Flexible parametrization of graph-theoretical features from individual-specific networks for prediction.

Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance.

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.

The Influence of Diffusion Weighted Imaging Lesions on Outcomes in Patients with Acute Spontaneous Intracerebral Hemorrhage.

BACKGROUND/OBJECTIVE: Diffusion weighted imaging (DWI) lesions have been well described in patients with acute spontaneous intracerebral hemorrhage (sICH). However, there are limited data on the influence of these lesions on sICH functional outcomes. We conducted a prospective observational cohort study with blinded imaging and outcomes assessment to determine the influence of DWI lesions on long-term outcomes in patients with acute sICH. We hypothesized that DWI lesions are associated with worse modified Rankin Scale (mRS) at 3 months after hospital discharge. METHODS: Consecutive sICH patients meeting study criteria were consented for an magnetic resonance imaging (MRI) scan of the brain and evaluated for remote DWI lesions by neuroradiologists blinded to the patients' hospital course. Blinded mRS outcomes were obtained at 3 months. Logistic regression was used to determine significant factors (p < 0.05) associated with worse functional outcomes defined as an mRS of 4-6. The generalized estimating equation (GEE) approach was used to investigate the effect of DWI lesions on dichotomized mRS (0-3 vs 4-6) longitudinally. RESULTS: DWI lesions were found in 60 of 121 patients (49.6%). The presence of a DWI lesion was associated with increased odds for an mRS of 4-6 at 3 months (OR 5.987, 95% CI 1.409-25.435, p = 0.015) in logistic regression. Using the GEE model, patients with a DWI lesion were less likely to recover over time between 14 days/discharge and 3 months (p = 0.005). CONCLUSIONS: DWI lesions are common in primary sICH, occurring in almost half of our cohort. Our data suggest that DWI lesions are associated with worse mRS at 3 months in good grade sICH and are predictive of impaired recovery after hospital discharge. Further research into the pathophysiologic mechanisms underlying DWI lesions may lead to novel treatment options that may improve outcomes associated with this devastating disease.

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.

Clinical, hemispheric, and autonomic changes associated with use of closed-loop, allostatic neurotechnology by a case series of individuals with self-reported symptoms of post-traumatic stress.

BACKGROUND: The objective of this pilot study was to explore the use of a closed-loop, allostatic, acoustic stimulation neurotechnology for individuals with self-reported symptoms of post-traumatic stress, as a potential means to impact symptomatology, temporal lobe high frequency asymmetry, heart rate variability (HRV), and baroreflex sensitivity (BRS). METHODS: From a cohort of individuals participating in a naturalistic study to evaluate use of allostatic neurotechnology for diverse clinical conditions, a subset was identified who reported high scores on the Posttraumatic Stress Disorder Checklist (PCL). The intervention entailed a series of sessions wherein brain electrical activity was monitored noninvasively at high spectral resolutions, with software algorithms translating selected brain frequencies into acoustic stimuli (audible tones) that were delivered back to the user in real time, to support auto-calibration of neural oscillations. Participants completed symptom inventories before and after the intervention, and a subset underwent short-term blood pressure recordings for HRV and BRS. Changes in temporal lobe high frequency asymmetry were analyzed from baseline assessment through the first four sessions, and for the last four sessions. RESULTS: Nineteen individuals (mean age 47, 11 women) were enrolled, and the majority also reported symptom scores that exceeded inventory thresholds for depression. They undertook a median of 16 sessions over 16.5 days, and 18 completed the number of sessions recommended. After the intervention, 89% of the completers reported clinically significant decreases in post-traumatic stress symptoms, indicated by a change of at least 10 points on the PCL. At a group level, individuals with either rightward (n = 7) or leftward (n = 7) dominant baseline asymmetry in temporal lobe high frequency (23-36 Hz) activity demonstrated statistically significant reductions in their asymmetry scores over the course of their first four sessions. For 12 individuals who underwent short-term blood pressure recordings, there were statistically significant increases in HRV in the time domain and BRS (Sequence Up). There were no adverse events. CONCLUSION: Closed-loop, allostatic neurotechnology for auto-calibration of neural oscillations appears promising as an innovative therapeutic strategy for individuals with symptoms of post-traumatic stress. TRIALS REGISTRATION: ClinicalTrials.gov #NCT02709369 , retrospectively registered on March 4, 2016.

Time of Admission to the PICU and Mortality.

OBJECTIVES: To evaluate for any association between time of admission to the PICU and mortality. DESIGN: Retrospective cohort study of admissions to PICUs in the Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database from 2009 to 2014. SETTING: One hundred and twenty-nine PICUs in the United States. PATIENTS: Patients less than 18 years old admitted to participating PICUs; excluding those post cardiac bypass. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 391,779 admissions were included with an observed PICU mortality of 2.31%. Overall mortality was highest for patients admitted from 07:00 to 07:59 (3.32%) and lowest for patients admitted from 14:00 to 14:59 (1.99%). The highest mortality on weekdays occurred for admissions from 08:00 to 08:59 (3.30%) and on weekends for admissions from 09:00 to 09:59 (4.66%). In multivariable regression, admission during the morning 06:00-09:59 and midday 10:00-13:59 were independently associated with PICU death when compared with the afternoon time period 14:00-17:59 (morning odds ratio, 1.15; 95% CI, 1.04-1.26; p = 0.006 and midday odds ratio, 1.09; 95% CI; 1.01-1.18; p = 0.03). When separated into weekday versus weekend admissions, only morning admissions were associated with increased odds of death on weekdays (odds ratio, 1.13; 95% CI, 1.01-1.27; p = 0.03), whereas weekend admissions during the morning (odds ratio, 1.33; 95% CI, 1.14-1.55; p = 0.004), midday (odds ratio, 1.27; 95% CI, 1.11-1.45; p = 0.0006), and afternoon (odds ratio, 1.17; 95% CI, 1.03-1.32; p = 0.01) were associated with increased risk of death when compared with weekday afternoons. CONCLUSIONS: Admission to the PICU during the morning period from 06:00 to 09:59 on weekdays and admission throughout the day on weekends (06:00-17:59) were independently associated with PICU death as compared to admission during weekday afternoons. Potential contributing factors deserving further study include handoffs of care, rounds, delays related to resource availability, or unrecognized patient deterioration prior to transfer.

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.

Association of physical function with connectivity in the sensorimotor and dorsal attention networks: why examining specific components of physical function matters.

Declining physical function with aging is associated with structural and functional brain network organization. Gaining a greater understanding of network associations may be useful for targeting interventions that are designed to slow or prevent such decline. Our previous work demonstrated that the Short Physical Performance Battery (eSPPB) score and body mass index (BMI) exhibited a statistical interaction in their associations with connectivity in the sensorimotor cortex (SMN) and the dorsal attention network (DAN). The current study examined if components of the eSPPB have unique associations with these brain networks. Functional magnetic resonance imaging was performed on 192 participants in the BNET study, a longitudinal and observational trial of community-dwelling adults aged 70 or older. Functional brain networks were generated for resting state and during a motor imagery task. Regression analyses were performed between eSPPB component scores (gait speed, complex gait speed, static balance, and lower extremity strength) and BMI with SMN and DAN connectivity. Gait speed, complex gait speed, and lower extremity strength significantly interacted with BMI in their association with SMN at rest. Gait speed and complex gait speed were interacted with BMI in the DAN at rest while complex gait speed, static balance, and lower extremity strength interacted with BMI in the DAN during motor imagery. Results demonstrate that different components of physical function, such as balance or gait speed and BMI, are associated with unique aspects of brain network organization. Gaining a greater mechanistic understanding of the associations between low physical function, body mass, and brain physiology may lead to the development of treatments that not only target specific physical function limitations but also specific brain networks.

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.

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.

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.

Cereset Research Standard Operating Procedures for Insomnia: A Randomized, Controlled Clinical Trial.

Background: Interventions for insomnia that also address autonomic dysfunction are needed. Objective: We evaluate Cereset Research™ Standard Operating Procedures (CR-SOP) in a pilot randomized, controlled trial. CR-SOP is a less operator-dependent, more generalizable innovation of HIRREM®, a noninvasive, closed-loop, allostatic, acoustic stimulation neurotechnology demonstrated to improve insomnia and autonomic function. Methods: Adults with Insomnia Severity Index (ISI) scores of ≥8 were randomized to receive ten sessions of CR-SOP, with tones linked to brainwaves (LB, intervention), or a sham condition of random tones not linked to brainwaves (NL, control). Measures were collected at enrollment and 0-14 days and 4-6 weeks post-allocated intervention. The primary outcome was differential change in ISI from baseline to 4-6 weeks post-intervention. Secondary self-report measures assessed sleep quality65 and behavioral outcomes. Ten-minute recordings of heart rate and blood pressure were collected to analyze autonomic function (heart rate variability [HRV] and baroreflex sensitivity). Results: Of 22 randomized, 20 participants completed the allocated condition. Intention to treat analysis of change from baseline to the 4-6 week outcome demonstrated mean ISI score reduction of 4.69 points among controls (SE 1.40). In the intervention group, there was an additional 2.58 point reduction in ISI score (SE 2.13; total reduction of 7.27, P = .24). Sleep quality and some measures of autonomic function improved significantly among the intervention group compared to control. Conclusions: This pilot study compared use of a standardized, allostatic, acoustic neurotechnology intervention with a sham, active control condition. The magnitude of change in insomnia severity was clinically relevant and similar to the findings in a prior, fully powered trial, but the differential improvement observed was not statistically significant. Significant improvements were demonstrated in sleep quality and some autonomic function measures.

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.

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.