Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables.

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Similarity-based multimodal regression.

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence.

With the increasing availability of neuroimaging data from multiple modalities-each providing a different lens through which to study brain structure or function-new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.

Voxel-wise intermodal coupling analysis of two or more modalities using local covariance decomposition.

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.

Multiple sclerosis diagnosis: Knowledge gaps and opportunities for educational intervention in neurologists in the United States.

BACKGROUND: Few studies have addressed the results of educational efforts concerning proper use of McDonald criteria (MC) revisions outside multiple sclerosis (MS) subspecialty centers. Neurology residents and MS subspecialist neurologists demonstrated knowledge gaps for core elements of the MC in a recent prior study. OBJECTIVE: To assess comprehension and application of MC core elements by non-MS specialist neurologists in the United States who routinely diagnose MS. METHODS: Through a cross-sectional study design, a previously developed survey instrument was distributed online. RESULTS: A total of 222 neurologists completed the study survey. Syndromes atypical for MS were frequently incorrectly considered "typical" MS presentations. Fourteen percent correctly identified definitions of both "periventricular" and "juxtacortical" lesions and 2% correctly applied these terms to 9/9 images. Twenty-four percent correctly identified all four central nervous system (CNS) regions for satisfaction of magnetic resonance imaging (MRI) dissemination in space. In two presented cases, 61% and 71% correctly identified dissemination in time (DIT) was not fulfilled, and 85% and 86% subsequently accepted nonspecific historical symptoms without objective evidence for DIT fulfillment. CONCLUSION: The high rate of knowledge deficiencies and application errors of core elements of the MC demonstrated by participants in this study raise pressing questions concerning adequacy of dissemination and educational efforts upon publication of revisions to MC.

A simple permutation-based test of intermodal correspondence.

Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.

Simulation-based design of pragmatic trials in psoriatic arthritis using propensity scores.

BACKGROUND/AIMS: Design of clinical trials requires careful decision-making across several dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with varying designs. We introduce a novel simulation-based approach using observational data to inform the design of a future pragmatic trial. METHODS: We utilize propensity score-adjusted models to simulate hypothetical trials under alternative endpoints and enrollment criteria. We apply our approach to the design of pragmatic trials in psoriatic arthritis, using observational data embedded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next, we compare simulated treatment effects in patient populations defined by traditional and broadened enrollment criteria, where the latter is consistent with a future pragmatic trial. In each trial, we also consider five candidate primary endpoints. RESULTS: Our results highlight how changes in the enrolled population and primary endpoints may qualitatively alter study findings and the ability to detect heterogeneous treatment effects between clinical subgroups. For treatments of interest in the study of psoriatic arthritis, broadened enrollment criteria led to diluted estimated treatment effects. Endpoints with greater responsiveness to treatment compared with a traditionally used endpoint were identified. These considerations, among others, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clinical practice. CONCLUSION: Observational data may be leveraged to inform design decisions in pragmatic trials. Our approach may be generalized to the study of other conditions where existing trial data are limited or do not generalize well to real-world clinical practice, but where observational data are available.

Modifiable factors associated with postoperative delirium after hip fracture repair: An age-stratified retrospective cohort study.

BACKGROUND: Postoperative delirium in hip fracture patients is common and is associated with substantial morbidity and consumption of resources. OBJECTIVE: Using data from the USA, we aimed to examine the relationship between postoperative delirium and (modifiable) peri-operative factors mentioned in the American Geriatrics Society Best Practice Statement on Postoperative Delirium in Older Adults, stratified by 'young old' (<80 years) and 'old-old' (≥80 years) categories. DESIGN: Retrospective cohort study from 2006 to 2016. SETTING: Population-based claims data from the USA. PARTICIPANTS: Patients undergoing 505 152 hip fracture repairs between 2006 and 2016 as recorded in the Premier Healthcare Database. MAIN OUTCOMES AND MEASURES: The main outcome was postoperative delirium; modifiable factors of interest were peri-operative opioid use (high, medium or low; <25th, 25 to 75th or >75th percentile of oral morphine equivalents), anaesthesia type (general, neuraxial, both), use of benzodiazepines (long acting, short acting, both), pethidine, nonbenzodiazepine hypnotics, ketamine, corticosteroids and gabapentinoids. Multilevel models assessed associations between these factors and postoperative delirium, in the full cohort, and separately in those aged less than 80 and at least 80 years. Odds ratios (ORs) and Bonferroni-adjusted 95% confidence intervals (95% CIs) are reported. RESULTS: Overall, postoperative delirium incidence was 15.7% (n = 79 547). After adjustment for relevant covariates, the use of long-acting (OR 1.82, CI 1.74 to 1.89) and combined short and long-acting benzodiazepines (OR 1.56, CI 1.48 to 1.63) and ketamine (OR 1.09, CI 1.03 to 1.15), in particular, was associated with increased odds for postoperative delirium, while neuraxial anaesthesia (OR 0.91 CI 0.85 to 0.98) and opioid use (OR 0.95, CI 0.92 to 0.98 and OR 0.88, CI 0.84 to 0.92 for medium and high dose compared with low dose) were associated with lower odds; all P < 0.05. When analysing data separately by age group, effects of benzodiazepines persisted, while opioid use was only relevant in those aged less than 80 years. CONCLUSION: We identified modifiable factors associated with postoperative delirium incidence among patients undergoing hip fracture repair surgery.

Diagnosis concealment behaviors and disclosure beliefs are associated with health and quality of life in people with multiple sclerosis.

BACKGROUND: People with multiple sclerosis (pwMS) struggle with whether, how, and how much to disclose their diagnosis. They often expend resources to conceal their diagnosis, and hold beliefs that it may negatively affect their personal relationships and/or professional opportunities. To better understand these effects, we developed a measure to quantify concealment behaviors and disclosure beliefs. Our main objective is to evaluate relationships of DISCO-MS responses to health and quality of life in a multinational cohort. METHODS: Survey responses were obtained for DISCO-MS and PROMIS-MS scales: global health, communication, social roles participation, anxiety, depression, emotional / behavioral dyscontrol, fatigue, lower extremity function, positive affect / well-being, social roles satisfaction, sleep, stigma, upper extremity function, cognitive function, bladder control, bowel control, visual function. Simple linear regression assessed associations. RESULTS: 263 pwMS were include. Higher concealment was associated with higher anxiety (beta= 0.15 [0.07, 0.23]), depression (beta = 0.13 [0.05, 0.21]), emotional dyscontrol (beta = 0.12 [0.04, 0.20]), lower affect / well-being (beta = -0.13 [-0.21, - 0.05]). Higher anticipation of negative consequences of disclosure was associated with lower self-reported physical (beta = -0.15) and mental health (beta = -0.14), lower positive affect / well-being, social roles satisfaction, higher anxiety, depression, emotional dyscontrol, sleep disturbance, and higher perceived stigma. DISCUSSION: These results reveal potential consequences of diagnosis concealment for physical and mental health and quality of life. Raising awareness and implementing interventions may mitigate negative repercussions of concealment.

Network Enrichment Significance Testing in Brain-Phenotype Association Studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about the spatial structure of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genomics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose Network Enrichment Significance Testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study phenotype associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Development of top-down cortical propagations in youth.

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

Impact of a Virtual Wellness Program on Quality of Life Measures for Patients Living With Multiple Sclerosis During the COVID-19 Pandemic.

BACKGROUND: Patients with multiple sclerosis (MS) were vulnerable to the effects of physical inactivity during the COVID-19 pandemic. As patients returned to in-person visits, providers reported seeing increased weakness, balance issues, falls, worsening pain, and spasticity. Social isolation also contributed to increased stress, depression, and anxiety. This study explored whether attending virtual wellness programs was associated with improvements in standard quality of life questionnaire scores for patients with MS. METHODS: The purposive convenience sample consisted of 43 patients in the treatment group and 28 in the control group. Patients in the treatment group attended 2 monthly programs for 6 months and completed a demographic questionnaire, the 36-Item Short Form Health Survey (SF-36), the Modified Fatigue Impact Scale, and the Medical Outcomes Study Pain Effects Scale (PES). Patients requested additional topics, resulting in 5 additional programs. The control group consisted of patients who chose not to attend the programs but agreed to complete the questionnaires. RESULTS: In comparing questionnaire responses (6 months minus baseline) among the participants in the treatment group, an association was found between higher meeting attendance and improvements in emotional well-being (P = .038), pain on the PES (P = .011), mindfulness on the SF-36 pain scale (P = .0472), and exercise on the PES (P = .0115). CONCLUSIONS: The results of this study suggest that a virtual wellness program may provide beneficial emotional support, physical exercise, and health promotion activities resulting in improved quality of life in people with MS. In addition, mindfulness and exercise programs may be beneficial in pain management.

Dissociable multi-scale patterns of development in personalized brain networks.

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.

Perioperative impact of sleep apnea in a high-volume specialty practice with a strong focus on regional anesthesia: a database analysis.

BACKGROUND AND OBJECTIVES: Obstructive sleep apnea (OSA) is a risk factor for adverse postoperative outcome and perioperative professional societies recommend the use of regional anesthesia to minimize perioperative detriment. We studied the impact of OSA on postoperative complications in a high-volume orthopedic surgery practice, with a strong focus on regional anesthesia. METHODS: After Institutional Review Board approval, 41 766 cases of primary total hip and knee arthroplasties (THAs/TKAs) from 2005 to 2014 were extracted from institutional data of the Hospital for Special Surgery (approximately 5000 THAs and 5000 TKAs annually, of which around 90% under neuraxial anesthesia).The main effect was OSA (identified by the International Classification of Diseases, ninth revision codes); outcomes of interest were cardiac, pulmonary, gastrointestinal, renal/genitourinary, thromboembolic complications, delirium, and prolonged length of stay (LOS). Multivariable logistic regression models provided ORs, corresponding 95% CIs, and p values. RESULTS: Overall, OSA was seen in 6.3% (n=1332) of patients with THA and 9.1% (n=1896) of patients with TKA. After adjustment for relevant covariates, OSA was significantly associated with 87% (OR 1.87, 95% CI 1.51 to 2.30), 52% (OR 1.52, 95% CI 1.13 to 2.04), and 44% (OR 1.44,95% CI 1.31 to 1.57) increased odds for pulmonary gastrointestinal complications, and prolonged LOS, respectively. The odds for other outcomes remained unaltered by OSA diagnosis. CONCLUSION: We showed that, even in a setting with almost universal regional anesthesia use, OSA was associated with increased odds for prolonged LOS, and pulmonary and gastrointestinal complications. This puts forward the question of how effective regional anesthesia is in mitigating postoperative complications in patients with OSA.

Risk factors for postoperative delirium in patients undergoing lower extremity joint arthroplasty: a retrospective population-based cohort study.

BACKGROUND: With an ageing population, the demand for joint arthroplasties and the burden of postoperative delirium is likely to increase. Given the lack of large-scale data, we investigated associations between perioperative risk factors and postoperative delirium in arthroplasty surgery. METHODS: This retrospective population-based cohort study, utilized national claims data from the all-payer Premier Healthcare database containing detailed billing information from >25% nationwide hospitalizations. Patients undergoing elective total hip/knee arthroplasty surgery (2006-2016) were included.The primary outcome was postoperative delirium, while potential risk factors included age, gender, race, insurance type, and modifiable exposures including anesthesia type, opioid prescription dose (low/medium/high), benzodiazepines, meperidine, non-benzodiazepine hypnotics, ketamine, corticosteroids, and gabapentinoids. RESULTS: Among 1 694 795 patients' postoperative delirium was seen in 2.6% (14 785/564 226) of hip and 2.9% (32 384/1 130 569) of knee arthroplasties. Multivariable models revealed that the utilization of long acting (OR 2.10 CI 1.82 to 2.42), combined long/short acting benzodiazepines (OR 1.74 CI 1.56 to 1.94), and gabapentinoids (OR 1.26 CI 1.16 to 1.36) was associated with increased odds of postoperative delirium. Lower odds of postoperative delirium were seen for neuraxial versus general anesthesia (OR 0.81 CI 0.70 to 0.93) and with the utilization of non-steroidal anti-inflammatory drugs (OR 0.85 CI 0.79 to 0.91) as well as cyclooxygenase-2 inhibitors (OR 0.82 CI 0.77 to 0.89). Age-stratified analysis revealed lower odds with high versus low opioid dose (OR 0.86 CI 0.76 to 0.98) in patients >65 years. Findings were consistent between hip and knee arthroplasties. CONCLUSIONS: In this large national cohort, we identified various modifiable risk factors (including anesthesia type and pharmaceutical agents) for postoperative delirium, demonstrating possible prevention pathways.

Lack of Association Between Levels and Length of Intraoperative Controlled Hypotension and Acute Kidney Injury in Total Hip Arthroplasty Patients Receiving Neuraxial Anesthesia.

BACKGROUND AND OBJECTIVES: Previous research suggests that increased duration and lower levels of intraoperative hypotension (IOH) are associated with postoperative acute kidney injury (AKI). However, this association has not been evaluated in the context of intraoperative controlled hypotension (IOCH), a practice that has been linked in the past to improved outcomes with respect to blood loss and transfusion needs. This study aimed to investigate whether IOCH is associated with postoperative AKI among total hip arthroplasty patients at an institution where this technique is commonly practiced. METHODS: We performed a retrospective cohort study of 2431 unilateral total hip arthroplasty patients who received IOCH under neuraxial anesthesia as well as invasive arterial monitoring between March 2016 and January 2017. Multiple logistic regression was used to compute the adjusted odds ratios of postoperative AKI, adjusting for covariates including duration of intraoperative mean arterial pressure of less than 60 mm Hg. Sensitivity analyses also considered the effects of IOH defined at mean arterial pressure of less than 55 and less than 65 mm Hg. RESULTS: Acute kidney injury occurred in 45 (1.85%) of the 2431 patients in this cohort. Longer duration of hypotension was not associated with increased odds of postoperative AKI. Preexisting differences, such as compromised renal function, best predicted increased odds of AKI. CONCLUSIONS: In this study, AKI was rare. We found a lack of association between IOH and postoperative AKI in a setting where neuraxial anesthesia-facilitated IOCH is routinely practiced. Therefore, it seems prudent for future research and clinical guidelines to consider the distinction between inadvertent and controlled hypotension.