Age and beta amyloid deposition impact gait speed, stride length, and gait smoothness while transitioning from an even to an uneven walking surface in older adults.

BACKGROUND: Capturing a measure of movement quality during a complex walking task may indicate the earliest signs of detrimental changes to the brain due to beta amyloid (Aß) deposition and be a potential differentiator of older adults at elevated and low risk of developing Alzheimer's disease. This study aimed to determine: 1) age-related differences in gait speed, stride length, and gait smoothness while transitioning from an even to an uneven walking surface, by comparing young adults (YA) and older adults (OA), and 2) if gait speed, stride length, and gait smoothness in OA while transitioning from an even to an uneven walking surface is influenced by the amount of Aß deposition present in an OA's brain. METHODS: Participants included 56 OA (>70 years of age) and 29 YA (25-35 years of age). In OA, Aß deposition in the brain was quantified by PET imaging. All participants completed a series of cognitive assessments, a functional mobility assessment, and self-report questionnaires. Then participants performed two sets of walking trials on a custom-built walkway containing a mixture of even and uneven surface sections, including three trials with a grass uneven surface and three trials with a rocks uneven surface. Gait data were recorded using a wireless inertial measurement unit system. Stride length, gait speed, and gait smoothness (i.e., log dimensionless lumbar jerk) in the anteroposterior (AP), mediolateral (ML), and vertical (VT) directions were calculated for each stride. Outcomes were retained for five stride locations immediately surrounding the surface transition. RESULTS: OA exhibited slower gait (Grass: p < 0.001; Rocks: p = 0.006), shorter strides (Grass: p < 0.001; Rocks: p = 0.008), and smoother gait (Grass AP: p < 0.001; Rocks AP: p = 0.002; Rocks ML: p = 0.02) than YA, but they also exhibited greater reductions in gait speed and stride length than YA while transitioning to the uneven grass and rocks surfaces. Within the OA group, those with greater Aß deposition exhibited decreases in smoothness with age (Grass AP: p = 0.02; Rocks AP: p = 0.03; Grass ML: p = 0.04; Rocks ML: p = 0.03), while those with lower Aß deposition exhibited increasing smoothness with age (Grass AP: p = 0.01; Rocks AP: p = 0.02; Grass ML: p = 0.08; Rocks ML: p = 0.07). Better functional mobility was associated with less smooth gait (Grass ML: p = 0.02; Rocks ML: p = 0.05) and with less variable gait smoothness (Grass and Rocks AP: both p = 0.04) in the OA group. CONCLUSION: These results suggest that, relative to YA, OA may be adopting more cautious, compensatory gait strategies to maintain smoothness when approaching surface transitions. However, OA with greater Aß deposition may have limited ability to adopt compensatory gait strategies to increase the smoothness of their walking as they get older because of neuropathological changes altering the sensory integration process and causing worse dynamic balance (i.e., jerkier gait). Functional mobility, in addition to age and Aß deposition, may be an important factor of whether or not an OA chooses to employ compensatory strategies to prioritize smoothness while walking and what type of compensatory strategy an OA chooses.

Differences in Brain Network Topology Based on Alcohol Use History in Adolescents.

Approximately 6 million youth aged 12 to 20 consume alcohol monthly in the United States. The effect of alcohol consumption in adolescence on behavior and cognition is heavily researched; however, little is known about how alcohol consumption in adolescence may alter brain function, leading to long-term developmental detriments. In order to investigate differences in brain connectivity associated with alcohol use in adolescents, brain networks were constructed using resting-state functional magnetic resonance imaging data collected by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) from 698 youth (12-21 years; 117 hazardous drinkers and 581 no/low drinkers). Analyses assessed differences in brain network topology based on alcohol consumption in eight predefined brain networks, as well as in whole-brain connectivity. Within the central executive network (CEN), basal ganglia network (BGN), and sensorimotor network (SMN), no/low drinkers demonstrated stronger and more frequent connections between highly globally efficient nodes, with fewer and weaker connections between highly clustered nodes. Inverse results were observed within the dorsal attention network (DAN), visual network (VN), and frontotemporal network (FTN), with no/low drinkers demonstrating weaker connections between nodes with high efficiency and increased frequency of clustered nodes compared to hazardous drinkers. Cross-sectional results from this study show clear organizational differences between adolescents with no/low or hazardous alcohol use, suggesting that aberrant connectivity in these brain networks is associated with risky drinking behaviors.

Neuroanatomical differences in Latinx children from rural farmworker families and urban non-farmworker families and related associations with pesticide exposure.

Exposure to pesticides in humans may lead to changes in brain structure and function and increase the likelihood of experiencing neurodevelopmental disorders. Despite the potential risks, there is limited neuroimaging research on the effects of pesticide exposure on children, particularly during the critical period of brain development. Here we used voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) from magnetic resonance images (MRI) to investigate neuroanatomical differences between Latinx children (n = 71) from rural, farmworker families (FW; n = 48) and urban, non-farmworker families (NFW; n = 23). Data presented here serves as a baseline for our ongoing study examining the longitudinal effects of living in a rural environment on neurodevelopment and cognition in children. The VBM analysis revealed that NFW children had higher volume in several distinct regions of white matter compared to FW children. Tract-based spatial statistics (TBSS) of DTI data also indicated NFW children had higher fractional anisotropy (FA) in several key white matter tracts. Although the difference was not as pronounced as white matter, the VBM analysis also found higher gray matter volume in selected regions of the frontal lobe in NFW children. Notably, white matter and gray matter findings demonstrated a high degree of overlap in the medial frontal lobe, a brain region predominantly linked to decision-making, error processing, and attention functions. To gain further insights into the underlying causes of the observed differences in brain structure between the two groups, we examined the association of organochlorine (OC) and organophosphate (OP) exposure collected from passive dosimeter wristbands with brain structure. Based on our previous findings within this data set, demonstrating higher OC exposure in children from non-farmworker families, we hypothesized OC might play a critical role in structural differences between NFW and FW children. We discovered a significant positive correlation between the number of types of OC exposure and the structure of white matter. The regions with significant association with OC exposure were in agreement with the findings from the FW-NFW groups comparison analysis. In contrast, OPs did not have a statistically significant association with brain structure. This study is among the first multimodal neuroimaging studies examining the brain structure of children exposed to agricultural pesticides, specifically OC. These findings suggest OC pesticide exposure may disrupt normal brain development in children, highlighting the need for further neuroimaging studies within this vulnerable population.

Fractal basins as a mechanism for the nimble brain.

An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-stability, the ability to switch between multiple stable states corresponding to specific patterns of brain activity/connectivity. In this article, we describe chimera states, which are patterns consisting of mixed synchrony and incoherence, in a brain-inspired dynamical systems model composed of a network with weak individual interactions and chaotic/periodic local dynamics. We illustrate the mechanism using synthetic time series interacting on a realistic anatomical brain network derived from human diffusion tensor imaging. We introduce the so-called vector pattern state (VPS) as an efficient way of identifying chimera states and mapping basin structures. Clustering similar VPSs for different initial conditions, we show that coexisting attractors of such states reveal intricately "mingled" fractal basin boundaries that are immediately reachable. This could explain the nimble brain's ability to rapidly switch patterns between coexisting attractors.

Comparing Longitudinal Measures of Cholinesterase as Biomarkers for Insecticide Exposure Among Latinx Children in Rural Farmworker and Urban Nonfarmworker Communities in North Carolina.

OBJECTIVE: In a 2-group prospective design, this study compared seasonal cholinesterase levels of Latinx children in rural farmworker families and comparable urban children to assess the impact of environmental exposure to cholinesterase-inhibiting insecticides. METHODS: Quarterly blood samples and passive dosimeter wristbands were collected over 2 years in 8-year-old children (74 rural, 62 urban). Laboratory analysis assessed total cholinesterase, acetylcholinesterase, and butyrylcholinesterase from blood samples, and insecticides from wristbands. RESULTS: In spring and summer, total cholinesterase and acetylcholinesterase levels were depressed in rural children compared with winter and fall. Butyrylcholinesterase was depressed in rural children in fall compared with spring and summer. Adjustment for insecticide exposure did not affect these associations. CONCLUSIONS: Environmental exposures to cholinesterase-inhibiting insecticides have measurable biochemical effects on blood cholinesterases in rural children from farmworker families.

Brain working memory network indices as landmarks of intelligence.

Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22-35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.

3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.

Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to phenotypic traits has lagged behind. Our previous work developed a novel analytic framework to assess the relationship between brain network architecture and phenotypic differences while controlling for confounding variables. More specifically, this innovative regression framework related distances (or similarities) between brain network features from a single task to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. Here we extend that work to the multitask and multisession context to allow for multiple brain networks per individual. We explore several similarity metrics for comparing distances between connection matrices and adapt several standard methods for estimation and inference within our framework: standard F test, F test with scan-level effects (SLE), and our proposed mixed model for multitask (and multisession) BrAin NeTwOrk Regression (3M_BANTOR). A novel strategy is implemented to simulate symmetric positive-definite (SPD) connection matrices, allowing for the testing of metrics on the Riemannian manifold. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

Pesticide Exposure among Latinx Children in Rural Farmworker and Urban Non-Farmworker Communities: Associations with Locality and Season.

This study uses repeated measures to document the pesticide exposure of rural and urban Latinx children (age eight at baseline), and to compare these children in terms of the frequency and concentration of their exposure to a large set of pesticides, accounting for season. We used silicone wristbands worn for one week up to ten times at quarterly intervals from 2018 to 2022 to assess pesticide exposure in children from rural farmworker (n = 75) and urban non-farmworker (n = 61) families. We determined the detection and concentrations (ng/g) of 72 pesticides and pesticide degradation products in the wristbands using gas chromatography electron capture detection and gas chromatography mass spectrometry. The most frequently detected pesticide classes were organochlorines, pyrethroids, and organophosphates. Controlling for season, organochlorine or phenylpyrazole detections were less likely for rural children than for urban children. Detections of organochlorines, pyrethroids, or organophosphates were lower in spring and summer versus winter. Controlling for season, urban children had greater concentrations of organochlorines, while rural children had greater concentrations of pyrethroids and Chlorpyrifos. Pesticide concentrations were lower in winter and spring compared with summer and fall. These results further document that pesticides are ubiquitous in the living environment for children in vulnerable, immigrant communities.

Associations of physical function and body mass index with functional brain networks in community-dwelling older adults.

Deficits in physical function that occur with aging contribute to declines in quality of life and increased mortality. There has been a growing interest in examining associations between physical function and neurobiology. Whereas high levels of white matter disease have been found in individuals with mobility impairments in structural brain studies, much less is known about the relationship between physical function and functional brain networks. Even less is known about the association between modifiable risk factors such as body mass index (BMI) and functional brain networks. The current study examined baseline functional brain networks in 192 individuals from the Brain Networks and mobility (B-NET) study, an ongoing longitudinal, observational study in community-dwelling adults aged 70 and older. Physical function and BMI were found to be associated with sensorimotor and dorsal attention network connectivity. There was a synergistic interaction such that high physical function and low BMI were associated with the highest network integrity. White matter disease did not modify these relationships. Future work is needed to understand the causal direction of these relationships.

Corrigendum: Examining the intersection of cognitive and physical function measures: Results from the brain networks and mobility (B-NET) study.

[This corrects the article DOI: 10.3389/fnagi.2023.1090641.].

Desire and craving measured using behavioral ratings and brain network topology differ significantly among moderate to heavy alcohol consumers.

BACKGROUND: "Craving" is a central concept in alcohol research, but the semantic interpretation of craving as a concept varies. Multiple studies that have investigated differences in operational definitions of craving have demonstrated a lack of agreement among them. This study investigated whether moderate to heavy drinkers would rate craving and "desire" for alcohol similarly and explored potential neurobiological differences underpinning feelings of craving and desire. METHODS: Thirty-nine individuals who consumed an average of at least 7 drinks/week for females and 14 drinks/week for males were studied across 3-day periods of their typical alcohol consumption and imposed abstinence. Ratings of desire and craving for alcohol were collected approximately every three hours during waking periods across the two experimental periods (n = 35, 17 males). At the end of each period, participants underwent functional MRI scanning during neutral and alcohol image viewing (n = 39, 17 males) followed by ratings of desire and craving for alcohol (n = 32, 16 males). Survey responses were analyzed using 2-level nested hierarchical modeling, image ratings were compared using a hierarchical mixed-effects regression, and brain networks constructed from fMRI data were assessed with a two-part mixed-effect regression (α = 0.05 in all analyses). RESULTS: Ratings of desire and craving differed significantly from one another in the survey data and in the ratings collected during image viewing. The strength of the desire experience was higher overall than craving, but the fluctuations over time were similar. Results for desire and craving differed on brain network attributes associated with distributed processing and those regional specific within the default mode network. Significant associations were found between ratings of desire and connection strength and between ratings of craving and connection probability. CONCLUSIONS: These results demonstrate that the difference between ratings of craving for alcohol and desire for alcohol is not trivial. The different ratings and their association with alcohol consumption or abstinence experiences may have significant biological and clinical implications.

Examining the intersection of cognitive and physical function measures: Results from the brain networks and mobility (B-NET) study.

Background and objectives: Although evidence exists that measures of mobility and cognition are correlated, it is not known to what extent they overlap, especially across various domains. This study aimed to investigate the intersection of 18 different objective cognitive and physical function measures from a sample of unimpaired adults aged 70 years and older. Research design and methods: Canonical correlation analysis was utilized to explore the joint cross-sectional relationship between 13 cognitive and 6 physical function measures in the baseline visit of the Brain Networks and Mobility Function (B-NET) Study (n = 192). Results: Mean age of participants was 76.4 years. Two synthetic functions were identified. Function 1 explained 26.3% of the shared variability between the cognition and physical function variables, whereas Function 2 explained 19.5%. Function 1 termed "cognitive and physical speed" related the expanded Short Physical Performance Battery (eSPPB), 400-m walk speed, and Dual Task gait speed measures of physical function to semantic fluency animals scores, Digit Symbol Coding (DSC), and Trail Making Test B. Function 2 termed "complex motor tasks and cognitive tasks" related the Force Plate Postural Sway Foam Task and Dual Task to the following cognitive variables: MoCA Adjusted Score, Verbal Fluency L words, Craft story immediate and delayed recall, and Trail Making Test B. Discussion and implications: We identified groups of cognitive and physical functional abilities that were linked in cross-sectional analyses, which may suggest shared underlying neural network pathway(s) related to speed (Function 1) or complexity (Function 2). Translational significance: Whether such neural processes decline before measurable functional losses or may be important targets for future interventions that aim to prevent disability also remains to be determined.

Differential Momentary Reports of Stress and Affect Associated With Alcohol Consumption in Middle-Aged Versus Younger Adults.

Background: Stress is a motivator to consume alcohol, a well-documented relapse risk, and is known to differentially affect biological and psychological processes as people age.Objectives: Because alcohol consumption is known to acutely decrease stress and increase affect, this study examined differences in ratings of stress and affect in middle-aged versus younger adults who regularly consume alcohol.Methods: A sample of younger (n = 17) and middle-aged (n = 18) drinkers was studied during a 3-day period of typical alcohol consumption. Resting levels of respiratory sinus arrhythmia (RSA) were measured during a baseline study visit since RSA is a well-documented biomarker of stress and is known to decrease with age. Ecological momentary assessment (EMA) survey ratings (n = 1,598) were modeled using hierarchical regression to assess differences in stress and affect throughout the day between the two age groups.Results: As anticipated, middle-aged participants had lower RSA than those who were younger. Although the middle-aged adults showed overall lower stress, generally they also experienced higher affect than the younger adults. Middle-aged adults experienced a significant reduction in stress following drinking while no such effect was observed in the younger adults.Conclusions: To our knowledge, this is the first investigation using EMA methodology to examine stress and affect between younger and middle-aged adults who habitually consume alcohol. These cross-sectional data suggest potential momentary stress relief to engaging with moderate alcohol consumption in a middle-aged population. Future work must address this important motivational process in curtailing maintenance of alcohol consumption and preventing escalation of consumption.

Association Between Contrast Sensitivity and Physical Function in Cognitively Healthy Older Adults: The Brain Networks and Mobility Function Study.

BACKGROUND: To evaluate whether contrast sensitivity is associated with lower extremity physical function in cognitively intact older adults. METHODS: Cross-sectional analysis of the relationship of binocular and worse eye log contrast sensitivity (LCS) to expanded Short Physical Performance Battery (eSPPB) and its components (gait speed, narrow walking speed, chair stand pace, and balance) in 192 cognitively healthy older adults. The association of LCS with postural sway and gait was also tested with tasks that further challenged functional reserve. RESULTS: Mean age was 76.4 years with 56% identifying as female and over 98.5% having good corrected visual acuity. Lower LCS was significantly associated with worse performance on the eSPPB, 4-M gait speed, narrow walking speed, and balance time in unadjusted and adjusted models. The relationship between worse eye LCS and larger postural sway was 3 times greater on a foam surface (beta 1.07, 95% CI [0.35, 1.80]) than a firm surface (beta 0.35, 95% CI [0.05, 0.65]), and both were robust to adjustment for confounders; similar findings were observed with binocular LCS. Lower binocular LCS had a greater decremental effect on gait velocity during the fast pace (beta -0.58, 95% CI [-0.90, -0.27]) than the usual pace (Beta -0.39 [-0.63, -0.15]) gait task. CONCLUSIONS: These findings suggest that cognitively unimpaired older adults without significant visual acuity impairment can have subtle preclinical deficits in contrast sensitivity and physical function that could place them at risk of mobility and balance issues. Future studies should determine whether this subset of older adults may benefit from targeted intervention to prevent disability.

Brain Network Analysis: A Review on Multivariate Analytical Methods.

Despite the explosive growth of neuroimaging studies aimed at analyzing the brain as a complex system, critical methodological gaps remain to be addressed. Most tools currently used for analyzing network data of the brain are univariate in nature and are based on assumptions borne out of previous techniques not directly related to the big and complex data of the brain. Although graph-based methods have shown great promise, the development of principled multivariate models to address inherent limitations of graph-based methods, such as their dependence on network size and degree distributions, and to allow assessing the effects of multiple phenotypes on the brain and simulating brain networks has largely lagged behind. Although some studies have been made in developing multivariate frameworks to fill this gap, in the absence of a "gold-standard" method or guidelines, choosing the most appropriate method for each study can be another critical challenge for investigators in this multidisciplinary field. Here, we briefly introduce important multivariate methods for brain network analyses in two main categories: data-driven and model-based methods. We discuss whether/how such methods are suited for examining connectivity (edge-level), topology (system-level), or both. This review will aid in choosing an appropriate multivariate method with respect to variables such as network type, number of subjects and brain regions included, and the interest in connectivity, topology, or both. This review is aimed to be accessible to investigators from different backgrounds, with a focus on applications in brain network studies, though the methods may be applicable in other areas too. Impact statement As the U.S. National Institute of Health notes, the rich biomedical data can greatly improve our knowledge of human health if new analytical tools are developed, and their applications are broadly disseminated. A major challenge in analyzing the brain as a complex system is about developing parsimonious multivariate methods, and particularly choosing the most appropriate one among the existing methods with respect to the study variables in this multidisciplinary field. This study provides a review on the most important multivariate methods to aid in helping the most appropriate ones with respect to the desired variables for each study.

Change and Continuity in Preventive Practices Across the COVID-19 Pandemic Among Rural and Urban Latinx Immigrant Worker Families.

Background: (1)The COVID-19 pandemic has put essential workers at high risk for contracting the disease. This study documents situational compliance with public health recommendations such as masking and social distancing among rural and urban Latinx families, with the goal of understanding change over time in COVID-19 risk reduction behaviors. Methods: (2)Respondents for 67 rural families and 44 urban families responded to repeated telephone surveys at three time points in the first year of the pandemic, providing data on use of masks and social distancing by themselves and family members while interacting with others at home, work, and in the community. Cumulative logistic regression models were employed to compare changes in risk behaviors between rural and urban groups over time. Results: (3)While group descriptive results indicated behaviors that posed low risk at each time point, regression models revealed greater change between time points for rural than urban residents. Rural residents also had gendered patterns. Conclusions: (4)Patterns of change appear to reflect structural issues such as seasonal labor demand and gender roles more than pandemic fatigue or changing public health recommendations. The findings suggest that structural factors play a role in individuals complying with public health prevention measures for COVID-19.

A mixed-modeling framework for whole-brain dynamic network analysis.

The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.

Comparing impact of pesticide exposure on cognitive abilities of Latinx children from rural farmworker and urban non-farmworker families in North Carolina.

Pesticide exposure remains a health hazard despite extensive study into adverse effects. Children in vulnerable populations, such as Latinx children in farmworker families, are particularly at risk for exposure. Several studies have demonstrated the detrimental cognitive effects of prenatal exposure to pesticides, particularly organophosphates (OPs) within this high-risk group. However, results from studies investigating the cognitive effects of early childhood pesticide exposure are equivocal. Most studies examining the effects of pesticide exposure have used correlative analyses rather than examining populations with expected high and low exposure. The current study compares 8-year-old children from rural families of farmworkers and urban, non-farmworker families. We used the Weschler Intelligence Scale for Children - Fifth Edition (WISC-V) to assess cognitive performance in these children. We designed this study with the expectation that children from farmworker families would have greater exposure to agricultural pesticides than urban, non-farmworker children. This assumption of exposure to agricultural pesticides was confirmed in a recent report that assessed exposure probabilities using life history calendars. However, data from passive wristband sampling of acute (1-week) pesticide exposure from these same children indicate that both study populations have considerable pesticide exposure but to different chemicals. As expected the children of farmworkers had greater OP exposure than non-farmworker children, but the non-farmworker children had greater exposure to two other classes of insecticides (organochlorines [OCs] and pyrethroids). Our analyses considered these findings. A comparison of the cognitive scores between groups revealed that children from farmworker families had slightly higher performance on the Visual-Spatial Index (VSI) and Verbal Comprehension Index (VCI) when compared to children from non-farmworker families. Regression analyses where pesticide exposure was included as covariates revealed that OC exposure accounted for the largest portion of the group differences for both VSI and VCI. However, a post-hoc moderation analysis did not find significant interactions. The main study outcome was that the non-farmworker children exhibited lower WISC-V scores than the children from farmworker families, and the analyses incorporating pesticide exposure measures raise the hypothesis the that pervasive and persistent nature of a variety of pesticides may have adverse effects on the neurodevelopment of young Latinx children whether living in rural or non-farmworker environments.

Correction: Peterson et al. Influence of Heart Rate Variability on Abstinence-Related Changes in Brain State in Everyday Drinkers. Brain Sci. 2021, 11, 817.

In the original article [...].

Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.

Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for functional brain networks (called "Geo-Net4Net" for short) to learn the intrinsic low-dimensional feature representations of resting-state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low-dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive-definite (SPD) form of the correlation matrices. Due to the lack of well-defined ground truth in the resting state, existing learning-based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self-supervise the feature representation learning of resting-state functional networks by leveraging the task-based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo-Net4Net allows us to establish a more reasonable understanding of resting-state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task-based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo-Net4Net not only achieves more accurate change detection results than other state-of-the-art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function.