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.

Microparticles incorporating dual apoptotic factors to inhibit anti-inflammatory effects in macrophages.

New approaches to treat autoimmune diseases are needed, and we can be inspired by mechanisms in immune tolerance to guide the design of these approaches. Efferocytosis, the process of phagocyte-mediated apoptotic cell (AC) disposal, represents a potent tolerogenic mechanism that we could draw inspiration from to restore immune tolerance to specific autoantigens. ACs engage multiple avenues of the immune response to redirect aberrant immune responses. Two such avenues are: phosphatidylserine on the outer leaflet of the cell and engaging the aryl hydrocarbon receptor (AhR) pathway. We incorporated these two avenues into one acetalated dextran (Ace-DEX) microparticle (MP) for evaluation in vitro. First phosphatidylserine (PS) was incorporated into Ace-DEX MPs and evaluated for cellular association and mediators of cell tolerance including IL-10 production and M2 associated gene expression when particles were cultured with peritoneal macrophages (PMacs). Further PS Ace-DEX MPs were evaluated as an agent to suppress LPS stimulated PMacs. Then, AhR agonist 2-(1'H-indole-3'-carbonyl)-thiazole-4-carboxylic acid methyl ester (ITE) was incorporated into Ace-DEX MPs and expression of M2 and IL-10 genes was evaluated in PMacs. Further the ITE and PS Ace-DEX MPs (PS/ITE MPs) were evaluated for suppression of T cell priming and Th1 polarization. Our results indicate that the PS/ITE-MPs stimulated anti-inflammatory cytokine expression and suppressed inflammation following LPS stimulation of PMacs. Moreover, PS/ITE MPs induced the anti-inflammatory enzyme IDO1 and suppressed macrophage-mediated T cell priming and Th1 polarization. These findings suggest that PS and ITE-loaded Ace-DEX MPs could be a promising therapeutic tool for suppressing inflammation.

Autotrophic adaptive laboratory evolution of the acetogen Clostridium autoethanogenum delivers the gas-fermenting strain LAbrini with superior growth, products, and robustness.

Microbes able to convert gaseous one-carbon (C1) waste feedstocks are increasingly important to transition to the sustainable production of renewable chemicals and fuels. Acetogens are interesting biocatalysts since gas fermentation using Clostridium autoethanogenum has been commercialised. However, most acetogen strains need complex nutrients, display slow growth, and are not robust for bioreactor fermentations. In this work, we used three different and independent adaptive laboratory evolution (ALE) strategies to evolve the wild-type C. autoethanogenum to grow faster, without yeast extract and to be robust in operating continuous bioreactor cultures. Multiple evolved strains with improved phenotypes were isolated on minimal media with one strain, named "LAbrini", exhibiting superior performance regarding the maximum specific growth rate, product profile, and robustness in continuous cultures. Whole-genome sequencing of the evolved strains identified 25 mutations. Of particular interest are two genes that acquired seven different mutations across the three ALE strategies, potentially as a result of convergent evolution. Reverse genetic engineering of mutations in potentially sporulation-related genes CLAU_3129 (spo0A) and CLAU_1957 recovered all three superior features of our ALE strains through triggering significant proteomic rearrangements. This work provides a robust C. autoethanogenum strain "LAbrini" to accelerate phenotyping and genetic engineering and to better understand acetogen metabolism.

An alcove at the acetyl-CoA synthase nickel active site is required for productive substrate CO binding and anaerobic carbon fixation.

One of seven natural CO2 fixation pathways, the anaerobic Wood-Ljungdahl Pathway (WLP) is unique in generating CO as a metabolic intermediate, operating through organometallic intermediates, and in conserving (versus utilizing) net ATP. The key enzyme in the WLP is acetyl-CoA synthase (ACS), which uses an active site [2Ni-4Fe-4S] cluster (A-cluster), a CO tunnel, and an organometallic (Ni-CO, Ni-methyl, and Ni-acetyl) reaction sequence to generate acetyl-CoA. Here we reveal that an alcove, which interfaces the tunnel and the A-cluster, is essential for CO2 fixation and autotrophic growth by the WLP. In vitro spectroscopy, kinetics, binding, and in vivo growth experiments reveal that a Phe229A substitution at one wall of the alcove decreases CO affinity thirty-fold and abolishes autotrophic growth; however, a F229W substitution enhances CO binding 80-fold. Our results indicate the structure of the alcove is exquisitely tuned to concentrate CO near the A-cluster; protect ACS from CO loss during catalysis, provide a haven for inhibitory CO, and stabilize the tetrahedral coordination at the Nip site where CO binds. The directing, concentrating, and protective effects of the alcove explain the inability of F209A to grow autotrophically. The alcove also could help explain current controversies over whether ACS binds CO and methyl through a random or ordered mechanism. Our work redefines what we historically refer to as the metallocenter "active site". The alcove is so crucial for enzymatic function that we propose it is part of the active site. The community should now look for such alcoves in all "gas handling" metalloenzymes.

Phylogenomics and genetic analysis of solvent-producing Clostridium species.

The genus Clostridium is a large and diverse group within the Bacillota (formerly Firmicutes), whose members can encode useful complex traits such as solvent production, gas-fermentation, and lignocellulose breakdown. We describe 270 genome sequences of solventogenic clostridia from a comprehensive industrial strain collection assembled by Professor David Jones that includes 194 C. beijerinckii, 57 C. saccharobutylicum, 4 C. saccharoperbutylacetonicum, 5 C. butyricum, 7 C. acetobutylicum, and 3 C. tetanomorphum genomes. We report methods, analyses and characterization for phylogeny, key attributes, core biosynthetic genes, secondary metabolites, plasmids, prophage/CRISPR diversity, cellulosomes and quorum sensing for the 6 species. The expanded genomic data described here will facilitate engineering of solvent-producing clostridia as well as non-model microorganisms with innately desirable traits. Sequences could be applied in conventional platform biocatalysts such as yeast or Escherichia coli for enhanced chemical production. Recently, gene sequences from this collection were used to engineer Clostridium autoethanogenum, a gas-fermenting autotrophic acetogen, for continuous acetone or isopropanol production, as well as butanol, butanoic acid, hexanol and hexanoic acid production.

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.

Enhancement of subunit vaccine delivery with zinc-carnosine coordination polymer through the addition of mannan.

Vaccines represent a pivotal health advancement for preventing infection. However, because carrier systems with repeated administration can invoke carrier-targeted immune responses that diminish subsequent immune responses (e.g., PEG antibodies), there is a continual need to develop novel vaccine platforms. Zinc carnosine microparticles (ZnCar MPs), which are composed of a one-dimensional coordination polymer formed between carnosine and the metal ion zinc, have exhibited efficacy in inducing an immune response against influenza. However, ZnCar MPs' limited suspendability hinders clinical application. In this study, we address this issue by mixing mannan, a polysaccharide derived from yeast, with ZnCar MPs. We show that the addition of mannan increases the suspendability of this promising vaccine formulation. Additionally, since mannan is an adjuvant, we illustrate that the addition of mannan increases the antibody response and T cell response when mixed with ZnCar MPs. Mice vaccinated with mannan + OVA/ZnCar MPs had elevated serum IgG and IgG1 levels in comparison to vaccination without mannan. Moreover, in the mannan + OVA/ZnCar MPs vaccinated group, mucosal washes demonstrated increased IgG, IgG1, and IgG2c titers, and antigen recall assays showed enhanced IFN-γ production in response to MHC-I and MHC-II immunodominant peptide restimulation, compared to the vaccination without mannan. These findings suggest that the use of mannan mixed with ZnCar MPs holds potential for subunit vaccination and its improved suspendability further promotes clinical translation.

Immunogenicity of an adjuvanted broadly active influenza vaccine in immunocompromised and diverse populations.

Influenza virus outbreaks are a major burden worldwide each year. Current vaccination strategies are inadequate due to antigenic drift/shift of the virus and the elicitation of low immune responses. The use of computationally optimized broadly reactive antigen (COBRA) hemagglutinin (HA) immunogens subvert the constantly mutating viruses; however, they are poorly immunogenic on their own. To increase the immunogenicity of subunit vaccines such as this, adjuvants can be delivered with the vaccine. For example, agonists of the stimulator of interferon genes (STING) have proven efficacy as vaccine adjuvants. However, their use in high-risk populations most vulnerable to influenza virus infection has not been closely examined. Here, we utilize a vaccine platform consisting of acetalated dextran microparticles loaded with COBRA HA and the STING agonist cyclic GMP-AMP. We examine the immunogenicity of this platform in mouse models of obesity, aging, and chemotherapy-induced immunosuppression. Further, we examine vaccine efficacy in collaborative cross mice, a genetically diverse population that mimics human genetic heterogeneity. Overall, this vaccine platform had variable efficacy in these populations supporting work to better tailor adjuvants to specific populations.

Generation of a Commercial-Scale Founder Population of Porcine Reproductive and Respiratory Syndrome Virus Resistant Pigs Using CRISPR-Cas.

Disease resistance genes in livestock provide health benefits to animals and opportunities for farmers to meet the growing demand for affordable, high-quality protein. Previously, researchers used gene editing to modify the porcine CD163 gene and demonstrated resistance to a harmful virus that causes porcine reproductive and respiratory syndrome (PRRS). To maximize potential benefits, this disease resistance trait needs to be present in commercially relevant breeding populations for multiplication and distribution of pigs. Toward this goal, a first-of-its-kind, scaled gene editing program was established to introduce a single modified CD163 allele into four genetically diverse, elite porcine lines. This effort produced healthy pigs that resisted PRRS virus infection as determined by macrophage and animal challenges. This founder population will be used for additional disease and trait testing, multiplication, and commercial distribution upon regulatory approval. Applying CRISPR-Cas to eliminate a viral disease represents a major step toward improving animal health.

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.

A postnatal network of co-hepato/pancreatic stem/progenitors in the biliary trees of pigs and humans.

A network of co-hepato/pancreatic stem/progenitors exists in pigs and humans in Brunner's Glands in the submucosa of the duodenum, in peribiliary glands (PBGs) of intrahepatic and extrahepatic biliary trees, and in pancreatic duct glands (PDGs) of intrapancreatic biliary trees, collectively supporting hepatic and pancreatic regeneration postnatally. The network is found in humans postnatally throughout life and, so far, has been demonstrated in pigs postnatally at least through to young adulthood. These stem/progenitors in vivo in pigs are in highest numbers in Brunner's Glands and in PDGs nearest the duodenum, and in humans are in Brunner's Glands and in PBGs in the hepato/pancreatic common duct, a duct missing postnatally in pigs. Elsewhere in PDGs in pigs and in all PDGs in humans are only committed unipotent or bipotent progenitors. Stem/progenitors have genetic signatures in liver/pancreas-related RNA-seq data based on correlation, hierarchical clustering, differential gene expression and principal component analyses (PCA). Gene expression includes representative traits of pluripotency genes (SOX2, OCT4), endodermal transcription factors (e.g. SOX9, SOX17, PDX1), other stem cell traits (e.g. NCAM, CD44, sodium iodide symporter or NIS), and proliferation biomarkers (Ki67). Hepato/pancreatic multipotentiality was demonstrated by the stem/progenitors' responses under distinct ex vivo conditions or in vivo when patch grafted as organoids onto the liver versus the pancreas. Therefore, pigs are logical hosts for translational/preclinical studies for cell therapies with these stem/progenitors for hepatic and pancreatic dysfunctions.

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.

Development of a broadly active influenza intranasal vaccine adjuvanted with self-assembled particles composed of mastoparan-7 and CpG.

Currently licensed vaccine adjuvants offer limited mucosal immunity, which is needed to better combat respiratory infections such as influenza. Mast cells (MCs) are emerging as a target for a new class of mucosal vaccine adjuvants. Here, we developed and characterized a nanoparticulate adjuvant composed of an MC activator [mastoparan-7 (M7)] and a TLR ligand (CpG). This novel nanoparticle (NP) adjuvant was co-formulated with a computationally optimized broadly reactive antigen (COBRA) for hemagglutinin (HA), which is broadly reactive against influenza strains. M7 was combined at different ratios with CpG and tested for in vitro immune responses and cytotoxicity. We observed significantly higher cytokine production in dendritic cells and MCs with the lowest cytotoxicity at a charge-neutralizing ratio of nitrogen/phosphate = 1 for M7 and CpG. This combination formed spherical NPs approximately 200 nm in diameter with self-assembling capacity. Mice were vaccinated intranasally with COBRA HA and M7-CpG NPs in a prime-boost-boost schedule. Vaccinated mice had significantly higher antigen-specific antibody responses (IgG and IgA) in serum and mucosa compared with controls. Splenocytes from vaccinated mice had significantly increased cytokine production upon antigen recall and the presence of central and effector memory T cells in draining lymph nodes. Finally, co-immunization with NPs and COBRA HA induced influenza H3N2-specific HA inhibition antibody titers across multiple strains and partially protected mice from a challenge against an H3N2 virus. These results illustrate that the M7-CpG NP adjuvant combination can induce a protective immune response with a broadly reactive influenza antigen via mucosal vaccination.

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.

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.

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.