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Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.

Purpose: The Centiloid project helps calibrate the quantitative amyloid-β (Aβ) load into a unified Centiloid (CL) scale that allows data comparison across multi-site. How the smaller regional amyloid converted into CL has not been attempted. We first aimed to express regional Aβ deposition in CL using [18F]Flutemetamol and evaluate regional Aβ deposition in CL with that in standardized uptake value ratio (SUVr). Second, we aimed to determine the presence or absence of focal Aβ deposition by measuring regional CL in equivocal cases showing negative global CL. Methods: Following the Centiloid project pipeline, Level-1 replication, Level-2 calibration, and quality control were completed to generate corresponding Centiloid conversion equations to convert SUVr into Centiloid at regional levels. In equivocal cases, the regional CL was compared with visual inspection to evaluate regional Aβ positivity. Results: 14 out of 16 regional conversions from [18F]Flutemetamol SUVr to Centiloid successfully passed the quality control, showing good reliability and relative variance, especially precuneus/posterior cingulate and prefrontal regions with good stability for Centiloid scaling. The absence of focal Aβ deposition could be detected by measuring regional CL, showing a high agreement rate with visual inspection. The regional Aβ positivity in the bilateral anterior cingulate cortex was most prevalent in equivocal cases. Conclusion: The expression of regional brain Aβ deposition in CL with [18F]Flutemetamol has been attempted in this study. Equivocal cases had focal Aβ deposition that can be detected by measuring regional CL. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-site global optimisation model of blending operations, alternative routings and order scheduling with a co-production flow. It is performed in a phosphoric supply chain owning three open-pit mines of different geological structures and chemical compositions, each having a dry blending plant. One main challenge of the phosphate industries is to produce merchantable ores (MO) that verify a quality charter (chemical composition) with chemical heterogeneous source ores (SO). Thus, a process of SO blending followed by a treatment is mostly necessary to obtain the required MO. Six alternative routings exist to produce an MO; one of them involving a calcination plant generates a co-product. The model objective is to determine the least costly production programme of a set of MO orders to be delivered within precise time windows, in given quantities and compliance with a specific quality charter, by assigning for each MO its blending plant, routing, blend of SOs and production schedule; it also defines the feeding of the blending plants. A real case study illustrates the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.

Abstract Background Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p

Jacob T Hall,1 Andrew M Moon,2,3 Michael Young,1 Xianming Tan,3,4 Rami Darawsheh,5 Flora Danquah,3 Joel E Tepper,1,3 Ted K Yanagihara1,3 1Department of Radiation Oncology, University of North Carolina Hospitals, Chapel Hill, NC, USA; 2Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina, Chapel Hill, NC, USA; 3Lineberger Comprehensive Cancer Center, University of North Carolina Hospitals, Chapel Hill, NC, USA; 4Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; 5University of North Carolina School of Medicine, Chapel Hill, NC, USACorrespondence: Ted K Yanagihara, Department of Radiation Oncology, 101 Manning Drive CB #7512, Chapel Hill, NC, 27514, USA, Tel +1-984-974-0400, Email tky@email.unc.eduBackground: We aim to better characterize stereotactic body radiation therapy (SBRT)-related hepatic biochemical toxicity in patients with multiple intrahepatic lesions from hepatocellular carcinoma (HCC).Methods: We conducted a retrospective analysis of patients with HCC who underwent SBRT for 2 or more synchronous or metachronous liver lesions. We collected patient characteristics and dosimetric data (mean liver dose [MLD], cumulative effective volume [Veff], cumulative volume of liver receiving 15 Gy [V15Gy], and cumulative planning target volume [PTV]) along with liver-related toxicity (measured by albumin-bilirubin [ALBI] and Child–Pugh [CP] scores). A linear mixed-effects model was used to assess the effect of multi-target SBRT on changes in ALBI.Results: There were 25 patients and 56 lesions with median follow-up of 29 months. Eleven patients had synchronous lesions, and 14 had recurrent lesions treated with separate SBRT courses. Among those receiving multiple SBRT courses, there were 7 lesions with overlap of V15Gy (median V15Gy overlap: 35 mL, range: 0.5– 388 mL). There was no association between cumulative MLD, Veff, V15Gy, or PTV and change in ALBI. Four of 25 patients experienced non-classic radiation-induced liver disease (RILD), due to an increase of CP score by ≥ 2 points 3 to 6 months after SBRT. Sixteen of 25 patients experienced an increase in ALBI grade by 1 or more points 3 to 6 months after SBRT. Comparing the groups that received SBRT in a single course versus multiple courses revealed no statistically significant differences in liver toxicity.Conclusion: Liver SBRT for multiple lesions in a single or in separate courses is feasible and with acceptable risk of hepatotoxicity. Prospective studies with a larger cohort are needed to better characterize safety in this population.Keywords: HCC, SBRT, reirradiation, multi-site, toxicity

Background: Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods: This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results: The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. Conclusions: This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset. [ABSTRACT FROM AUTHOR]

The present study focuses on using qualitative and quantitative data to evaluate the functionality, user experience (UX), and aesthetic approach offered by an academic multi-site Web ecosystem consisting of multiple interconnected websites. Large entities in various industry fields often have the need for an elaborate Web presence. In an effort to address the challenges posed by this need specifically in the field of academia, the authors developed, over a period of many years, a multi-site ecosystem within the Ionian University, which focuses on interconnectivity and a collaborative approach to academic content management. This system, known as "Publish@Ionio", uses a singular content management infrastructure to allow for the creation of content for different websites that share both information and resources while at the same time allowing for individual variations in both functionality and aesthetics. The ecosystem was evaluated through quantitative data from its operation and qualitative feedback from a focus-group interview with experts, including website editors and administrative staff. The collected data were used to assess the strengths and weaknesses of the multi-site approach based on the actions and needs of the individuals in charge of generating content. The study led to conclusions on the advantages that interoperability offers in terms of digital and human resource management, the benefits of a unified aesthetic approach that allows for variability, and the necessity of collaborative content management tools that are tailored to the content's nature. [ABSTRACT FROM AUTHOR]

ObjectiveMethodsResultsConclusionThe LIMBIC Military and Tactical Athletic Research Study (MATARS) framework was established to confirm and extend understanding of concussion with initial studies driven by clinical data collected between 2015 and 2020 in a collegiate sports setting. The LIMBIC MATARS framework will be leveraged to apply gold-standard and innovative research designs to advance the science of concussion. This manuscript provides the background, methodology, and initial demographic data associated with the LIMBIC MATARS.Consensus-based common data elements were used to conduct a retrospective chart review, specific to collegiate athletes diagnosed with concussions between 2015 and 2020 at 11 universities.A final sample of 1,311 (47.8% female) concussions were diagnosed during the five-year study period from athletes participating in a variety of National Collegiate Athlete Association (NCAA) sports. The LIMBIC MATARS demographic data, align with the NCAA and other pioneering multi-site concussion-related studies in terms of biological sex, race and ethnicity, and sport participation.This pragmatic, methodological approach was used to address several a priori hypotheses related to concussion, align with other multi-site studies of concussion, and establish a consortium for future investigations. [ABSTRACT FROM AUTHOR]

The majority of studies in agroforestry have primarily focused on boosting agricultural production, income generation, component interaction and less emphasized on the aspect of biodiversity. Several studies have called for more investigation to address the lack of understanding regarding the significance of agroforestry in conservation of plant biodiversity. This paper examined the role of agroforestry practices in conservation of perennial plant species diversity and the relationship that the perennial plant species diversity has with altitude, slope, and household wealth status. Data was collected from 216 sample plots of agroforestry practices using a multi-stage sampling system. The relationship between diversity indices and explanatory variables such as agroforestry practices, household wealth, altitude, and slope gradients were assessed using a generalized linear mixed model. The impact of these variables on plant species composition was evaluated using the Adonis2 function. Sorenson dissimilarity measures were used to determine the dissimilarities in species composition. A total of 135 perennial plant species with 58 families were identified, with natives accounting 68.15% of the total. The Fruit-tree-based agroforestry practices maintained significantly higher observed and expected number of species (p < 0.05). The values of the mean species richness, Shannon diversity, and Margalef indices per plot, were found to be 6.1 ± 0.65, 1.53 ± 0.32 and 6.37 ±.045 respectively. The dissimilarity in species composition between the two agroforestry practices was 0.53 while the Sorensen multiple-site dissimilarity measures among the kebeles was 0.72. The coffee-shade-tree based agroforestry practices deserved higher importance value index of species. The species richness, Shannon diversity and the importance value indices were significantly differed with respect to slope, altitude, agroforestry practices and household wealth status (p < 0.001). The species composition varied along altitudinal gradients, household wealth status and across the study kebeles (p = 0.005, 0.013, and 0.029, respectively). Finally, it is argued that agroforestry practices should be given more emphasis as a way of perennial plant species reservoirs in relation to the various socioeconomic and ecological factors in order to counterbalance the local threat of perennial plant species from their natural habitats. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

PurposeHeating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.MethodA standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).ResultsOf the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.DiscussionThis study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

The study of the church is a prominent issue among New Testament writers. Though the church has progressed and may look different in the twenty-first century than it did in the first century, its purpose and function described in the New Testament ought to remain the same. One such progression that many modern churches have promoted is the use of multiple locations. The multi-campus phenomenon of the church is a recent development in the history of Christianity. Even more popular and perhaps even more acceptable is the multiple gatherings of the church that occur in one place but at different times. In many ways, the multi-site and multiple-service church are an application of the hierarchical structure of the church which developed in the third century. Such practices compromise the principle of congregationalism, foundational to Baptist churches, for the benefit of pragmatic purposes. Therefore, this article will argue a necessary connection between congregationalism and the assembly to show that the local church is to be a physical and singular gathering of covenanted believers at one time and in one location.

Abstract Introduction: Operationalizing multi-site Community Engagement (CE) Studios to inform a research program is valuable for researchers. We describe the process and outcomes of hosting three CE Studios with Community Experts aged 65 years or older with chronic conditions and care partners of older adults. Experts gave feedback about processes for testing the feasibility, efficacy, effectiveness, and implementation of audio recording clinic visits and sharing recordings with patients who have multimorbidity and their care partners. Methods: The CE Cores of the Clinical and Translational Science Awards Programs at three academic health science centers created a joint CE Studio guide. Studios were conducted iteratively by site. Following receipt of the final reports, responses were compared to find themes, similarities, and differences on four topics in addition to overall commentary: Recruitment and Retention, Study Protocol, Study Reminders and Frequency, and Recording Technology. Results: Eighteen older adults and care partners in three states provided valuable feedback to inform multi-site trials. Feedback influenced multiple aspects of trials in process or subsequently funded. Experts provided critique on the wording of study invitations, information sheets, and reminders to engage in study procedures. Experts were concerned for participants being disappointed by randomization to a control arm and advised how investigators should prepare to address that. Conclusions: Multi-site CE Studios should be consecutive, so each team can learn from the previous teams. Using the CES Toolkit ensures that final reports were easily comparable and utilized to develop a research program that now includes three federally funded clinical trials.

Depression is an incapacitating psychiatric disorder with increased risk through adolescence. Among other factors, children with family history of depression have significantly higher risk of developing depression. Early identification of pre-adolescent children who are at risk of depression is crucial for early intervention and prevention. In this study, we used a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9–10 years at baseline), we applied advanced machine learning methods to predict depression risk at the two-year follow-up from the baseline assessment, using a set of comprehensive multimodal neuroimaging features derived from structural MRI, diffusion tensor imaging, and task and rest functional MRI. Prediction performance underwent a rigorous cross-validation method of leave-one-site-out. Our results demonstrate that all brain features had prediction scores significantly better than expected by chance, with brain features from rest-fMRI showing the best classification performance in the high-risk group of participants with parental history of depression (N = 625). Specifically, rest-fMRI features, which came from functional connectomes, showed significantly better classification performance than other brain features. This finding highlights the key role of the interacting elements of the connectome in capturing more individual variability in psychopathology compared to measures of single brain regions. Our study contributes to the effort of identifying biological risks of depression in early adolescence in population-based samples.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC’s full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

The analysis of time series studies linking daily counts of a health indicator with environmental variables (e.g., mortality or hospital admissions with air pollution concentrations or temperature; or motor vehicle crashes with temperature) is usually conducted with Poisson regression models controlling for long-term and seasonal trends using temporal strata. When the study includes multiple zones, analysts usually apply a two-stage approach: first, each zone is analyzed separately, and the resulting zone-specific estimates are then combined using meta-analysis. This approach allows zone-specific control for trends. A one-stage approach uses spatio-temporal strata and could be seen as a particular case of the case-time series framework recently proposed. However, the number of strata can escalate very rapidly in a long time series with many zones. A computationally efficient alternative is to fit a conditional Poisson regression model, avoiding the estimation of the nuisance strata. To allow for zone-specific effects, we propose a conditional Poisson regression model with a random slope, although available frequentist software does not implement this model. Here, we implement our approach in the Bayesian paradigm, which also facilitates the inclusion of spatial patterns in the effect of interest. We also provide a possible extension to deal with overdispersed data. We first introduce the equations of the framework and then illustrate their application to data from a previously published study on the effects of temperature on the risk of motor vehicle crashes. We provide R code and a semi-synthetic dataset to reproduce all analyses presented. [ABSTRACT FROM AUTHOR]

Dark microbial CO2 fixation plays a crucial role in soil organic carbon (SOC) sequestration, but the fungus contributions to this process in varying agroecosystems remain unclear. This study investigated fungal CO2 fixation in 29 soil samples collected from the major agricultural regions across China. Phospholipid fatty analysis-stable isotope probing revealed variations in fungal fixation by 2.2 to 65.5% of the total microbial CO2 fixation. CO2 assimilation of ten fungal strains belonging to the phyla Ascomycota and Basidiomycota was determined in three different media using an isotope labeling experiment. Trichocladium uniseriatum had the highest CO2 fixation capacity. T. uniseriatum had the reductive tricarboxylic acid cycle (rTCA) for CO2 assimilation associated with sulfite metabolism. T. uniseriatum was thus selected for use in soil inoculation experiments, aimed to trace the fractionation of its fixed carbon in SOC. The CO2 fixation rate of T. uniseriatum was 0.07–0.09 μg C per g of soil per day. Notably, 77–82% of the fixed C was partitioned into the mineral-associated soil organic carbon. This study highlights the significance of fungal CO2 fixation in soil carbon sequestration. [ABSTRACT FROM AUTHOR]

Larix gmelinii var. principis-rupprechtii (Mayr.) Pilger is an important native tree species in North China with advantages of fast growth, straight trunk, and good wood properties. The multi-year and multi-site breeding research of families of the species has not been reported previously. Based on diameter at breast height (DBH), height and volume of 25 families on four experimental sites, we calculated variance components, genetic parameters, juvenile and mature trait correlations and made genotype main effect plus genotype × environment interaction effect (GGE) biplot based on the breeding values estimated using the method of best linear unbiased prediction (BLUP). Compared with height, DBH and volume had higher heritability and larger variation coefficients, making them the more suitable traits for family selection and evaluation. Based on these, GGE biplots containing 20 combinations of site × age were drawn using data at 13 to 17 years when the interactions between family and location were strong. Test sites classifications based on DBH, and volume were inconsistent, with two categories for DBH and one for volume. The Guyuan site was the most suitable with strong discriminating ability, high representativeness and stability among tree ages. Integrating the ranking results of DBH and volume, families 66, 76, 82 and 111 were high-yielding and stable, families 78 and 96 were high-yielding with above average stability, families 72 and 79 were high-yielding with below average stability, whereas stability of family 100 was inconsistent between DBH and volume. Early selection based on DBH was convenient and reliable, and can be made at seven years. This study provides support for the selection of Larix gmelinii var. principis-rupprechtii families in Hebei province and an example for the application of stem analysis data from multiple sites in tree breeding. [ABSTRACT FROM AUTHOR]

Combining magnetic resonance imaging (MRI) data from multi‐site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi‐site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA‐based and GLM‐based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects‐related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP‐based ICA harmonisation method. Results show that DP‐based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM‐based and conventional ICA harmonisation methods. [ABSTRACT FROM AUTHOR]

The present study focuses on using qualitative and quantitative data to evaluate the functionality, user experience (UX), and aesthetic approach offered by an academic multi-site Web ecosystem consisting of multiple interconnected websites. Large entities in various industry fields often have the need for an elaborate Web presence. In an effort to address the challenges posed by this need specifically in the field of academia, the authors developed, over a period of many years, a multi-site ecosystem within the Ionian University, which focuses on interconnectivity and a collaborative approach to academic content management. This system, known as “Publish@Ionio”, uses a singular content management infrastructure to allow for the creation of content for different websites that share both information and resources while at the same time allowing for individual variations in both functionality and aesthetics. The ecosystem was evaluated through quantitative data from its operation and qualitative feedback from a focus-group interview with experts, including website editors and administrative staff. The collected data were used to assess the strengths and weaknesses of the multi-site approach based on the actions and needs of the individuals in charge of generating content. The study led to conclusions on the advantages that interoperability offers in terms of digital and human resource management, the benefits of a unified aesthetic approach that allows for variability, and the necessity of collaborative content management tools that are tailored to the content’s nature.

Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To construct a more effective model with good generalization performance for inter-site autism spectrum disorder (ASD) diagnosis, domain adaptation based ASD diagnostic models are proposed to alleviate the inter-site heterogeneity. However, most existing methods only reduce the marginal distribution difference without considering class discriminative information, and are difficult to achieve satisfactory results. In this paper, we propose a low rank and class discriminative representation (LRCDR) based multi-source unsupervised domain adaptation method to reduce the marginal and conditional distribution differences synchronously for improving ASD identification. Specifically, LRCDR adopts low rank representation to alleviate the marginal distribution difference between domains by aligning the global structure of the projected multi-site data. To reduce the conditional distribution difference of data from all sites, LRCDR learns the class discriminative representation of data from multiple source domains and the target domain to enhance the intra-class compactness and inter-class separability of the projected data. For inter-site prediction on all ABIDE I data (1102 subjects from 17 sites), LRCDR obtains the mean accuracy of 73.1%, superior to the results of the compared state-of-the-art domain adaptation methods and multi-site ASD identification methods. In addition, we locate some meaningful biomarkers: Most of the top important biomarkers are inter-network resting-state functional connectivities (RSFCs). The proposed LRCDR method can effectively improve the identification of ASD, and has great potential as a clinical diagnostic tool.

Introduction: The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multisite studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data. Methods: The data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates. Results: Models 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences. Conclusion: Using data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects. [ABSTRACT FROM AUTHOR]

Millions of children sustain a concussion annually. Concussion disrupts cellular signaling and neural pathways within the brain but the resulting metabolic disruptions are not well characterized. Magnetic resonance spectroscopy (MRS) can examine key brain metabolites (e.g., N‐acetyl Aspartate (tNAA), glutamate (Glx), creatine (tCr), choline (tCho), and myo‐Inositol (mI)) to better understand these disruptions. In this study, we used MRS to examine differences in brain metabolites between children and adolescents with concussion versus orthopedic injury. Children and adolescents with concussion (n = 361) or orthopedic injury (OI) (n = 184) aged 8 to 17 years were recruited from five emergency departments across Canada. MRS data were collected from the left dorsolateral prefrontal cortex (L‐DLPFC) using point resolved spectroscopy (PRESS) at 3 T at a mean of 12 days post‐injury (median 10 days post‐injury, range 2–33 days). Univariate analyses for each metabolite found no statistically significant metabolite differences between groups. Within each analysis, several covariates were statistically significant. Follow‐up analyses designed to account for possible confounding factors including age, site, scanner, vendor, time since injury, and tissue type (and interactions as appropriate) did not find any metabolite group differences. In the largest sample of pediatric concussion studied with MRS to date, we found no metabolite differences between concussion and OI groups in the L‐DLPFC. We suggest that at 2 weeks post‐injury in a general pediatric concussion population, brain metabolites in the L‐DLPFC are not specifically affected by brain injury. [ABSTRACT FROM AUTHOR]

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

Abstract The alpine treeline ecotone is expected to move upwards in elevation with global warming. Thus, mapping treeline ecotones is crucial in monitoring potential changes. Previous remote sensing studies have focused on the usage of satellites and aircrafts for mapping the treeline ecotone. However, treeline ecotones can be highly heterogenous, and thus the use of imagery with higher spatial resolution should be investigated. We evaluate the potential of using unmanned aerial vehicles (UAVs) for the collection of ultra‐high spatial resolution imagery for mapping treeline ecotone land covers. We acquired imagery and field reference data from 32 treeline ecotone sites along a 1100 km latitudinal gradient in Norway (60–69°N). Before classification, we performed a superpixel segmentation of the UAV‐derived orthomosaics and assigned land cover classes to segments: rock, water, snow, shadow, wetland, tree‐covered area and five classes within the ridge‐snowbed gradient. We calculated features providing spectral, textural, three‐dimensional vegetation structure, topographical and shape information for the classification. To evaluate the influence of acquisition time during the growing season and geographical variations, we performed four sets of classifications: global, seasonal‐based, geographical regional‐based and seasonal‐regional‐based. We found no differences in overall accuracy (OA) between the different classifications, and the global model with observations irrespective of data acquisition timing and geographical region had an OA of 73%. When accounting for similarities between closely related classes along the ridge‐snowbed gradient, the accuracy increased to 92.6%. We found spectral features related to visible, red‐edge and near‐infrared bands to be the most important to predict treeline ecotone land cover classes. Our results show that the use of UAVs is efficient in mapping treeline ecotones, and that data can be acquired irrespective of timing within a growing season and geographical region to get accurate land cover maps. This can overcome constraints of a short field‐season or low‐resolution remote sensing data.

BackgroundCoal workers are inclined to report multi-site work-related musculoskeletal disorders (WMSDs) due to their specific working environment and extensive exposure to occupational hazards, which seriously affect the physical and mental health of coal workers.objectiveTo investigate the prevalence of multi-site WMSDs among workers in coal mining enterprises in Xinjiang, and analyze the related factors influencing multi-site WMSDs.MethodsUsing stratified cluster random sampling method, Musculoskeletal Disorders Questionnaire, Job Burnout Questionnaire, and Brief Occupational Stress Questionnaire were distributed in six Xinjiang coal mining enterprises to collect data on general information, prevalence of WMSDs in the past year, work-related information, job burnout, and occupational stress among coal miners with a working experience ≥ 1 year. A multiple logistic regression model was used to analyze the influencing factors of multi-site WMSDs.ResultsA total of 1730 questionnaires were distributed in this survey, 1448 valid questionnaires were recovered, and the valid recovery rate was 83.7%. Among the 1448 coal miners, 93.2% were males and 6.8% were females; 39.2% were aged ≤ 35 years, 31.1% were aged 35-45 years, and 29.6% were aged > 45 years; 58.4% reported ≤ 10 years of working experience, 20.0% reported 10-20 years, and 21.6% reported > 20 years. The total prevalence rate of WMSDs in the coal miners was 75.6% (1095/1448), the prevalence rate of multi-site WMSDs was 58.4% (846/1448), and the prevalence rate of one-site WMSDs was 17.2% (249/1448). The results of multiple logistic regression analysis showed that working experience > 20 years (OR=2.245, 95%CI: 1.655-3.045), severe burnout (OR=2.644, 95%CI: 1.549-4.516), and multiple repetitive operations per minute very often (OR=2.038, 95%CI: 1.339-3.101), often bending at larger angles (OR=1.467, 95%CI: 1.111-1.938), long-time bending the neck forward (OR=1.671, 95%CI: 1.294-2.159), and slipping or falling down (OR=2.459, 95%CI: 1.908-3.168) were positively correlated with multi-site WMSDs; sufficient rest time (OR=0.718, 95%CI: 0.565-0.913) was negatively correlated with multi-site WMSDs.ConclusionMulti-site WMSDs is common among workers in coal mining enterprises, and the prevalence rate of multi-site WMSDs is higher than that of one-site WMSDs. Working experience, job burnout, frequent multiple repetitive operations per minute, often bending at larger angles, long-time bending the neck forward, slipping or falling down, and sufficient rest time are influencing factors of multi-site WMSDs in coal workers.

BackgroundThe prevalence rate of work-related musculoskeletal disorders (WMSDs) among manufacturing workers is relatively high and has been widely concerned. However, research on the prevalence and risk factors of multi-site WMSDs is still insufficient. ObjectiveTo explore the prevalence and risk factors of multi-site WMSDs of assembly workers in four manufacturing enterprises. MethodsThe "Chinese Musculoskeletal Questionnaire" was used to conduct a cross-sectional survey among 1401 assembly workers selected by convenient sampling in four manufacturing enterprises, and their demographic characteristics, the prevalence of WMSDs in various body parts, and the information about biomechanical factors, work organizations, work environment, and psychosocial factor exposure were collected. The prevalence of WMSDs and multi-site WMSDs were estimated, Pearson χ2 test was used for univariate analysis of multi-site WMSDs risk factors, and logistic regression was used for multi-factor analysis. ResultsThe total prevalence rate of WMSDs was 54.9% (769/1 401). The top three affected sites were neck (41.4%), shoulders (30.7%), and wrist/hand (25.1%). The prevalence rate of multi-site WMSDs was 41.9% (587/1 401). The prevalence rates of multi-site WMSDs in railway vehicle assembly and riveting workers were higher than those in electronic parts processing and glass manufacturing workers (P

IntroductionThe effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data.MethodsThe data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates.ResultsModels 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences.ConclusionUsing data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects.

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of themethod itself such as its sensitivity tomotion. The lack of standardization from a data acquisition and data processing perspective makes it dificult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies. [ABSTRACT FROM AUTHOR]

To construct a more effective model with good generalization performance for inter-site autism spectrum disorder (ASD) diagnosis, domain adaptation based ASD diagnostic models are proposed to alleviate the inter-site heterogeneity. However, most existing methods only reduce the marginal distribution difference without considering class discriminative information, and are difficult to achieve satisfactory results. In this paper, we propose a low rank and class discriminative representation (LRCDR) based multi-source unsupervised domain adaptation method to reduce the marginal and conditional distribution differences synchronously for improving ASD identification. Specifically, LRCDR adopts low rank representation to alleviate the marginal distribution difference between domains by aligning the global structure of the projected multi-site data. To reduce the conditional distribution difference of data from all sites, LRCDR learns the class discriminative representation of data from multiple source domains and the target domain to enhance the intra-class compactness and inter-class separability of the projected data. For inter-site prediction on all ABIDE I data (1102 subjects from 17 sites), LRCDR obtains the mean accuracy of 73.1%, superior to the results of the compared state-of-the-art domain adaptation methods and multi-site ASD identification methods. In addition, we locate some meaningful biomarkers: Most of the top important biomarkers are inter-network resting-state functional connectivities (RSFCs). The proposed LRCDR method can effectively improve the identification of ASD, and has great potential as a clinical diagnostic tool. [ABSTRACT FROM AUTHOR]

Background and objectives: Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes. Methods: We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM). Results: Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy. Discussion: We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.

IntroductionThe early diagnosis of major depressive disorder (MDD) is very important for patients that suffer from severe and irreversible consequences of depression. It has been indicated that functional connectivity (FC) analysis based on functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers for clinical diagnosis. However, previous studies mainly focus on brain disease classification in small sample sizes, which may lead to dramatic divergences in classification accuracy.MethodsThis paper attempts to address this limitation by applying the deep graph convolutional neural network (DGCNN) method on a large multi-site MDD dataset. The resting-state fMRI data are acquired from 830 MDD patients and 771 normal controls (NC) shared by the REST-meta-MDD consortium.ResultsThe DGCNN model trained with the binary network after thresholding, identified MDD patients from normal controls and achieved an accuracy of 72.1% with 10-fold cross-validation, which is 12.4%, 9.8%, and 7.6% higher than SVM, RF, and GCN, respectively. Moreover, the process of dataset reading and model training is faster. Therefore, it demonstrates the advantages of the DGCNN model with low time complexity and sound classification performance.DiscussionBased on a large, multi-site dataset from MDD patients, the results expressed that DGCNN is not an extremely accurate method for MDD diagnosis. However, there is an improvement over previous methods with our goal of better understanding brain function and ultimately providing a biomarker or diagnostic capability for MDD diagnosis.

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.