Search

In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents, and planning communication strategy with all study team members. Section 2 focuses on what to do once data collection has already begun. Topics covered include: (1) how to effectively monitor and maintain EEG data quality, (2) how to ensure consistent implementation of experimental protocols, and (3) how to develop rigorous preprocessing procedures that are feasible for use in a large-scale study. Links to resources are also provided, including sample protocols, sample equipment and software tracking forms, sample code, and tutorial videos (to access resources, please visit: https://osf.io/wdrj3/).

Introduction: N6-methyladenosine (m6A) modification contributes to the pathogenesis and development of various cancers, including bladder cancer (BCa). In particular, integrin α6 (ITGA6) promotes BCa progression by cooperatively regulating multisite m6A modification. However, the therapeutic effect of targeting ITGA6 multisite m6A modifications in BCa remains unknown. Objectives: We aim to develop a multisite dCasRx– m6A editor for assessing the effects of the multisite dCasRx–m6A editor targeted m6A demethylation of ITGA6 mRNA in BC growth and progression. Methods: The multisite dCasRx– m6A editor was generated by cloning. m6A-methylated RNA immunoprecipitation (meRIP), luciferase reporter, a single-base T3 ligase-based qPCR-amplification, Polysome profiling and meRIP-seq experiments were performed to determine the targeting specificity of the multisite dCasRx–m6A editor. We performed cell phenotype analysis and used in vivo mouse xenograft models to assess the effects of the multisite dCasRx–m6A editor in BC growth and progression. Results: We designed a targeted ITGA6 multi-locus guide (g)RNA and established a bidirectional deactivated RfxCas13d (dCasRx)-based m6A-editing platform, comprising a nucleus-localized dCasRx fused with the catalytic domains of methyltransferase-like 3 (METTL3-CD) or α-ketoglutarate-dependent dioxygenase alkB homolog 5 (ALKBH5-CD), to simultaneously manipulate the methylation of ITGA6 mRNA at four m6A sites. The results confirmed the dCasRx–m6A editor modified m6A at multiple sites in ITGA6 mRNA, with low off-target effects. Moreover, targeted m6A demethylation of ITGA6 mRNA by the multisite dCasRx–m6A editor significantly reduced BCa cell proliferation and migration in vitro and in vivo. Furthermore, the dCasRx–ALKBH5-CD and ITGA6 multi-site gRNA delivered to 5-week-old BALB/cJNju-Foxn1nu/Nju nude mice via adeno-associated viral vectors significantly inhibited BCa cell growth. Conclusion: Our study proposes a novel therapeutic tool for the treatment of BC by applying the multisite dCasRx–m6A editor while highlighting its potential efficacy for treating other diseases associated with abnormal m6A modifications.

[Display omitted] • We for the first time designed a targeted ITGA6 multi-locus guide (g)RNA and established a bidirectional deactivated RfxCas13d (dCasRx)-based m6A-editing platform. • The dCasRx–m6A editors have a high on-target efficiency. • Multi-target demethylation using dCasRx–m6A editors reduces ITGA6 translation. • Multi-target demethylation of ITGA6 mRNA inhibits BCa progression. • Adeno-associated delivery of dCasRx–ALKBH5-CD and ITGA6-multi-gRNA inhibits BCa progression. N6-methyladenosine (m6A) modification contributes to the pathogenesis and development of various cancers, including bladder cancer (BCa). In particular, integrin α6 (ITGA6) promotes BCa progression by cooperatively regulating multisite m6A modification. However, the therapeutic effect of targeting ITGA6 multisite m6A modifications in BCa remains unknown. We aim to develop a multisite dCasRx– m6A editor for assessing the effects of the multisite dCasRx–m6A editor targeted m6A demethylation of ITGA6 mRNA in BC growth and progression. The multisite dCasRx– m6A editor was generated by cloning. m6A-methylated RNA immunoprecipitation (meRIP), luciferase reporter, a single-base T3 ligase-based qPCR-amplification, Polysome profiling and meRIP-seq experiments were performed to determine the targeting specificity of the multisite dCasRx–m6A editor. We performed cell phenotype analysis and used in vivo mouse xenograft models to assess the effects of the multisite dCasRx–m6A editor in BC growth and progression. We designed a targeted ITGA6 multi-locus guide (g)RNA and established a bidirectional deactivated RfxCas13d (dCasRx)-based m6A-editing platform, comprising a nucleus-localized dCasRx fused with the catalytic domains of methyltransferase-like 3 (METTL3-CD) or α-ketoglutarate-dependent dioxygenase alkB homolog 5 (ALKBH5-CD), to simultaneously manipulate the methylation of ITGA6 mRNA at four m6A sites. The results confirmed the dCasRx–m6A editor modified m6A at multiple sites in ITGA6 mRNA, with low off-target effects. Moreover, targeted m6A demethylation of ITGA6 mRNA by the multisite dCasRx–m6A editor significantly reduced BCa cell proliferation and migration in vitro and in vivo. Furthermore, the dCasRx–ALKBH5-CD and ITGA6 multi-site gRNA delivered to 5-week-old BALB/cJNju-Foxn1nu/Nju nude mice via adeno-associated viral vectors significantly inhibited BCa cell growth. Our study proposes a novel therapeutic tool for the treatment of BC by applying the multisite dCasRx–m6A editor while highlighting its potential efficacy for treating other diseases associated with abnormal m6A modifications. [ABSTRACT FROM AUTHOR]

A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72–28.61%, cost in the range of 5.16–45.16%, and reliability in the range of 3.11–25% over well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Background: The “Bridge Project” is a Mexico in Alliance with St. Jude (MAS) initiative developed in 2019 to improve access, accuracy, and timeliness of specialized diagnostic studies for patients with suspected acute lymphoblastic leukemia (ALL). The project strategy relies on service centralization to improve service delivery, biological characterization, risk-group classification, and support proper treatment allocation. Methods: This is an ongoing prospective multisite intersectoral quality improvement (QI) project available to all patients 0-18 years of age presenting with suspected ALL to the 14 actively participating institutions in 12 Mexican states. Institutions send specimens to one centralized laboratory. From a clinical standpoint, the project secures access to a consensus-derived comprehensive diagnostic panel. From a service delivery standpoint, we assess equity, timeliness, effectiveness, and patient-centeredness. From an implementation science standpoint, we document feasibility, utility, and appropriateness of the diagnostic panel and centralized approach. This analysis spans from July 2019 to June 2023. Results: 612 patients have accessed the project. The median age was 6 years (IQR 3-11), and 53% were males. 94% of the specimens arrived within 48 hours, which documents the feasibility of the centralized model, and 100% of the patients received precise and timely diagnostic results, which documents the effectiveness of the approach. Of 505 (82.5%) patients with confirmed ALL, 463/505 (91.6%) had B-cell ALL, and 42/505 (8.3%) had T-cell ALL. High-hyperdiploidy was detected by DNA index in 36.6% and hypodiploidy in 1.6%. 76.6% of the patients had conclusive karyotype results. FISH studies showed t (12;21) in 15%, iAMP21 in 8.5%, t(1;19) in 7.5%, t(4;11) in 4.2%, t(9;22) in 3.2%, del (9)(p21) in 1.8%, and TRA/D (14)(q11.2) rearrangement in 2.4%. Among B-cell ALL patients, 344/403 (85.1%) had Day 15 MRD<1% and 261/305 (85.6%) Day 84 MRD<0.01. For T-cell ALL patients 20/28 (71.4%) had Day 29 MRD<0.01% and 19/22 (86.4%) Day 84 MRD<0.01%. Conclusions: By securing access to a standardized consensus-derived diagnostic panel, the Bridge Project has allowed better characterization of childhood ALL in Mexico while producing unprecedented service improvements and documenting key implementation outcomes. We are using these results to inform iterative changes to the diagnostic panel and an associated treatment guideline (MAS-ALL18). [ABSTRACT FROM AUTHOR]

BackgroundMagnetic resonance imaging (MRI) with hyperpolarized [1-13C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US.MethodsThree healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1-13C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13C-pyruvate to 13C-lactate conversion were calculated, including the kPL, a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models.ResultsThe mean kPL across all examinations in the multisite comparison was 0.024 ± 0.0016 s-1. The ICC of the kPL was 0.83 (p = 0.14) between sites and 0.7 (p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio.ConclusionsEstimation of metabolic conversion from hyperpolarized [1-13C]pyruvate to lactate using model-based metrics such as kPL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Background: This study aimed to compare clinical and surgical outcomes of robotic single‐port hysterectomy (RSPH) using the da Vinci® SP surgical system and robotic multisite hysterectomy (RMSH) with the da Vinci Xi system in benign gynecologic disease. Methods: The retrospective study included 134 patients who underwent RSPH or RMSH between November 2019 and December 2020. Total operation time, amount of blood loss, and the change in hemoglobin (Hb) after surgery and the weight of the removed uteri were also measured. Data on complications such as post‐operative fever and length of hospitalization were also compared and analyzed. Results: There was no significant difference in the total operation time between the two groups, although the operation time was slightly longer in the RSPH group. Results in the RSPH group were superior to the RMSH group in docking time and wound incision time (1.67 ± 0.79 vs. 5.46 ± 2.25 min, p‐value <0.01; 6.48 ± 4.29 vs. 9.10 ± 4.64 min, p‐value <0.01, respectively). On the other hand, wound suture time took longer in the RSPH group (18.12 ± 5.66 vs. 10.69 ± 3.18 min, p‐value <0.01). The weights of the removed specimens were higher in the RMSH group (302.64 ± 190.56 vs. 369.24 ± 181.70 g, p‐value <0.04). The amount of blood loss during surgery and the difference in hemoglobin (Hb) before and after surgery were less in the RSPH group (97.39 ± 113.79 vs. 224.93 ± 152.29 mL, p‐value <0.01, 1.51 ± 1.08 vs. 2.54 ± 1.08 g/dL, p‐value <0.01). When considering the weight difference as a correction between the two surgical groups (because there were many heavier samples in the RMSH group), the blood loss of the RSPH group was also less than that of the RMSH group by 115.95 ± 23.78 mL (p‐value <0.01). Conclusions: On the basis of our data, the robotic hysterectomy using the da Vinci SP surgical system might be feasible and safe, even if the hysterectomy is complex, and comparable to robotic multisite surgery by the da Vinci Xi system. [ABSTRACT FROM AUTHOR]

Cerebral venous oxygenation (Yv) is a valuable biomarker for a variety of brain diseases. T2 relaxation under spin tagging (TRUST) MRI is a widely used method for Yv quantification. In this work, there were two main objectives. The first was to evaluate the reproducibility of TRUST Yv measurements across MRI scanners from different vendors. The second was to examine the correlation between Yv and end‐tidal CO2 (EtCO2) in a multisite, multivendor setting and determine the usefulness of this correlation to account for variations in Yv caused by normal variations and physiological fluctuations. Standardized TRUST pulse sequences were implemented on three scanners from major MRI vendors (GE, Siemens, Philips). These scanners were located at two research institutions. Ten healthy subjects were scanned. On each scanner, the subject underwent two scan sessions, each of which included three TRUST scans, to evaluate the intrasession and intersession reproducibility of Yv. Each scanner was also equipped with a capnograph device to record the EtCO2 of the subject during the MRI scan. We found no significant bias in Yv measurements across the three scanners (P = 0.18). The measured Yv values on the three scanners were also strongly correlated with each other (intraclass correlation coefficients > 0.85, P < 0.001). The intrasession and intersession coefficients of variation of Yv were less than 4% and showed no significant difference among the scanners. In addition, our results revealed that (1) within the same subject, Yv increased with EtCO2 at a rate of 1.24 ± 0.17%/mmHg (P < 0.0001), and (2) across different subjects, individuals with a higher EtCO2 had a higher Yv, at a rate of 0.94 ± 0.36%/mmHg (P = 0.01). These results suggest that (1) the standardized TRUST sequences had similar accuracies and reproducibilities for the quantification of Yv across the scanners, and (2) recording of EtCO2 may be a useful complement to Yv measurement to account for CO2‐related physiological fluctuations in Yv in multisite, multivendor studies. [ABSTRACT FROM AUTHOR]

Summary: Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Methods: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. Findings: IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p

Introduction: The gender disparity in autism spectrum disorder (ASD) has been one of the salient features of condition. However, its relationship between the pathogenesis and genetic transcription in patients of different genders has yet to reach a reliable conclusion. Methods: To address this gap, this study aimed to establish a reliable potential neuro-marker in gender-specific patients, by employing multi-site functional magnetic resonance imaging (fMRI) data, and to further investigate the role of genetic transcription molecules in neurogenetic abnormalities and gender differences in autism at the neuro-transcriptional level. To this end, age was firstly used as a regression covariate, followed by the use of ComBat to remove the site effect from the fMRI data, and abnormal functional activity was subsequently identified. The resulting abnormal functional activity was then correlated by genetic transcription to explore underlying molecular functions and cellular molecular mechanisms. Results: Abnormal brain functional activities were identified in autism patients of different genders, mainly located in the default model network (DMN) and precuneus-cingulate gyrus-frontal lobe. The correlation analysis of neuroimaging and genetic transcription further found that heterogeneous brain regions were highly correlated with genes involved in signal transmission between neurons' plasma membranes. Additionally, we further identified different weighted gene expression patterns and specific expression tissues of risk genes in ASD of different genders. Discussion: Thus, this work not only identified the mechanism of abnormal brain functional activities caused by gender differences in ASD, but also explored the genetic and molecular characteristics caused by these related changes. Moreover, we further analyzed the genetic basis of sex differences in ASD from a neuro-transcriptional perspective. [ABSTRACT FROM AUTHOR]

Medical specialty camps can play an important role in the positive development of psychosocial outcomes for children and youth with sickle cell disease (SCD). This study examined how sense of mastery and attitude towards illness outcomes changed over 6 months for 100 campers aged 8–16 years with SCD. The outcomes were measured twice before and twice after camp. Latent growth curve modeling was used to analyze data. Results showed no changes in the outcomes for this study population. Implications for future research designs, populations, and outcomes are discussed, as are implications for communications about camp, and policy and practice. [ABSTRACT FROM AUTHOR]

Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are present in the data of one or more sites. It remains unclear how the presence of outliers could affect the effectiveness of data harmonization and consequently the results of analyses using harmonized data. To address this question, we generated a normal simulation dataset without outliers and a series of simulation datasets with outliers of varying properties (e.g., outlier location, outlier quantity, and outlier score) based on a real large-sample neuroimaging dataset. We first verified the effectiveness of the most commonly used ComBat harmonization method in the removal of inter-site heterogeneity using the normal simulation data, and then characterized the effects of outliers on the effectiveness of ComBat harmonization and on the results of association analyses between brain imaging-derived phenotypes and a simulated behavioral variable using the simulation datasets with outliers. We found that, although ComBat harmonization effectively removed the inter-site heterogeneity in multisite data and consequently improved the detection of the true brain-behavior relationships, the presence of outliers could damage severely the effectiveness of ComBat harmonization in the removal of data heterogeneity or even introduce extra heterogeneity in the data. Moreover, we found that the effects of outliers on the improvement of the detection of brain-behavior associations by ComBat harmonization were dependent on how such associations were assessed (i.e., by Pearson correlation or Spearman correlation), and on the outlier location, quantity, and outlier score. These findings help us better understand the influences of outliers on data harmonization and highlight the importance of detecting and removing outliers prior to data harmonization in multisite neuroimaging studies. [ABSTRACT FROM AUTHOR]

Data from multisite magnetic resonance imaging (MRI) studies contain variance attributable to the scanner that can reduce statistical power and potentially bias results if not appropriately managed. The Adolescent Cognitive Brain Development (ABCD) study is an ongoing, longitudinal neuroimaging study acquiring data from over 11,000 children starting at 9–10 years of age. These scans are acquired on 29 different scanners of 5 different model types manufactured by 3 different vendors. Publicly available data from the ABCD study include structural MRI (sMRI) measures such as cortical thickness and diffusion MRI (dMRI) measures such as fractional anisotropy. In this work, we 1) quantify the variance attributable to scanner effects in the sMRI and dMRI datasets, 2) demonstrate the effectiveness of the data harmonization approach called ComBat to address scanner effects, and 3) present a simple, open-source tool for investigators to harmonize image features from the ABCD study. Scanner-induced variance was present in every image feature and varied in magnitude by feature type and brain location. For almost all features, scanner variance exceeded variability attributable to age and sex. ComBat harmonization was shown to effectively remove scanner induced variance from all image features while preserving the biological variability in the data. Moreover, we show that for studies examining relatively small subsamples of the ABCD dataset, the use of ComBat harmonized data provides more accurate estimates of effect sizes compared to controlling for scanner effects using ordinary least squares regression. [ABSTRACT FROM AUTHOR]

BackgroundMRI is the imaging modality of choice for diagnosis and intervention assessment in neurological disease. Its full potential has not been realized due in part to challenges in harmonizing advanced techniques across multiple sites.PurposeTo develop a method for the assessment of reliability and repeatability of advanced multisite-multisession neuroimaging studies and specifically to assess the reliability of an advanced MRI protocol, including multiband fMRI and diffusion tensor MRI, in a multisite setting.Study typeProspective.PopulationTwice repeated measurement of a single subject with stable relapsing-remitting multiple sclerosis (MS) at seven institutions.Field strength/sequenceA 3 T MRI protocol included higher spatial resolution anatomical scans, a variable flip-angle longitudinal relaxation rate constant (R1 ≡ 1/T1 ) measurement, quantitative magnetization transfer imaging, diffusion tensor imaging, and a resting-state fMRI (rsFMRI) series.AssessmentMultiple methods of assessing intrasite repeatability and intersite reliability were evaluated for imaging metrics derived from each sequence.Statistical testsStudent's t-test, Pearson's r, and intraclass correlation coefficient (ICC) (2,1) were employed to assess repeatability and reliability. Two new statistical metrics are introduced that frame reliability and repeatability in the respective units of the measurements themselves.ResultsIntrasite repeatability was excellent for quantitative R1 , magnetization transfer ratio (MTR), and diffusion-weighted imaging (DWI) based metrics (r > 0.95). rsFMRI metrics were less repeatable (r = 0.8). Intersite reliability was excellent for R1 , MTR, and DWI (ICC >0.9), and moderate for rsFMRI metrics (ICC∼0.4).Data conclusionFrom most reliable to least, using a new reliability metric introduced here, MTR > R1 > DWI > rsFMRI; for repeatability, MTR > DWI > R1 > rsFMRI. A graphical method for at-a-glance assessment of reliability and repeatability, effect sizes, and outlier identification in multisite-multisession neuroimaging studies is introduced.Level of evidence1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:878-888.

Background: Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD. Aims: Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. Method: Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age). Results: Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD. Conclusions: Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD. [ABSTRACT FROM AUTHOR]

Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal‐based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site‐effect correction methods in removing the site‐related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site‐related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo‐connectivity within sensory areas (Net1), hypo‐connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper‐connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set (p <.05). Finally, the three subnet‐specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out‐of‐sample public data set (Z = −1.583, p =.114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet‐specific composite FC measures might be replicable neuroimaging markers for schizophrenia. [ABSTRACT FROM AUTHOR]

• Optimization of a quality control routine across sites in a 3T multicentric study. • More than 80% of ACR quality control measures were within the acceptance ranges. • All measures demonstrated good reproducibility over time. • Inter-vendor differences were observed in the uniformity and geometric metrics. • The automated analysis allows each site to independently monitor scanner performance. Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers. [ABSTRACT FROM AUTHOR]

This study compares single-site and multisite calibration methods with upscaled parameters for thermal modelling. Multisite calibration and parameter upscaling are novel for calibrating a hydrological–thermal model. To this end, model calibration and simulations are carried out using a semi-distributed hydrological–thermal model (CEQUEAU). First, a hydrological calibration is carried out. The same calibrated hydrological model outputs are used for all thermal calibrations. The covariance matrix adaptation evolution strategy (CMA-ES) single-objective optimization algorithm is applied to calibrate the thermal module using observed water temperatures at seven monitoring sites within the Nechako Watershed in British Columbia, Canada. Single-site calibration provided the best performance for individual sites, but these sets of parameters performed poorly when applied to the other sites. The multisite calibration and the upscaled methods perform adequately for all sites with overall Root Mean Square Error (RMSE)<2°C as an acceptable threshold. However, the multisite method resulted in the best performance metrics and shortest computing time. [ABSTRACT FROM AUTHOR]

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R2*) mapping from multiecho T2*‐weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R2* reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross‐site variability through postprocessing approaches. Twenty‐four healthy subjects traveled between three sites and were scanned twice at each site. Scan‐rescan measurements from seven deep gray matter regions were used for assessing within‐site and cross‐site reproducibility using intraclass correlation coefficient (ICC) and within‐subject standard deviation (SDw) measures. In addition, multiple QSM and R2* postprocessing options were investigated with the aim to minimize cross‐site sequence‐related variations, including: mask generation approach, echo‐timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same‐subject cross‐site region of interest measurements for QSM and R2* were highly correlated (R2 ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R2*, respectively). The mean cross‐site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s−1 for R2*. For within‐site measurements of QSM and R2*, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s−1, respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross‐site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts. [ABSTRACT FROM AUTHOR]

In MRI studies, the aggregation of imaging data from multiple acquisition sites enhances sample size but may introduce site-related variabilities that hinder consistency in subsequent analyses. Deep learning methods for image translation have emerged as a solution for harmonizing MR images across sites. In this study, we introduce IGUANe (Image Generation with Unified Adversarial Networks), an original 3D model that leverages the strengths of domain translation and straightforward application of style transfer methods for multicenter brain MR image harmonization. IGUANe extends CycleGAN by integrating an arbitrary number of domains for training through a many-to-one architecture. The framework based on domain pairs enables the implementation of sampling strategies that prevent confusion between site-related and biological variabilities. During inference, the model can be applied to any image, even from an unknown acquisition site, making it a universal generator for harmonization. Trained on a dataset comprising T1-weighted images from 11 different scanners, IGUANe was evaluated on data from unseen sites. The assessments included the transformation of MR images with traveling subjects, the preservation of pairwise distances between MR images within domains, the evolution of volumetric patterns related to age and Alzheimer's disease (AD), and the performance in age regression and patient classification tasks. Comparisons with other harmonization and normalization methods suggest that IGUANe better preserves individual information in MR images and is more suitable for maintaining and reinforcing variabilities related to age and AD. Future studies may further assess IGUANe in other multicenter contexts, either using the same model or retraining it for applications to different image modalities. Codes and the trained IGUANe model are available at https://github.com/RocaVincent/iguane&#95;harmonization.git., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Vincent Roca reports financial support was provided by Philips. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Speaking up is an important yet challenging aspect of health professional communication. To overcome social-cognitive influences and improve speaking up, an intervention based on Kolb's experiential learning cycle was developed, which integrated Virtual Simulation, curriculum, and practice speaking up. The present study investigated if integrating Virtual Simulation influenced Respiratory Therapy students' ability to challenge a physician compared to a control condition at multiple time points during training., Methods: A multi-institutional longitudinal randomized control trial was conducted. Students from two schools completed a Virtual Simulation or No Virtual Simulation before classroom instruction on speaking up and an in-person simulation requiring speaking up. After three-to-six months and post-clinical placement, students completed a second simulation requiring speaking up. The student's ability to speak up and use CUS (Concerned, Uncomfortable, Safety Issue) was measured., Results: No significant effects for the intervention were observed across time points, p>.05 , with a small effect for using CUS, ϕ=.28. During the study, two unexpected findings emerged with theoretical and practical implications. The multi-institutional design created a natural experiment that allowed for the identification of instructor effects on speaking up and Bloom's Two-Sigma problem. Observations were also made related to perceptual limitations that diminish the ability to speak up., Conclusions: Single speaking-up interventions continue to appear to be ineffective. To substantially influence behaviour, consistent mentorship through a "champion" is likely necessary to train for and create a culture of speaking up. Training in situational awareness is also likely needed to counter human perceptual limitations in complex situations., Competing Interests: The authors have no relevant financial or non-financial interests to disclose. The authors declare no conflict of interest.

When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.

Domestic violence is a pernicious social problem in the United States and a cause of national concern. The South Asian culture disempowers individuals experiencing domestic violence from recognizing and reporting victimization. Structural inequities may also discourage reporting victimization. These issues have led to inconsistent findings of domestic violence prevalence rates. Additionally, domestic violence studies with South Asians in the United States have predominantly focused on women, omitting men from this purview of research. The purpose of this study was to examine domestic violence victimization rates as well as their correlates among South Asian immigrant men and women. The sample for this cross-sectional study included 468 South Asians across the 50 U.S. states. Descriptive statistics were used to establish rates of domestic violence victimization. Hierarchical logistic regression was used to examine the correlates of domestic violence victimization. All of the sociodemographic information was added in step 1, and acculturation and gender-role attitudes were added as covariates in step 2. The most prevalent type of domestic violence victimization was physical violence (48%), followed by emotional (38%), economic (35%), verbal (27%), immigration-related (26%), in-laws related (19%), and ultimately sexual abuse (11%). Prevalence rates were higher for women than for men in each type of violence. According to the logistic regression results, education, generational position, family type, and employment were significant correlates of domestic violence victimization. Prior to development of prevention programs by community agencies, it is essential to understand the nature and prevalence of domestic violence experiences among South Asians. The victimization of men in addition to women adds to the novelty of this research study and paves the way for practitioners and scholars to engage in conversations about providing both male and female victims of domestic violence with the needed resources and support. The article will discuss implications for research, practice, and policy. [ABSTRACT FROM AUTHOR]

Background: Setting up and conducting a randomised controlled trial (RCT) has many challenges—particularly trials that include vulnerable individuals with behavioural problems or who reside in facilities that focus on care as opposed to research. These populations are underrepresented in RCTs. Approach: In our paper, we describe the challenges and practical lessons learned from two RCTs in two care settings involving long‐stay psychiatric inpatients and people with intellectual disabilities. We describe five main difficulties and how these were overcome: (1) multisite setting, (2) inclusion of vulnerable participants, (3) nutritional supplements and placebos, (4) assessment of behavioural outcomes, and (5) collecting bio samples. Conclusions: By sharing these practical experiences, we hope to inform other researchers how to optimally design their trials, while avoiding and minimising the difficulties that we encountered, and to facilitate the implementation of a trial. Both trials were registered in the Clinical Trials Register (RCT A: NCT02498106; RCT B: NCT03212092). [ABSTRACT FROM AUTHOR]

We do not know if the delivery of Motivational Interviewing (MI) differs across countries. In an international study targeting Elderly people with Alcohol Use Disorder, The Elderly Study, MI was part of the treatment applied. Treatment delivery was measured by means of the Motivational Interviewing Treatment Integrity code version 4 (MITI 4). Mixed effects models explored potential differences in delivery of MI between the countries. Delivery of MI differed significantly between participating countries: Denmark, Germany and the US. These findings are important to consider when comparing measures of MI integrity across studies from different cultures. [ABSTRACT FROM AUTHOR]

Multicenter studies are essential in clinical and public health research with several advantages compared to single-center studies. Multicenter studies are attractive and advantageous, allowing quicker recruitment, diverse population coverage and increased generalizability. However, these studies often suffer from methodological, implementation and statistical challenges that can compromise the validity of the study. To meet the technical and interpretative integrity, a multicenter study must be conducted with sound study design, uniform implementation methodology, assured standardization, high-quality data and appropriate statistical considerations. A systematic site selection, rigorous study protocols, stringent quality assurance measures and appropriate analytical approach are indispensable to ensure high internal validity and minimize inter-site variability. For effective implementation of multicenter study, a well-organized coordination center and functional governance mechanism are critical. Transparent and effective network communication among the investigators with cultural sensitivities assists in building productive collaboration. This manuscript summarizes the design, organization, implementation and governance aspects of multicenter studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Four green morpholino-based ionic liquids were synthesized in one step for the separation of NHNCs. • [C 1 Mor][PA] has multiple interaction sites. • Hydrogen bonding and van der Waals forces were shown to be the driving forces for extraction. • The extraction efficiency of [C 1 Mor][PA] for indole and pyrrole can reach 94.16% and 93.05%. • [C 1 Mor][PA] is renewable and the extraction efficiency is basically unchanged after recycling for many times. The separation of neutral heterocyclic nitrogen compounds (NHNCs) from coal tar contributes to the rational utilization of coal tar resources while reducing environmental pollution. In this study, four green morpholino-based ionic liquids were synthesized through a one-step scheme and utilized for extracting indole and pyrrole. The research findings showed that the anionic surface of the N -methyl morpholine pyruvate extractant, [C 1 Mor][PA], exhibits multiple interaction sites, which facilitates the extraction ability to interact with indole/pyrrole. Furthermore, its anion formed N–H⋯O hydrogen bonds with indole/pyrrole (also validated by infrared spectroscopy), while its cation was observed to form van der Waals forces interactions. Subsequently, experimental tests were employed to determine the extraction efficiency (EE) of [C 1 Mor][PA] for indole/pyrrole. High efficiencies were achieved under the optimal conditions. This work presents a simplified IL extractant and a more effective method for extracting potentially pollutant and high-value-added NHNCs. Also, to understand the molecular interactions between its complexes would have significant implications in developing more effective extraction techniques for indole and pyrrole. These discoveries have played an essential role in reducing atmospheric pollution and alleviating environmental issues, paving the way for subsequent developments in the field. [ABSTRACT FROM AUTHOR]

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSEp 7.6%, R p 2 0.87) and DMY (nRMSEp 12.9%, R p 2 0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies. [ABSTRACT FROM AUTHOR]

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures. [ABSTRACT FROM AUTHOR]

Pandemic restrictions have led to changes in therapy plans and disrupted rehabilitation services for people with multiple sclerosis. CogEx is an international, multicentre MS dual-intervention (cognitive rehabilitation, aerobic exercise) randomized, controlled rehabilitation trial confined to people with progressive disease. The primary outcome is cognition (processing speed).There are 11 treatment sites in six countries with participants required to make 27 site visits over 12 weeks. Collectively, the large, in-person demands of the trial, and the varying international policies for the containment of COVID-19, might disproportionately impact the administration of CogEx. During the first lockdown, all centres closed on average for 82.9 (SD = 24.3) days. One site was required to lockdown on two further occasions. One site remained closed for 16 months. Ten staff (19.2%) were required to quarantine and eight staff (15.4%) tested positive for COVID. 10 of 264 (3.8%) participants acquired COVID-19. All survived. The mean duration of enrollment delay has been [236.7 (SD = 214.5) days]. Restarting participants whose interventions were interrupted by the pandemic meant recalculating the intervention prescriptions for these individuals. While the impact of the pandemic on CogEx has been considerable, all study sites are again open. Participants and staff have shown considerable flexibility and resilience in keeping a complex, international endeavour running. The future in general remains uncertain in the midst of a pandemic, but there is cautious optimism the study will be completed with sufficient sample size to robustly evaluate our hypothesis and provide meaningful results to the MS community on the impact of these interventions on people with progressive MS. Trial registration: The trial was registered on September 20th 2018 at www.clinicaltrials.gov having identifier NCT03679468. Registration was performed before recruitment was initiated. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) with hyperpolarized [1-13C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US. Methods: Three healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1-13C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13C-pyruvate to 13C-lactate conversion were calculated, including the kPL, a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models. Results: The mean kPL across all examinations in the multisite comparison was 0.024 ± 0.0016 s−1. The ICC of the kPL was 0.83 (p = 0.14) between sites and 0.7 (p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio. Conclusions: Estimation of metabolic conversion from hyperpolarized [1-13C]pyruvate to lactate using model-based metrics such as kPL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design. [ABSTRACT FROM AUTHOR]

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.

IntroductionNeuropathologic assessment is the current "gold standard" for evaluating the Alzheimer's disease (AD), but there is no consensus on the methods used.MethodsFifteen unstained slides (8 brain regions) from each of the 14 cases were prepared and distributed to 10 different National Institute on Aging AD Centers for application of usual staining and evaluation following recently revised guidelines for AD neuropathologic change.ResultsCurrent practice used in the AD Centers Program achieved robustly excellent agreement for the severity score for AD neuropathologic change (average weighted κ = .88, 95% confidence interval: 0.77-0.95) and good-to-excellent agreement for the three supporting scores. Some improvement was observed with consensus evaluation but not with central staining of slides. Evaluation of glass slides and digitally prepared whole-slide images was comparable.DiscussionAD neuropathologic evaluation as performed across AD Centers yields data that have high agreement with potential modifications for modest improvements.

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 ( P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 ( P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 ( P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 ( P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

Background: Neurodegenerative diseases require collaborative, multisite research to comprehensively grasp their complex and diverse pathological progression; however, there is caution in aggregating global data due to data heterogeneity. In the current study, we investigated brain structure across stages of Alzheimer's disease (AD) and how relationships vary across sources of heterogeneity., Methods: Using 6 international datasets (N > 27,000), associations of structural neuroimaging markers were investigated in relation to the AD continuum via meta-analysis. We investigated whether associations varied across elements of magnetic resonance imaging acquisition, study design, and populations., Results: Modest differences in associations were found depending on how data were acquired; however, patterns were similar. Preliminary results suggested that neuroimaging marker-AD relationships differ across ethnic groups., Conclusions: Diversity in data offers unique insights into the neural substrate of AD; however, harmonized processing and transparency of data collection are needed. Global collaborations should embrace the inherent heterogeneity that exists in the data and quantify its contribution to research findings at the meta-analytical stage., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image‐acquisition protocols—negatively influence the reliability and reproducibility of image‐analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling‐subject (TS) dataset including 56 T1‐weighted MRI scans of 20 healthy participants in three different MRI procedures—20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively—was considered to compare the reproducibility of TS‐GLM, ComBat, and TS‐ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement‐bias indicator. The measurement‐bias reduction realized with different methods was evaluated by comparing test–retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test–retest datasets. The results revealed that TS‐GLM and TS‐ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS‐based harmonization outperforms ComBat for measurement‐bias reduction and is optimal for MRI data in well‐prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large‐scale study is an interesting future problem. [ABSTRACT FROM AUTHOR]

Purpose: The performance of computer-aided detection (CAD) software depends on the quality and quantity of the dataset used for machine learning. If the data characteristics in development and practical use are different, the performance of CAD software degrades. In this study, we investigated changes in detection performance due to differences in training data for cerebral aneurysm detection software in head magnetic resonance angiography images. Materials and methods: We utilized three types of CAD software for cerebral aneurysm detection in MRA images, which were based on 3D local intensity structure analysis, graph-based features, and convolutional neural network. For each type of CAD software, we compared three types of training pattern, which were two types of training using single-site data and one type of training using multisite data. We also carried out internal and external evaluations. Results: In training using single-site data, the performance of CAD software largely and unpredictably fluctuated when the training dataset was changed. Training using multisite data did not show the lowest performance among the three training patterns for any CAD software and dataset. Conclusion: The training of cerebral aneurysm detection software using data collected from multiple sites is desirable to ensure the stable performance of the software. [ABSTRACT FROM AUTHOR]

Background: Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures—including the hippocampus, amygdala, and entorhinal cortex—in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases. Purpose: To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset. Material and Methods: Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data. Results: ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349–0.529, DSC = 0.380–0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods. Conclusion: This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies. [ABSTRACT FROM AUTHOR]

Robust methods are needed to efficiently conduct large, multisite, randomized, controlled clinical trials of acupuncture protocols. The Southwest Oncology Group (SWOG) S1200 trial is a randomized, controlled (i.e., sham-controlled and waitlist-controlled) trial of a standardized acupuncture protocol for treating aromatase inhibitor (AI)-associated arthralgias in early-stage breast cancer patients (n = 228). The primary objective of this study was to determine whether true acupuncture administered twice weekly for 6 weeks, as compared to sham acupuncture or a waitlist control, reduced AI-associated joint pain at 6 weeks as assessed by patient reports. The study was conducted at 11 institutions across the United States. The true acupuncture protocol was developed using a consensus-based process. The true acupuncture and the sham acupuncture protocols each consisted of 12 sessions administered for 6 weeks, followed by one weekly session for 6 weeks. The true acupuncture protocol used standardized protocol points, and the standardized acupoints were tailored to a patient's joint symptoms. The similarly standardized sham acupuncture protocol utilized superficial needling of nonacupoints. Standardized methods were developed to train and monitor acupuncturists and included online and in-person training, study manuals, monthly phone calls, and remote quality assurance monitoring throughout the study period. The research staff similarly received online and in-person training and monthly phone calls.

[Display omitted] • PCN-222 (Cu, 1200 nm) shows superior catalytic ability in the regio- and stereo-selective hydroboration of alkynes as multifunctional heterogeneous catalyst. • The deuterium labelling experiments show that proton source come from the H 2 O molecule of solvents. • DFT calculations and the control experiments demonstrate that the excellent catalytic activity of PCN-222(Cu) is originated from the synergistic effect of Cu (II) ions, N atoms of porphyrin rings and terminal –OH groups on Zr-nodes. • Five-membered ring centering on "Cu-N-B-C-C" skeleton is the key species. Zirconium-based porphyrinic MOFs (PMOFs, MOF = metal-organic framework) have gained considerable attention in the field of electric/thermo/photo-catalysis as heterogeneous single-site catalysts; however, the study on multisite synergistic catalysis of PMOFs are rare. Herein, we have successfully developed a highly regio- and stereo-selective synthetic method of (E)- β -vinylboronates through hydroboration of alkynes using PCN-222 (Cu, PCN = porous coordination network) as multifunctional heterogeneous catalyst. Compared with homogeneous catalytic system, high TOF value is achieved in the developed method, originating from the synergistic effect of central Cu(II) ions, N atoms of porphyrin rings and terminal –OH groups on Zr-nodes. Notably, density functional theory (DFT) study reveals that five-membered "Cu-N-B-C-C" ring is the key species responsible for the hydroboration process with specific selectivity. [ABSTRACT FROM AUTHOR]

Abstract Introduction Molecular, functional, and structural neuroimaging biomarkers are largely used to study neurodegenerative diseases, but their benefits to patients/science might be greatly enhanced by improving standardization and cross‐validation. In this EU Joint Programme‐Neurodegenerative Diseases Research–funded project, we surveyed the neuroimaging community to assess perceived barriers in multicentric neuroimaging harmonization and actions to overcome them. Methods An anonymous survey addressed researchers, clinicians, pharma industry, and professional associations, inquiring about both general and modality‐specific harmonization barriers. Results Survey participants (459) represented an international (37 countries) multidisciplinary community. We identified two sets of funding actions, one proposing the creation of an updated hub of documents to help researchers plan and execute multicentric neuroimaging studies capitalizing from previous studies, and the other focused on modality‐specific harmonization challenges in future neurodegenerative diseases clinical trials. Discussion This large survey of priorities and actions may help define harmonization calls launched by worldwide science funding agencies.

• For a multisite study to succeed, frequent communication and securing a site facilitator are key to success. • Techniques may be performed differently by participants from different sites. To address this, processes can be developed for participants to follow to aid in consistency. • Evaluating a site's commitment to following the research protocol is critical for success. • Consistent use of fidelity during this multisite simulation study was needed to ensure that differences in fidelity across sites did not result in confounding variables. Multisite simulation studies are critical in nursing research as they allow researchers to obtain larger sample sizes, however, many components need to be addressed to ensure adequate compliance with research protocols. Multisite studies are important in research as they help promote generalizability. Many components go into successfully conducting multisite simulation studies. The purpose of this paper is to describe how the four-phase process of planning, project development, study execution, and dissemination of study findings were contributed to the success of this multisite simulation study. Locating sites that met inclusion criteria, keeping consistent communication with all sites, and ensuring that participants strictly follow the research protocol to maintain simulation fidelity were found to be important components of success in multisite studies. [ABSTRACT FROM AUTHOR]

Background: Diffusion tensor imaging (DTI) has been widely used to identify structural integrity and to delineate white matter (WM) degeneration in Alzheimer's disease (AD). However, the validity and replicability of the ability to discriminate AD patients and normal controls (NCs) of WM measures are limited due to the use of small cohorts and diverse image processing methods. As yet, we still do not have a clear idea of whether WM characteristics are biomarkers for AD. Methods: We conducted a competition with diffusion measurements along 18 fiber tracts as features extracted via the automated fiber quantification (AFQ) method based on one of the largest worldwide DTI multisite biobanks (862 individuals, consisting of 279 NCs, 318 ADs, and 265 MCIs). After quality control, 825 subjects (276 NCs, 294 ADs, and 255 MCIs) were divided into a public training set (N=700) and a private testing set (N=125). Forty-eight teams submitted 130 solutions that were estimated on the private testing samples. We reported the final results of the top ten models. Results: The performance of white matter features in AD classification was stable and generalizable, which indicated the potential of WM to be a biomarker for AD. The best model achieved a prediction accuracy of 82.35% (with a sensitivity of 86.36% and a specificity of 78.05%) on the private testing set. The average accuracy of the top ten solutions was over 80%. Conclusions: The results of this competition demonstrated that DTI is a powerful tool to identify AD. A larger dataset and additional independent cohort cross-validation may improve the discriminant performance and generalization power of the classification models, thus revealing more precise disease severity factors associated with AD. For this purpose, we have released this database (https://github.com/YongLiuLab/AI4AD_AFQ) to the community, with the expectation of new solutions for the accurate diagnosis of AD.

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSEp 7.6%, Rp2 0.87) and DMY (nRMSEp 12.9%, Rp2 0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies.

Multisite research (MSR) offers the key advantages of greater statistical power and external validity via larger and more diverse participant pools. In second language acquisition (SLA) research, recent developments in meta‐analysis have created a robust foundation for MSR. Although logistical and financial obstacles can complicate expansion beyond a single site, we show that MSR's benefits can justify the investment of resources. We begin by outlining how developments in meta‐analytic research, replication, and access to data and materials have created an especially opportune moment for MSR. Next, we discuss the methodology for a classroom study on metacognitive instruction as an illustrative case, outlining the major elements of its design and implementation. Finally, we review how four issues critical to MSRs (funding, compliance, logistics, and analysis) were addressed in our example study, including a discussion on multilevel modeling. We conclude with a discussion of how the field can build upon existing research to advance multisite work. [ABSTRACT FROM AUTHOR]

It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power. [ABSTRACT FROM AUTHOR]