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.

Altered default mode network associated with pesticide exposure in Latinx children from rural farmworker families.

Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.

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.

Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks.

Mounting evidence shows that brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the relatively small changes in BOLD (blood-oxygen-level-dependent) signals across tasks, it is difficult to detect the change of cognitive status without requiring prior knowledge of the experimental design. To address this challenge, we present a dynamic graph learning approach to generate an ensemble of subject-specific dynamic graph embeddings, which allows us to use brain networks to disentangle cognitive events more accurately than using raw BOLD signals. The backbone of our method is essentially a representation learning process for projecting BOLD signals into a latent vertex-temporal domain with the greater biological underpinning of brain activities. Specifically, the learned representation domain is jointly formed by (1) a set of harmonic waves that govern the topology of whole-brain functional connectivities and (2) a set of Fourier bases that characterize the temporal dynamics of functional changes. In this regard, our dynamic graph embeddings provide a new methodology to investigate how these self-organized functional fluctuation patterns oscillate along with the evolving cognitive status. We have evaluated our proposed method on both simulated data and working memory task-based fMRI datasets, where our dynamic graph embeddings achieve higher accuracy in detecting multiple cognitive states than other state-of-the-art methods.

Entropic regression with neurologically motivated applications.

The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. The key to this study is understanding the structure of both the structural and functional connectivity between anatomical regions. In this paper, we use an information theoretic approach, which defines direct information flow in terms of causation entropy, to improve upon the accuracy of the recovery of the true network structure over popularly used methods for this task such as correlation and least absolute shrinkage and selection operator regression. The method outlined above is tested on synthetic data, which is produced by following previous work in which a simple dynamical model of the brain is used, simulated on top of a real network of anatomical brain regions reconstructed from diffusion tensor imaging. We demonstrate the effectiveness of the method of AlMomani et al. [Chaos 30, 013107 (2020)] when applied to data simulated on the realistic diffusion tensor imaging network, as well as on randomly generated small-world and Erdös-Rényi networks.

Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

Analysis of brain subnetworks within the context of their whole-brain networks.

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole-brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed-effects modeling framework that allows analyzing subnetworks within the context of their whole-brain networks, and show that it can better disentangle global (whole-brain) and local (subnetwork) differences when compared to standard t-test analyses. T-test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system-level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model-quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks-but for local, subnetwork-level hypotheses.

A MATLAB toolbox for multivariate analysis of brain networks.

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

Wake Forest Alcohol Imagery Set: Development and Validation of a Large Standardized Alcohol Imagery Dataset.

BACKGROUND: The study of alcohol use frequency utilizes alcohol-related cue imagery. Although a number of alcohol-image databases currently exist, they have several limitations: Many are not publicly available, some use stock images or clip art rather than real photographs, several eliminate any photographs displaying brand information, and predominantly they contain relatively few images. The aim of this project was to develop a large, open-access database of alcohol-related cue images, containing photographs with and without brand information, taken in real-world environments, with images in a variety of orientations and dimensions. METHODS: The study collected 1,650 images voluntarily from the larger community, to capture photographs with a wide range of content, environments, and relation to alcohol. All images were then rated on scales of valence, arousal, and relation to alcohol by 1,008 Amazon Mechanical Turk workers, using classical emotion validation methods based on the International Affective Picture System (IAPS). Survey respondents were screened with the Alcohol Use Disorders Identification Test (AUDIT), and Cronbach's alpha scores were calculated to determine the interrater reliability of scores across the whole sample, and within low-risk, moderate-risk, and high-risk drinkers for each rating domain. Univariate ANOVAs were run to determine differences in ratings across drinking groups. RESULTS: All Cronbach's alpha scores indicated high interrater reliability within the whole sample, and across drinking severity groups. Tukey's HSD post hoc results indicated greater arousal and affect in response to image viewing in moderate- and high-risk drinkers, and higher relation-to-alcohol ratings in low-risk drinkers. All images had categorization tags assigned by members of the study team. CONCLUSIONS: The established imagery set includes 1,650 alcohol-related images, rated on scales of valence, arousal, and relation to alcohol, and categorized by type of alcohol depicted. The imagery database will be available for open-access download and use through Google Photos.

Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to examine whether these models have clinical utility in tailoring treatment.

Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories.

A common approach in memory research is to isolate the function(s) of individual brain regions, such as the hippocampus, without addressing how those regions interact with the larger network. To investigate the properties of the hippocampus embedded within large-scale networks, we used functional magnetic resonance imaging and graph theory to characterize complex hippocampal interactions during the active retrieval of vivid versus dim visual memories. The study yielded 4 main findings. First, the right hippocampus displayed greater communication efficiency with the network (shorter path length) and became a more convergent structure for information integration (higher centrality measures) for vivid than dim memories. Second, vivid minus dim differences in our graph theory measures of interest were greater in magnitude for the right hippocampus than for any other region in the 90-region network. Moreover, the right hippocampus significantly reorganized its set of direct connections from dim to vivid memory retrieval. Finally, beyond the hippocampus, communication throughout the whole-brain network was more efficient (shorter global path length) for vivid than dim memories. In sum, our findings illustrate how multivariate network analyses can be used to investigate the roles of specific regions within the large-scale network, while also accounting for global network changes.

Effects of supervised exercise and dietary nitrate in older adults with controlled hypertension and/or heart failure with preserved ejection fraction.

Aerobic exercise training is an effective therapy to improve peak aerobic power (peak VO2) in individuals with hypertension (HTN, AHA/ACC class A) and heart failure patients with preserved ejection fraction (HFpEF). High nitrate containing beetroot juice (BRJ) also improves sub-maximal endurance and decreases blood pressure in both HTN and HFpEF. We hypothesized that combining an aerobic exercise and dietary nitrate intervention would result in additive or even synergistic positive effects on exercise tolerance and blood pressure in HTN or HFpEF. We report results from two pilot studies examining the effects of supervised aerobic exercise combined with dietary nitrate in patients with controlled HTN (n = 26, average age 65 ± 5 years) and in patients with HFpEF (n = 20, average age 69 ± 7 years). All patients underwent an aerobic exercise training regimen; half were randomly assigned to consume a high nitrate-containing beet juice beverage (BRJ containing 6.1 mmol nitrate for the HFpEF study consumed three times a week and 8 mmol nitrate for the HTN study consumed daily) while the other half consumed a beet juice beverage with the nitrate removed (placebo). The main result was that there was no added benefit observed for any outcomes when comparing BRJ to placebo in either HTN or HFpEF patients undergoing exercise training (p ≥ 0.14). There were within-group benefits. In the pilot study in patients with HFpEF, aerobic endurance (primary outcome), defined as the exercise time to volitional exhaustion during submaximal cycling at 75% of maximal power output, improved during exercise training within each group from baseline to end of study, 369 ± 149 s vs 520 ± 257 s (p = 0.04) for the placebo group and 384 ± 129 s vs 483 ± 258 s for the BRJ group (p = 0.15). Resting systolic blood pressure in patients with HFpEF also improved during exercise training in both groups, 136 ± 16 mm Hg vs 122 ± 3 mm Hg for the placebo group (p < 0.05) and 132 ± 12 mm Hg vs 119 ± 9 mm Hg for the BRJ group (p < 0.05). In the HTN pilot study, during a treadmill graded exercise test, peak oxygen consumption (primary outcome) did not change significantly, but time to exhaustion (also a primary outcome) improved in both groups, 504 ± 32 s vs 601 ± 38 s (p < 0.05) for the placebo group and 690 ± 38 s vs 772 ± 95 s for the BRJ group (p < 0.05) which was associated with a reduction in supine resting systolic blood pressure in BRJ group. Arterial compliance also improved during aerobic exercise training in both the HFpEF and the HTN patients for both BRJ and placebo groups. Future work is needed to determine if larger nitrate doses would provide an added benefit to supervised aerobic exercise in HTN and HFpEF patients.

Mechanisms of human erythrocytic bioactivation of nitrite.

Nitrite signaling likely occurs through its reduction to nitric oxide (NO). Several reports support a role of erythrocytes and hemoglobin in nitrite reduction, but this remains controversial, and alternative reductive pathways have been proposed. In this work we determined whether the primary human erythrocytic nitrite reductase is hemoglobin as opposed to other erythrocytic proteins that have been suggested to be the major source of nitrite reduction. We employed several different assays to determine NO production from nitrite in erythrocytes including electron paramagnetic resonance detection of nitrosyl hemoglobin, chemiluminescent detection of NO, and inhibition of platelet activation and aggregation. Our studies show that NO is formed by red blood cells and inhibits platelet activation. Nitric oxide formation and signaling can be recapitulated with isolated deoxyhemoglobin. Importantly, there is limited NO production from erythrocytic xanthine oxidoreductase and nitric-oxide synthase. Under certain conditions we find dorzolamide (an inhibitor of carbonic anhydrase) results in diminished nitrite bioactivation, but the role of carbonic anhydrase is abrogated when physiological concentrations of CO2 are present. Importantly, carbon monoxide, which inhibits hemoglobin function as a nitrite reductase, abolishes nitrite bioactivation. Overall our data suggest that deoxyhemoglobin is the primary erythrocytic nitrite reductase operating under physiological conditions and accounts for nitrite-mediated NO signaling in blood.

Alcohol Consumption and Risk for Dependence Among Male Latino Migrant Farmworkers Compared to Latino Nonfarmworkers in North Carolina.

BACKGROUND: Our aim was to describe alcohol consumption behavior of male Latino migrant farmworkers, compare their alcohol consumption behavior with that of other male Latino immigrants, and determine factors associated with risk for alcohol dependence among Latino immigrant workers. METHODS: Cross-sectional data were drawn from baseline interviews conducted as part of a larger community-based participatory research project examining the cognitive and neurological outcomes of pesticide exposure. A total of 235 farmworkers and 212 nonfarmworkers completed interviews between May and August 2012. RESULTS: Although 17.5% of the North Carolina Latino farmworkers report never having drunk alcohol, and a total of 34.5% report not having drunk alcohol in the previous 3 months, 48.5% engaged in heavy episodic drinking (HED) in the previous 3 months, and 23.8% frequently engaged in HED during this period. Farmworkers and nonfarmworkers did not differ significantly in alcohol consumption behavior. Farmworkers and nonfarmworkers did differ significantly in each component of the CAGE scale, with 37.9% of farmworkers and 16.0% of nonfarmworkers being at risk for alcohol dependence (p < 0.0001). Significant factors for being at risk for alcohol dependence were stress (odds ratio 1.06, 95% confidence interval 1.03, 1.09) and being a farmworker (odds ratio 3.58, 95% confidence interval 2.12, 6.06). Being married reduced the risk of alcohol dependence (odds ratio 0.45, 95% confidence interval 0.23, 0.87). CONCLUSIONS: Latino farmworkers and nonfarmworkers consume relatively large amounts of alcohol and engage in HED at relatively high rates. Latino farmworkers have very high rates of risk for alcohol dependence. Policy changes and public health interventions are needed to address these concerns for a population that is vital to the agricultural economy.

Urinary Cotinine Levels Among Latino Tobacco Farmworkers in North Carolina Compared to Latinos Not Employed in Agriculture.

INTRODUCTION: This analysis describes urinary cotinine levels of North Carolina Latino farmworkers, compares cotinine levels of farmworkers to those of Latinos non-farmworkers, determines factors associated with farmworker cotinine levels, and determines if differences in farmworker and non-farmworker cotinine levels are associated with smoking. METHODS: Data are from 63 farmworkers and 44 non-farmworkers who participated in a larger study of occupational exposures. Questionnaire data and urine samples collected in 2012 and 2013 are analyzed. RESULTS: Farmworkers had urinary cotinine levels that were far greater than the non-farmworker group. Geometric mean (GM) urinary cotinine levels for farmworkers were 1808.22ng/ml in 2012, and 396.03ng/ml in 2013; corresponding GM levels for non-farmworkers were 4.68ng/ml and 9.03ng/ml. Farmworker GM cotinine levels were associated with harvesting tobacco (1242.77ng/ml vs. 471.26ng/ml; P = .0048), and working in wet shoes (1356.41ng/ml vs. 596.93ng/ml; P = .0148). Smoking did not account for cotinine level differences; the GM cotinine level for farmworkers who did not smoke was 541.31ng/ml; it was 199.40ng/ml for non-farmworkers who did smoke. CONCLUSION: North Carolina farmworkers experience large nicotine doses. The long-term health effects of these doses are not known. Although procedures to reduce occupational nicotine exposure are known, no changes in work practices or in policies to protect workers have been implemented. Research on the health effects of occupational nicotine exposure must become a priority. Current knowledge of occupational transdermal nicotine exposure must be used to improve occupational safety practice and policy for tobacco workers. IMPLICATIONS: This study documents the heavy burden of nicotine exposure and dose experienced by tobacco workers in North Carolina. Hundreds of thousands of farmworkers and farmers in the United States and Canada, as well as agricultural workers around the world, share this burden of nicotine exposure and dose. These results support the need to change work practices and regulations to protect workers. They also document the need to delineate the health effects of long-term exposure to high transdermal nicotine doses.

A two-part mixed-effects modeling framework for analyzing whole-brain network data.

Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function.

The effects of chronic alcohol self-administration in nonhuman primate brain networks.

BACKGROUND: Long-term alcohol abuse is associated with change in behavior, brain structure, and brain function. However, the nature of these changes is not well understood. In this study, we used network science to analyze a nonhuman primate model of ethanol self-administration to evaluate functional differences between animals with chronic alcohol use and animals with no exposure to alcohol. Of particular interest was how chronic alcohol exposure may affect the resting state network. METHODS: Baseline resting state functional magnetic resonance imaging was acquired in a cohort of vervet monkeys. Animals underwent an induction period where they were exposed to an isocaloric maltose dextrin solution (control) or ethanol in escalating doses over three 30-day epochs. Following induction, animals were given ad libitum access to water and a maltose dextrin solution (control) or water and ethanol for 22 h/d over 12 months. Cross-sectional analyses examined region of interests in hubs and community structure across animals to determine differences between drinking and nondrinking animals after the 12-month free access period. RESULTS: Animals were classified as lighter (<2.0 g/kg/d) or heavier drinkers (≥2.0 g/kg/d) based on a median split of their intake pattern during the 12-month ethanol free access period. Statistical analysis of hub connectivity showed significant differences in heavier drinkers for hubs in the precuneus, posterior parietal cortices, superior temporal gyrus, subgenual cingulate, and sensorimotor cortex. Heavier drinkers were also shown to have less consistent communities across the brain compared to lighter drinkers. The different level of consumption between the lighter and heavier drinking monkeys suggests that differences in connectivity may be intake dependent. CONCLUSIONS: Animals that consume alcohol show topological differences in brain network organization, particularly in animals that drink heavily. Differences in the resting state network were linked to areas that are associated with spatial association, working memory, and visuomotor processing.

The human functional brain network demonstrates structural and dynamical resilience to targeted attack.

In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.

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.

Amyloid-ß Deposition Predicts Grocery Shopping Performance in Older Adults Without Cognitive Impairment.

Background: A screening tool sensitive to Alzheimer's disease (AD) risk factors, such as amyloid-ß (Aß) deposition, and subtle cognitive changes, best elicited by complex everyday tasks, is needed. Objective: To determine if grocery shopping performance could differentiate older adults at elevated risk of developing AD (OAer), older adults at low risk of developing AD (OAlr), and young adults (YA), and if amount of Aß deposition could predict grocery shopping performance in older adults (OA). Methods: Twenty-one OAer (78±5 years), 33 OAlr (78±5 years), and 28 YA (31±3 years) performed four grocery shopping trials, with the best and worst performances analyzed. Measures included trial time, number of correct items, number of grocery note fixations, and number of fixations and percentage of time fixating on the correct shelving unit, correct brand, and correct shelf. Linear mixed effects models compared measures by performance rank (best, worst) and group (OAer, OAlr, YA), and estimated the effect of Aß deposition on measures in OA. Results: Relative to their best performance, OAer and OAlr exhibited more correct shelving unit fixations and correct brand fixations during their worst performance, while YA did not. Within OA's worst performance, greater Aß deposition was associated with a smaller percentage of time fixating on the correct shelving unit, correct shelf, and correct brand. Within OA, greater Aß deposition was associated with more grocery note fixations. Conclusions: OA with elevated Aß deposition may exhibit subtle working memory impairments and less efficient visual search strategies while performing a cognitively demanding everyday task.