A hybrid demultiplexing strategy that improves performance and robustness of cell hashing.

Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.

MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction.

Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out node information or lose the connection of the original graph; therefore, it is difficult to identify continuous subtructures. Importantly, they lacked interpretability on molecular graphs. To this end, we proposed a novel Molecular Edge Shrinkage Pooling (MESPool) method, which is based on edges (or chemical bonds). MESPool preserves crucial edges and shrinks others inside the functional groups and is able to search for key structures without breaking the original connection. We compared MESPool with various well-known pooling methods on different benchmarks and showed that MESPool outperforms the previous methods. Furthermore, we explained the rationality of MESPool on some datasets, including a COVID-19 drug dataset.

Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes.

Post- and co-transcriptional RNA modifications are found to play various roles in regulating essential biological processes at all stages of RNA life. Precise identification of RNA modification sites is thus crucial for understanding the related molecular functions and specific regulatory circuitry. To date, a number of computational approaches have been developed for in silico identification of RNA modification sites; however, most of them require learning from base-resolution epitranscriptome datasets, which are generally scarce and available only for a limited number of experimental conditions, and predict only a single modification, even though there are multiple inter-related RNA modification types available. In this study, we proposed AdaptRM, a multi-task computational method for synergetic learning of multi-tissue, type and species RNA modifications from both high- and low-resolution epitranscriptome datasets. By taking advantage of adaptive pooling and multi-task learning, the newly proposed AdaptRM approach outperformed the state-of-the-art computational models (WeakRM and TS-m6A-DL) and two other deep-learning architectures based on Transformer and ConvMixer in three different case studies for both high-resolution and low-resolution prediction tasks, demonstrating its effectiveness and generalization ability. In addition, by interpreting the learned models, we unveiled for the first time the potential association between different tissues in terms of epitranscriptome sequence patterns. AdaptRM is available as a user-friendly web server from http://www.rnamd.org/AdaptRM together with all the codes and data used in this project.

GraphADT: Empowering interpretable predictions of acute dermal toxicity with Multi-View graph pooling and structure remapping.

MOTIVATION: Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and effective development of contact drugs. Currently, graph neural networks (GNNs), a form of deep learning technology, accurately model the structure of compound molecules, enhancing predictions of their ADT. However, many existing methods emphasize atom-level information transfer and overlook crucial data conveyed by molecular bonds and their interrelationships. Additionally, these methods often generate" equal" node representations across the entire graph, failing to accentuate" important" substructures like functional groups, pharmacophores, and toxicophores, thereby reducing interpretability. RESULTS: We introduce a novel model, GraphADT, utilizing structure remapping and multi-view graph pooling technologies to accurately predict compound ADT. Initially, our model applies structure remapping to better delineate bonds, transforming" bonds" into new nodes and" bond-atom-bond" interactions into new edges, thereby reconstructing the compound molecular graph. Subsequently, we employ multi-view graph pooling to amalgamate data from various perspectives, minimizing biases inherent to single-view analyses. Following this, the model generates a robust node ranking collaboratively, emphasizing critical nodes or substructures to enhance model interpretability. Lastly, we apply a graph comparison learning strategy to train both the original and structure remapped molecular graphs, deriving the final molecular representation. Experimental results on public datasets indicate that the GraphADT model outperforms existing state-of-the-art models. The GraphADT model has been demonstrated to effectively predict compound ADT, offering potential guidance for the development of contact drugs and related treatments. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/mxqmxqmxq/GraphADT.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study.

PURPOSE: To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping. METHODS: This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers. RESULTS: Quantitative maps obtained with 3D-QALAS showed high linearity (R2 = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis. CONCLUSION: The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

Reduction of product composition variability using pooled microbiome ecosystem therapy and consequence in two infectious murine models.

Growing evidence demonstrates the key role of the gut microbiota in human health and disease. The recent success of microbiotherapy products to treat recurrent Clostridioides difficile infection has shed light on its potential in conditions associated with gut dysbiosis, such as acute graft-versus-host disease, intestinal bowel diseases, neurodegenerative diseases, or even cancer. However, the difficulty in defining a "good" donor as well as the intrinsic variability of donor-derived products' taxonomic composition limits the translatability and reproducibility of these studies. Thus, the pooling of donors' feces has been proposed to homogenize product composition and achieve higher taxonomic richness and diversity. In this study, we compared the metagenomic profile of pooled products to corresponding single donor-derived products. We demonstrated that pooled products are more homogeneous, diverse, and enriched in beneficial bacteria known to produce anti-inflammatory short chain fatty acids compared to single donor-derived products. We then evaluated pooled products' efficacy compared to corresponding single donor-derived products in Salmonella and C. difficile infectious mouse models. We were able to demonstrate that pooled products decreased pathogenicity by inducing a structural change in the intestinal microbiota composition. Single donor-derived product efficacy was variable, with some products failing to control disease progression. We further performed in vitro growth inhibition assays of two extremely drug-resistant bacteria, Enterococcus faecium vanA and Klebsiella pneumoniae oxa48, supporting the use of pooled microbiotherapies. Altogether, these results demonstrate that the heterogeneity of donor-derived products is corrected by pooled fecal microbiotherapies in several infectious preclinical models.IMPORTANCEGrowing evidence demonstrates the key role of the gut microbiota in human health and disease. Recent Food and Drug Administration approval of fecal microbiotherapy products to treat recurrent Clostridioides difficile infection has shed light on their potential to treat pathological conditions associated with gut dysbiosis. In this study, we combined metagenomic analysis with in vitro and in vivo studies to compare the efficacy of pooled microbiotherapy products to corresponding single donor-derived products. We demonstrate that pooled products are more homogeneous, diverse, and enriched in beneficial bacteria compared to single donor-derived products. We further reveal that pooled products decreased Salmonella and Clostridioides difficile pathogenicity in mice, while single donor-derived product efficacy was variable, with some products failing to control disease progression. Altogether, these findings support the development of pooled microbiotherapies to overcome donor-dependent treatment efficacy.

Method for high-plex analysis of immune cells in human skin using the GeoMx system.

Specific T cell populations in the skin have been demonstrated as important disease drivers in several dermatoses. Due to the unique skin architecture, these cells are not grouped together in structures but dispersedly spread out throughout the epidermis. Following tissue disruption and isolation, only about 10% of skin T cells are recovered and any in vitro expansion may alter their bona fide phenotype. The Nanostring GeoMx system was developed to address cellular phenotype and protein expression in a tissue spatial context. To do so, regions of interest (ROI) must exceed a certain area threshold (usually 100 µm in diameter) to generate a sufficient signal-to-noise ratio. Here, we present an approach that allows for the pooling of numerous smaller ROIs within the skin, enabling T cell and melanocyte phenotyping. Skin samples from healthy individuals and vitiligo patients were analysed using the GeoMx system and several immune profiling panels. A sufficient signal-to-noise ratio was achieved by pooling smaller ROIs and analysing them as a single group. While this prevents spatial analysis, this method allows for detailed analysis of cells as a population in the context of their physiological environment, making it possible to investigate in situ phenotype of rare cells in different tissue compartments.

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing.

High-throughput transcriptomics is of increasing fundamental biological and clinical interest. The generation of molecular data from large collections of samples, such as biobanks and drug libraries, is boosting the development of new biomarkers and treatments. Focusing on gene expression, the transcriptomic market exploits the benefits of next-generation sequencing (NGS), leveraging RNA sequencing (RNA-seq) as standard for measuring genome-wide gene expression in biological samples. The cumbersome sample preparation, including RNA extraction, conversion to cDNA and amplification, prevents high-throughput translation of RNA-seq technologies. Bulk RNA barcoding and sequencing (BRB-seq) addresses this limitation by enabling sample preparation in multi-well plate format. Sample multiplexing combined with early pooling into a single tube reduces reagents consumption and manual steps. Enabling simultaneous pooling of all samples from the multi-well plate into one tube, our technology relies on smart labware: a pooling lid comprising fluidic features and small pins to transport the liquid, adapted to standard 96-well plates. Operated with standard fluidic tubes and pump, the system enables over 90% recovery of liquid in a single step in less than a minute. Large scale manufacturing of the lid is demonstrated with the transition from a milled polycarbonate/steel prototype into an injection molded polystyrene lid. The pooling lid demonstrated its value in supporting high-throughput barcode-based sequencing by pooling 96 different DNA barcodes directly from a standard 96-well plate, followed by processing within the single sample pool. This new pooling technology shows great potential to address medium throughput needs in the BRB-seq workflow, thereby addressing the challenge of large-scale and cost-efficient sample preparation for RNA-seq.

Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Pooling sputum samples for the Xpert MTB/RIF assay: a practical screening strategy for highly infectious tuberculosis cases.

The Xpert MTB/RIF test (Xpert) can help in the accurate screening of tuberculosis, however, its widespread use is limited by its high cost and lack of accessibility. Pooling of sputum samples for testing is a strategy to cut expenses and enhance population coverage but may result in a decrease in detection sensitivity due to the dilution of Mycobacterium tuberculosis (Mtb) by sample mixing. We investigated how the mixing ratio affected the detection performance of Xpert. We used frozen sputum samples that had been kept after individual Xpert assays of the sputa from Mtb-confirmed TB patients and non-TB patients. Our results showed that the overall sensitivity of the Xpert pooling assay remained higher than 80% when the mixing ratio was between 1/2 and 1/8. When the mixing ratio was raised to 1/16, the positive detection rate fell to 69.0%. For patients with either a high sputum Mtb smear score ≥ 2+, a time-to-positive culture ≤ 10 days, or an Xpert test indicating a high or medium abundance of bacteria, the pooling assay positivity rates were 93.3%, 96.8%, and 100% respectively, even at a 1/16 mixing ratio. For participants with cavities and cough, the pooling assay positivity rates were 86.2% and 90.0% at a 1/8 ratio, higher than for those without these signs. Our results show that the Xpert pooled assay has a high overall sensitivity, especially for highly infectious patients. This pooling strategy with lower reagent and labor costs could support TB screening in communities with limited resources, thereby facilitating reductions in the community transmission and incidence of TB worldwide.

Enhancing pooling levels strengthens the risk resilience of healthcare insurance: a case study of basic medical insurance fund operations data in Gansu, China.

BACKGROUND: In China, enhancing the pooling levels of basic health insurance has consistently been regarded as a pivotal measure to promote the refinement of the healthcare insurance system. From 2020 to 2022, the widespread outbreak of COVID-19 posed new challenges to China's basic health insurance. METHODS: The research utilizes Data Envelopment Analysis (DEA), Malmquist index assessment, and fixed-effects panel Tobit models to analyze panel data from 2020 to 2022, assessing the efficiency of basic health insurance in Gansu Province. RESULTS: From 2020 to 2022, the average overall efficiency of the municipal pooling of Basic Medical Insurance for Urban and Rural Residents was 0.941, demonstrating a stable trend with a modest increase. The efficiency frontier regions have expanded from 5 (35.71%) to 7 (50%). Operational efficiency exhibited a negative correlation with per capita hospitalization expenses and per capita fund balance but a positive correlation with per capita accumulated fund balance and reimbursement rates for hospitalized patients. In 2021, compared to 2020, the county-pooling Basic Medical Insurance for Urban Employees saw a decline of 0.126 in overall efficiency, reducing the efficiency frontier regions from 8 to 3. However, from 2021 to 2022, the municipal-coordinated Basic Medical Insurance for Urban Employees experienced a 0.069 increase in overall efficiency, with the efficiency frontier regions expanding from 3 to 5. Throughout 2020 to 2022, the operational efficiency of the Urban Employee Basic Medical Insurance showed a consistent negative correlation with per capita fund balance. CONCLUSION: From 2020 to 2022, the overall operational performance of basic health insurance in Gansu Province was satisfactory, and enhancing the pooling level is beneficial in addressing the impact of unforeseen events on the health insurance system.

High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information.

BACKGROUND: Despite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness. METHODS: We study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated- though imperfect- risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a 'high risk pool' using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers' incentives for risk selection and cost control, and compare this to the situation without HRP. RESULTS: The results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending. CONCLUSION: Our main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

Additive partially linear model for pooled biomonitoring data.

Human biomonitoring involves monitoring human health by measuring the accumulation of harmful chemicals, typically in specimens like blood samples. The high cost of chemical analysis has led researchers to adopt a cost-effective approach. This approach physically combines specimens and subsequently analyzes the concentration of toxic substances within the merged pools. Consequently, there arises a need for innovative regression techniques to effectively interpret these aggregated measurements. To address this need, a new regression framework is proposed by extending the additive partially linear model (APLM) to accommodate the pooling context. The APLM is well-known for its versatility in capturing the complex association between outcomes and covariates, which is particularly valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors. Consistent estimators of the APLM are obtained through an iterative process that disaggregates information from the pooled observations. The performance is evaluated through simulations and an environmental health study focused on brominated flame retardants using data from the National Health and Nutrition Examination Survey.

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images.

In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet.

Delaying Suspension Syndrome Onset in Aerially Suspended Victims Through Leg Raising.

INTRODUCTION: Suspension syndrome (SS) develops when venous blood pools in extremities of passively suspended individuals, resulting in presyncopal symptoms and potential unconsciousness or death independent of additional injuries. We investigated use of leg raising to delay onset of SS, as it can decrease venous pooling and increase cardiac return and systemic perfusion. METHODS: Participants were suspended in rock climbing harnesses at an indoor climbing wall in a legs-dangling control position or a legs-raised interventional position to compare physiological outcomes between groups. Participants were suspended for a maximum of 45 min. Onset of 2 or more symptoms of SS, such as vertigo, lightheadedness, or nausea, halted suspension immediately. We recorded each participant's heart rate, blood pressure, oxygen saturation, lower leg oxygen saturation, pain rating, and presyncope scores presuspension, midsuspension, and postsuspension, as well as total time suspended. RESULTS: There were 24 participants. There was a significant difference in total time suspended between groups (43.05±6.7 min vs 33.35±9.02 min, p=0.007). There was a significant difference in heart rate between groups overall (p=0.012), and between groups, specifically at the midsuspension time interval (80±11 bpm vs 100±17 bpm, p=0.003). Pain rating was significantly different between groups (p=0.05). Differences in blood pressure, oxygen saturation, lower leg oxygen saturation, and presyncope scores were not significant. CONCLUSION: Leg raising lengthened the time individuals tolerated passive suspension and delayed symptom onset.

Combining reaction-time distributions to conserve shape.

To improve the estimate of the shape of a reaction-time distribution, it is sometimes desirable to combine several samples, drawn from different sessions or different subjects. How should these samples be combined? This paper provides an evaluation of four combination methods, two that are currently in use (the bin-means histogram, often called "Vincentizing", and quantile averaging) and two that are new (linear-transform pooling and shape averaging). The evaluation makes use of a modern method for describing the shape of a distribution, based on L-moments, rather than the traditional method, based on central moments. Also provided is an introduction to shape descriptors based on L-moments, whose advantages over central moments-less biased and less sensitive to outliers-are demonstrated. Whether traditional or modern shape descriptions are employed, the combination methods currently in use, especially bin-means histograms, based on averaged bin means, prove to be substantially inferior to the new methods. Averaged bin-means themselves are less deficient when estimating differences between distribution shapes, as in delta plots, but are nonetheless inferior to linear-transform pooling.

Pooling test statistics across multiply imputed datasets for nonnormal items.

In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al. (Statistica Sinica, 1(1), 65-92, 1991) and Meng and Rubin (Biometrika, 79(1), 103-111, 1992). When the assumption of normality is violated, it is not clear how well these pooling approaches work with the test statistics generated from various robust estimators and multiple imputation methods. Jorgensen and colleagues (2021) implemented these pooling approaches in their R package semTools; however, no systematical evaluation has been conducted. In this simulation study, we examine the performance of these approaches in working with different imputation methods and robust estimators under nonnormality. We found that the naïve pooling approach based on Meng and Rubin (Biometrika, 79(1), 103-111, 1992; D3SN) worked the best when combining with the normal-theory-based imputation and either MLM or MLMV estimator.

scSNPdemux: a sensitive demultiplexing pipeline using single nucleotide polymorphisms for improved pooled single-cell RNA sequencing analysis.

BACKGROUND: Here we present scSNPdemux, a sample demultiplexing pipeline for single-cell RNA sequencing data using natural genetic variations in humans. The pipeline requires alignment files from Cell Ranger (10× Genomics), a population SNP database and genotyped single nucleotide polymorphisms (SNPs) per sample. The tool works on sparse genotyping data in VCF format for sample identification. RESULTS: The pipeline was tested on both single-cell and single-nuclei based RNA sequencing datasets and showed superior demultiplexing performance over the lipid-based CellPlex and Multi-seq sample multiplexing technique which incurs additional single cell library preparation steps. Specifically, our pipeline demonstrated superior sensitivity and specificity in cell-identity assignment over CellPlex, especially on immune cell types with low RNA content. CONCLUSIONS: We designed a streamlined pipeline for single-cell sample demultiplexing, aiming to overcome common problems in multiplexing samples using single cell libraries which might affect data quality and can be costly.

Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion.

To embrace big-data neuroimaging, harmonizing the site effect in resting-state functional magnetic resonance imaging (R-fMRI) data fusion is a fundamental challenge. A comprehensive evaluation of potentially effective harmonization strategies, particularly with specifically collected data, has been scarce, especially for R-fMRI metrics. Here, we comprehensively assess harmonization strategies from multiple perspectives, including tests on residual site effect, individual identification, test-retest reliability, and replicability of group-level statistical results, on widely used R-fMRI metrics across various datasets, including data obtained from participants with repetitive measures at different scanners. For individual identifiability (i.e., whether the same subject could be identified across R-fMRI data scanned across different sites), we found that, while most methods decreased site effects, the Subsampling Maximum-mean-distance based distribution shift correction Algorithm (SMA) and parametric unadjusted CovBat outperformed linear regression models, linear mixed models, ComBat series and invariant conditional variational auto-encoder in clustering accuracy. Test-retest reliability was better for SMA and parametric adjusted CovBat than unadjusted ComBat series and parametric unadjusted CovBat in the number of overlapped voxels. At the same time, SMA was superior to the latter in replicability in terms of the Dice coefficient and the scale of brain areas showing sex differences reproducibly observed across datasets. Furthermore, SMA better detected reproducible sex differences of ALFF under the site-sex confounded situation. Moreover, we designed experiments to identify the best target site features to optimize SMA identifiability, test-retest reliability, and stability. We noted both sample size and distribution of the target site matter and introduced a heuristic formula for selecting the target site. In addition to providing practical guidelines, this work can inform continuing improvements and innovations in harmonizing methodologies for big R-fMRI data.