Large-scale Multi-site Studies Research Articles

Prediction of gestational age using urinary metabolites in term and preterm pregnancies

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.

Standardized Data Acquisition for Neuromelanin-sensitive Magnetic Resonance Imaging of the Substantia Nigra.

The dopaminergic system plays a crucial role in healthy cognition (e.g., reward learning and uncertainty) and neuropsychiatric disorders (e.g., Parkinson's disease and schizophrenia). Neuromelanin is a byproduct of dopamine synthesis that accumulates in dopaminergic neurons of the substantia nigra. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a noninvasive method for measuring neuromelanin in those dopaminergic neurons, providing a direct measure of dopaminergic cell loss in the substantia nigra and a proxy measure of dopamine function. Although NM-MRI has been shown to be useful for studying various neuropsychiatric disorders, it is challenged by a limited field-of-view in the inferior-superior direction resulting in the potential loss of data from the accidental exclusion of part of the substantia nigra. In addition, the field is lacking a standardized protocol for the acquisition of NM-MRI data, a critical step in facilitating large-scale multisite studies and translation into the clinic. This protocol describes a step-by-step NM-MRI volume placement procedure and online quality control checks to ensure the acquisition of good-quality data covering the entire substantia nigra.

Denoising scanner effects from multimodal MRI data using linked independent component analysis

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.

Methodological considerations for large-scale breath analysis studies: lessons from the U-BIOPRED severe asthma project

Methods for breath sampling and analysis require robust quality assessment to minimise the risk of false discoveries. Planning large-scale multi-site breath metabolite profiling studies also requires careful consideration of systematic and random variation as a result of sampling and analysis techniques. In this study we use breath sample data from the recent U-BIOPRED cohort to evaluate and discuss some important methodological considerations such as batch variation and correction, variation between sites, storage and transportation, as well as inter-instrument analytical differences. Based on this we provide a summary of recommended best practices for new large scale multi-site studies.

Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer's Disease.

Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer's disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ICC=0.96 , SI = 0.62±0.16 for ADC data set and ICC=0.78 , SI=0.51±0.15 for PREVENT-AD data set. The proposed method was robust in the independent sample yielding SI=0.64±0.17 with ICC=0.93 for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.

The effectiveness of orthoses in the conservative management of thumb CMC joint osteoarthritis: An analysis of functional pinch strength

Study DesignThe study was a retrospective cohort analysis for a 19-month period from May 2013 to December 2014. IntroductionAlthough the use of orthoses has long been a staple of conservative treatment measures for individuals with osteoarthritis of the thumb carpometacarpal (CMC) joint, there remains little evidence exploring its effectiveness in improving functional outcomes for this client population. Purpose of the StudyThe purpose of this study was to assess the effectiveness of 3 frequently used orthoses in improving the functional pinch strength of adults with a diagnosis of thumb CMC joint osteoarthritis. MethodsA retrospective cohort analysis was conducted to determine whether pinch strength improved after orthotic fabrication, and fitting in patients referred to a hand therapy clinic. ResultsPatients who received a Colditz design orthosis had a mean increase of 2.64 lb with regard to functional pinch strength after orthotic fabrication and fitting. Patients who received a Comfort Cool orthosis (North Coast Medical, Morgan Hill, CA) had a mean increase of 2.47 lb, whereas patients who received a Thumb Spica orthosis had a mean increase of 3.25 lb. There was no evidence of any statistically significant difference in the average improvements in pinch strength between the Colditz design orthosis and the Comfort Cool orthosis. ConclusionsResults from this study demonstrate that orthosis wear consistently increases the functional pinch strength of individuals with thumb CMC joint osteoarthritis. Large-scale multisite research studies comparing various orthotic designs are necessary to help therapists determine best practice interventions for the conservative management of thumb CMC joint osteoarthritis. Level of evidence2(c).

Future clinical uses of neurophysiological biomarkers to predict and monitor treatment response for schizophrenia.

Advances in psychiatric neuroscience have transformed our understanding of impaired and spared brain functions in psychotic illnesses. Despite substantial progress, few (if any) laboratory tests have graduated to clinics to inform diagnoses, guide treatments, and monitor treatment response. Providers must rely on careful behavioral observation and interview techniques to make inferences about patients' inner experiences and then secondary deductions about impacted neural systems. Development of more effective treatments has also been hindered by a lack of translational quantitative biomarkers that can span the brain-behavior treatment knowledge gap. Here, we describe an example of a simple, low-cost, and translatable electroencephalography (EEG) measure that offers promise for improving our understanding and treatment of psychotic illnesses: mismatch negativity (MMN). MMN is sensitive to and/or predicts response to some pharmacologic and nonpharmacologic interventions and accounts for substantial portions of variance in clinical, cognitive, and psychosocial functioning in schizophrenia (SZ). This measure has recently been validated for use in large-scale multisite clinical studies of SZ. Finally, MMN greatly improves our ability to forecast which individuals at high clinical risk actually develop a psychotic illness. These attributes suggest that MMN can contribute to personalized biomarker-guided treatment strategies aimed at ameliorating or even preventing the onset of psychosis.

Genetics and brain morphology.

A wealth of empirical evidence is accumulating on the genetic mediation of brain structure phenotypes. This comes from twin studies that assess heritability and genetic covariance between traits, candidate gene associations, and genome-wide association studies (GWAS) that can identify specific genetic variants. Here we review the major findings from each of these approaches and consider how they inform on the genetic architecture of brain structure. The findings from twin studies show there is a strong genetic influence (heritability) on brain structure, and overlap of genetic effects (pleiotropy) between structures, and between structure and cognition. However, there is also evidence for genetic specificity, with distinct genetic effects across some brain regions. Candidate gene associations show little convergence; most have been under powered to detect effect sizes of the magnitude now expected. GWAS have identified 19 genetic variants for brain structure, though no replicated associations account for more than 1% of the variance. Together these studies are revealing new insights into the genetic architecture of brain morphology. As the scope of inquiry broadens, including measures that capture the complexity of the brain, along with larger samples and new analyses, such as genome-wide common trait analysis (GCTA) and polygenic scores, which combine variant effects for a phenotype, as well as whole-genome sequencing, more genetic variants for brain structure will be identified. Increasingly, large-scale multi-site studies will facilitate this next wave of studies, and promise to enhance our understanding of the etiology of variation in brain morphology, as well as brain disorders.

Validation of mismatch negativity and P3a for use in multi-site studies of schizophrenia: Characterization of demographic, clinical, cognitive, and functional correlates in COGS-2

Mismatch negativity (MMN) and P3a are auditory event-related potential (ERP) components that show robust deficits in schizophrenia (SZ) patients and exhibit qualities of endophenotypes, including substantial heritability, test–retest reliability, and trait-like stability. These measures also fulfill criteria for use as cognition and function-linked biomarkers in outcome studies, but have not yet been validated for use in large-scale multi-site clinical studies. This study tested the feasibility of adding MMN and P3a to the ongoing Consortium on the Genetics of Schizophrenia (COGS) study. The extent to which demographic, clinical, cognitive, and functional characteristics contribute to variability in MMN and P3a amplitudes was also examined. Participants (HCS n=824, SZ n=966) underwent testing at 5 geographically distributed COGS laboratories. Valid ERP recordings were obtained from 91% of HCS and 91% of SZ patients. Highly significant MMN (d=0.96) and P3a (d=0.93) amplitude reductions were observed in SZ patients, comparable in magnitude to those observed in single-lab studies with no appreciable differences across laboratories. Demographic characteristics accounted for 26% and 18% of the variance in MMN and P3a amplitudes, respectively. Significant relationships were observed among demographically-adjusted MMN and P3a measures and medication status as well as several clinical, cognitive, and functional characteristics of the SZ patients. This study demonstrates that MMN and P3a ERP biomarkers can be feasibly used in multi-site clinical studies. As with many clinical tests of brain function, demographic factors contribute to MMN and P3a amplitudes and should be carefully considered in future biomarker-informed clinical studies.

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.

A15 MRI brain imaging as a potential marker for Huntington's disease

As disease-modifying treatment trials for HD become a realistic possibility, there is an urgent need to establish robust markers of disease progression with the potential to assess the efficacy of future neuroprotective agents. Such measures need to be sensitive to disease-related change in early HD and ideally premanifest subjects, have the ability to track disease progression over time and be reproducible across sites. Current clinical rating scales lack sensitivity and are subject to floor or ceiling effects, particularly in premanifest subjects. However, neuroimaging techniques are showing promise for detecting disease-related changes in the brain even in the very earliest premanifest stages. Structural MRI allows in vivo assessment of the macroscopic effects of underlying neuropathology, namely neuronal damage and ultimately brain atrophy. Large-scale multi-site studies such as TRACK-HD and PREDICT-HD have identified reliable measures such as striatal and white-matter atrophy with the ability to track disease progression in both early HD and even the premanifest stages, over timescales which are feasible for clinical trials. Such measures have already been applied to trials of novel therapies in conditions such as Alzheimer9s disease and multiple sclerosis. I will discuss imaging metrics which might prove useful for future clinical trials in HD, together with the quality control and validation measures necessary for multi-site studies. I will present effect sizes and sample size estimates and also discuss the relationship of imaging measures to functional readouts. <h3>Funding</h3> Rachael Scahill is supported by the CHDI Foundation, a not for profit organisation dedicated to finding treatments for HD.

Feasibility and Pilot Efficacy Results From the Multisite Cognitive Remediation in the Schizophrenia Trials Network (CRSTN) Randomized Controlled Trial

The true benefit of pharmacologic intervention to improve cognition in schizophrenia may not be evident without regular cognitive enrichment. Clinical trials assessing the neurocognitive effects of new medications may require engagement in cognitive remediation exercises to stimulate the benefit potential. However, the feasibility of large-scale multisite studies using cognitive remediation at clinical trials sites has not been established. 53 adult patients with DSM-IV schizophrenia from 9 university-affiliated sites were randomized to a cognitive remediation condition that included the Posit Science Brain Fitness auditory training program with weekly Neuropsychological and Educational Approach to Remediation (NEAR) "bridging groups" or a control condition of computer games and weekly healthy lifestyles groups. Patients were expected to complete 3 to 5 one-hour sessions weekly for 40 sessions or 12 weeks, whichever came first. The primary outcomes were feasibility results as measured by rate of enrollment, retention, and completion rate of primary outcome measures. The study was conducted from July 2009 through January 2010. During a 3-month enrollment period, 53 (of a projected 54) patients were enrolled, and 41 (77%) met criteria for study completion. Thirty-one patients completed all 40 sessions, and all patients completed all primary outcome measures. Preliminary efficacy results indicated that, after 20 sessions, patients in the cognitive remediation condition demonstrated mean MATRICS Consensus Cognitive Battery composite score improvements that were 3.7 (95% CI, 0.05-7.34) T-score points greater than in patients in the computer-games control group (F(1,46) = 4.16, P = .047). At the end of treatment, a trend favoring cognitive remediation was not statistically significant (F(1,47) = 2.26, P = .14). Multisite clinical trials of cognitive remediation using the Posit Science Brain Fitness auditory training program with the NEAR method of weekly bridging groups at traditional clinical sites appear to be feasible. ClinicalTrials.gov identifier: NCT00930150.

Assessing Academic Attainment: a Brief, Psychometrically Sound Measure for Use in Middle Childhood

Background: Assessing educational outcomes in high-risk populations is crucial for defining long term functional outcomes. Standardised tests are costly and time-consuming for large-scale investigations. A valid and reliable teacher-report measure would be advantageous. Aim: To assess the psychometric properties of the 'Teacher Academic Attainment Scale' (TAAS). Methods: Participants comprised 340 children aged 10-11 years (n=187 born extremely preterm [< 26 weeks] and n=153 born at term) were assessed using the reading and mathematics scales of the Wechsler Individual Achievement Test-II (WIAT-II), a Gold Standard measure. Class teachers completed the TAAS, a 7-item questionnaire for assessing academic attainment. The TAAS was also completed at 6 years of age for 266 children. Results: Cronbach's alpha=0.95 indicated excellent internal consistency, and a significant correlation (r=0.77, p< 0.001) between TAAS scores at 6 and 11 years indicated good long-term test-retest reliability. Significantly higher TAAS scores for term vs. preterm children demonstrated discriminative validity. TAAS scores at both 6 and 11 years were significantly correlated with WIAT-II reading (r=0.69 & r=0.75, p< 0.001) and mathematics (r=0.75 & r=0.82, p< 0.001) scores demonstrating good predictive and concurrent validity, respectively. TAAS scores < 2.5 were good predictors of learning impairment in reading (sensitivity 93% specificity 78%) and mathematics (sensitivity 90% specificity 87%). Conclusions: The TAAS is a brief, psychometrically sound teacher-report of academic attainment that yields both continuous and categorical outcomes. It provides a highly reliable, valid, cost- and time-efficient outcome measure for large-scale multi-site studies both within and across countries.

The future of fMRI and genetics research

I provide a brief and subjective view of where the field of imaging genetics is heading. After recapitulating early debates between imagers and geneticists revolving around the topic of candidate gene studies, I point out the importance of genome-wide significant, rare and common variants. I propose that the next stages will be dominated by large-scale multi-site studies that will enable the examination of rare-high penetrance variants and methodological developments that will be required to properly assess the effects of pleiotropy, epistasis, and gene-by environment interactions. The incorporation of new sources of biological information such as whole genome sequencing, proteomic, lipidomic and expression profiles and cellular models derived from induced pluripotent stem cells opens new vistas for imaging genetics in a translational enterprise that is ultimately hoped to improve and create therapeutic options for psychiatric disorders.

Premature Discharge from Methadone Treatment: Patient Perspectives

Longer retention in drug abuse treatment is associated with better patient outcomes, and research indicates the first 12 months of methadone treatment are critical to patient success. Nevertheless, large-scale multisite longitudinal studies over the past three decades indicate that the majority of patients drop out during the first year of methadone treatment. Through an examination of 42 qualitative interviews with patients prematurely discharged from six methadone treatment programs in Baltimore, this study highlights factors patients describe as contributing to their reasons for being discharged within the first 12 months of the treatment. The two most consistent themes are program-related factors and incarceration. The former factors are richly described through patients' words and underscore the ways in which patients' perceptions of control exerted by the program and by the medication and misunderstandings of program structure can lead to premature discharge. Patients' reasons for discharge were compared to counselors' reasons as indicated in discharge summary forms. An analysis of the patterns of agreement and disagreement are presented. Patient-centered program and policy implications are discussed.