Effects of various exercise programs on postural balance control in athletes and their underlying physiological mechanisms have been extensively investigated. However, little is known regarding how challenging sport-specific conditions contribute to the improvement of body balance and to what extent these changes may be explained by sensorimotor and/or neuromuscular function adaptations. Analysis of the literature could provide useful information on the interpretation of changes in postural sway variables in response to long-term sport-specific training and their association with performance measures. Therefore, the aim of this scoping review was (1) to analyze the literature investigating postural control adaptations induced by sport-specific training and their relationship with measures of athletic performance, and (2) to identify gaps in the existing research and to propose suggestions for future studies. A literature search conducted with Scopus, Web of Science, MEDLINE and Cochrane Library was completed by Elsevier, SpringerLink and Google Scholar with no date restrictions. Overall, 126 articles were eligible for inclusion. However, the association between variables of postural balance control and measures of sport-specific performance was investigated in only 14 of the articles. A relationship between static and/or dynamic balance and criterion measures of athletic performance was revealed in shooting, archery, golf, baseball, ice-hockey, tennis, and snowboarding. This may be ascribed to improved ability of athletes to perform postural adjustments in highly balanced task demands. However, the extent to which sport-specific exercises contribute to their superior postural stability is unknown. Although there is a good deal of evidence supporting neurophysiological adaptations in postural balance control induced by body conditioning exercises, little effort has been made to explain balance adaptations induced by sport-specific exercises and their effects on athletic performance. While an enhancement in athletic performance is often attributed to an improvement of neuromuscular functions induced by sport-specific balance exercises, it can be equally well ascribed to their improvement by general body conditioning exercises. Therefore, the relevant experiments have yet to be conducted to investigate the relative contributions of each of these exercises to improving athletic performance. Long-term adaptations in postural balance control following a variety of exercises have been extensively researched . In partPostural adaptation may be ascribed to improved ability to regulate the center of mass (CoM) movement more precisely with less effort and to perceive movement of the CoM more accurately via the use of proprioceptors. This is mostly true for task-oriented balance exercises based on visual feedback control of the CoM position . AlthougHowever, research to date has only marginally addressed their relevance with respect to performance in sports where static and/or dynamic balance plays an essential role. Postural adaptations are specific to the context in which the physical activity is practiced, so there is only slight transfer to non-experienced motor tasks . HoweverA novel approach is necessary to provide the basis for transfer of underlying sensorimotor processes of postural control to specific sport environments. There is a need to analyze the existing literature and elucidate whether the environment in which exercises are performed plays a role in an improvement of postural stability relevant to sport-specific skills. Provided that body balance is a complex adaptive system interacting with the environment in a functionally integrated manner, the interrelation between the sensorimotor processes of postural control and sport-specific tasks may be assumed. What is lacking is a review of the evidence investigating how these components are modified with training and subsequently how they influence performance in sports with high demands on body balance.Therefore, the aim of this scoping review was to analyze literature investigating postural control adaptations induced by sport-specific training and their relationship with measures of athletic performance. This provided a basis for identifying gaps in the existing research and suggesting recommendations for future studies.This paper is presented in a form of a scoping review . It addrA literature review was made to analyze existing research related to the relationship between variables of postural balance control and athletic performance measures, and related neurophysiological adaptations induced by sport-specific balance exercises. The search conducted with Scopus, Web of Science, MEDLINE and Cochrane Library was completed by Elsevier, SpringerLink and Google Scholar with no date restrictions. Articles in peer-reviewed journals were analyzed. However, references included in reviews were also manually searched to identify other relevant studies. If overlapping data were found in multiple articles, resulting from similar or the same research, those with the most recent publication date were considered for analysis. Books, theses, case reports, abstracts or articles published in conference proceedings were excluded. Incomplete articles and studies that did not include original research were also excluded. The inclusion criteria included research papers that sufficiently described participants, study design, and relevant measures. Studies written in English were preferred. Papers that failed to meet the eligibility criteria were excluded.The search was focused on studies close to the main aim of this review. The key inclusion criterion was that (a) the training included specific balance exercises performed within a given sport, (b) variables of postural balance control were related to athletic performance measures, and (c) postural adaptations induced by sport-specific training were analyzed. However, only a small number of studies was revealed using this approach. Therefore, the search was widened to studies investigating adaptations in postural balance control and athletic performance induced by sport-specific as well as general body conditioning exercises. This helped us to identify gaps in the existing research and propose recommendations for further studies on this topic.The search and appraisal of studies selected by inclusion and exclusion criteria was conducted by both authors. Some concerns were about the representativeness of samples, missing information related to balance exercises included in sport-specific training programs, imprecisely described variables of postural balance control and related performance measures and/or non-controlled compliance of experiments. Athletes of individual and team sports where postural stability plays an important role in their performance were considered a target population.In the search strategy were included suggested sports combined with these terms: “balance exercises” AND “sport-specific exercise” AND “balance” AND “postural control” AND “athletes” AND “athletic performance” AND “postural sway variables” AND “performance measures” AND “neurophysiological adaptations,” AND “neuromuscular functions,” AND “sensorimotor functions,” AND “physiological mechanisms.” Further searches were performed using words from subheadings that specified other balance related exercises within particular sports, and other variables related to performance except for body balance . Altogether 193 papers were found through database searching and other sources when these keywords were connected with particular sports. After an initial screening and assessing for eligibility, studies that did not meet the inclusion criteria were removed. Out of 126 articles included in this scoping review, only 14 investigated the association between variables of postural balance control and measures of sport-specific performance. In summary, studies in following sports were reviewed:(a)shooting and air-rifle shooting ,b, air-p(b)biathlon ;(c)archery ;(d)gymnastics , rhythmi(e)dancing , and bal(f)golf ;(g)baseball ;(h)basketball ;(i)handball ;(j)ice hockey ;(k)soccer ;(l)tennis ;(m)volleyball ;(n)judo ;(o)alpine skiing , cross c(p)surfing , canoein(r)swimming ;(s)others, such as track and field , cascadeA review of the literature revealed that training under sport-specific conditions that include various balance exercises can lead to an improvement of postural stability. A recent analysis of postural sway variables in 936 athletes ranging from 6 to 47 years  identified that practicing any kind of sport improves bipedal balance . HoweverMost studies compared postural sway variables in athletes of different sports and levels of expertise, or physically active individuals with a control group of sedentary individuals. In general, athletes are superior to non-athletes in balance performance . More spFurther studies have been conducted to investigate acute  and adapBalance training can improve some sport-related performance measures, however its transfer to general functional enhancement is unclear . BalanceIn comparison with a number of studies dealing with neurophysiological adaptations in postural control induced by general body conditioning exercises, scarce research has been conducted to investigate adaptive changes in postural balance control in response to sport-specific balance exercises and their effects on athletic performance.Static balance is important for performance in shooters. Both postural sway and rifle stability are different in national- and elite-level air-rifle and small-bore shooters . While aIn addition to the CoP movement, also arm joints and the pistol end point motions are lower in expert than in novice shooters , 2018. KThe strategy of biathletes is different from the one used by rifle shooters due to adaptive changes of their respective specializations . PosturaMaximum sway speed, draw force, and clicker reaction time are important factors of shot performance in elite recurve archers . More spExpertise in gymnastics enhances postural stability only in situations where it is practised, i.e., while standing on one leg with eyes open . The CoPGymnasts have greater postural stability than non-gymnasts . SpecifiAge influences static balance in acrobatic gymnastics . ProprioStatic and dynamic balance, and whole-body coordination are better in elite rhythmic gymnasts than in controls . Static The stability of standing and handstand is differentiated by the sport advancement level so that the control of both body positions is better in seniors than in juniors . In partPostural sway is more stationary (lower absolute trend), less stable (lower maxline), less regular (lower recurrence), and less complex (lower entropy) in ballet dancers than in track athletes . DancersHowever, postural stability may also play a role in performance of less trained individuals, such as entry level university dancers  or olderSport-specific balance in team sports athletes may depend on their expertise and level of performance. For example, dynamic balance depends on the competition level of baseball players .Dynamic balance is lower and hand coordination is higher in elite male basketball players than in controls. They are also better on prediction measures, selective attention, and memory-retention . The cooBetter balance in experienced handball players is associated with the maturation of the motor system rather than performance level . Slower Static balance is only slightly different on the non-preferred and preferred leg determined by the manipulation task in the form of kicking a ball in highly-trained tennis players, whereas there are no differences when the classification is based on the preference for performing a single-leg jump . SimilarThe model of sensory integration in postural balance control is different in volleyball players and non-athlete controls . BetweenParallel stance and dominant leg postural sway is better in deaf volleyball than basketball players, whereas there are no significant between-group differences in non-dominant leg postural sway . PosturaDynamic balance is better in gymnasts than in soccer players and swimmers, whereas there are no differences between two remaining groups of athletes . HoweverPostural parameters improve with age until zero maturity offset is achieved . It seemPostural strategy and performance is influenced by the competition level in soccer players. Playing experience also affects postural measures and strategies in test conditions specific to playing soccer . MoreoveProfessional soccer players have also greater postural stability on the non-dominant leg . Their bIn practice, both static and dynamic balance tests should be performed in soccer players because balance variables in these two conditions are not related . For insBalance, core strength and stability, peripheral muscle strength, and flexibility correlate with performance in golf . These aPractice of high-skill activities that include proprioceptive afferences enhances both postural stability and performance . DancersThe model of sensory integration is different in young kayakers and canoeists than in non-athletes as a result of their sport specializations, which may be attributed to re-adaptation deficit after disembarking to stable surface with diminished sensitivity of the vision and vestibular systems .Stand-up paddle boarding athletes have increased static and dynamic balance, aerobic and anaerobic fitness, and isometric trunk endurance .There is an association of postural stability with the competition level of surfers . The senRelative dynamic postural stability index improves annually in competitive youth skiers . HoweverPracticing many other sports may contribute to the improvement of balance, however a significant relationship with athletic performance has been rarely documented. For instance, proprioceptive functions of posture and postural muscle tone during bipodal dynamic perturbations are developed by horseback riding . RefinedAnalysis of the literature revealed that postural balance control is a key determinant of performance in several sports . HoweverStatic bipedal balance is associated with shooting accuracy in rifle shooters  but onlySimilarly, postural sway displacement in the X direction prior to the arrow release is associated with performance score in archers . There iFurthermore, static unipedal balance correlates with putt distance after a chip shot and greens in regulation in elite golfers, suggesting that standing on uneven ground and weight shift during the golf swing may require good postural balance control . UnilateAn investigation of associations of balance with tennis expertise and performance revealed a lack of correlations, suggesting that postural stability measured in non-specific conditions is not a key factor of performance in the tennis serve . HoweverWith regard to dynamic balance, it is associated with maximum skating speed in young ice hockey players . UnipedaWhile most of the analyzed studies reported an improvement of static and/or dynamic balance as a result of training including a variety of sport-specific balance exercises, a direct relationship between postural sway variables and athletic performance measures has been investigated and demonstrated only in few of them.A relationship between static bipedal balance and shooting accuracy in rifle shooters was found  but onlyTaking these findings into account, it is clear that little attention has been paid to the relationship between postural sway variables and athletic performance measures in the existing research. Though balance exercises performed within a given sport may contribute to the improvement of neuromuscular control of postural stability, the transfer to sport-specific performance has not been sufficiently demonstrated. Therefore, further studies including general body conditioning exercises and specific exercises within particular sports and their applications for enhancing athletic performance should be conducted. Specifically, the effectiveness of various exercise programs in generalizing transfer to sport-specific skills should be evaluated. Neurophysiological mechanisms underpinning adaptive changes in postural balance control following these exercises should be more precisely addressed. A better understanding of long-term changes in body balance under conditions specific to particular sports and their associations with athletic performance can provide useful information for designing exercise programs best suited to individual athlete needs.Out of 126 articles, only 14 investigated the association between variables of postural balance control and measures of sport-specific performance. A relationship between static and/or dynamic balance and criterion measures of athletic performance was revealed in shooting, archery, golf, baseball, ice-hockey, tennis, and snowboarding. This may be ascribed to improved ability of athletes to perform postural adjustments in highly balanced task demands. However, the extent to which sport-specific exercises contribute to their better postural stability is unknown. Although there is a good deal of evidence supporting neurophysiological adaptations in postural balance control induced by body conditioning exercises, little effort has been made to explain balance adaptations induced by sport-specific exercises and their effects on athletic performance. While an enhancement in the athletic performance is often attributed to an improvement of neuromuscular functions induced by sport-specific balance exercises, it can be equally well ascribed to their improvement by general body conditioning exercises. Therefore, the relevant experiments have yet to be conducted to investigate the relative contributions of each of these exercises to improving athletic performance.Both authors have made substantial, direct, and intellectual contribution to the work, and approved it for publication.


The wide range of gerontological cancer research necessitates a variety of methodological approaches. In our symposium, we bring together researchers who represent varied approaches to studying multiple cancer types—with a focus on demonstrating how to apply different methods. First, Ye, will discuss the use of a unique cross-sequential design to facilitate comparison between health change in long-term older cancer survivors and demographically-matched older adults with no history of cancer. Zanwar, will present disparities in cancer screening using secondary nationally representative complex survey data, provide examples of survey data that can be utilized in aging and cancer prevention and control research, and present challenges and opportunities for using survey data. Von Ah, will discuss research methods pertaining to a series of non-pharmacological clinical trials and offer insight to reducing barriers and improving acceptability to technology-based intervention programs in older breast cancer survivors. Next, Castaneda will present quantitative  and qualitative approaches to understand the role of monoclonal gammopathy of undetermined significance in healthcare utilization and progression to multiple myeloma. Last, Halpin will discuss the use of naturally occurring data such as participant observation and audio recordings to evaluate education for patients with multiple myeloma preparing for autologous stem cell transplant. Understanding how a variety of methodological approaches are applied to gerontological cancer research will help facilitate a broader understanding of the tools available for these studies.


Gastroesophageal reflux disease (GERD) is one of the most commonly encountered disorders in clinical practice nowadays, with an increasing burden on healthcare systems worldwide. GERD-related respiratory symptoms such as unexplained chronic cough, bronchial asthma or chronic obstructive pulmonary disease (COPD) with frequent exacerbations often pose diagnostic and therapeutic challenges and may require a multidisciplinary approach. Moreover, a potential role of GERD as a risk factor has been proposed for chronic rejection in patients who underwent lung transplantation. Pepsin has gained considerable attention from the scientific community in the last few years as a possible surrogate biomarker for GERD. The aim of this narrative review was to provide an overview of the potential utility of pepsin detection as a marker of micro-aspiration in various biological fluids retrieved from patients with suspected GERD-induced respiratory manifestations and in lung transplant patients with allograft dysfunction. Data on the subject remains highly contradictory, and while certain studies support its applicability in investigating atypical GERD manifestations, at the moment, it would be realistic to accept a modest utility at best. A major lack of consensus persists regarding topics such as the optimal timeframe for fluid collection and cut-off values. Further research is warranted in order to address these issues. Gastroesophageal reflux disease (GERD) is one of the most commonly encountered disorders in clinical practice nowadays, with an increasing burden on healthcare systems worldwide . It is cIn addition to the classic symptoms of heartburn and regurgitation, a wide range of atypical and extra-digestive manifestations have been linked to GERD. GERD-related respiratory symptoms such as unexplained chronic cough, bronchial asthma or chronic obstructive pulmonary disease (COPD) with frequent exacerbations often pose diagnostic and therapeutic challenges and may require a multidisciplinary approach. Moreover, a potential role of GERD as a risk factor has also been proposed for chronic rejection in patients who underwent lung transplantation.Physiologically, oesophageal mucosal injury and other GERD-related symptoms are prevented by protective mechanisms, such as the proper function of the upper and lower oesophageal sphincters, adequate volume clearance by oesophageal peristalsis, and the esophago-glottic closure reflex. Any disturbances in the structure or function of these protective mechanisms may represent contributing factors in the pathophysiology of GERD-induced oesophageal lesions and extra-digestive manifestations . The retIn order to explain the occurrence of GERD-induced respiratory manifestations, two main pathways have been hypothesised. First is the reflux theory, which incriminates the micro-aspiration of gastric contents as the main deleterious event. This may induce respiratory symptoms by causing direct damage to the airways and pulmonary parenchyma. The second proposed mechanism is the reflex theory, which states that acid reflux into the distal oesophagus may determine bronchoconstriction via the vagally-mediated esophago-bronchial reflex ,6. In rePepsin is one of the main components of gastric refluxate, secreted by chief gastric cells as the zymogen pepsinogen, which is then further activated to pepsin in the acidic environment created by the hydrochloric acid in the stomach. Pepsinogen has seven isoforms, which have been classified into two main types: pepsinogen C, which can be found in various tissues and organs , and pepsinogen A, which is believed to be produced exclusively in the stomach by gastric chief cells ,8. HowevThe aim of this narrative review was to provide an overview of the potential utility of pepsin detection in various biological fluids retrieved from patients with suspected GERD-induced respiratory manifestations, such as bronchial asthma, COPD and chronic cough. In addition, we also reviewed the available literature on pepsin as a marker of micro-aspiration in lung transplant patients with allograft dysfunction.GERD is associated with a wide range of chronic respiratory diseases, and many authors have hypothesised its role in airway remodelling. Damage to the respiratory tract is thought to be caused not solely by the acidic nature of the refluxate but also due to the digestive enzymes it contains, such as pepsin . Recent Studies on human bronchial epithelial cell cultures experimentally simulated the effect of gastric fluid aspiration by exposing cell cultures to various acidic pH levels and pepsin ,11. ThesIn the early phase, aspiration of gastric contents causes alveolitis, which is subsequently followed by gradually increasing degrees of fibrosis. Markedly higher levels of fibrosis-promoting cytokines such as transforming growth factor β (TGF-β) and connective tissue growth factor (CTGF), along with increased deposition of type I collagen in the pulmonary interstitium and extracellular matrix structure disorders, support these findings . NotablyIn cases of respiratory manifestations thought to be secondary to GERD, acid-suppressive treatment with proton pump inhibitors (PPIs) is often recommended. Even though they can reduce the acidity and volume of gastric contents, these medications do not eliminate the possibility of weakly acidic and non-acidic reflux. Two in vitro studies investigated the effect of gastric juice retrieved from paediatric patients with and without PPI therapy ,18. InteFurther research on the effects of pepsin on the respiratory tract in non-acidic conditions found that it could induce mucus hypersecretion in both upper and lower airway epithelial cells . This efAn overview of the reflux theory, summarizing the effects of pepsin on the respiratory tract, is presented in There has been increasing interest in the potential utility of detecting and measuring pepsin levels in various biologic fluids retrieved from patients with respiratory manifestations suspected to be due to GERD, including bronchial asthma. An association was observed between GERD and asthma symptoms, as well as frequent exacerbations and poor symptom control ,21. HoweEmilsson et al. found significantly higher levels of pepsin and inflammatory biomarkers in the exhaled breath condensates (EBC) of asthmatic patients with nocturnal GERD when compared to controls . AdditioOn the other hand, a study by Marshall et al. performed on 25 asthmatic patients showed no significant correlation between salivary pepsin levels and clinical measures of asthma severity . HoweverPepsin levels have also been measured from the broncho-alveolar lavage (BAL) fluid collected from asthmatic individuals. Even though pepsin was present in the airways of asthma patients and may represent a marker of aspiration, a correlation between its presence and disease severity could not be demonstrated ,28. ThesWhen it comes to the paediatric population, a study by Soyer et al., which included asthmatic patients, found that EBC pepsin levels were below the level of detection , with thA summary of the studies investigating pepsin levels in biological fluids retrieved from adult and paediatric subjects and the relationship between pepsin and obstructive lung disease, focusing on bronchial asthma, is presented in Associations between GERD and a higher frequency of COPD exacerbations, increased drug usage and subsequent hospitalizations have been described in the literature ,32,33,34A study on patients with obstructive lung diseases by Timms et al. demonstrated that EBC pepsin concentrations in COPD patients with questionnaire-diagnosed GERD were significantly higher when compared both to patients with COPD only and to healthy controls . A recent descriptive cross-sectional study by Hashemi-Bajgani et al. performed on 52 COPD patients found a higher mean level of pepsin in BAL fluid retrieved from individuals with a history of COPD exacerbations . HoweverStarosta et al. investigated the potential role of pepsin in BAL fluid as a marker of aspiration in a paediatric population with various chronic lung diseases, comprised mainly of chronic bronchitis patients . They foAn observational study by Lee et al. found that pepsin could be detected in 33% of the sputum samples retrieved from clinically stable COPD patients, irrespective of a formal GERD diagnosis . HoweverIn another study that analysed EBC samples retrieved from 10 stable COPD patients, pepsin levels were found to be higher in chronic lung disease patients compared to controls; however, this was in the absence of a relationship to a GERD diagnosis or to markers of proximal or distal reflux . In addiA summary of the studies investigating pepsin levels in biological fluids retrieved from adult and paediatric subjects and the relationship between pepsin and COPD and chronic bronchitis symptoms is presented in Even though the Montreal consensus recognises chronic cough as an extra-digestive manifestation of GERD , a causaDecalmer et al. reached a similar conclusion in a study on 100 chronic cough patients, observing that coughing frequency is directly related to the total number of reflux episodes, which were mostly limited to the distal oesophagus . Given tIn contrast to these findings, results from the pioneer study, which assessed pepsin levels in the BAL fluid collected from chronic cough paediatric patients, incriminate GERD-related micro-aspiration, thus supporting the reflux theory. In their research, Farrell et al. found significantly increased concentrations of pepsin in chronic cough subjects compared to negative controls, while this difference was not observed in GERD patients without chronic cough . Additional evidence supporting this pathogenic mechanism comes from a real-life setting cohort study by Strugala et al. . Their rIt is important to mention the fact that although GERD is considered to be one of the main contributing factors to chronic cough in adults, it is a much less common cause in the paediatric population . Martin A summary of the studies investigating pepsin levels in biological fluids retrieved from adult and paediatric subjects and the relationship between pepsin and chronic cough is presented in The potential role of GERD as a cause of non-allogenic injury and a risk factor for chronic rejection in patients who underwent lung transplantation (LT) has been hypothesised and investigated in recent literature. The clinical utility of clearly establishing its involvement lies in the fact that it is a modifiable risk factor, with a generous array of potential treatment options, including fundoplication.Blondeau et al. found that in LT patients, aspiration of gastric contents occurs frequently, as indicated by the presence of pepsin in all BAL fluid samples retrieved from the individuals included in their study . HoweverThe results of another study by Fisichella et al. point to a similar conclusion that pepsin detection in BAL fluid collected from LT patients could indeed demonstrate aspiration . They alThe same team went on to further investigate BAL fluid cellularity and composition in immune mediators in 105 LT patients in order to identify specific patterns of dysregulation . They foIn a study performed on a smaller population of only 18 LT patients, Griffin et al. aimed to prospectively assess GERD and reflux in the first month after transplantation . Pepsin Render et al. investigated the utility of pepsin detection in BAL fluid samples collected from LT patients in order to identify those with GERD-related aspirations . Their rA summary of the studies investigating pepsin levels in biological fluids retrieved from adult patients who underwent lung transplantation is presented in Of important note, it should be stated that some studies also demonstrated the presence of pepsin in various biological fluids collected from healthy individuals. For example, in a study that compared chronic cough patients to controls, pepsin BAL fluid positivity percentage and concentrations were similar between the two groups . In a siPepsin has gained considerable attention from the scientific community in the last few years as a possible surrogate biomarker for GERD. Many studies have aimed to investigate the potential utility of pepsin detection in individuals suffering from possibly GERD-induced respiratory manifestations, such as chronic cough, bronchial asthma and frequent-exacerbator phenotype COPD. In addition to that, a distinct category of patients is represented by those who underwent lung transplantation and in whom GERD may play a contributing role to chronic rejection. Taking into consideration all recent publications, it is obvious that data on the subject remain highly contradictory, and no clear conclusion can be drawn on the current usefulness of pepsin detection. While certain studies support its applicability in investigating atypical GERD manifestations, at the moment, it would be realistic to accept a modest utility at best. A major lack of consensus still persists regarding essential topics such as the optimal timeframe for biological fluid collection  and specific cut-off values. Further research is warranted in order to address these issues.


We examine the relationship between wholesale funding and liquidity creation using a sample of 825 banks in 84 countries during the post-crisis period of 2010–2020. We find that asset-side liquidity creation is consistently negatively associated with short-term wholesale funding, but not with long-term wholesale funding. Our results suggest that the relationship of short-term wholesale funding with asset-side liquidity creation is significantly driven by a negative relationship with illiquid lending. Moreover, our results show that the negative relationship between wholesale funding and liquidity creation is positively moderated by asset risk, suggesting the presence of moral hazard incentives. Our results are robust to a series of tests and have important implications for bank liquidity regulation. This paper aims to investigate whether limiting banks’ reliance on wholesale funding affects their main function in the economy, which is creating liquidity. Wholesale funding includes several financing resorts, such as interbank lending, repurchase agreements (repo) or debt securities issued for money market mutual funds, which are used by banks as a supplement to deposits or to expand their balance sheet. The literature has attempted to understand the consequences of reliance on wholesale funding but it remains unclear whether relying on wholesale funding is beneficial. There are many questions remaining unanswered: What is the relationship between wholesale funding and lending after the crisis? What is the relationship between wholesale funding and more comprehensive measures of bank output such as liquidity creation? Our study aims to answer these questions.Focusing on the post-2009 period, our study provides three main findings. First, we document a consistently negative association of asset-side liquidity creation with short-term wholesale funding, but not with long-term wholesale funding. Second, we find a negative relationship between short-term wholesale funding and illiquid lending. Third, we show that the relationship between wholesale funding and asset-side liquidity creation is moderated by asset risk. Consistent with moral hazard theory, our findings suggest that banks are less likely to adjust their liquidity creation levels as a response to increased funding risk, thus engaging in suboptimal risk-taking. These results are robust to a battery of tests.Our study makes a novel contribution to the literature on bank liquidity creation. Banks’ ability to fund illiquid loans with liquid deposits is one of their main functions Bryant . ThroughThe determinants of bank liquidity creation, however, have not been well explored due to difficulties in measurement. Since Berger and Bouwman  introducWholesale funding is often raised on a short-term rollover basis, and this leaves banks vulnerable as wholesale financiers may refuse to rollover debt based on superior information on the bank . We introduce an interaction term between poor asset quality, measured by problem loans (PL) and WFST to our baseline regression models.. We intrThe positive moderation of asset risk on the negative relationship between WFST and asset-side liquidity creation can be attributed to the presence of moral hazard incentives. The literature argues that asset risk increases the risk-taking behaviour of banks due to conflicts of interest between shareholders and creditors  instead of PL. LLR stands for the ratio of loan loss reserves to total loans and leases. The results are reported in Table An important issue is that changes in bank liquidity creation could make banks adjust their wholesale funding structure, raising endogeneity concerns for our regressions. We employ two tests to address this. First, we run the regressions using the independent variables in a lagged form.11Second, we employ the two-step System GMM estimatorSince the system GMM is a complicated estimator that can easily generate invalid results, we are careful and follow the guidelines of Roodman  to increTo test the reliability of our results estimated with the system GMM we conduct two specification tests. First, we use Arellano and Bond’s  test forThe results of the two-step System GMM estimator are presented in Table The gradual reduction of banks’ reliance on wholesale funding can largely be attributed to the post-2009 liquidity requirements recommended by the Basel Committee. The banks of the member states of BCBS are subject to the mandatory implementation of these liquidity requirements (i.e. the LCR and NSFR ratios)  We also use two additional approaches to account for differences of banks and banking systems across the large number of jurisdictions in our sample. First, we run the regressions using robust standard errors clustered at the country level to control for heteroskedasticity as well as for correlation across observations of the same country in different years and include macroeconomic variables. Our country-level control variables are GDP that stands for real GDP growth, UNEMP that stands for the unemployment rate and INF that stands for the inflation of the country in which each bank is based. Second, we include country dummies and their interaction with the year dummies to control for heterogeneity across jurisdictions in the sample. In both tests presented in Table Banks resort to wholesale funding to finance themselves as a supplement to customer deposits. Although the prevailing view on wholesale funding before the financial crisis of 2007–2009 was that it benefits banks by increasing borrower monitoring under the threat of liquidation, banks’ vulnerability during the crisis has raised concerns about the risk associated with wholesale funding. In this paper, we aim to examine the effect of wholesale funding on asset-side liquidity creation, the most valuable function of banks in the economy.Against the prevailing view on wholesale funding before the crisis but consistent with recent criticism, our results indicate that wholesale funding is negatively associated with asset-side liquidity creation and its main component, illiquid lending. Such a relationship is mainly attributable to short-term wholesale funding. These results have important implications for regulatory policies aimed at increasing bank liquidity buffers as well as for the management of the institutions at which these policies target. It is widely accepted that liquidity creation and risk transformation are two important functions of banks in the economy, and they are somehow related. Although banks have privileged access to central bank liquidity and government-guaranteed deposits which makes them superior liquidity risk managers, when creating liquidity in the market, banks’ own liquidity is reduced and they become vulnerable to liquidity risk (Berger and Bouwman Finally, we also document that poor asset quality can positively moderate the negative relationship between wholesale funding and liquidity creation. This shows that as asset risk increases, banks are less willing to adjust their liquidity risk exposures as a response to greater reliance on wholesale funding, thus shifting the suboptimal risk to their creditors. Regulators may want to monitor moral hazard problems as well as design policy strategies to enhance bank asset quality.Tables and Figures.


N = 275; N = 279; N = 285), a subset of whom (N = 112) underwent diffusion magnetic resonance imaging. Although no single tract in isolation appears to be necessary or sufficient to predict social network characteristics, distributed patterns of white matter microstructural integrity in brain networks supporting social and affective processing predict eigenvector centrality (how well-connected someone is to well-connected others) and brokerage (how much one connects otherwise unconnected others). Thus, where individuals sit in their real-world social networks is reflected in their structural brain networks. More broadly, these results suggest that the application of data-driven methods to neuroimaging data can be a promising approach to investigate how brains shape and are shaped by individuals’ positions in their real-world social networks.Human behavior is embedded in social networks. Certain characteristics of the positions that people occupy within these networks appear to be stable within individuals. Such traits likely stem in part from individual differences in how people tend to think and behave, which may be driven by individual differences in the neuroanatomy supporting socio-affective processing. To investigate this possibility, we reconstructed the full social networks of three graduate student cohorts ( Diffusion magnetic resonance imaging reveals that students’ standing in their real-world social networks is reflected in patterns of brain network connectivity. However, recent work has shown that social network position characteristics constitute significantly heritable individual difference variables that are stable across contexts13. The heritability of social network position characteristics may be driven, at least in part, by the heritability of social, affective, and behavioral tendencies, which may be reflected in individual differences in the networks of brain regions that support relevant aspects of social perception, cognition, and affective processing. Here, we sought to investigate this possibility by integrating structural neuroimaging data with characterizations of participants’ positions in their real-world social networks.All human cognition and behavior are embedded within the context of real-world social networks. In any social network, people vary systematically with respect to the number of friends that they have, the extent to which they are well-connected to well-connected others, and the extent to which they connect people who would otherwise be unconnected to each other. Although these social network position characteristics have meaningful consequences for individuals and their communities14, and the structural integrity of white matter tracts has been linked to a variety of individual differences in social, cognitive, and behavioral traits20. Although past work has largely focused on how the structural integrity of single white matter tracts relate to sociobehavioral tendencies, using data-driven machine learning models to map relationships between distributed patterns of white matter microstructural integrity and sociobehavioral tendencies can provide an informative window into the complex web of connectivity between brain regions that supports social cognition21.A growing body of research has begun to highlight the critical role of white matter connectivity in supporting social cognitionHere, we used diffusion magnetic resonance imaging (dMRI) to test whether individual differences in distributed patterns of white matter microstructural integrity are predictive of individual differences in social network position characteristics. To this end, we characterized the complete social networks of three different bounded communities of individuals, a subset of whom underwent dMRI. We then used probabilistic tractography to delineate groups of white matter tracts associated with three key facets of social processing: face perception, mentalizing, and mirroring, as well as affective processing. Finally, we used a machine learning algorithm to predict characteristics of individuals’ social network positions based on patterns of white matter microstructural integrity across tracts in these brain networks. Rather than only examining univariate relationships between single tracts and social network position characteristics, leveraging a data-driven, multivariate approach can improve predictive performance by taking into account distributed connectivity signatures .Patterns of microstructural integrity distributed across white matter tracts in brain networks involved in social and affective processing were predictive of structural characteristics of individuals’ positions in their real-world social networks, such as the extent to which they bridge between disparate people or groups (brokerage) and the extent to which they are well-connected to well-connected others . In addition, while distributed patterns of white matter microstructural integrity were predictive of social network position characteristics, no single white matter tract appeared to be necessary or sufficient for predicting social network position characteristics. These findings suggest that individual differences in brain networks that support social perception, affective processing, and understanding others’ actions may be particularly important in determining the structural positions that individuals occupy in their real-world social networks.Ncohort-1 = 275; Ncohort-2 = 279; Ncohort-3 = 285) completed an online survey (see Methods); data from this survey was used to characterize each cohort’s social network , in which diffusion-weighted images were collected (see Methods for more details).We first characterized the complete social networks of three cohorts of a graduate program. Members of each cohort , in-degree centrality (the number of people who name the participant as a friend), eigenvector centrality , betweenness centrality , and constraint . Thus, two measures of brokerage were considered: constraint and betweenness centrality. We examined betweenness centrality to be consistent with prior work that used betweenness centrality as a measure of brokerage in establishing the heritability of this aspect of social network position24  recognizing and extracting information from others’ faces (via regions in the face perception network), (2) quickly understanding their actions, emotions, and intentions through brain regions involved in both producing and observing actions (via regions in the putative mirroring network), and (3) representing and reasoning about their mental states . Probabilistic tractography was then conducted to trace the white matter tracts connecting every possible pair of ROIs in each brain network (see Methods). Ultimately, the affective processing network consisted of 33 white matter tracts, the face perception network consisted of 50 white matter tracts, the mentalizing network consisted of 41 white matter tracts, and the mirroring network consisted of 49 white matter tracts. To characterize the microstructural integrity of each white matter tract, average fractional anisotropy (FA) values were extracted such that each white matter tract had a corresponding single FA value. FA captures the directional coherence of water in white matter tracts and has been shown to be highly sensitive to factors such as myelination, axonal packing density, and axonal diameter25.Regions of interest (ROIs) in four a priori defined brain networks associated with different aspects of socio-affective processing  were functionally defined using the meta-analysis tool Neurosynthp-values were corrected for multiple comparisons using false discovery rate (FDR) thresholding (see Methods).Using a leave-one-subject-out cross-validation scheme, we used a machine learning algorithm based on ridge regression (see Methods) to test whether patterns of microstructural integrity of white matter tracts within each of the four aforementioned brain networks were predictive of out-degree centrality, in-degree centrality, eigenvector centrality, betweenness centrality, and constraint Fig. . Model pr = 0.263, p = 0.002, pFDR-corrected = 0.010), betweenness centrality , and eigenvector centrality . Patterns of microstructural integrity across white matter tracts in the mirroring network significantly predicted individuals’ constraint , eigenvector centrality , and out-degree centrality . Patterns of microstructural integrity across white matter tracts in the mentalizing network significantly predicted individuals’ eigenvector centrality  and betweenness centrality . Patterns of microstructural integrity across white matter tracts in the face perception network significantly predicted individuals’ betweenness centrality .Patterns of microstructural integrity across white matter tracts in the affective processing network significantly predicted individuals’ constraint  while controlling for these variables. Furthermore, patterns of microstructural integrity across white matter tracts in the mirroring network were significantly predictive of eigenvector centrality  and constraint  when controlling for these variables , as well as handedness and academic cohort. We also controlled for self-reported extraversion, given that extraversion has been associated with social network position characteristics, such as eigenvector centrality26, an algorithm for automated global probabilistic tractography, to reconstruct 18 major white matter tracts for each subject. We then used the same analytic procedure to test if patterns of microstructural integrity across these major, well-established white matter tracts can predict social network position characteristics. We observed results similar to those in the primary analysis  toolP predictors, where P is the number of tracts in a given brain network). We first calculated the true difference in predictive performance between the full model and a model leaving one tract out . This true difference value was then compared against a null distribution of 1,000 difference values in predictive performance generated by permutation testing. This procedure was repeated P times  for each of these three models, such that the relative contribution of each predictor to each model’s predictive performance was evaluated (see Methods).Our primary results suggest that patterns of microstructural integrity distributed across white matter tracts in the mirroring network are predictive of eigenvector centrality, and that patterns of microstructural integrity distributed across white matter tracts in the affective processing and mirroring networks are predictive of constraint, when controlling for covariates. We then sought to conduct exploratory analyses to investigate whether certain white matter tracts were disproportionately contributing to the predictive performance of these models. To this end, we tested whether the exclusion of any single white matter tract would significantly diminish the predictive performance of each full model . For each of the 173 white matter tracts, we used ordinary least squares regression to test whether its microstructural integrity was predictive of any of the five social network position characteristics . The microstructural integrity of single tracts was not predictive of any of the five social network position characteristics, even when a relaxed threshold for determining significance was used (see Methods). This procedure was also repeated using the set of predictors derived from the TRACULA analysis, and this analysis also returned null results.We next sought to test whether any single white matter tract in the affective processing, mirroring, face perception, or mentalizing networks would be The results of our primary analysis demonstrate that distributed patterns of white matter microstructural integrity across tracts in brain networks supporting social and affective processes are predictive of structural characteristics of people’s positions in their real-world social networks. Specifically, patterns of white matter microstructural integrity amongst white matter tracts between brain regions associated with affective processing and mirroring were predictive of the extent to which individuals connect otherwise unconnected people and the extent to which individuals are well-connected to other well-connected people in their real-world social networks, above and beyond the effects of demographic variables and extraversion Fig. .27. The genetic basis of social network position characteristics may operate in part via individuals’ passive characteristics, which influence how others behave toward them . Consistent with this possibility, physical attractiveness has been shown to be predictive of social status, popularity, and social acceptance31, and people can somewhat accurately infer aspects of strangers’ social network position characteristics  based on their physical appearance32. On the other hand, the genetic basis of social network position characteristics may also manifest through active characteristics–e.g., sociobehavioral tendencies that facilitate the occupation of certain kinds of social network positions13. For example, such active characteristics might include an individual’s sociability, their tendency to introduce their friends to one another, the extent to which they express empathy toward others, their propensity to engage in behavioral mimicry in social interactions, or some combination of these factors. Individual differences in such sociobehavioral tendencies are likely driven by individual differences in brain structure, but little is known about the relationship between neuroanatomy and social network position characteristics.These findings expand on past work demonstrating that various social network position characteristics are heritable individual difference variables that are stable across contexts37. The studies referred to here linked individual differences in neural predictors to social network size, which was measured in a variety of ways. However, these different measures of social network size all correspond to out-degree centrality . Thus, we use the more precise term out-degree centrality here . To characterize out-degree centrality, these studies used an egocentric network approach, in which participants enumerate their contacts via free recall or the number of friends that participants have in online communities. This work has yielded important insights into the relationship between brain structure and sociality. At the same time, such approaches have important limitations. For example, it is difficult to disentangle variability in out-degree centrality (self-reported network size) from individual differences in social perception or memory (when self-report is used) or from individual differences in engagement with a particular online platform 42. In contrast, the sociocentric network approach used here incorporates data on social ties provided by each individual in the network and can be used to calculate characteristics of individuals’ social network position that take into account broader patterns of social ties . Thus, the sociocentric network approach can complement the egocentric network approach by capturing a more complete picture of a social network, thereby expanding the types of inferences that can be drawn about people’s relative social network position characteristics.Past research has demonstrated that out-degree centrality is linked to individual differences in structural properties of particular brain regions, as well as white matter connectivity44, as well as social influence2, professional success9, others’ perceptions of one’s competence and leadership45, and the likelihood of becoming the target of negative gossip and scapegoating46. Furthermore, whereas out-degree centrality has not been found to be heritable, other, often sociocentrically-derived, social network position characteristics have been shown to be heritable individual difference variables12. Thus, the latter may constitute stable traits that are relatively invariant across contexts27. Indeed, a growing body of research has integrated sociocentric network analysis and neuroimaging to demonstrate that people spontaneously encode and track the extent to which others hold positions of in-degree centrality48, eigenvector centrality, and brokerage22 in real-world social networks. These findings suggest that individuals spontaneously retrieve complex knowledge about people’s relative social network position characteristics that may be crucial for informing cognition and behavior. However, the neural predictors and sociobehavioral tendencies associated with such social network position characteristics are not well understood in social neuroscience and psychology, given that the data necessary to calculate these characteristics  is seldom collected.Research in sociology and ecology has demonstrated that social network position characteristics whose calculation often depends on sociocentric network data  have particularly impactful consequences in real-world social networks. These include measures of evolutionary fitness and likelihood of survival across a variety of social species49. Given that occupying positions of brokerage involves interacting with different groups of people, brokers may be exceptionally skilled in adapting their thoughts and behavior to meet the variable demands of their diverse social environment. Indeed, past work has shown that across different contexts, people occupying positions of brokerage are characteristically high in self-monitoring53, which is associated with an intuitive sensitivity to subtle social cues and with the ability to modify one’s behavior to adapt to social circumstances55. Individuals high in self-monitoring have been shown to closely monitor the thoughts, actions, and feelings of people around them57 and also invest considerable effort in providing emotional help58 and advice59 to their contacts. Such sociobehavioral tendencies may be driven by individual differences in patterns of white matter microstructural integrity across the affective processing and mirroring networks. The affective processing network may support the monitoring and interpretation of emotions and the regulation of one’s own emotions62, and the mirroring network may mediate the representation, understanding, and mimicry of the actions of others65. Given that brokers are highly attuned to cues of situational appropriateness, they are likely exceptionally skilled at accurately perceiving and interpreting the emotions and actions of others. Brokers may also be particularly likely to exhibit social chameleon-like behavior such that they engage in nonconscious mimicry and imitate the behaviors of their social contacts. Such behavior has been shown to be associated with increased mutual feelings of affiliation, rapport, and liking66 and would be conducive to bridging disparate groups of people that may behave in different ways.The localization of the current results can shed light on the types of active characteristics, or sociobehavioral tendencies, that may be associated with particular social network position characteristics. In particular, patterns of microstructural integrity of white matter tracts in the affective processing and mirroring networks were predictive of constraint (an inverse measure of brokerage), beyond the effects of demographics , handedness, cohort, and extraversion. Brokers connect people who would not otherwise be connected and thus wield leverage in controlling the flow of resources  and in coordinating behavior across local social ties67. Given the relative dearth of previous research investigating cognitive and behavioral traits associated with eigenvector centrality, the link between eigenvector centrality and the cognitive and behavioral tendencies associated with the mirroring network is unclear. However, being highly attuned to social cues and having the ability to effectively understand and imitate the actions of others may be particularly characteristic of individuals occupying positions of high eigenvector centrality, as such tendencies are conducive to being well-liked66 and may also lead to the formation and maintenance of social ties with other well-connected individuals.Additionally, patterns of microstructural integrity of white matter tracts in the mirroring network were predictive of eigenvector centrality, a prestige-based measure of centrality that takes into account not only an individual’s own centrality but also the centralities of their contactsPatterns of white matter microstructure were not significantly predictive of out-degree centrality, in-degree centrality, and betweenness centrality beyond the effects of covariates. While it is difficult to interpret null findings, we note that this may be attributable to a variety of factors. For example, given that a sociocentric network approach was used here, participants only enumerated contacts within their bounded social network; thus, the measure of out-degree centrality used here was limited to capturing nominations of friends within the social network. While the bounded social networks characterized here were composed of people in a rural, isolated location who largely live, eat, socialize, and study with one another (see Methods), participants may have had friends outside of their academic cohorts that were not characterized. It is possible that patterns of white matter microstructure in social processing networks would be predictive of out-degree centrality if friendships with people outside of participants’ academic cohorts were taken into account. Alternatively, more targeted analyses in tracts defined a priori may yield significant predictions of out-degree centrality, whereas the current study corrected for multiple comparisons across multiple sets of analyses.Patterns of white matter microstructural integrity were also not predictive of in-degree centrality, but they were significantly predictive of eigenvector centrality. This suggests that individual differences in patterns of white matter microstructural integrity are related to individual differences in characteristics of the social network position that take into account indirect relationships and broader patterns of social ties  that go beyond more local characteristics .41), patterns of white matter microstructural integrity were not predictive of betweenness centrality, but were predictive of constraint.At the same time, consistent with the possibility that constraint, as a more local measure of brokerage, is more strongly related to individuals’ sociobehavioral tendencies  of others74.While patterns of white matter microstructural integrity within the affective processing and mirroring networks were predictive of social network position characteristics when controlling for covariates, those in the mentalizing network were not. Thus, structural characteristics of individuals’ positions in their social networks appear to be linked to connectivity in brain networks involved in rapid, automatic processes involved in understanding and relating to others and their emotional states , but not in those supporting more cognitive facets of interpersonal understanding. Understanding cues to the internal states of others – i.e., the construct of empathy – may be broken down into emotional and cognitive empathy, which are distinct processes with distinct neural mechanismsboth cognitive and emotional empathy, terms such as “mirror neuron” and “mirror” that were used to define regions in the mirroring network are aligned with emotional, but not cognitive, empathy. Thus, it is possible that individual differences in patterns of white matter microstructural integrity in the mirroring network reflect individual differences in emotional empathy (and not cognitive empathy) and that these individual differences are linked to individuals’ social network position characteristics. Indeed, past work has shown that individual differences in the microstructural integrity of white matter tracts connecting perception and action-related regions  and regions involved in affective processing are predictive of emotional, but not cognitive, empathy62. In contrast, individual differences in patterns of white matter microstructural integrity in the mentalizing network likely at least partially reflect individual differences in cognitive empathy. It is possible that cognitive empathy is not linked to people’s social network position characteristics. It is also possible that cognitive empathy relies on more domain-general neural mechanisms76, whereas emotional empathy relies on more domain-specific neural circuitry77, which could make associations between cognitive empathy and its structural correlates less robust than those between emotional empathy and its structural correlates. Furthermore, a limitation of the current study is that a group-level meta-analytic map was used to define regions of interest associated with the mentalizing network. However, recent work has demonstrated that the spatial specificity of the mentalizing network is highly heterogeneous across individuals78. This finding may explain the current study’s null results when using patterns of white matter microstructural integrity within the mentalizing network to predict social network position characteristics, and future work may benefit from using functional localizers to identify mentalizing regions of interest that would be used as seed regions in probabilistic tractography.Here, “empathy” was one of the terms used to define brain regions in the mentalizing network (see Methods). Whereas this term may have yielded brain regions associated with We suggest that future work build on the current findings by examining the extent to which individual differences in the processes supported by the aforementioned brain networks  mediate the relationship between brain structure and social network position characteristics. Future work should also examine the extent to which individual differences in brain structure precede or result from individual differences in social network position characteristics. It is possible that brokers attain their advantageous social network position characteristics through distinctive capacities for interpersonal understanding, which are reflected in structural characteristics of brain networks involved in mirroring and affective processing. It is also possible that occupying a high-brokerage position in one’s social network places more demands on one’s capacity for social and affective information processing . Over time, this may lead to structural differences in the brain networks that support such processing.In addition, while the current study pooled data from three different bounded communities, all participants were graduate students at a university in the United States. Future work could extend these findings by examining the relationship between white matter microstructure and social network position characteristics in different cultural settings and at different stages of development. Furthermore, given a large enough sample size, future work would also benefit from training and testing models on different samples of individuals, as this would shed light on whether relationships between white matter microstructural integrity and social network position characteristics are consistent across contexts and communities. Additionally, while the current study used FA as a measure of microstructural integrity, future work may benefit from testing if other measures of white matter microstructure, such as axial diffusivity, mean diffusivity, and radial diffusivity, or the number of probabilistic tractography streamlines, are predictive of social network position characteristics.79. In particular, aspects of the mentalizing network have recently been shown to vary substantially across individuals78. Thus, using functional localizers to define subject-specific seed regions of interest would likely confer greater sensitivity for detecting systematic links between patterns of white matter microstructural integrity and traits such as social network position characteristics. Furthermore, with functionally localized subject-specific ROIs, it is also possible to test if the size of ROIs may systematically affect FA-based prediction of social network position characteristics.Future work may also benefit from using functional localizers to identify seed regions of interest, as the location of functionally defined brain regions may vary from person to personwithin brain networks, as it was motivated by an interest in linking individuals’ social network position characteristics to anatomical connectivity among brain regions involved in particular mental functions . We did not test whether patterns of white matter microstructure across functionally defined tracts between brain networks were predictive of social network position characteristics. It is conceivable that patterns of white matter microstructure across between-network tracts and, correspondingly, interactions between different mental functions , also play an important role in shaping social behavioral tendencies and therefore an individual’s social network position characteristics. We did, however, find that patterns of white matter microstructure across TRACULA-defined major white matter tracts were predictive of social network position characteristics. Future work could benefit from specifically considering tracts between different functionally defined brain networks.Additionally, the current work focused on patterns of white matter microstructure across tracts 85. In particular, recent work has demonstrated that whole-brain patterns of resting-state functional connectivity are predictive of one’s location in a real-world social network86. Taken together with the current results, these findings suggest that applying machine learning to high-dimensional neuroimaging data is a fruitful approach for gaining insight into how brain structure and function relate to individuals’ positions in their real-world social networks.Our exploratory analyses indicated that no single white matter tract in particular was necessary for predicting social network position characteristics. Furthermore, no single white matter tract was sufficient for predicting social network position characteristics on its own. Rather, our results suggest that multivariate patterns of microstructural integrity values derived from sets of white matter tracts were necessary for predicting social network position characteristics. These findings expand on recent neuroimaging research demonstrating the utility of data-driven machine learning models in mapping relationships between multivariate sets of neural predictors and person-level outcomes. Such data-driven predictive modeling frameworks have been adopted to link functional neuroimaging data to a wide range of social, cognitive, and behavioral traitsNCohort-1 = 275, 91 females, response rate = 99.3%; NCohort-2 = 279, 89 females, response rate = 100%; NCohort-3 = 285, 120 females, response rate = 99.7%). For each cohort, an online social network survey was administered 3-4 months after the subjects had arrived on campus. Subjects followed an e-mailed link to the study website where they responded to a survey designed to assess their position in the social network of students in their cohort of the academic program. The survey was adapted from prior work88. It read, “Consider the people with whom you like to spend your free time. Since you arrived at [institution name], who are the classmates you have been with most often for informal social activities, such as going out to lunch, dinner, drinks, films, visiting one another’s homes, and so on?” A roster-based name generator was used to avoid inadequate or biased recall. Subjects indicated the presence of a social tie with an individual by placing a checkmark next to their name. Subjects could indicate any number of social ties and were not constrained by a time limit. The bounded social networks characterized here were composed of people in a rural, isolated location who predominantly lived, ate, socialized, and studied with one another. The social network survey used here inquired only about students’ interactions with other members of their academic cohort. Thus, the current approach does not capture the students’ social ties that exist outside of their cohort of classmates . That being said, the current study was conducted at a relatively insular and remotely located institution where subjects’ contacts outside of campus likely play a smaller role in their daily lives relative to their everyday, in-person interactions with their classmates. All data collection procedures were performed in accordance with the standards of the Dartmouth College Institutional Review Board.Subjects who completed the social network survey were from three different cohorts of first-year students in a graduate program at a private university in the United States who participated as part of their coursework on leadership. The total size of all three cohorts was 842 students, and 839 students participated in the social network survey, resulting in an overall response rate of 99.6% .Each cohort’s social network data was analyzed using igraph in RThe out-degree centrality of an individual was calculated as the sum of the individual’s outgoing social ties .The in-degree centrality of individual was calculated as the sum of individual’s incoming social ties .A, populated by elements such that aij = 1 if nodes i and j are directly connected, and aij = 0 if these nodes are not connected. The eigenvector centrality of each node is given by the eigenvector of A in which all elements are positive. The requirement that all elements of the eigenvector must be positive yields a unique eigenvector solution . Here, when computing eigenvector centrality, the directionality of the graph was preserved; in the event of asymmetric relationships, only incoming, rather than outgoing, ties were used to compute eigenvector centrality.A graph consisting of nodes connected by edges can be characterized by an adjacency matrix The betweenness centrality of an individual was calculated as the proportion of shortest paths between two given nodes that pass through the individual. An unweighted, undirected graph was used to estimate betweenness centrality.i is given by the following equation, where Pij corresponds to the proportion of i’s direct social ties accounted for by their tie to actor j. The inner summation approximates the indirect constraint imposed on i by other actors, q, who are socially connected to both i and j :The constraint of actor z-scored) within-cohort. These data were then concatenated across all three cohorts. For each of the social network position characteristics, we then applied the square-root transformation, given that the distributions for each of the social network position characteristics were positively skewed. Correlations between social network characteristics are reported in Supplementary Table All social network position characteristic data were normalized . Subjects in cohort 1 were scanned 6-7 months after their arrival on campus, subjects in cohort 2 were scanned 7-8 months after their arrival on campus, and subjects in cohort 3 were scanned within the first month of their arrival on campus. Subjects provided informed consent in accordance with the policies of the institution’s ethical review board. Of the 130 subjects, data from 18 subjects were excluded from analysis due to excess movement. Data from the resulting 112 subjects  aged 24–35  were used for analysis. The neuroimaging study was advertised to all students in each cohort via email. All students who were interested in participating and who passed a standard MRI safety screening participated in the DTI scan.A subset of 130 individuals who had completed the social network survey completed a subsequent dMRI study . Thirty-three diffusion-weighted volumes were collected per subject. High-resolution anatomical images were also acquired using a T1-weighted MPRAGE protocol .Magnetic resonance imaging was conducted with a Philips Achieva 3.0 Tesla scanner using a 32-channel phased array head coil. Diffusion-weighted images were collected using 70 contiguous 2 mm thick axial slices with 32 diffusion directions 25. We also used FSL’s BEDPOSTX91 to model crossing fibers and white matter fiber orientations in each voxel. Both linear and non-linear methods were used to align subjects’ fractional anisotropy images in native space to MNI152 standard space. FSL FLIRT  was used to generate an affine transformation matrix to align fractional anisotropy images to T1 anatomical images. FSL FLIRT was used to generate an affine transformation matrix to align T1 space to MNI152 standard space, and FSL FNIRT used this “affine guess” to generate a non-linear warpfield to align T1 space to MNI152 standard space. FSL FNIRT was then used to apply the first affine FLIRT matrix  and the FNIRT warpfield in one step to transform fractional anisotropy images to MNI152 standard space.We performed standard preprocessing steps using the Diffusion toolbox in FSL 5.0.10Probabilistic tractography was conducted to reconstruct white matter tracts between ROIs associated with each of four facets of social processing: affective processing, mirroring, mentalizing, and face perception. This process was used to define the affective processing, mirroring, mentalizing, and face perception networks . A schematic of this procedure is visualized in Fig. 24 to generate whole-brain meta-analysis-based images of networks of brain regions involved in affective processing , mirroring , mentalizing , and face perception . For each brain network, the meta-analysis-based images associated with each keyword were aggregated, and the FSL cluster command was used to identify the discrete ROIs in each of the aggregated meta-analysis images. The FSL FLIRT and FNIRT commands were used to transform ROIs from standard space to each subject’s native diffusion space. This method provides an approximation of the location of a given ROI if it had been mapped with fMRI in each individual. Each ROI was then dilated by a single voxel and was masked using a brain mask in order to create more liberal ROI masks to ensure that ROIs would extend into neighboring white matter. Due to being exceptionally large and/or spanning multiple regions, a small subset of ROIs were then masked using subject-specific anatomical masks generated using the Freesurfer anatomical parcellation algorithm94 in order to split the ROI into smaller constituent ROIs, which were subsequently used for analysis. Details regarding the size of each ROI, the anatomical regions associated with each ROI, and whether the ROI was masked using subject-specific anatomical masks are provided in Supplementary Tables Keywords were submitted to Neurosynthprobtrackx2 command was then used to perform probabilistic tractography between every possible pair of ROIs within each of the left and right hemispheres , with a contralateral hemisphere exclusion mask and a brainstem exclusion mask. Two-mask seeding was used, and 1,000 probabilistic tract streamlines were taken at each voxel within each mask, which allows resulting tractography maps to include streamlines originating from and terminating in each ROI. For each proposed white matter tract, if more than half of the subjects yielded zero fiber tracts , the corresponding white matter tract was excluded from further analysis. The remaining output connectivity distribution maps were divided by the corresponding total number of existing streamlines to normalize and convert the images into probabilistic maps. These probabilistic maps were then thresholded such that all voxels with a probability below 1% were zeroed in order to reduce false-positive fiber tracts. The resulting probabilistic maps were then binarized within each subject and summed across all subjects in standard space to create group-level tractography images. The group-level tractography images were manually inspected to determine thresholds that minimized spurious connections. Tracts that were identified as spurious upon manual inspection were excluded from further analysis. The resulting group-level images were then transformed back into each subject’s native diffusion space. For each subject, we then masked the resulting images using subject-specific white matter tissue masks (generated by FSL), conservatively thresholded at 50% in order to eliminate spurious white matter, and binarized. For each subject, FA values were extracted from each voxel from each white matter tract image using subject-specific FA maps thresholded at 0.2, which is a standard practice to create conservative FA maps. The resulting FA values were averaged across voxels within each tract to yield a single FA value reflecting the microstructural integrity of a given white matter tract.For each brain network, the FSL 95 to implement the predictive modeling analysis. Using Scikit-learn’s Pipeline function, we created an algorithm that performed two steps in sequence on the training data for each fold : (1) normalize the predictors using Scikit-learn’s StandardScaler function  and (2) implement ridge regression. Given the multicollinearity among the predictors, regularized ridge regression was used. We used a nested cross-validation scheme to perform hyperparameter tuning using a grid search procedure  regularization hyperparameter from a grid/range of values logarithmically spaced between 10−5 and 10), such that the training data of each of the 10 outer data folds was further subdivided into 10 inner folds consisting of sub-training and validation datasets. Within each of these inner folds, for each hyperparameter value provided in the hyperparameter grid, the algorithm was trained on the sub-training data and tested on the validation data. The hyperparameter value used in the model with the best performance across all validation sets was identified as the optimal hyperparameter for the corresponding outer training fold. Using this optimal hyperparameter, the algorithm was trained on the outer fold’s training data and tested on the outer fold’s testing data. This process was repeated independently for each of the ten outer data folds. This procedure yielded a predicted social network position characteristic value for each subject in the sample. Out-of-sample performance was evaluated by calculating the Pearson r-value between predicted and actual social network position characteristic values. All reported p-values reflect one-sided tests of if participants’ actual social network position characteristics were positively associated with the social network position characteristics predicted by the trained models, based on participants’ dMRI data (negative associations would not be interpreted in this context). Given that we tested if patterns of white matter microstructural integrity associated with brain networks were predictive of five different social network position characteristics, we corrected for multiple comparisons across these five sets of statistical tests using FDR thresholding.We tested if inter-individual variability in patterns of white matter microstructural integrity would be predictive of individuals’ positions in their real-world social network Fig. . That is96.To test whether inter-individual variability in patterns of white matter microstructural integrity would be predictive of social network position characteristics above and beyond the effects of demographics and extraversion, we repeated the analysis described above while controlling for age, gender, handedness, cohort, and self-reported extraversion. Extraversion was assessed using the relevant items of the Big Five 44-item inventoryThe primary results demonstrate that patterns of microstructural integrity distributed across white matter tracts in the affective processing network are predictive of constraint, and that patterns of microstructural integrity distributed across white matter tracts in the mirroring network are predictive of constraint and eigenvector centrality, even while controlling for variables including age, gender, handedness, cohort, and extraversion. Thus, in these three models , we tested if any predictors  in particular were disproportionately contributing to significant predictions of social network position characteristics.P additional ‘leave-one-tract-out’ models where P reflects the number of predictors (tracts) in the corresponding full model. Each of these additional models excluded one of the P predictors that had been included in the corresponding full model such that the number of predictors in the resulting leave-one-tract-out model was equal to P - 1. The permutation testing procedure used in the primary analysis was then used to calculate the performance of each of the P leave-one-tract-out models in predicting the relevant social network position characteristic. For each of the P leave-one-tract-out models, the difference in the model’s predictive performance  and that of the full model was calculated. We then tested the statistical significance of this difference in predictive performance . To this end, we used a permutation testing procedure where social network position characteristics were randomly shuffled across subjects in each of 1,000 permuted datasets. Here, in each permuted dataset, r-values were obtained for the full model and for each of the P leave-one-tract-out models. For each of the leave-one-tract-out models, we then calculated the difference between its r-value and that of the full model within each permuted dataset; this produced a null distribution of 1,000 difference values for each of the leave-one-tract-out models. We then calculated a p-value measuring the frequency with which the true difference value was greater than the permuted difference values in this null distribution.For each of the three models mentioned above, the following procedure was implemented. We created sufficient to predict social network position characteristics while controlling for extraversion and demographics. To do so, we used Scikit-learn95 to implement ordinary least squares regression in a predictive modeling framework. There were 173 white matter tracts in total across the four examined brain networks. Thus, to test if microstructural integrity of each of these 173 white matter tracts were predictive of the five social network position characteristics , we conducted 865 statistical tests.We next tested if the microstructural integrity of any single white matter tract in the social processing brain networks defined above was alone ” for details of the data-folding procedure): (1) normalize the predictors using Scikit-learn’s StandardScaler function and (2) implement linear regression. Out-of-sample performance was evaluated by calculating the Pearson r-value between predicted and actual social network position characteristic values. All reported p-values reflect one-sided tests of if participants’ actual social network position characteristics were positively associated with those predicted by each trained single-tract model (negative associations would not be interpreted in this context). To correct for multiple comparisons across all 865 statistical tests, we used FDR thresholding. This procedure corrects for the total number of models tested and corresponding results indicated that data from single tracts could not predict social network position characteristics, unlike the data from distributed patterns of tracts used in our main analyses. At the same time, we note that the threshold used in these exploratory single-tract analyses  was much more conservative than that used in our main analyses, where predictors from each brain network were combined into a single model to predict each examined social network position characteristic. Therefore, as an additional point of comparison, we also examined results of single-tract analyses using a less conservative threshold for determining statistical significance: Given that we tested if microstructural integrity associated with single tracts was predictive of five different social network position characteristics, we corrected for multiple comparisons across these five sets of statistical tests. This relaxed threshold yielded identical results to the results obtained using a more conservative threshold (see Results and Discussion section).For each statistical test, we used Scikit-learn’s Pipeline function to create an algorithm that performed two steps in sequence on the training data for each fold (models fit to each data fold’s training data were used to predict social network position characteristics based on the white matter microstructural integrity value in the corresponding testing data; see “Structural connectome-based predictive modeling of social network position characteristicsStructural connectome-based predictive modeling of social network position characteristics, Testing whether any single white matter tracts were necessary for predicting social network position characteristics, and Testing whether any single white matter tracts alone were sufficient for predicting social network position characteristics sections in Methods. As discussed in the Conclusions and future directions section, the extent to which the observed relationships between white matter microstructural integrity and social network position characteristics are consistent across contexts and communities is unknown. Future work may shed light on how such relationships vary across contexts and communities.Neuroimaging data were normalized within cohorts and were then pooled across cohorts to yield a sample size of 112 participants. Statistical analyses were conducted as described in the Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Eosinophilic esophagitis (EoE) is the second most common cause of chronic esophageal inflammation after GERD, with increasing incidence and prevalence across all age groups. Since current diagnosis and follow up of EoE require endoscopy with biopsies, there is an increased interest in non or less invasive tests for its diagnosis and follow up. Baseline mucosal impedance measurement allows evaluation of mucosal barrier properties and is widely accepted as an adjunct method in GERD diagnosis. As EoE is associated with increased mucosal permeability, the use of baseline impedance to evaluate mucosal integrity has been investigated in several studies. It was found that baseline mucosal impedance, measured either during 24 h reflux monitoring or during endoscopy, was significantly lower in all parts of the esophagus in EoE patients. Impedance measurement correlated with eosinophil counts on biopsies, offering a tool to monitor treatment response. Additionally, baseline impedance patterns differed between those responding to proton pump inhibitor (PPI) treatment and those resistant to PPI, potentially allowing to tailor future treatment to the individual patient. In summary, baseline impedance measurement offers a potential tool for diagnosis and follow up in EoE. Its exact place in EoE treatment is yet to be determined and requires further future studies. Eosinophilic esophagitis (EoE) is a chronic, immune-mediated or antigen-mediated esophageal disease, second only to GERD as a cause of chronic esophageal inflammation. Incidence and prevalence of EoE is increasing in both children and adults . The exaIntraluminal impedance monitoring measures changes in conductivity to an alternating electrical current along electrodes mounted on a catheter, which in a tubular organ such as the esophagus, in the absence of swallowing or reflux, are in close contact with the mucosa. The electrical impedance, expressed in ohms, is the equivalent to the resistance to the electrical current . MultichLow baseline impedance measurements have been used as a marker for impaired mucosal integrity, a finding initially demonstrated in acid-induced heartburn in healthy volunteers and in GERD patients . MoreoveSeveral previous studies demonstrated specific mucosal alterations in EoE that result in increased mucosal permeability. The dilation of the intercellular space in EoE is well documented and is considered one of the histologic hallmarks for the disease . PreviouIn their study in 2013, Van Rhijn et al  investigAs high prevalence of EoE in patients with esophageal atresia was reported, Pesce et al.  investigIn addition to the measurement of MNBI during MII-pH monitoring, other methods for impedance measurement have been investigated in EoE. The question whether direct mucosal impedance could differentiate between GERD and non-GERD patients has been explored by Ates et al , using aInvestigating the influence of reflux on EoE, Frazzoni et al.  demonstrIn conclusion, measurement of mucosal baseline impedance during 24 h reflux monitoring or instant mucosal impedance during endoscopy offers a potential tool for diagnosis and follow up in EoE. Its exact place in EoE treatment is yet to be determined and requires further future studies.