR ranged from 0.74-0.87). Among the native-born, a positive gradient for mortality was found (Q5 RR ranged from 1.31-1.45), although this was reduced when accounting for neighborhood deprivation (Q5 RR ranged from 1.01 -1.11). For both migrants and the native-born, the mutual information index showed no clear association with mortality, although a mortality advantage is observed for migrants in the most segregated areas after individual and neighborhood level adjustments (Q5 RR ranged from 0.73-0.86). Conclusions Our findings show that the manner in which residential segregation is conceptualized and operationalized alters associations with health. However, individual and other contextual level confounders are important determinants which influences the patterns. Much of the negative health effects of residential segregation can be perceived through the lack of economic opportunities. Key messages • Associations between segregation and all-cause mortality depends on how segregation is conceptualized and measured. • The mortality disadvantage observed in local areas with high migrant density may be attributed to the lack of economic opportunities in the neighborhood.

Underlying Dimensions of Racial Residential Segregation and Police-Caused Homicide of Blacks: A Cross-Sectional Analysis of Core Based Statistical Areas

This work builds upon previous research to assess the associations between underlying dimensions of racial residential segregation and police-caused homicide of Black civilians. Previous scholars have observed that much of the existing work on residential segregation and police-caused homicide has used one measure of segregation, most commonly the dissimilarity index, although recent work has expanded the focus to include multiple indicators of segregation. Our research extends this research by computing multiple measures of racial residential segregation for a sample of U.S. Core Based Statistical Areas in 2010 and factor analyzing them to identify underlying dimensions. Measures of the resulting dimensions are merged with the Mapping Police Violence data on police-caused homicide. The analyses reaffirm and extend previous conclusions about the importance of spatial relationships within residential locations for understanding contemporary police-caused homicide, while raising questions about the underlying theoretical mechanisms.

Abstract A040: Effects of food environment, income inequality, and racial segregation on obesity- related cancer mortality

Abstract BACKGROUND: Rural areas experience 7-12% higher risk of obesity-related cancer mortality compared to their urban counterparts. Complex interactions between social and structural factors in rural areas, combined with limited access to healthy foods and lower income, warrant further study to understand drivers of this disparity. Methods: County-level rates of age-adjusted obesity-related cancer mortality per 100,000 (2009-2020 were obtained from the Centers for Disease Control and Prevention’s Wide-Ranging ONline Data for Epidemiologic Research database. Based on data from the Food Environment Atlas, we calculated food swamp using the modified Retail Food Environment Index (mRFEI) as the ratio of fast-food restaurants and convenience stores to grocery and specialized food stores, supercenters, and farmers markets. Food swamp scores were then divided into quartiles. Next, we used Global G and local Gi statistics to identify the presence of spatial autocorrelation in food swamp and subsequently calculated a spatial moving average (SMA) across counties. Measures of economic segregation, racial segregation, and racialized economic segregation were calculated using the Index of Concentration at the Extremes (ICE) using population and demographic estimates from the 2005- 2009 American Community Survey. Larger values reflect concentrated extremes of disadvantage. Poisson regression was used to assess the impact of food swamp, urban-rural status, economic segregation, racial segregation, and racialized economic segregation on rates of obesity-related cancer mortality. Results: Among the 3014 counties included, median food swamp score was 3.6 (interquartile range=2.6-4.9), and counties in the 2nd, 3rd, and 4th quartiles for food swamp had 2%, 4%, and 6% higher rates (respectively) of obesity-related cancer mortality than those in the 1st quartile. Incidence rate ratios for the association of food swamp quartile with obesity-related cancer mortality were 1-3% smaller after control for food swamp SMA and for racialized economic Urban-rural differences in obesity-related cancer mortality varied with all ICE measures. Rural counties had 5% higher rates of obesity-related cancer mortality (IRR=1.05, 95%CI=1.04-1.06), even after controlling for food swamp and food swamp SMA, but there was no longer an association after controlling for economic segregation (IRR=0.99, 95%CI=0.98-1.00). Rural counties had 5% higher rates of obesity-related cancer mortality (IRR=1.05, 95%CI=1.04-1.07) when controlling for racial segregation and 2% higher rates when controlling for racialized economic segregation (IRR=1.02, 95%CI=1.01-1.03). Thus, we stratified our models by urban-rural status and found that each ICE measure had larger effects on obesity-related cancer mortality in rural counties than in urban. CONCLUSION: While food swamp is important, the socioeconomic environment of a county has a larger impact on obesity- related cancer mortality. Individuals may have access to healthy food stores, but whether they can afford the healthy food is more important. Citation Format: Elizabeth S. Davis, Sara Myers, Jeffrey A. Franks, Michael R. Poulson, Julie R. Palmer, Kelly M. Kenzik. Effects of food environment, income inequality, and racial segregation on obesity- related cancer mortality [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A040.

The effect of chess on cognition: a graph theory study on cognitive data.

We aimed to advance our understanding of the effect of chess on cognition by expanding previous univariate studies with the use of graph theory on cognitive data. Specifically, we investigated the cognitive connectome of adult chess players. We included 19 chess players and 19 controls with ages between 39 and 69 years. Univariate analysis and graph theory included 27 cognitive measures representing multiple cognitive domains and subdomains. Graph analysis included global and nodal measures of integration, segregation, and centrality. We also performed an analysis of community structures to gain an additional understanding of the cognitive architecture of chess players. The analysis of global graph measures showed that chess players had a higher local efficiency than controls at the cost of a lower global efficiency, which did not permeate segregation aspects of their connectome. The nodal graph measures showed that executive/attention/processing speed and visuoconstructive nodes had a central role in the connectome of chess players. The analysis of communities showed that chess players had a slightly reorganized cognitive architecture into three modules. These graph theory findings were in the context of better cognitive performance in chess players than controls in visuospatial abilities. We conclude that the cognitive architecture of chess players is slightly reorganized into functionally and anatomically coherent modules reflecting a distinction between visual, verbal, and executive/attention/processing speed-related functions, perhaps reminiscent of right hemisphere and left hemisphere subnetworks orchestrated by the frontal lobe and its white matter connections.

Mediators of racial and ethnic inequities in clinical trial participation among patients with cancer, 2011-2023.

Although racially and ethnically minoritized populations are less likely to participate in cancer trials, it is unknown whether social determinants of health (SDOH) explain these inequities. Here we identify SDOH factors that contribute to racial and ethnic inequities in clinical trial participation among patients with 22 common cancers. This retrospective cohort study used electronic health record data (2011-2023) linked to neighborhood (US Census tract) data from multiple sources. Patients were followed from diagnosis to clinical study drug receipt (proxy for trial participation), death, or last recorded activity. Associations were assessed using Cox proportional hazards models adjusted for clinical factors (year of diagnosis, age, sex, performance status, disease stage, cancer type). To elucidate which area-level SDOH underlie racial and ethnic inequities, mediation analysis was performed using nonlinear multiple additive regression tree models. This study included 250 105 patients (64.7% non-Latinx White, 8.9% non-Latinx Black, 5.2% Latinx). Black and Latinx patients were more likely to live in economically or socially marginalized areas (eg, disproportionately minoritized [measure of segregation], limited English proficiency, low vehicle ownership) than White patients. Black (3.7%; hazard ratio = 0.55, 95% confidence interval [CI] = 0.52 to 0.60) and Latinx patients (4.4%; hazard ratio = 0.63, 95% CI = 0.58 to 0.69) were less likely to participate in trials than White patients (6.3%). Fewer patients in economically or socially marginalized neighborhoods participated in trials. Mediators explained 62.2% (95% CI = 49.5% to 74.8%) of participation inequities between Black and White patients; area-level SDOH-including segregation (29.9%, 95% CI = 21.2% to 38.6%) and vehicle ownership (11.6%, 95% CI = 7.0% to 16.1%)-were the most important mediators. Similarly, Latinx-White participation inequities were mediated (65.1%, 95% CI = 49.8% to 80.3%) by area-level SDOH, such as segregation (39.8%, 95% CI = 28.3% to 51.3%), limited English proficiency (11.6%, 95% CI = 2.8% to 20.4%), and vehicle ownership (9.6%, 95% CI = 5.8% to 13.5%). To improve racial and ethnic diversity in cancer trials, efforts to address barriers related to adverse neighborhood SDOH factors are necessary.

Language networks of normal-hearing infants exhibit topological differences between resting and steady states: An fNIRS functional connectivity study.

Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.

Provider care segregation and hospital-region racial disparities for carotid interventions in the USA

BackgroundReasons for racial disparities in the utilization and outcomes of carotid interventions (carotid endarterectomy (CEA) and carotid artery stenting (CAS)) are not well understood, especially segregation of care associated with...

Segregation in Hyderabad: Introspecting Multiple Indexes and Multiple Patterns

Segregation based on caste, religion, and ethnicity exists primarily in the urban housing market in India even in cosmopolitan urban centres like Mumbai, Delhi, Bengaluru, Kolkata, and Hyderabad. The present study maps segregation in Hyderabad and explores its intricate linkages with access to public amenities. This study examines caste-based segregation in urban space using multiple indexes like dissimilarity index (DI), isolation index, interaction index, correlation analysis, and mandal development index (MDI). It is observed that there is no clear link between caste-based DI and MDI. The correlation of MDI and caste-based DI illustrates that it is the location (core or periphery) that decides the level of infrastructural development. This investigation of the link between residential segregation measurement of DI and MDI leads us to conclude that the historical factors are equally crucial along with the social identities to explain urban segregation and discrimination.

Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study.

Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.

Characterizing normal perinatal development of the human brain structural connectivity.

Early brain development is characterized by the formation of a highly organized structural connectome, which underlies brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development, inherently low signal quality, imaging difficulties, and high inter-subject variability. These factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational method based on spatio-temporal averaging in the image space for determining such baselines. We used this method to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in the perinatal stage. We observed increases in measures of network integration and segregation, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. Our proposed method also showed considerable agreement with an alternative technique based on connectome averaging. The new computational method and results of this study can be useful for assessing normal and abnormal development of the structural connectome early in life.

Sociodemographic and Socioeconomic Factors Correlate with Late-Stage Pediatric Hodgkin Lymphoma and Rhabdomyosarcoma: A Report from the Children's Oncology Group Registries.

We examined the association between late-stage diagnosis and individual- and community-level sociodemographic and socioeconomic characteristics among patients with pediatric Hodgkin lymphoma and rhabdomyosarcoma (RMS). We obtained Children's Oncology Group data from 1999 to 2021 including summary stage [local (L), regional (R), and distant (D)], tumor subtype, demographics, and ZIP Code at diagnosis. We linked ZIP Codes to county-level redlining scores (C, D = greatest redlining), the Child Opportunity Index, and measures of segregation (racial dissimilarity indices). Logistic regressions calculated odds ratios for late-stage diagnosis and by race within tumor subtype. In total, 5,956 patients with Hodgkin lymphoma and 2,800 patients with RMS were included. Late-stage diagnosis of Hodgkin lymphoma was correlated with Black race [ORDistant(D) vs. regional/local (R&L) = 1.38 (1.13-1.68)], being uninsured [ORD vs. R&L = 1.38 (1.09-1.75)], and subtype [nodular sclerosis vs. Other Hodgkin lymphoma: ORD vs. R&L = 1.64 (1.34-2.01), Untyped: ORD vs. R&L = 1.30 (1.04-1.63)]. Late-stage RMS was correlated with bilingual households [ORDistant/regional(D&R) vs. local(L) = 2.66 (1.03-6.91)] and tumor type [alveolar vs. embryonal ORD vs. R&L = 6.16 (5.00-7.58)]. Community-level factors associated with late-stage Hodgkin lymphoma were greater Black (OR80-100% = 1.83; 95% CI = 1.11-3.02) and Hispanic (OR60-79% = 1.30; 95% CI = 1.05-1.60) dissimilarity indices. Late-stage diagnosis for RMS was associated with more redlined census tracts within counties (OR = 1.54; 95% CI = 1.02-2.35) and low/very low Child Opportunity Index (OR = 1.21; 95% CI = 1.02-1.45). Novel markers of community deprivation, such as redlining and racial segregation, were correlated with cancer outcomes for children with Hodgkin lymphoma and RMS in this first disparities study using Children's Oncology Group registries. The interplay of multilevel risk factors provides important consideration for efforts to improve early detection of pediatric cancer diagnosis.

Role of green and sustainable practices in shaping the future of medical imaging technology: A cross-sectional multi-stakeholder analysis among students, radiographers, and academic experts

IntroductionThe detection and treatment of diseases like COVID, diabetes, cancer, cardiovascular conditions, etc., have made medical imaging technology more necessary, so it is expected that the demands of imaging modalities are also increasing and are major contributors to carbon emissions in the healthcare industry. Hence, the Radiology departments, like the rest of the healthcare industry should adapt the procedures to become more sustainable. MethodsA total of 1016 respondents completed the online survey to assess the perception, current practices, and challenges in adopting green and sustainable practices in medical imaging. The radio technologists, teaching faculties, and students of medical imaging were recruited for the study. The survey tool was distributed to the closed groups through social media and emails. ResultsThe majority of participants (66.6%) highlighted the importance of green and sustainable practices in medical imaging whereas only 21.06% of participants seem to have implemented these practices. Most of the participants give positive responses on the use of zero-lead aprons (77%), refurbished medical systems (85.8%), and eco-friendly packaging (89.5%). The mixed response was received from waste segregation and energy-saving measures. The majority (60.3%) of them have no formal education or training. However, they have a good attitude towards the willingness to adopt green practices. ConclusionsThere is a gap between perception and implementation of green and sustainable practices due to leadership and information barriers. Comprehensive training for stakeholders of medical imaging is crucial to fully integrate sustainability practices, possibly through webinars or educational modules. Implications for practiceThe study's findings shed light on how important medical imaging stakeholders view green and sustainable practices as well as potential obstacles to their implementation at the local level whilst suggesting the need for exclusive training on these practices to promote sustainability.

Segregation and the onset of COVID-19 in American cities

PurposeThis paper investigates one of the potential costs of rising segregation in American cities by evaluating empirically the extent at which ethnic-based segregation contributes to the onset and the speed of propagation of the COVID-19 pandemic.Design/methodology/approachRegression analysis based on matched data on early incidence of COVID-19 cases, segregation and covariates. Identification resorts on variations in segregation across MSAs and heterogeneity in the geography and timing of stay-at-home orders.FindingsOne cross-MSA standard deviation increase in segregation leads to a significant and robust rise of COVID-19 cases of 8.7 per 100,000 residents across urban counties.Originality/valueCombines spatial data on COVID-19 cases and segregation; use of a new segregation measure; focus on early incidence of the pandemic and its drivers.

Census tract geospatial analysis comparing social determinants of health with tooth loss in California seniors: An ecologic study.

Individual-level social determinant of health (SDOH) measures alone may insufficiently explain disparities in edentulism among seniors. Therefore, the authors examined the correlation of census tract-level SDOH and residential racial segregation measures with edentulism in Californian adults aged ≥65 years old. Explanatory variables were obtained from Healthy Places Index (HPI), the National Cancer Institute and diversitydatakids.org. The edentulism outcome variable was obtained from CDC's PLACES small area estimates from the 2018 Behavioral Risk Factor Surveillance System data. Pearson and Spearman rank correlations were estimated. Multiple linear regression and multi-collinearity evaluations were performed. The Global Moran's I statistic assessed partial autocorrelation within census tracts. Pearson and Spearman correlations were similar, supporting robustness. HPI, an area measure of advantage, strongly negatively correlated with edentulism prevalence [correlation coefficient: -0.87; 95% confidence interval (CI): -0.87, -0.86]. A change of 1.0 in HPI corresponded to an estimated decrease in edentulism prevalence of 5.9% (linear model adjusted R2 = 0.78). Racially segregated census tracts with Hispanics or Blacks alone were positively correlated with edentulism prevalence [0.60, 95% CI: 0.58, 0.62; and 0.33, 95% CI: 0.31, 0.35, respectively]. The converse was seen in census tracts with non-Hispanic Whites alone [-0.57, 95% CI: -0.58, -0.55]. Global Moran's I statistic for edentulism (0.13) and HPI scores (0.19) were significant (both p < .001) indicating geospatial autocorrelation. Higher disadvantage and minority racial segregation within census tracts were positively correlated with edentulism prevalence. Future research and policy should consider possible interventions improving SDOH to reduce oral health inequities.

Residential segregation and summertime air temperature across 13 northeastern U.S. states: potential implications for energy burden

High ambient summertime temperatures are an increasing health concern with climate change. This is a particular concern for minoritized households in the United States, for which differential energy burden may compromise adaptive capacity to high temperatures. Our research question was: Do minoritized groups experience hotter summers than the area average, and do non-Hispanic white people experience cooler summers? Using a fine-scaled spatiotemporal air temperature model and U.S. census data, we examined local (within-county) differences in warm season cooling degree days (CDDs) by ethnoracial group as a proxy for local energy demand for space cooling across states of the northeast and mid-Atlantic U.S. in 2003–2019. Using state-specific regression models adjusted for year and county, we found that Black and Latino people consistently experienced more CDDs, non-Hispanic white people experienced fewer CDDs, and Asian populations showed mixed results. We also explored a concentration-based measure of residential segregation for each ethnoracial group as one possible pathway towards temperature disparities. We included the segregation measure as a smooth term in a regression model adjusted for county and year. The results were nonlinear, but higher concentrations of white people were associated with lower annual CDDs and higher concentrations of Latino people were associated with higher annual CDDs than the county average. Concentrations for Black and Asian people were nonmonotonic, sometimes with bowed associations. These findings suggest that present-day residential segregation, as modeled by spatially smoothed ethnoracial subgroup concentrations, may contribute to summertime air temperature disparities and influence adaptive capacity. We hope these findings can support place-based interventions, including targeting of energy insecurity relief programs.

Probabilistic Tractography-Based Tremor Network Connectivity in Tremor Dominant Parkinson's Disease and Essential Tremor plus.

Background: The basal ganglia-thalamocortical (BGTC) and cerebello-thalamocortical (CTC) networks are implicated in tremor genesis; however, exact contributions across disorders have not been studied. Objective: Evaluate the structural connectivity of BGTC and CTC in tremor-dominant Parkinson's disease (TDPD) and essential tremor plus (ETP) with the aid of probabilistic tractography and graph theory analysis. Methods: Structural connectomes of the BGTC and CTC were generated by probabilistic tractography for TDPD (n = 25), ETP (ET with rest tremor, n = 25), and healthy control (HC, n = 22). The Brain Connectivity Toolbox was used for computing standard topological graph measures of segregation, integration, and centrality. Tremor severity was ascertained using the Fahn-Tolosa-Marin tremor rating scale (FTMRS). Results: There was no difference in total FTMRS scores. Compared with HC, TDPD had a lower global efficiency and characteristic path length. Abnormality in segregation, integration, and centrality of bilateral putamen, globus pallidus externa (GPe), and GP interna (GPi), with reduction of centrality of right caudate and cerebellar lobule 8, was observed. ETP showed reduction in segregation and integration of right GPe and GPi, ventrolateral posterior nucleus, and centrality of right putamen, compared with HC. Differences between TDPD and ETP were a reduction of strength of the right putamen, and lower clustering coefficient, local efficiency, and strength of the left GPi in TDPD. Conclusions: Contrary to expectations, TDPD and ETP may not be significantly different with regard to tremor pathogenesis, with definite overlaps. There may be fundamental similarities in network disruption across different tremor disorders with the same tremor activation patterns, along with disease-specific changes.

SuperAging functional connectomics from resting-state functional MRI.

Understanding the relationship between functional connectivity (FC) of higher-order neurocognitive networks and age-related cognitive decline is a complex and evolving field of research. Decreases in FC have been associated with cognitive decline in persons with Alzheimer's disease and related dementias (ADRD). However, the contributions of FC have been less straightforward in typical cognitive aging. Some investigations suggest relatively robust FC within neurocognitive networks differentiates unusually successful cognitive aging from average aging, while others do not. Methodologic limitations in data processing and varying definitions of 'successful aging' may have contributed to the inconsistent results to date. The current study seeks to address previous limitations by optimized MRI methods to examine FC in the well-established SuperAging phenotype, defined by age and cognitive performance as individuals 80 and older with episodic memory performance equal to or better than 50-to-60-year-olds. Within- and between-network FC of large-scale neurocognitive networks were compared between 24 SuperAgers and 16 cognitively average older-aged control (OACs) with stable cognitive profiles using resting-state functional MRI (rs-fMRI) from a single visit. Group classification was determined based on measures of episodic memory, executive functioning, verbal fluency and picture naming. Inclusion criteria required stable cognitive status across two visits. First, we investigated the FC within and between seven resting-state networks from a common atlas parcellation. A separate index of network segregation was also compared between groups. Second, we investigated the FC between six subcomponents of the default mode network (DMN), the neurocognitive network commonly associated with memory performance and disrupted in persons with ADRD. For each analysis, FCs were compared across groups using two-sample independent t-tests and corrected for multiple comparisons. There were no significant between-group differences in demographic characteristics including age, sex and education. At the group-level, within-network FC, between-network FC, and segregation measurements of seven large-scale networks, including subcomponents of the DMN, were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Thus, FC within or between large-scale networks does not appear to be a primary driver of the exceptional memory performance observed in SuperAgers. These results have relevance for differentiating the role of FC changes associated with cognitive aging from those associated with ADRD.