Biology subjects

Cui, Z.

Publications and source records attributed to Cui, Z..

At least 109 records · Page 6Linked to original sources

bioRxiv2020

A proof of concept for neutralizing antibody-guided vaccine design against SARS-CoV-2

Mutations and transient conformational movements of receptor binding domain (RBD) that make neutralizing epitopes momentarily unavailable, present immune escape routes to SARS-CoV-2. To mitigate viral escape, we developed a cocktail of neutralizing antibodies (NAbs) targeting epitopes located on different domains of spike (S) protein. Screening of a library of monoclonal antibodies generated from peripheral blood mononuclear cells of COVID-19 convalescent patients yielded potent NAbs, targeting N-terminal domain (NTD) and RBD domain of S, effective at nM concentrations. Remarkably, combination of RBD-targeting NAbs and NTD-binding NAb, FC05, dramatically enhanced the neutralization potency in cell-based assays and animal model. Results of competitive SPR assays and cryo-EM structures of Fabs bound to S unveil determinants of immunogenicity. Combinations of immunogens, identified in NTD and RBD of S, when immunized in rabbits elicited potent protective immune responses against SARS-CoV-2. These results provide a proof-of-concept for neutralization-based immunogen design targeting SARS-CoV-2 NTD and RBD. One sentence summaryImmunogens identified in the NTD and RBD of the SARS-CoV-2 spike protein using a cocktail of non-competing NAbs when injected in rabbits elicited a potent protective immune response against SARS-CoV-2.

microbiology↗

bioRxiv2020

Codon-optimized FAM132b prevents diet-induced obesity by modulating adrenergic response and insulin action

FAM132b, also known as myonectin, has been identified as a myokine produced by exercise. It is a secreted protein precursor that belongs to the adipolin/erythroferrone family, and has hormone activity in circulation to regulate cellular iron homeostasis and lipid metabolism via unknown receptors. Here, adeno-associated viral vectors (AAV9) were engineered to induce overexpression of FAM132b with 2 codon mutations (A136T and P159A). Treatment of mice under high-fat diet feeding with FAM132b gene transfer resulted in marked reductions in body weight, fat depot, adipocytes size, glucose intolerance and insulin resistance. Moreover, FAM132b overproduction reduced glycemic response to epinephrine (EPI) in whole body and increased lipolytic response to EPI in adipose tissues. This adrenergic response of adipose tissue led to the result that gene transfer reduced glycogen utilization and increased fat consumption in skeletal muscle during exercise. FAM132b knockdown by shRNA significantly increased glycemic response to EPI in vivo and reduced adipocytes response to EPI and adipose tissue browning. Structural analysis suggested that FAM132b mutants delivered by AAV9 may form a weak bond with ADRB2, and potentially bind to insulin against insulin receptor by blocking the receptor binding sites on insulin B-chain. Our study underscores the potential of FAM132b gene therapy with codon optimization to treat obesity by modulating adrenergic response and interfering insulin action. SignificanceWe show here that AAV9-mediated expression of FAM132b with A136T and P159A is a safe and effective therapeutic strategy for improving glucose homeostasis. This is the first demonstration of a therapeutic effect on metabolic disorders in mice with FAM132b codon optimization. These therapeutic effects indicate that FAM132b gene transfer with selective codon mutants in vivo might be a valid therapy for diabetes that can be extended to other metabolic disorders.

molecular biology↗

bioRxiv2020

MiRNA expression profiling and zeatin dynamic changes in a new model system of in vivo indirect regeneration of tomato

Callus formation and adventitious shoot differentiation could be observed on the cut surface of completely decapitated tomato plants. We propose that this process can be used as a model system to investigate the mechanisms that regulate indirect regeneration of higher plants without the addition of exogenous hormones. This study analyzed the patterns of trans-zeatin and miRNA expression during in vivo regeneration of tomato. Analysis of trans-zeatin revealed that the hormone cytokinin played an important role in in vivo regeneration of tomato. Among 183 miRNAs and 1168 predicted target genes sequences identified, 93 miRNAs and 505 potential targets were selected based on differential expression levels for further characterization. Expression patterns of six miRNAs, including sly-miR166, sly-miR167, sly-miR396, sly-miR397, novel 156, and novel 128, were further validated by qRT-PCR. We speculate that sly-miR156, sly-miR160, sly-miR166, and sly-miR397 play major roles in callus formation of tomato during in vivo regeneration by regulating cytokinin, IAA, and laccase levels. Overall, our microRNA sequence and target analyses of callus formation during in vivo regeneration of tomato provide novel insights into the regulation of regeneration in higher plants.

molecular biology↗

bioRxiv2020

Single-cell screening of SARS-CoV-2 target cells in pets, livestock, poultry and wildlife

A few animals have been suspected to be intermediate hosts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, a large-scale single-cell screening of SARS-CoV-2 target cells on a wide variety of animals is missing. Here, we constructed the single-cell atlas for 11 representative species in pets, livestock, poultry, and wildlife. Notably, the proportion of SARS-CoV-2 target cells in cat was found considerably higher than other species we investigated and SARS-CoV-2 target cells were detected in multiple cell types of domestic pig, implying the necessity to carefully evaluate the risk of cats during the current COVID-19 pandemic and keep pigs under surveillance for the possibility of becoming intermediate hosts in future coronavirus outbreak. Furthermore, we screened the expression patterns of receptors for 144 viruses, resulting in a comprehensive atlas of virus target cells. Taken together, our work provides a novel and fundamental strategy to screen virus target cells and susceptible species, based on single-cell transcriptomes we generated for domesticated animals and wildlife, which could function as a valuable resource for controlling current pandemics and serve as an early warning system for coping with future infectious disease threats.

cell biology↗

bioRxiv2020

Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody

The COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in an unprecedented public health crisis. There are no approved vaccines or therapeutics for treating COVID-19. Here we reported a humanized monoclonal antibody, H014, efficiently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 at nM level by engaging the S receptor binding domain (RBD). Importantly, H014 administration reduced SARS-CoV-2 titers in the infected lungs and prevented pulmonary pathology in hACE2 mouse model. Cryo-EM characterization of the SARS-CoV-2 S trimer in complex with the H014 Fab fragment unveiled a novel conformational epitope, which is only accessible when the RBD is in open conformation. Biochemical, cellular, virological and structural studies demonstrated that H014 prevents attachment of SARS-CoV-2 to its host cell receptors. Epitope analysis of available neutralizing antibodies against SARS-CoV and SARS-CoV-2 uncover broad cross-protective epitopes. Our results highlight a key role for antibody-based therapeutic interventions in the treatment of COVID-19. One sentence summaryA potent neutralizing antibody conferred protection against SARS-CoV-2 in an hACE2 humanized mouse model by sterically blocking the interaction of the virus with its receptor.

biophysics↗

bioRxiv2020

Temporal and spatial dynamics in the apple flower microbiome in the presence of the phytopathogen Erwinia amylovora

Plant microbiomes have important roles in plant health and productivity. However, despite flowers being directly linked to reproductive outcomes, little is known about the microbiomes of flowers and their potential interaction with pathogen infection. Here, we investigated the temporal dynamics and spatial traits of the apple stigma microbiome when challenged with a phytopathogen Erwinia amylovora, the causal agent of fire blight disease. We profiled the microbiome from the stigmas of a single flower, greatly increasing the resolution at which we can characterize shifts in the composition of the microbiome. Individual flowers harbored unique microbiomes at the OTU level. However, taxonomic analysis of community succession showed a population gradually dominated by bacteria within the families Enterobacteriaceae and Pseudomonadaceae. Flowers inoculated E. amylovora established large populations of the phytopathogen, with pathogen specific gene counts of >3.0 x 107 in 90% of the flowers. Yet, only 42% of inoculated flowers later developed fire blight symptoms. This reveals pathogen amount on the stigma is not sufficient to predict disease outcome. Our data demonstrate that apple flowers represent an excellent model in which to characterize how plant microbiomes establish, develop, and interact with biological processes such as disease progression in an experimentally tractable plant organ.

microbiology↗

bioRxiv2020

Efficient Coding in the Economics of Human Brain Connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior--beyond the conventional network efficiency metric--for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity. Author SummaryMacroscale communication between interconnected brain regions underpins most aspects of brain function and incurs substantial metabolic cost. Understanding efficient and behaviorally meaningful information transmission dependent on structural connectivity has remained challenging. We validate a model of communication dynamics atop the macroscale human structural connectome, finding that structural networks support dynamics that strike a balance between information transmission fidelity and lossy compression. Notably, this balance is predictive of behavior and explanatory of biology. In addition to challenging and reformulating the currently held view that communication occurs by routing dynamics along metabolically efficient direct anatomical pathways, our results suggest that connectome architecture and behavioral demands yield communication dynamics that accord to neurobiological and information theoretical principles of efficient coding and lossy compression.

neuroscience↗

bioRxiv2019

Stress-induced myonectin improves glucose homeostasis by inhibiting glycemic response to HPA axis

Inhibiting glycemic response to HPA axis contributes to glycemic control for diabetic patients. Here, mice were subjected to high-fat diet and intermittent chronic stress, and glucose homeostasis and lipolysis were determined during the intervention. Firstly, we found that glucose intolerance appears at the earliest, followed by reduced insulin sensitivity and increased epinephrine (EPI) sensitivity in the early stage of diet-induced obesity. Next we investigated whether chronic stress impairs glycemic control and which mediates its effects. Short-term stress training raises serum and skeletal muscle myonectin (Myn) levels and improves glucose intolerance. Stress attenuates blood glucose and glycerol responses to EPI, but enhances lipolytic response to EPI in adipose tissues. Myn overexpression in vivo improves glucose tolerance and enhances insulin sensitivity at the cost of blunting glycemic responses to EPI. Myn knockdown reduces beneficial effects of stress or exercise on glucose homeostasis. Together, myonectin is a stress-induced myokine that readjusts glycemic and metabolic responses to HPA axis, and thus prevent the progression of glucose intolerance and obesity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/838003v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@42b09forg.highwire.dtl.DTLVardef@59a744org.highwire.dtl.DTLVardef@1f4a01dorg.highwire.dtl.DTLVardef@13bda27_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryChronic stress breaks glucose intolerance cycle to resist diet-induced obesity, through myonectin-mediated inhibition of glycemic response to epinephrine (EPI) and activation of insulin signaling in adipose tissues. HighlightsO_LIEPI sensitivity increases after glucose intolerance and with reduced insulin sensitivity in diet-induced obesity C_LIO_LIChronic stress blunts glycemic responses to EPI and increases myonectin levels in serum and skeletal muscle C_LIO_LIMyonectin attenuates glycemic response to EPI and improves metabolic profile in HFD-fed mice C_LIO_LIReducing myonectin reverses beneficial effects of stress on glucose homeostasis C_LI

physiology↗

bioRxiv2019

Long Read based Human Genomic Structural Variation Detection with cuteSV

Long-read sequencing enables the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high sensitivity and performance simultaneously due to the complex SV characteristics implied by noisy long reads. Therefore, we propose cuteSV, a sensitive, fast and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to analyze the signatures to implement sensitive SV detection. Benchmarks on real PacBio and ONT datasets demonstrate that cuteSV has better yields and scalability than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.

bioinformatics↗

bioRxiv2019

Neurocognitive and Functional Heterogeneity in Depressed Youth

OBJECTIVEDepression is a common psychiatric illness that often begins in youth, and is associated with cognitive symptoms. However, there is significant variability in the cognitive burden, likely reflecting biological heterogeneity. This study sought to identify neurocognitive subtypes in a large sample of depressed youth, and evaluated the neural signatures of these subtypes.\n\nMETHODSParticipants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (n=368, TD=200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A semi-supervised machine-learning algorithm, HYDRA (Heterogeneity through Discriminative Analysis), was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task.\n\nRESULTSHYDRA identified three neurocognitive subtypes in the depressed group. Overall, Subtype 1 had better performance than TD comparators across many cognitive tasks (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), whereas Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition.\n\nCONCLUSIONSUsing a data-driven approach, three neurocognitive subtypes of depression were identified that differed in neural signatures despite similar clinical psychopathology. These data suggest disparate mechanisms of cognitive vulnerability and resilience in depression, which may inform the identification of biomarkers for prognosis and treatment response.

neuroscience↗

bioRxiv2019

Improving personalized prediction of cancer prognoses with clonal evolution models

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, known as mutator phenotypes, which act in different combinations or degrees in different cancers. Here we explore the question of how and to which degree different mutator phenotypes act in a cancer predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational preferences, followed by a machine learning frame-work to identify key features predictive of progression. We build phylogenies tracing the evolution of subclones of cells in tumor tissues using a variety of somatic genomic alterations, including single nucleotide variations, copy number alterations, and structural variations. We demonstrate that mutation preference features derived from the phylogenies are predictive of clinical outcomes of cancer progression - overall survival and disease-free survival - based on the analyses on breast invasive carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. We further show that mutational phenotypes have predictive power even after accounting for traditional clinical and driver-centric predictors of progression. These results confirm the power of mutational phenotypes as an independent class of predictive biomarkers and suggest a strategy for enhancing the predictive power of conventional clinical or driver-centric genomic features.

cancer biology↗

bioRxiv2019

RNA Helicase DDX5 Negatively Regulates Wnt Signaling and Hepatocyte Reprogramming in Hepatitis B Virus-related Hepatocellular Carcinoma

Background and AimsRNA helicase DEAD box protein 5 (DDX5) is downregulated during hepatitis B virus (HBV) replication, and associates with poor prognosis HBV-related hepatocellular carcinoma (HCC). The aim of this study is to determine the mechanism and significance of DDX5 downregulation for HBV-driven HCC.\n\nApproach & ResultsWe used established cellular models of HBV replication, HBV infection, as well as HBV-related liver tumors. HBV replicating DDX5 knockdown hepatocytes were analyzed by RNAseq; differentially expressed genes were validated by qRT-PCR, and bioinformatic analyses of HCCs from The Cancer Genome Atlas. Our results show reduced expression of DDX5 in HCCs of all etiologies is associated with poor survival. In HBV replicating hepatocytes, downregulation of DDX5 is mediated by miR17[~]92 and miR106b[~]25, induced by HBV infection. Increased expression of these miRNAs was quantified in HBV-associated HCCs expressing a hepatic cancer stem cell (hCSC)-like gene signature and reduced DDX5 mRNA, suggesting a role for DDX5 in hCSC formation. Interestingly, DDX5 knockdown in HBV replicating hepatocyte cell lines resulted in hepatosphere formation, sorafenib and cisplatin resistance, Wnt signaling activation and pluripotency gene expression, all characteristics of hCSCs. Moreover, DDX5 knockdown increased viral replication. RNA-seq analyses of HBV-replicating DDX5 knockdown cells, identified enhanced expression of key genes of the Wnt/{beta}-catenin pathway, including Frizzled7 (FZD7) and Matrix Metallopeptidase7 (MMP7), indicative of Wnt signaling activation. Clinically, elevated FZD7 expression correlates with poor patient survival. Importantly, inhibitors to miR17[~]92 and miR106b[~]25 restored DDX5 levels and suppressed both Wnt/{beta}-catenin activation and viral replication.\n\nConclusionDDX5 is a negative regulator of Wnt signaling and hepatocyte reprogramming in HCCs. Restoration of DDX5 levels in HBV-infected patients can exert both antitumor and antiviral effects.

cancer biology↗

bioRxiv2019

GRASP: a Bayesian network structure learning method using adaptive sequential Monte Carlo

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex dependence structures. BNs can be used to infer complex biological networks using heterogeneous data from different sources with missing values. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the diversity of sampled networks which were further improved by a new stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASPs potential in discovering novel biological relationships in integrative genomic studies.

bioinformatics↗

bioRxiv2019

Visualizing in vitro-in vivo correlation of miktoarm copolymer nanomicelles in cancer cellular uptake and trafficking

In this study, a clear correlation between the in vitro and in vivo cellular uptake and trafficking was discovered by delivering miktoarm copolymer nanomicelles (MCNs) to cancer cells and tumor tissues. To monitor this process, two different FRET pairs, DiO and DiI, DiD and DiR, were loaded into MCNs to monitor the Forster resonance energy transfer (FRET) efficiency. The change in FRET efficiency in vitro and in vivo demonstrated a similar sequence of events for the transport of MCNs: hyperbranched block PCL inserted into cytomembrane, while the loaded hydrophobic fluorescence probes were released and followed by time-dependent intracellular clustering within endocytic vesicles. Additionally, uptake of loaded fluorescence probes with successively increasing ratios of copolymers suggested that with the increase of mass ratio of copolymer to fluorescence probes, cellular uptake of probes significantly decreased. This result was also consistent with the uptake behavior in cancer tissues. Collectively, the interaction between MCNs and cellular membrane dictated the uptake and trafficking of core-loaded hydrophobic probes. This concept paves a new way to analyze in vitro-in vivo correlation of other nanocarriers for endocytosis mechanism studies as well as further novel copolymers design in biomedical applications.

pharmacology and toxicology↗

bioRxiv2019

Development of structure-function coupling in human brain networks during youth

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as working memory. However, it remains unclear how white matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure-function coupling using diffusion-weighted imaging and n-back fMRI data in a sample of 727 individuals (ages 8-23 years). We found that spatial variability in structure-function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure-function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n=294). Moreover, structure-function coupling in rostrolateral prefrontal cortex was associated with executive performance, and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

neuroscience↗

bioRxiv2019

Individual Variation in Control Network Topography Supports Executive Function in Youth

The spatial distribution of large-scale functional networks on the anatomic cortex differs between individuals, and is particularly variable in networks responsible for executive function. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing upon advances in machine learning and a large sample of youth (n=693, ages 8-23y) imaged with 27 minutes of high-quality fMRI data, we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of an unseen individuals brain maturity. Furthermore, the cortical representation of executive networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering both the plasticity and diversity of functional neuroanatomy during development, and suggest advances in personalized therapeutics.

neuroscience↗

bioRxiv2019

Asymmetrical cleavages of Sleeping Beauty transposons generate multiple excised transposon fragments during transposition

Although the Sleeping Beauty (SB) transposon is the most validated DNA transposon used as a gene delivery vehicle in vertebrates, many details of the excision and integration steps in the transposition process are unclear. We have probed in detail the products of the excision step and apparent selective integration of a subset of those products during transposition. The standard model of SB transposase-mediated transposition includes symmetrical cleavages at both ends of the transposon for excision and re-integration in another DNA sequence. In our analysis of excised transposon fragments (ETFs), we found evidence for the requirement of certain flanking sequences for efficient cleavage and a significant rate of asymmetrical cleavage during the excision process that generates multiple ETFs. Our results suggest that the cleavage step by SB transposase is not as precise as indicated in most models. Repair of the donor ends can produce eight footprint sequences (TACTGTA, TACAGTA, TACATA, TACGTA, TATGTA, TACTA, TAGTA and TATA). Our data also suggest that mismatch repair (MMR) is not an essential requirement for footprint formation. Among the twenty liberated ETFs, only eight appear to effectively re-integrate into TA sites distributed across the genome, supporting earlier findings of unequal rates of excision and reintegration during SB transposition. These findings may be important in considerations of efficiency of SB transposon remobilization, selection of TA integration sites and detection of SB excision and integration loci, all of which may be important in human gene therapy.

molecular biology↗

bioRxiv2019

Multi-Scale Network Regression for Brain-Phenotype Associations

Complex brain networks are increasingly characterized at different scales, including global summary statistics, community connectivity, and individual edges. While research relating brain networks to demographic and behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale, thus failing to incorporate rich information present at other scales. Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge- and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant association between functional connectivity patterns with brain development, sex differences, and motion-related artifacts. Notably, compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge- and community-level information, MSNR has the potential to yield novel insights into brain-behavior relationships.

neuroscience↗