A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Distributed Cox proportional hazards regression using summary-level information.

Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission.

COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.

OBJECTIVES: We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites. METHODS: We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity. RESULTS: Simulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70. CONCLUSION: COMMUTE improves the risk prediction in a target population with limited samples and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.

Meta-analysis with sample-standardization in multi-site studies.

PURPOSE: To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses. METHODS: The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency. RESULTS: A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand. CONCLUSIONS: Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.

The unmet information needs, quality of life, and care experiences of patients with neuroendocrine tumours (NETs) at follow-up: 6 months from diagnosis.

OBJECTIVES: To identify changes in the healthcare preferences, patient experiences, and quality of life of patients with NETs at 6-month follow-up, informing the design of supportive care services. METHODS: This study presents 6-month follow-up data of a mixed-methods multi-site study. Demographic, clinical, and patient-reported outcome questionnaire data was collected. RESULTS: High percentages of suboptimal experiences of care were reported. Patients reported less positive experiences with being involved in decisions about their care and treatment; their family or someone close to them having the opportunity to talk to their cancer doctor, or having their family or someone close to them receive all the information they need to help care for them at home. Patients also reported negative experiences for on the information about their cancer accessible online and the usefulness of the information they accessed. Differences between baseline and follow-up scores were mostly not significant apart from anxiety and sleep disturbance scales, CONCLUSIONS: Patients with NETs report difficulties in accessing and understanding written information that is persistent over time. PRACTICE IMPLICATIONS: Outcomes will inform the design and development of an informational resource aimed at facilitating improved understanding for patients with NETs.

Association of greenness surrounding school with aggression among adolescents: A multi-site study in China.

Association of greenness surrounding school with aggression among adolescents was not well understood. This study aimed to investigate the associations of greenness surrounding school with adolescents' total and sub-types of aggression and explore potential mediators underlying these associations. A multi-site study of 15,301 adolescents aged 11-20 years were recruited through a multistage, random cluster sampling across 5 representative provinces in mainland China. Satellite-derived Normalized Difference Vegetation Index (NDVI) values within circular buffers of 100 m, 500 m, and 1000 m surrounding school were used to indicate the adolescents' greenness exposure. We used the Chinese version of Buss and Warren's Aggression Questionnaire to assess total and sub-types of aggression. Daily concentrations of PM2.5, and NO2 were obtained from the China High Air Pollutants datasets. Per IQR increment of NDVI 100 m and 500 m surrounding school was associated with lower odds of total aggression; odds ratio [OR] with 95% CI was 0.958 (0.926-0.990) for the 100 m buffer and 0.963 (0.932-0.996) for the 500 m buffer, respectively. Similar associations can be observed in two sub-types of aggression, including verbal (NDVI 100 m: 0.960 (0.925-0.995); NDVI500m: 0.964 (0.930-0.999)) and indirect aggression (NDVI 100 m: 0.956 (0.924-0.990); NDVI500m: 0.953 (0.921-0.986)). There were no sex and age differences in the associations of school surrounding greenness with aggression, except that the beneficial associations of greenness exposure with total aggression (0.933(0.895-0.975) vs.1.005(0.956-1.056)), physical aggression (0.971(0.925-1.019) vs.1.098(1.043-1.156)), and hostility (0.942(0.901-0.986) vs.1.016(0.965-1.069)) were greater among participants aged ≥16 years than those aged<16 years. PM2.5 (proportion mediated estimates: 0.21; 95% CI: 0.08, 0.94) and NO2 (-0.78, 95% CI: -3.22, -0.37) mediated the association of NDVI 500 m surrounding school with total aggression. Our data indicated that exposure to greenness in school surroundings was associated with reduced aggression, particularly in verbal and indirect aggression. The presence of PM2.5 and NO2 partially mediated these associations.

Better understanding care transitions of adults with complex health and social care needs: a study protocol.

BACKGROUND: Adults with chronic conditions who also suffer from mental health comorbidities and/or social vulnerability require services from many providers across different sectors. They may have complex health and social care needs and experience poorer health indicators and high mortality rates while generating considerable costs to the health and social services system. In response, the literature has stressed the need for a collaborative approach amongst providers to facilitate the care transition process. A better understanding of care transitions is the next step towards the improvement of integrated care models. The aim of the study is to better understand care transitions of adults with complex health and social care needs across community, primary care, and hospital settings, combining the experiences of patients and their families, providers, and health managers. METHODS/DESIGN: We will conduct a two-phase mixed methods multiple case study (quantitative and qualitative). We will work with six cases in three Canadian provinces, each case being the actual care transitions across community, primary care, and hospital settings. Adult patients with complex needs will be identified by having visited the emergency department at least three times over the previous 12 months. To ensure they have complex needs, they will be invited to complete INTERMED Self-Assessment and invited to enroll if positive. For the quantitative phase, data will be obtained through questionnaires and multi-level regression analyses will be conducted. For the qualitative phase, semi-structured interviews and focus groups will be conducted with patients, family members, care providers, and managers, and thematic analysis will be performed. Quantitative and qualitative results will be compared and then merged. DISCUSSION: This study is one of the first to examine care transitions of adults with complex needs by adopting a comprehensive vision of care transitions and bringing together the experiences of patients and family members, providers, and health managers. By using an integrated knowledge translation approach with key knowledge users, the study's findings have the potential to inform the optimization of integrated care, to positively impact the health of adults with complex needs, and reduce the economic burden to the health and social care systems.

Persuasive messaging for human papillomavirus vaccination by adolescent providers in a five-country multi-site study.

OBJECTIVE: Strong persuasive messaging by providers is a key predictor for patient acceptance of prophylactic human papillomavirus vaccination. We aimed to determine optimal messaging to promote human papillomavirus adolescent vaccination across different geographical sites. METHODS: Adolescent providers (n = 151) from Argentina, Malaysia, South Africa, South Korea, and Spain were surveyed on messages, family decision makers, and sources of communication to best motivate parents to vaccinate their adolescent daughters overall, and against human papillomavirus. Multivariate logistic regression assessed the likelihood of recommending messages specifically targeted at cervical cancer with providers' characteristics: gender, medical specialization, and previous administration of human papillomavirus vaccination. RESULTS: Mothers were considered the most important human papillomavirus vaccination decision makers for their daughters (range 93%-100%). Television was cited as the best source of information on human papillomavirus vaccination in surveyed countries (range 56.5%-87.1%), except Spain where one-on-one discussions were most common (73.3%). Prevention messages were considered the most likely to motivate parents to vaccinate their daughters overall, and against human papillomavirus, in all five countries (range 30.8%-55.9%). Optimal messages emphasized cervical cancer prevention, and included strong provider recommendation to vaccinate, vaccine safety and efficacy, timely vaccination, and national policy for human papillomavirus vaccination. Pediatricians and obstetricians/gynecologists were more likely to cite that the best prevention messages should focus on cervical cancer (OR: 4.2, 95% CI: 1.17 to 15.02 vs other medical specialists). CONCLUSIONS: Provider communication messages that would motivate parents to vaccinate against human papillomavirus were based on strong recommendation emphasizing prevention of cervical cancer. To frame convincing messages to increase vaccination uptake, adolescent providers should receive updated training on human papillomavirus and associated cancers, while clearly addressing human papillomavirus vaccination safety and efficacy.

Big GABA: Edited MR spectroscopy at 24 research sites.

Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study's protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.

Derivation and Evaluation of a Risk-Scoring Tool to Predict Participant Attrition in a Lifestyle Intervention Project.

Participant attrition in clinical trials and community-based interventions is a serious, common, and costly problem. In order to develop a simple predictive scoring system that can quantify the risk of participant attrition in a lifestyle intervention project, we analyzed data from the Special Diabetes Program for Indians Diabetes Prevention Program (SDPI-DP), an evidence-based lifestyle intervention to prevent diabetes in 36 American Indian and Alaska Native communities. SDPI-DP participants were randomly divided into a derivation cohort (n = 1600) and a validation cohort (n = 801). Logistic regressions were used to develop a scoring system from the derivation cohort. The discriminatory power and calibration properties of the system were assessed using the validation cohort. Seven independent factors predicted program attrition: gender, age, household income, comorbidity, chronic pain, site's user population size, and average age of site staff. Six factors predicted long-term attrition: gender, age, marital status, chronic pain, site's user population size, and average age of site staff. Each model exhibited moderate to fair discriminatory power (C statistic in the validation set: 0.70 for program attrition, and 0.66 for long-term attrition) and excellent calibration. The resulting scoring system offers a low-technology approach to identify participants at elevated risk for attrition in future similar behavioral modification intervention projects, which may inform appropriate allocation of retention resources. This approach also serves as a model for other efforts to prevent participant attrition.

A socio-structural approach to preventing injection drug use initiation: rationale for the PRIMER study.

BACKGROUND: Injection drug use remains a primary driver of HIV and HCV-related harms globally. However, there is a gap in efforts to prevent individuals from transitioning into injecting. People who inject drugs (PWID) play a key role in the transition of others into injecting, and while behavioral interventions have been developed to address this phenomenon, socio-structural approaches remain unexplored. To that end, we hypothesize that certain interventions designed to reduce injecting-related risk behaviors may also reduce the risk that PWID expose and introduce others into injecting. Identifying the preventive potential of existing interventions will inform broader efforts to prevent injecting and related harms. METHODS: The Preventing Injecting by Modifying Existing Responses (PRIMER) study is a multi-country mixed methods study with an aim to investigate whether specific interventions (e.g., opioid substitution therapy, supervised injection facilities, stable housing, incarceration environments) and related factors (e.g., public injecting and gender) influence the likelihood that PWID initiate others into injecting. This study will (1) investigate the PWID participation in injection initiation; (2) identify factors influencing the risk that PWID expose others to or facilitate injection initiation; (3) describe drug scene roles that increase the risk of PWID facilitating injection initiation; and (4) evaluate the impact of structural, social, or biomedical interventions on the risk that PWID facilitate injection initiation. It does so by pooling observational data from cohort studies of PWID in six cities: Vancouver, Canada; San Diego, USA; Tijuana, Mexico; Paris, Marseille, and Bordeaux, France. RESULTS: Team members are conducting a prospective, multi-site study of PWID (n = 3050) in North America and France that includes quantitative and qualitative data collection through four separate cohort studies of PWID (San Diego, STAHR II; Tijuana, El Cuete IV; Vancouver, V-DUS; Bordeaux, Marseille, Paris and Strasbourg, COSINUS). CONCLUSIONS: PRIMER is the largest study of injection initiation to date and the first to investigate structural approaches to preventing injection drug use initiation. Findings have the potential to inform the development and scale up of new and existing interventions to prevent transitions into injecting. TRIAL REGISTRATION: Preventing Injecting by Modifying Existing Responses (PRIMER), NIDA DP2-DA040256-01 .

Cartilage T1ρ and T2 relaxation times: longitudinal reproducibility and variations using different coils, MR systems and sites.

OBJECTIVE: To evaluate the longitudinal reproducibility and variations of cartilage T1ρ and T2 measurements using different coils, MR systems and sites. METHODS: Single-Site study: Phantom data were collected monthly for up to 29 months on four GE 3T MR systems. Data from phantoms and human subjects were collected on two MR systems using the same model of coil; and were collected on one MR system using two models of coils. Multi-site study: Three participating sites used the same model of MR systems and coils, and identical imaging protocols. Phantom data were collected monthly. Human subjects were scanned and rescanned on the same day at each site. Two traveling human subjects were scanned at all three sites. RESULTS: Single-Site Study: The phantom longitudinal RMS-CVs ranged from 1.8% to 2.7% for T1ρ and 1.8-2.8% for T2. Significant differences were found in T1ρ and T2 values using different MR systems and coils. Multi-Site Study: The phantom longitudinal RMS-CVs ranged from 1.3% to 2.6% for T1ρ and 1.2-2.7% for T2. Across three sites (n = 16), the in vivo scan-rescan RMS-CV was 3.1% and 4.0% for T1ρ and T2, respectively. Phantom T1ρ and T2 values were significantly different between three sites but highly correlated (R > 0.99). No significant difference was found in T1ρ and T2 values of traveling controls, with cross-site RMS-CV as 4.9% and 4.4% for T1ρ and T2, respectively. CONCLUSION: With careful quality control and cross-calibration, quantitative MRI can be readily applied in multi-site studies and clinical trials for evaluating cartilage degeneration.

IRB challenges in multisite studies: A case report and commentary from the Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing (IDS-TILDA).

A shift from single to multi-site health studies enabled a range of research benefits including faster recruitment of larger and more diverse samples; increased statistical power, greater rigour, generalisability, and external reliability; and increased likelihood of impacting policy and clinical practice. However, ethical review of multi-site studies by Institutional Review Boards (IRBs) raises specific challenges compared with single site studies, with requirements to apply to multiple local IRBs increasing the burden on research, possibly endangering the integrity of the research process or inhibiting development of multi-site studies. The option of a single centralised IRB may offer a clearer, more consistent and efficient review process. This study presents a case report and commentary from 15 years engaging with IRBs in multiple sites in Ireland by the Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing (IDS-TILDA). It examines the ethics review process for IDS-TILDA through its first four waves. While the majority of 48 IRBs granted ethical approval within 13 weeks, six IRBs took 21-47 weeks to approve, leading to delays in data collection of up to 11 months. Despite additional review time, no changes were required to the study protocol. Therefore, a critical impact of the process was the delay in starting data collection within a small number of organisations, and reduced involvement in the study for one organisation. The ethical review process with multiple IRBs increased the degree of complexity of the process, with added bureaucracy and far greater communication required across 48 IRBs, substantially adding to the resource commitment for the review process. The relatively quick approval from the majority of IRBs was partially a result of the longitudinal study building relationships with organisations throughout multiple waves. That other health studies may not accrue this benefit supports calls for a single IRB system for multi-site health studies.

Community-Based Medications First for Opioid Use Disorder - Care Utilization and Mortality Outcomes.

Purpose: A large treatment gap exists for people who could benefit from medications for opioid use disorder (MOUD). People OUD accessing services in harm reduction and community-based organizations often have difficulty engaging in MOUD at opioid treatment programs and traditional health care settings. We conducted a study to test the impacts of a community-based medications first model of care in six Washington (WA) State communities that provided drop-in MOUD access. Participants and Methods: Participants included people newly prescribed MOUD. Settings included harm reduction and homeless services programs. A prospective cohort analysis tested the impacts of the intervention on MOUD and care utilization. Intervention impacts on mortality were tested via a synthetic comparison group analysis matching on demographics, MOUD history, and geography using WA State agency administrative data. Results: 825 people were enrolled in the study of whom 813 were matched to state records for care utilization and outcomes. Cohort analyses indicated significant increases for days' supply of buprenorphine, months with any MOUD, and months with any buprenorphine for people previously on buprenorphine (all results p<0.05). Months with an emergency department overdose did not change. Months with an inpatient hospital stay increased (p<0.05). The annual death rate in the first year for the intervention group was 0.45% (3 out of 664) versus 2.2% (222 out of 9893) in the comparison group in the 12 months; a relative risk of 0.323 (95% CI 0.11-0.94). Conclusion: Findings indicated a significant increase in MOUD for the intervention group and a lower mortality rate relative to the comparison group. The COVID-19 epidemic and rapid increase in non-pharmaceutical-fentanyl may have lessened the intervention impact as measured in the cohort analysis. Study findings support expanding access to a third model of low barrier MOUD care alongside opioid treatment programs and traditional health care settings.

Heterogeneity in Measures of Illness among Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Is Not Explained by Clinical Practice: A Study in Seven U.S. Specialty Clinics.

Background: One of the goals of the Multi-site Clinical Assessment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (MCAM) study was to evaluate whether clinicians experienced in diagnosing and caring for patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) recognized the same clinical entity. Methods: We enrolled participants from seven specialty clinics in the United States. We used baseline data (n = 465) on standardized questions measuring general clinical characteristics, functional impairment, post-exertional malaise, fatigue, sleep, neurocognitive/autonomic symptoms, pain, and other symptoms to evaluate whether patient characteristics differed by clinic. Results: We found few statistically significant and no clinically significant differences between clinics in their patients' standardized measures of ME/CFS symptoms and function. Strikingly, patients in each clinic sample and overall showed a wide distribution in all scores and measures. Conclusions: Illness heterogeneity may be an inherent feature of ME/CFS. Presenting research data in scatter plots or histograms will help clarify the challenge. Relying on case-control study designs without subgrouping or stratification of ME/CFS illness characteristics may limit the reproducibility of research findings and could obscure underlying mechanisms.

Combining meta-analysis with multiple imputation for one-step, privacy-protecting estimation of causal treatment effects in multi-site studies.

Missing data complicates statistical analyses in multi-site studies, especially when it is not feasible to centrally pool individual-level data across sites. We combined meta-analysis with within-site multiple imputation for one-step estimation of the average causal effect (ACE) of a target population comprised of all individuals from all data-contributing sites within a multi-site distributed data network, without the need for sharing individual-level data to handle missing data. We considered two orders of combination and three choices of weights for meta-analysis, resulting in six approaches. The first three approaches, denoted as RR + metaF, RR + metaR and RR + std, first combined results from imputed data sets within each site using Rubin's rules and then meta-analyzed the combined results across sites using fixed-effect, random-effects and sample-standardization weights, respectively. The last three approaches, denoted as metaF + RR, metaR + RR and std + RR, first meta-analyzed results across sites separately for each imputation and then combined the meta-analysis results using Rubin's rules. Simulation results confirmed very good performance of RR + std and std + RR under various missing completely at random and missing at random settings. A direct application of the inverse-variance weighted meta-analysis based on site-specific ACEs can lead to biased results for the targeted network-wide ACE in the presence of treatment effect heterogeneity by site, demonstrating the need to clearly specify the target population and estimand and properly account for potential site heterogeneity in meta-analyses seeking to draw causal interpretations. An illustration using a large administrative claims database is presented.

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses.

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

Between-site equivalence of turning speed assessments using inertial measurement units.

BACKGROUND: Turning is a component of gait that requires planning for movement of multiple body segments and the sophisticated integration of sensory information from the vestibular, visual, and somatosensory systems. These aspects of turning have led to growing interest to quantify turning in clinical populations to characterize deficits or identify disease progression. However, turning may be affected by environmental differences, and the degree to which turning assessments are comparable across research or clinical sites has not yet been evaluated. RESEARCH QUESTION: The aim of this study was to determine the extent to which peak turning speeds are equivalent between two sites for a variety of mobility tasks. METHODS: Data were collected at two different sites using separate healthy young adult participants (n = 47 participants total), but recruited using identical inclusion and exclusion criteria. Participants at each site completed three turning tasks: a one-minute walk (1 MW) along a six-meter walkway, a modified Illinois Agility Test (mIAT), and a custom clinical turning course (CCTC). Peak yaw turning speeds were extracted from wearable inertial sensors on the head, trunk, and pelvis. Between-site differences and two one-sided tests (TOST) were used to determine equivalence between sites, based on a minimum effect size reported between individuals with mild traumatic brain injury and healthy control subjects. RESULTS: No outcomes were different between sites, and equivalence was determined for 6/21 of the outcomes. These findings suggest that some turning tasks and outcome measures may be better suited for multi-site studies. The equivalence results are also dependent on the minimum effect size of interest; nearly all outcomes were equivalent across sites when larger minimum effect sizes of interest were used. SIGNIFICANCE: Together, these results suggest some tasks and outcome measures may be better suited for multi-site studies and literature-based comparisons.

Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan.

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

Polygenic risk scores for alcohol involvement relate to brain structure in substance-naïve children: Results from the ABCD study.

Brain imaging-derived structural correlates of alcohol involvement have largely been speculated to arise as a consequence of alcohol exposure. However, they may also reflect predispositional risk. In substance naïve children of European ancestry who completed the baseline session of the Adolescent Brain Cognitive Development (ABCD) Study (n = 3013), mixed-effects models estimated whether polygenic risk scores (PRS) for problematic alcohol use (PAU-PRS) and drinks per week (DPW-PRS) are associated with magnetic resonance imaging-derived brain structure phenotypes (i.e., total and regional: cortical thickness, surface area and volume; subcortical volume; white matter volume, fractional anisotropy, mean diffusivity). Follow-up analyses evaluated whether any identified regions were also associated with polygenic risk among substance naïve children of African ancestry (n = 898). After adjustment for multiple testing correction, polygenic risk for PAU was associated with lower volume of the left frontal pole and greater cortical thickness of the right supramarginal gyrus (|ßs| > 0.009; ps < 0.001; psfdr < 0.046; r2 s < 0.004). PAU PRS and DPW PRS showed nominally significant associations with a host of other regional brain structure phenotypes (e.g., insula surface area and volume). None of these regions showed any, even nominal association among children of African ancestry. Genomic liability to alcohol involvement may manifest as variability in brain structure during middle childhood prior to alcohol use initiation. Broadly, alcohol-related variability in brain morphometry may partially reflect predisposing genomic influence. Larger discovery genome-wide association studies and target samples of diverse ancestries are needed to determine whether observed associations may generalize across ancestral origins.