Examining differential success in recruitment using respondent driven sampling (RDS) in a multi-site study of gay, bisexual and other men who have sex with men.

BACKGROUND: The Engage Study is a longitudinal biobehavioral cohort study of gay, bisexual and other men who have sex with men (GBM) in Toronto, Montreal, and Vancouver. Baseline data (2,449 participants) were collected from February 2017 - August 2019 using respondent-driven sampling (RDS). Recruitment in Montreal required fewer seeds, had a much shorter recruitment period, and recruited the largest sample. METHODS: To better understand why RDS recruitment was more successful in Montreal compared to other sites, we conducted an analysis to examine RDS recruitment characteristics for GBM in each of the three study sites, explore demographic characteristics and measures of homophily, that is, the tendency of individuals to recruit other study participants who are like themselves, and compared motivations for study participation. RESULTS: Montreal had the greatest proportion of participants over the age of 45 (29.1% in Montreal, 24.6% in Vancouver, and 21.0% in Toronto) and the highest homophily for this age group, but homophily was high across the three cities. Montreal also reported the lowest percentage of participants with an annual income greater or equal to $60,000 (7.9% in Montreal, 13.1% in Vancouver and 10.6% in Toronto), but homophily was similar across all three cities. The majority of participants indicated interest in sexual health and HIV as the main reason for participating (36.1% in Montreal, 34.7% in Vancouver, and 29.8% in Toronto). Financial interest as the main reason for participation was low (12.7% in Montreal, 10.6% in Vancouver, and 5.7% in Toronto). CONCLUSION: Taken together, although we found some differences in study demographic characteristics and homophily scores, we were unable to fully explain the different recruitment success based on the data available. Our study underlines the fact that success of RDS implementation may vary by unknown factors, and that researchers should be proactive and flexible to account for variability.

Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

BACKGROUND: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. METHOD: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. RESULT: We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. CONCLUSION: The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

Facilitating Clinical Studies in Rare Diseases.

In recent years, there have been many scientific advances and new collaborations for rare diseases research and, ultimately, the health of patients living with rare diseases. However, for too many rare diseases, there still is no effective treatment, and our understanding of the incidence, prevalence, and underlying etiology is incomplete. To facilitate the studies needed to answer the many open questions there is a great need for the active involvement of all stakeholders, most importantly of patient groups. Also, the creation of streamlined infrastructure for performing multi-site clinical studies is critical, as is the engagement of multi-disciplinary teams with shared focus on a group of diseases. Another essential component of such efforts is to collect standardized data so that downstream meta-analyses and data sharing can be facilitated. To ensure high-quality protocols and datasets, a central data management and coordinating center is important. Since there are more than 6000 rare diseases, instead of focusing on single rare disease, it is more impactful to create platforms and methods that can support a group of rare diseases.

Advancing the Science of Palliative Care: Contributions of the Palliative Care Research Cooperative Group.

The Palliative Care Research Cooperative Group (PCRC) formed to lead, catalyze, and empower a community of scientists to build an evidence base to ensure high-quality care and optimal well-being for persons with serious illness and their caregivers. The PCRC grew to 630 members representing 220 distinct sites. The PCRC awarded 44 pilot grant awards (total investment $1.4 million), resulting in $15.8 million in extramural grant funding, supported monthly webinars, an annual mentorship selective, "Clinical Trials Intensives," research consultation, and grant review. Among the 169 Clinical Trials Intensive participants, 74 subsequently achieved extramural grant award funding with direct costs of over $139 million. The PCRC supported the submission of extramural research applications and fostered community through annual meetings, special interest groups, newsletters, and its website. The PCRC filled an important void in serious illness science and set the stage for the next era of advancing serious illness research.

Harmonization of repetition time and scanner effects on estimates of brain hemodynamic response function.

Multisite contributions are essential to improve the reliability and statistical power of imaging studies but introduce a complexity because of different acquisition protocols and scanners. The hemodynamic response function (HRF) is the transform that relates neural activity to the measured blood oxygenation level-dependent (BOLD) signal in MRI and contains information about the latency, amplitude, and duration of neuronal activations. Acquisition variabilities, without adding harmonization techniques, can severely limit our ability to characterize spatial effects. To address this problem, we propose to study and remove variabilities of the sampling rate and scanners on estimates of the HRF. We computed the HRF using a blind deconvolution method in 547 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) across 62 sites and 18 scanners. The approach consists of studying the changes of the response according to repetition times (TR) and scanner models. We applied ComBAT, a statistical multi-site harmonization technique, to evaluate and reduce the scanner and repetition time effects and used the Wilcoxon rank sum test to assess the performance of the harmonization. Results show high scanner and repetition time variabilities (|d| ≥ 0.38, p = 4.5 × 10-5) across features, indicating that using harmonization is crucial in multi-site studies. ComBAT successfully removes the sampling effects and reduces the variance between scanners for 7 out of 10 of the HRF features (|d| ≤ 0.05, p = 0.0052). Scanners effects have been characterized on multi-site datasets, but the repetition time impact has been less studied. We showed that the use of different values of repetition time leads to changes in HRF behavior. Regression modeling changes in the HRF on the harmonized data are not significant (p = 0.0401) which does not allow to conclude how HRF changes with aging.

Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults.

Subject motion is a well-known confound in resting-state functional MRI (rs-fMRI) and the analysis of functional connectivity. Consequently, several clean-up strategies have been established to minimize the impact of subject motion. Physiological signals in response to cardiac activity and respiration are also known to alter the apparent rs-fMRI connectivity. Comprehensive comparisons of common noise regression techniques showed that the "Independent Component Analysis based strategy for Automatic Removal of Motion Artifacts" (ICA-AROMA) was a preferred pre-processing technique for teenagers and adults. However, motion and physiological noise characteristics may differ substantially for older adults. Here, we present a comprehensive comparison of noise-regression techniques for older adults from a large multi-site clinical trial of exercise and intensive pharmacological vascular risk factor reduction. The Risk Reduction for Alzheimer's Disease (rrAD) trial included hypertensive older adults (60-84 years old) at elevated risk of developing Alzheimer's Disease (AD). We compared the performance of censoring, censoring combined with global signal regression, non-aggressive and aggressive ICA-AROMA, as well as the Spatially Organized Component Klassifikator (SOCK) on the rs-fMRI baseline scans from 434 rrAD subjects. All techniques were rated based on network reproducibility, network identifiability, edge activity, spatial smoothness, and loss of temporal degrees of freedom (tDOF). We found that non-aggressive ICA-AROMA did not perform as well as the other four techniques, which performed table with marginal differences, demonstrating the validity of these techniques. Considering reproducibility as the most important factor for longitudinal studies, given low false-positive rates and a better preserved, more cohesive temporal structure, currently aggressive ICA-AROMA is likely the most suitable noise regression technique for rs-fMRI studies of older adults.

Effective Recruitment Strategies for a Sickle Cell Patient Registry Across Sites from the Sickle Cell Disease Implementation Consortium (SCDIC).

Sickle cell disease (SCD) is a genetic disorder predominantly affecting people of African descent and is associated with significant morbidity and mortality. To improve SCD outcomes, the National Heart Lung and Blood Institute funded eight centers to participate in the SCD Implementation Consortium. Sites were required to each recruit 300 individuals with SCD, over 20 months. We aim to describe recruitment strategies and challenges encountered. Participants aged 15-45 years with confirmed diagnosis of SCD were eligible. Descriptive statistics were used to analyze the effectiveness of each recruitment strategy. A total of 2432 participants were recruited. Majority (95.3%) were African American. Successful strategies were recruitment from clinics (68.1%) and affiliated sites (15.6%). Recruitment at community events, emergency departments and pain centers had the lowest yield. Challenges included saturation of strategies and time constraints. Effective recruitment of participants in multi-site studies requires multiple strategies to achieve adequate sample sizes.

Open science in psychophysiology: An overview of challenges and emerging solutions.

The present review is the result of a one-day workshop on open science, held at the Annual Meeting of the Society for Psychophysiological Research in Washington, DC, September 2019. The contributors represent psychophysiological researchers at different career stages and from a wide spectrum of institutions. The state of open science in psychophysiology is discussed from different perspectives, highlighting key challenges, potential benefits, and emerging solutions that are intended to facilitate open science practices. Three domains are emphasized: data sharing, preregistration, and multi-site studies. In the context of these broader domains, we present potential implementations of specific open science procedures such as data format harmonization, power analysis, data, presentation code and analysis pipeline sharing, suitable for psychophysiological research. Practical steps are discussed that may be taken to facilitate the adoption of open science practices in psychophysiology. These steps include (1) promoting broad and accessible training in the skills needed to implement open science practices, such as collaborative research and computational reproducibility initiatives, (2) establishing mechanisms that provide practical assistance in sharing of processing pipelines, presentation code, and data in an efficient way, and (3) improving the incentive structure for open science approaches. Throughout the manuscript, we provide references and links to available resources for those interested in adopting open science practices in their research.

Comparing discrimination among people with schizophrenia, affective and anxiety disorders. A multilevel study in five European countries.

BACKGROUND: Most research on mental illness stigma has involved people with psychosis; less information is available for people with affective and anxiety disorders. We aimed to compare experienced and anticipated discrimination among people with schizophrenia, and affective and anxiety disorders. METHODS: People with schizophrenia (n=773), affective (n=1010) and anxiety disorders (n=372) were recruited during psychiatric admission across 5 EU countries. The Discrimination and Stigma Scale (DISC-12) was used. Multivariate mixed effect logistic regression models with a random effect for hospital and country were performed to explore patient characteristics associated with experienced and anticipated discrimination. RESULTS: With anxiety disorders, there were more reports of experiences of discrimination in social life (35%), intimate relationships (23.5%), and physical healthcare (19%); in schizophrenia, in relations with neighbours (23.6%) and mental health staff (21.7%); and in affective disorders, in parental role (22.8%). In multivariate analyses, anxiety was associated with increased likelihood of experiencing discrimination in police interactions (OR=1.675; p=0.038) and physical healthcare (OR=1.816; p=0.003), and reduced likelihood when starting a family (OR=0.474; p=0.01) as compared with schizophrenia. Affective (OR=1.367; p=0.004) and anxiety disorders (OR=1.354; p=0.034) were associated with increased likelihood of concealing a diagnosis compared with schizophrenia. LIMITATIONS: As patients with affective and anxiety disorders were recruited from hospital inpatient units, their experiences may not be representative of all people with these disorders. CONCLUSIONS: In a sample of people receiving inpatient treatment, experienced and anticipated discrimination are perceived, at least in some life domains, as more of a burden for people with affective and anxiety disorders than those with schizophrenia.

Brain network dynamics fingerprints are resilient to data heterogeneity.

CONTEXT: Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. OBJECTIVE: We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models. APPROACH: Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions. MAIN RESULTS: Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data. SIGNIFICANCE: These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol.

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Better Together: The Making and Maturation of the Palliative Care Research Cooperative Group.

OBJECTIVE: To describe the growth and outcomes of the Palliative Care Research Cooperative Group (PCRC). BACKGROUND: Despite advances, significant gaps remain in the evidence base to inform care for people with serious illness. To generate this needed evidence and bolster research capacity, the Palliative Care Research Cooperative (PCRC) group was formed. METHODS: The PCRC supports investigators in the conduct of multisite clinical studies. After developing a governance structure and completing a proof of concept demonstration study, the PCRC expanded its infrastructure to include additional resource cores (Clinical Studies; Measurement; Data Informatics and Statistics; and Caregiver Studies). The PCRC also supports an Investigator Development Center as many palliative care investigators valued opportunities to advance their skills. Additional key aspects of PCRC resources include a Scientific Review Committee, a Publications Committee, and initiatives to purposefully engage investigators in a community of palliative care science. RESULTS: The PCRC has grown to over 300 members representing more than 130 distinct sites. To date, the PCRC has supported the submission of 51 research applications and has engaged in 27 studies. The PCRC supports investigator research development needs through webinars and clinical trials "intensives." To foster a sense of community, the PCRC has convened biannual meetings, developed special interest groups, and regularly communicates via a newsletter and its website. CONCLUSION: With a particular focus on facilitating conduct of rigorous multisite clinical studies, the PCRC fosters an engaged multidisciplinary research community, filling an important void in generating and disseminating evidence that informs the provision of high-quality care to people with serious illness.

Use of a Single, Independent IRB: Case Study of an NIH Funded Consortium.

In 2014, the Request for Applications from the National Institutes of Health (NIH) for continued funding of a multi-site clinical and mechanistic research network, Inner City Asthma Consortium (ICAC), called for "efficient IRB review and approval for multi-center studies" and "IRB approval within 30 days from submission". These requirements were precursors to the NIH policy of single IRB review for multi-site studies. Here we share our challenges, implementation processes, results, and recommendations, using a single, independent IRB.

A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia.

BACKGROUND: This multi-site study compares resting state fMRI amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) between patients with schizophrenia (SZ) and healthy controls (HC). METHODS: Eyes-closed resting fMRI scans (5:38 min; n = 306, 146 SZ) were collected from 6 Siemens 3T scanners and one GE 3T scanner. Imaging data were pre-processed using an SPM pipeline. Power in the low frequency band (0.01-0.08 Hz) was calculated both for the original pre-processed data as well as for the pre-processed data after regressing out the six rigid-body motion parameters, mean white matter (WM) and cerebral spinal fluid (CSF) signals. Both original and regressed ALFF and fALFF measures were modeled with site, diagnosis, age, and diagnosis × age interactions. RESULTS: Regressing out motion and non-gray matter signals significantly decreased fALFF throughout the brain as well as ALFF in the cortical edge, but significantly increased ALFF in subcortical regions. Regression had little effect on site, age, and diagnosis effects on ALFF, other than to reduce diagnosis effects in subcortical regions. There were significant effects of site across the brain in all the analyses, largely due to vendor differences. HC showed greater ALFF in the occipital, posterior parietal, and superior temporal lobe, while SZ showed smaller clusters of greater ALFF in the frontal and temporal/insular regions as well as in the caudate, putamen, and hippocampus. HC showed greater fALFF compared with SZ in all regions, though subcortical differences were only significant for original fALFF. CONCLUSIONS: SZ show greater eyes-closed resting state low frequency power in frontal cortex, and less power in posterior lobes than do HC; fALFF, however, is lower in SZ than HC throughout the cortex. These effects are robust to multi-site variability. Regressing out physiological noise signals significantly affects both total and fALFF measures, but does not affect the pattern of case/control differences.