Biology subjects

Fan, Y.

Publications and source records attributed to Fan, Y..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Structural basis for DNA double-strand break sensing by human MRE11-RAD50-NBS1 and its TRF2 complex

The MRE11-RAD50-NBS1 (MRN) complex is a central, multifunctional factor in the detection, signaling and nucleolytic processing of DNA double-strand breaks (DSBs). To clarify how human MRN binds generic and telomeric DNA ends and can separate DNA end sensing from nuclease activities, we determined cryo-electron microscopy structures of human MRN bound to DNA and to DNA and the telomere protection factor TRF2. MRN senses DSBs through a tight clamp-like sensing state with closed coiled-coil domains, but auto-inhibited MRE11 nuclease. NBS1 wraps around the MRE11 dimer, with NBS1s ATM recruitment motif sequestered by binding to the regulatory RAD50 S site, necessitating an allosteric switch for ATM activation. At telomeric DNA, TRF2 blocks the second S site via the iDDR motif to prevent nuclease and ATM activation. Our results provide a structural framework for topological DNA sensing and separation of sensing, signaling and processing activities of mammalian MRN. Highlights- Human MRN senses DNA ends with an autoinhibited nuclease - NBS1s C-terminus binds one RAD50 S site in the sensing state - TRF2 binds MRNs second S site at telomeres - RAD50 and ATM compete for the NBS1 C-terminus

biochemistry↗

bioRxiv2025

Chromatin remodeling integrates vertebrate body axis elongation and cell fate determination

The effective elongation of the vertebrate body axis depends on the precise coordination of cell movements and cell fate determination within the posterior tailbud region. However, the specific biological determinant that integrates these cellular processes during vertebrate tail bud elongation has remained unclear. Here, we demonstrate that the spatiotemporal heterogeneity of chromatin condensation states governs both cell fate and cell motility within the mesodermal progenitor zone (MPZ) and the presomitic mesoderm (PSM) during zebrafish tail bud elongation. By integrating biological experiments with computational modeling, we reveal that nuclear volume heterogeneity, mediated by chromatin remodeling, acts as an active driver of collective cell migration. Reducing chromatin condensation heterogeneity in the MPZ through modulation of chromatin modifications impairs collective cell movements and delays tailbud elongation. Single cell ATAC-seq and RNA-seq analyses reveal that global chromatin accessibility heterogeneity and region-specific chromatin remodeling differences in the PSM and MPZ are functionally associated with gene expression pathways involving in cell lineage specification. These findings establish a molecular connection between cell fate determination and tissue-scale cellular dynamics. Collectively, our findings unveil a unifying framework for understanding the coordination of cell fate determination and large-scale cellular movements during tissue morphogenesis.

developmental biology↗

bioRxiv2025

A new corrector rescues F508del-CFTR folding through stabilization of the TMD1-NBD1 linker

While Trikafta has turned many CF patients into people that lead relatively healthy lives, additional modulators are still needed. We developed an acylsulfonamide-type C2 corrector X307810 that is potent and shows a distinctive mode of action in rescuing F508del-CFTR from degradation at the endoplasmic reticulum. We tracked the de-novo folding and assembly of each CFTR domain with a radio-active pulse chase - limited proteolysis folding assay and determined when and where X307810 works on the newly synthesized protein. X307810 rescued the Golgi form of F508del-CFTR to 80% of wild-type values. To our knowledge, such a dramatic increase has never been attained with a single corrector. Zooming in on X307810 mode of action revealed that the corrector changed protease resistance of the linker between TMD1 and NBD1 during late stages of CFTR folding. X307810 is the first corrector that added a unique new fragment to the typical limited-proteolysis pattern. Other C2 correctors do not change fragments, but portray an increase in fragment quantity characteristic of domain assembly in wild-type and corrected F508del-CFTR. X307810 instead extended protection of TMD1 from intracellular loop 2 to the unstructured regulatory insertion of NBD1. These results provide new and important insights into the impact of acylsulfonamide correctors on CFTR structure and function. SIGNIFICANCE STATEMENTCFTR is an anion channel, mutations in which cause cystic fibrosis (CF). Corrector compounds allow misfolded missense CFTR mutants to escape from degradation in the endoplasmic reticulum, which is the basis for CF therapy, in combination with a potentiator. How correctors improve folding is incompletely understood. We here report on X307810 having a novel mode of action on post-translational domain assembly of newly synthesized CFTR. Our in-cell folding assay shows that X307810 stabilizes the linker between the first transmembrane domain (TMD1) and the first nucleotide binding domain (NBD1) into the Regulatory Insertion; it strengthens the TMD1-NBD1-ICL4 interface. The action of X307810 boosts egress of F508del-CFTR from the ER to an unprecedented 80% for a single modulator.

biochemistry↗

bioRxiv2025

Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling

Functional MRI studies have identified functional connectivity (FC) patterns associated with behavioral traits using whole-brain or region-wise predictive models. However, whole-brain approaches often suffer from limited generalizability and interpretability due to the high-dimensionality of FC data. Conversely, region-wise models inherently isolate predictions, ineffective for characterizing contributions of the whole brain FC patterns in predicting a target trait. In this study, we propose an interpretable predictive model that learns fine-grained FC patterns predictive of behavioral traits, jointly at the regional and participant levels, to characterize the overall association of FC patterns with a target trait. Our model learns both a relevance score and a dedicated prediction model for each brain region, then integrates the regional predictions to generate a participant-level prediction, capturing the collective association of FC patterns with the trait. We validated our method using FC data from 6798 participants in the Adolescent Brain and Cognitive Development (ABCD) study for predicting cognition. Our interpretable predictive model identified the cingulo-parietal, retrosplenial-temporal, dorsal attention, salience, and cingulo-opercular networks as collectively predictive of cognitive traits. The interpretable model significantly improved prediction accuracy and facilitated the characterization of fine-grained differences in FC patterns across cognitive domains. Furthermore, the learned relevance scores enhanced region-wise predictions of longitudinal cognitive measures in the ABCD cohort and cognitive traits in an external Human Connectome Project Development (HCP-D) cohort. These findings suggest that our method effectively characterizes generalizable and fine-grained FC patterns linked to cognition in youth.

neuroscience↗

bioRxiv2025

cfDecon: Accurate and Interpretable methylation-based cell type deconvolution for cell-free DNA

Cell-free DNA (cfDNA) analysis has emerged as a powerful tool for noninvasive diagnostics and monitoring of aberrant methylation. However, computational deconvolution of cfDNA remains challenging due to the complexity of methylation data, the diverse cellular compositions in cfDNA mixtures, and the limited interpretability of current methods. We present cfDecon, an efficient deep-learning framework for cfDNA deconvolution at the resolution of individual reads. cfDecon employs a multichannel autoencoder core module and an iterative refinement process to estimate cell-type proportions and generate condition-aware cell-type-specific methylation profiles. We rigorously evaluate cfDecon through comprehensive simulation experiments, including scenarios with normal cellular compositions, rare cell types, and unknown cell types, where cfDecon consistently outperforms previous state-of-the-art methods in deconvolution. Using strict data separation and careful control for potential data leakage, cfDecon shows scalability and applicability to large-scale studies by raising Lins Concordance Correlation Coefficient (CCC) over 33% on an atlas-level reference dataset. We further validate cfDecons utility in disease diagnosis on two clinical datasets, Amyotrophic Lateral Sclerosis (ALS) and Hepatocellular Carcinoma (HCC). cfDecon raises the performance of disease detection from 0.53 to 0.79 compared to existing methods on the ALS dataset and 0.55 to 0.77 on the HCC dataset. The capacity of cfDecon to predict condition-specific DNA methylation patterns across cell types offers a valuable framework for studying differentially methylated CpGs. Furthermore, integrating cfDecon with enrichment analysis facilitates the exploration of functional variations in CpGs across different conditions. cfDecon represents a significant advancement in cfDNA analysis, offering improved accuracy, interpretability, and potential for personalized medicine applications. The codebase is available at https://github.com/Susanxuan/cfDecon.

bioinformatics↗

bioRxiv2025

Intermittent Fasting Reprograms Chromatin Accessibility to Modulate Gene Expression in Brain and Muscle

Intermittent fasting (IF), a dietary regimen that mimics the natural feeding patterns observed across diverse organisms--from single-celled life to mammals--is thought to activate systemic survival responses and confer health benefits. However, the molecular mechanisms underlying these effects remain poorly understood. In this study, we utilized ATAC-Seq and RNA-Seq to investigate how IF influences chromatin accessibility and gene expression in brain and muscle tissues of mice, compared to ad libitum feeding. Our results reveal that IF induces significant changes in chromatin accessibility, modulating pathways related to metabolism, ribosome function, HIF-1 signaling, and glycolysis. Motif analysis identified tissue-specific transcription factors enriched in IF-regulated regions, including Sp1, Mef2a, NeuroD2, Banp, and NFIA in the brain, and SMAD4, TCF4, STAT5B, NKX3-1, and ZEB2 in muscle. Integrative analysis of ATAC-Seq and RNA-Seq data demonstrated that IF upregulates 50 genes and downregulates 15 in the cortex, while upregulating 31 genes and downregulating 10 in muscle. These gene expression changes are linked to pathways associated with neuroprotection and enhanced muscle function, offering mechanistic insights into the health benefits of IF. Our findings underscore the role of IF-induced chromatin remodeling in driving adaptive gene regulation.

bioinformatics↗

bioRxiv2025

SMCLMDA: statistical meta-paths contrastive learning for predicting miRNA-disease multidimensional relationships

Accurately identifying miRNA-disease association (MDA) is of great importance in biomedical research and clinical applications. However, most existing computational methods rely on similarity, making it difficult to effectively capture the deep semantic information among heterogeneous nodes in complex networks. In addition, the inherent "black-box" nature of traditional deep learning models leads to a lack of transparency in their decision-making process. Therefore, we propose an interpretable MDA prediction method (iDRKAN) based on dual-graph representation learning and Kolmogorov-Arnold Network. First, iDRKAN constructs similarity views and meta-path views based on the similarity matrix and association matrix respectively, and learns higher-order feature representation of each view by graph convolutional network (GCN). Subsequently, the multi-channel attention (MCA) mechanism is introduced to adaptively fuse the contextual information of each similarity view in different convolutional layers, and the semantic layer attention (SLA) mechanism is used to integrate the semantic information contained in different meta-path views. Next, the contrastive learning strategy is used to optimize the consistency between the dual-graph representation. Finally, the dual-graph features are weighted fused, and fed into the interpretable Kolmogorov-Arnold Network (KAN) for prediction. Experimental results on two public datasets show that iDRKAN significantly outperforms existing computational approaches in multiple performance indicators. In addition, experiments with different classifiers verify that iDRKAN achieves a good balance between prediction performance and interpretability. The case study further demonstrates the effectiveness of iDRKAN in mining potential associations.

bioinformatics↗

bioRxiv2025

SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations

To achieve fast and accurate cortical surface reconstruction from brain magnetic resonance images (MRIs), we develop a method to jointly reconstruct the inner (white-gray matter interface), outer (pial), and midthickness surfaces, regularized by their interdependence. Rather than reconstructing these surfaces separately without taking into consideration their interdependence as in most existing methods, our method learns three diffeomorphic deformations jointly to optimize the midthickness surface to lie halfway between the inner and outer cortical surfaces and simultaneously deforms it inward and outward towards the inner and outer cortical surfaces, respectively. The surfaces are encouraged to have a spherical topology by regularization terms for non-negativeness of the cortical thickness and symmetric cycle-consistency of the coupled surface deformations. The coupled reconstruction of cortical surfaces also facilitates an accurate estimation of the cortical thickness based on the diffeomorphic deformation trajectory of each vertex on the surfaces. Validation experiments have demonstrated that our method achieves state-of-the-art cortical surface reconstruction performance in terms of accuracy and surface topological correctness on large-scale MRI datasets, including ADNI, HCP, and OASIS.1

neuroscience↗

bioRxiv2025

Spatial Heterogeneity and Subtypes of Functional Connectivity Development in Youth

The brain functional connectome development is fundamental to neurocognitive growth in youth. While brain age prediction has been widely used to assess connectome development at the individual level, traditional approaches providing a global index overlook the spatial variability and inter-individual heterogeneity of functional connectivity (FC) development across the cortex. In this study, we introduced a regional brain development index to assess spatially fine-grained FC development. We examined the spatial variability of FC development and stratified individuals into subtypes with distinct patterns of spatial heterogeneity in region-wise FC development across the cortex through clustering. An evaluation conducted on a sample of youths aged 8-23 years using fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) revealed three distinct FC development subtypes. Individuals with advanced FC development aligned with the hierarchical brain organization along the sensorimotor-association (S-A) axis demonstrated superior cognitive performance compared to those with other patterns. These patterns were replicated in the Human Connectome Project Development (HCP-D) cohort, confirming their robustness. Further analysis revealed associations between FC development and gene expression, with enriched genes linked to neural differentiation, synaptogenesis, and myelination. These findings suggest that spatial heterogeneity in FC development reflects underlying cortical microstructure and hierarchical cortical organization, underscoring its critical role in understanding neurocognitive maturation and individual variability during youth.

neuroscience↗

bioRxiv2025

Three annotated chromosome-level de novo genome assemblies of Lomentospora prolificans provide evidence for a chromosomal translocation event

Lomentospora prolificans is a fungal pathogen responsible for serious, often fatal, illness in patients with compromised immune systems. Treatment is rarely successful because L. prolificans is inherently resistant to all major classes of antifungal drugs. In this study we publish three chromosome-level de novo genome assemblies, including the first complete-level assembly of L. prolificans, along with genome annotations. The L. prolificans genome is packaged in 11 nuclear chromosomes and one mitochondrial chromosome, has 36.7-37.1 Mb, and encodes for a putative 7357-7640 genes. The length and composition of contigs in one stain varied from those of the other two strains, supporting the hypothesis that a chromosomal translocation took place. These assemblies were confirmed with pulsed-field gel electrophoresis. The availability of more complete genomes will hopefully help the search for new antifungal drugs and provides insights into the evolutionary history of this pathogenic fungus.

genomics↗

bioRxiv2025

Epigenetic phase variation in the gut microbiome enhances bacterial adaptation

The human gut microbiome within the gastrointestinal tract continuously adapts to variations in diet, medications, and host physiology. A strategy for bacterial genetic adaptation is epigenetic phase variation (ePV) mediated by bacterial DNA methylation, which can regulate gene expression, enhance clonal heterogeneity, and enable a single bacterial strain to exhibit variable phenotypic states. Genome-wide and site-specific ePVs have been characterized in human pathogens antigenic variation and virulence factor production. However, the role of ePV in facilitating adaptation within the human microbiome remains poorly understood. Here, we comprehensively cataloged genome-wide and site-specific ePV in human infant and adult gut microbiomes. First, using long-read metagenomic sequencing, we detected genome-wide ePV mediated by complex structural variations of DNA methyltransferases, highlighting those associated with antibiotics or fecal microbiota transplantation. Second, we analyzed a collection of public short-read metagenomic sequencing datasets, uncovering a great prevalence of genome-wide ePV in the human gut microbiome. Third, we quantitatively detected site-specific ePVs using single-molecule methylation analysis to identify dynamic variation associated with antibiotic treatment or probiotic engraftment. Finally, we performed an in-depth assessment of an Akkermansia muciniphila isolate from an infant, highlighting that ePVs can regulate gene expression and enhance the bacterial adaptive capacity by employing a bet-hedging strategy to increase tolerance to differing antibiotics. Our findings indicate that epigenetic modifications are a common strategy used by gut bacteria to adapt to the fluctuating environment.

microbiology↗

bioRxiv2025

Non-mutated human tau stimulates Alzheimer's disease-relevant neurodegeneration in a microglia-dependent manner

The accumulation of abnormal, non-mutated tau protein is a key pathological hallmark of Alzheimers disease (AD). Despite its strong association with disease progression, the mechanisms by which tau drives neurodegeneration in the brain remain poorly understood. Here, we selectively expressed non-mutated or mutated human microtubule-associated protein tau (hMAPT) in neurons across the brain and observed neurodegeneration in the hippocampus, especially associated with non-mutated human tau. Single-nuclei RNA sequencing confirmed a selective loss of hippocampal excitatory neurons by the wild-type tau and revealed the upregulation of neurodegeneration-related pathways in the affected populations. The accumulation of phosphorylated tau was accompanied by cellular stress in neurons and reactive gliosis in multiple brain regions. Notably, the lifelong absence of microglia significantly and differentially influenced the extent of neurodegeneration in the hippocampus and thalamus. Therefore, our study established an AD-relevant tauopathy mouse model, elucidated both neuron-intrinsic and neuron-extrinsic responses, and highlighted critical and complex roles of microglia in modulating tau-driven neurodegeneration.

neuroscience↗

bioRxiv2025

Local erythropoiesis directs oxygen availability in bone fracture repair

Oxygen tension dynamically regulates stem cell fate and tissue regeneration, yet how local oxygen availability is controlled within the bone marrow niche remains poorly understood. While bone marrow injury, such as by bone fracture, disrupts marrow vasculature, the consequences on local oxygen tension remain unclear. Here, we show in mice that while the tissue oxygen tension in bone marrow is low (25 mmHg, [~]4% O2), intracellular oxygenation is heterogeneous and erythroid cells are high in oxygen. Bone fracture elevates oxygen tension in the injured bone marrow (>55 mmHg, [~]8%), which persists for over a week post-injury. This oxygen elevation results not from angiogenesis, but rather from localized expansion of erythroid precursor cells in the injured bone marrow. The activated erythroid precursors synthesize hemoglobin and accumulate oxygen, acting as local modulators of oxygen tension. Blocking transferrin receptor 1 (CD71)-mediated iron uptake impairs hemoglobin synthesis, reduces local oxygen levels, and enhances bone regeneration through increased angiogenesis and osteogenesis. These findings identify erythroid precursors as active regulators of local oxygen availability in the bone marrow niche, which may be targetable to enhance tissue regeneration.

cell biology↗

bioRxiv2025

Insights into the spool-like architecture and infectionstrategy of the archaeal virus SEV1

Archaeal viruses are well known for their diverse morphologies and extreme stability. In this study, we used cryo-electron tomography to analyze the structure of SEV1 and its infection strategies in its native state. The results show that SEV1 nucleocapsid adopts a coil-stacking architecture which displays a degree of flexibility. VP4, whose homologues are widespread in the thermo-acidic environment globally, is identified as the major capsid protein and binds genomic DNA forming a "beads-on-a-string" arrangement. Simulations in various extreme environments indicate that the envelope of SEV1 is crucial to the thermostability. SEV1 infects the host by membrane fusion revealed by the membrane fusion assay. The infected cell undergoes cytoplasm condensation to form a "viral factory", leading to the successive production of nascent virions. A series of assembly intermediates of SEV1 are identified revealing an integrated picture of the virus assembly process. The nascent virions are found to be released through virus-associated pyramids (VAPs), composed of unique proteins encoded by SEV1 distinct from other known VAP proteins. Our study provides novel insights to the survival strategies of SEV1, a flexible and enveloped archaeal virus, by using the unique "coil-stacking" architecture and the characteristic infection strategies.

microbiology↗

bioRxiv2024

MethylQUEEN: A Methylation Encoded DNA Foundation Model

DNA 5-methylcytosine (5mC) modification plays a pivotal role in many biological processes, yet 5mC information and pattern hidden behind remains to be explored. Here, we develop Methylation Language Model based on Quintuple Bidirectional Transformer (MethylQUEEN), a novel pre-trained DNA methylation foundation model capable of sensing methylation states and covering the genome-wide methylation landscape. Through tailored methylation-prone pre-training, MethylQUEEN effectively captured epigenetics information hidden within the DNA sequences: it accurately traces DNAs tissue-of-origin, and successfully recovers the expression profile through methylation states. Integrative analysis on MethylQUEENs attention scores also enables us to reveal the unique methylation status of a tissue for precise disease detection, and identifying key regulatory 5mC sites for disease intervention. As a result, MethylQUEEN signifies a new paradigm in methylation analysis for various biological problems. Besides, our study demonstrates the effectiveness of directly integrating methylation information into pre-training, offering new perspectives and methodologies for a range of methylation-related biological processes. It serves as an initial exploration for the development of more comprehensive epigenomic models.

genomics↗

bioRxiv2024

Effect of Stroma-directed Drugs in Combination with Chemotherapy Against Pancreatic Cancer- a Preclinical Study

Cytotoxic chemotherapy plays an important role for extending the survival of patients with pancreatic ductal adenocarcinoma (PDAC). To enhance the efficacy of chemotherapy for eradicating the cancer cells, we have compared the standard care chemotherapy (combination of nab-paclitaxel, gemcitabine and cisplatin, NGC) versus NGC plus stroma-directed agents (calcipotriol and losartan, respectively) in a genetically engineered mouse model of PDAC. Over a 2-week study period, MRI was conducted to measure the tumor size and to test the sensitivity of imaging markers derived from diffusion-weighted imaging (DWI), dynamic contrast enhanced MRI (DCE) and magnetization transfer ratio (MTR) for assessing the tumor cellularity and stromal changes. Detailed immunohistochemistry and preliminary single cell RNA sequencing (scRNAseq) study were applied to tumor tissues collected upon euthanasia on day-14. Our major findings are: 1. Compared the untreated controls, NGC chemotherapy induced significant tumor growth inhibition and stromal changes including pronounced reduction of fibroblast associated protein (FAP) level accompanied by increased matrix collagen content, significantly reduced microvascular permeability revealed by DCE corroborated with reduced microvascular density. 2. Losartan+NGC significantly enhanced inhibition of tumor growth beyond NGC and increased lymphocytes infiltration in the tumor which may contribute to enhanced cancer cells eradication. 3. NGC treatment enriched the fraction of mesenchymal (M) subtype while reducing the epithelial (E) subtype of cancer cells compared to the controls, and this trend was reversed by calcipotriol+NGC. In conclusion, our study captured changes in cancer cell and tumor microenvironment in response to chemo stromal therapy versus chemotherapy alone with mechanistic insights.

cancer biology↗

bioRxiv2024

Noninvasively real-time monitoring in-vivo immune cell and tumor cell interaction by NIR-II nanosensor

Immunocytotherapy holds significant promise as a novel cancer treatment, but its effectiveness is often hindered by delayed responses, requiring evaluations every two to three weeks based on current diagnostic methods. Early assessment of immune cell-tumor cell interactions could provide more timely insights into therapeutic efficacy, enabling adjustments to treatment plans. In this study, we report a noninvasive nanosensor (C8R-DSNP) for real-time monitoring of in vivo immune cell activities in the second near-infrared long-wavelength (NIR-II-L) window (1500-1900 nm), which offers deep tissue transparency. The C8R-DSNP responds rapidly to caspase-8, a key apoptotic signaling molecule generated during interactions between natural killer (NK-92) immune cells and tumor cells. Using ratiometric NIR-II-L fluorescence imaging, we captured dynamic in vivo observations of NK-92 cell engagement with tumor cells in a mouse model. These results demonstrated cancer cell apoptosis that happens as early as 4.5 hours after NK-92 cell infusion. Additionally, in vitro urine imaging confirmed the initiation of apoptosis via cleaved fluorescent small molecules, while single-cell tracking within blood vessels and tumors further elucidated immune cell dynamics. This real-time NIR-II-L monitoring approach offers valuable insights for optimizing immunocytotherapy strategies.

bioengineering↗

bioRxiv2024

dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts

Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in imaging neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great potential for modeling the dFC patterns. However, these methods face challenges in effectively characterizing the modularity organization of brain networks and capturing varying dFC state patterns. To address these limitations, we propose dFCExpert, a novel method designed to learn robust representations of dFC patterns in fMRI data with modularity experts and state experts. Specifically, the modularity experts optimize multiple experts to characterize the brain modularity organization during graph feature learning process by combining GNN and mixture of experts (MoE), with each expert focusing on brain nodes within the same functional network module. The state experts aggregate temporal dFC features into a set of distinctive connectivity states using a soft prototype clustering method, providing insight into how these states support diverse brain functions and how they vary across brain conditions. Experiments on three large-scale fMRI datasets have demonstrated the superiority of our method over existing alternatives. The learned dFC representations not only enhance interpretability but also hold promise for advancing our understanding of brain function across a range of conditions, including development, sex difference, and Autism Spectrum Disorder. Our code is publicly available at MLDataAnalytics/dFCExpert1.

neuroscience↗