Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2.

The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2  years of age. Our findings highlight network-level neural substrates underlying early cognitive development.

Development of segregation and integration of functional connectomes during the first 1,000 days.

The first 1,000 days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.

DCP: A pipeline toolbox for diffusion connectome.

The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.

A portable magnetic electrochemical sensor for highly efficient Pb(II) detection based on bimetal composites from Fe-on-Co-MOF.

The practical, sensitive, and real-time detection of heavy metal ions is an essential and difficult problem. This study presents the design of a unique magnetic electrochemical detection system that can achieve real-time field detection. To enhance the electrochemical performance of the sensor, Fe2O3@C-800, Co/CoO@/C-600, and CoFe2O4@C-600 magnetic composites were synthesized using three MOFs precursors by the solvothermal method. And the morphology structure and electrochemical properties of as-prepared magnetic composites were researched by X-ray diffraction (XRD), Scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS), vibrating sample magnetometer (VSM), specific surface area and porosity analyzer (BET) and differential pulse voltammetry (DPV). The results shown that these composites improve conductivity and stability while preserving the MOFs basic frame structure. Compared with the monometallic MOFs-derived composites, the synergistic effect of the bimetallic composite CoFe2O4@C-600 can significantly enhance the electrochemical performance of the sensor. The linear range for the detection of lead ions was 0.001-60 µM, and the detection limit was 0.0043 µM with a sensitivity of 22.22 µA µM·cm-2 by differential pulse voltammetry. The sensor has good selectivity, stability, reproducibility and can be used for actual sample testing.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence.

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Endoscopy-guided intraoral extraction of ectopic mandibular third molar in the subcondylar region planned with three-dimensional imaging: A case report and literature review.

Ectopic mandibular third molar (EMTM) in the subcondylar region is a rare clinical condition, especially for a subtype confined between the mandibular foramen and condylar neck. The etiology is currently uncertain and the optimal management of this specific subtype remains not well defined. We reported a case of this specific subtype of EMTM that was minimally invasively extracted by endoscopy-guided intraoral surgery, planned preoperatively using three-dimensional (3D) imaging of cone beam computed tomography (CBCT), with no complications postoperatively caused by the routine surgery. We also reviewed nine relevant literature to expand the clinical features and therapeutic management of this specific subtype of EMTM. Etiologically, persistent cystic pressure may be a major cause of EMTM displaced into the subcondylar region. For extraction of this specific EMTM, the combination of 3D CBCT-based imaging and endoscopy-assisted intraoral minimally invasive surgery could be considered as the priority option for patients without facial fistula.

Quantitative detection of naphthenic acids in wastewater based on superior fluorescence performance of nitrogen-rich carbon quantum dots.

With the development of the petrochemical industry, a large amount of naphthenic acids in petrochemical wastewater was accumulated in the environment, causing serious environmental pollution. Most of the commonly used methods for the determination of naphthenic acids have the characteristics of high energy consumption, complicated pretreatment, long detection cycle, and the need to send samples to analytical laboratories. Therefore, it is essential to develop an efficient and low-cost field analytical method for rapidly naphthenic acids quantify. In this study, nitrogen-rich carbon quantum dots (N-CQDs) based on natural deep eutectic solvents (NADESs) was successfully synthesized by a one-step solvothermal method. The fluorescence property of carbon quantum dots was used to achieve the quantitative detection of naphthenic acids in wastewater. The prepared N-CQDs showed excellent fluorescence and stability, showed a good response to naphthenic acids and a linear relationship in the concentration range of naphthenic acids from 0.03 to 0.09 mol‧L-1. The effect of common interferents in petrochemical wastewater on the detection of naphthenic acids by N-CQDs was investigated. The results showed that N-CQDs had good specificity for the detection of naphthenic acids. N-CQDs was applied to the naphthenic acids wastewater, and the concentration of naphthenic acids in the wastewater was successfully calculated according to the fitting equation.

3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images.

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

Development of functional connectome gradients during childhood and adolescence.

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum that ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (aged 6-14 years), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis and synaptic transmission-related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.

Meta-connectomic analysis maps consistent, reproducible, and transcriptionally relevant functional connectome hubs in the human brain.

Human brain connectomes include sets of densely connected hub regions. However, the consistency and reproducibility of functional connectome hubs have not been established to date and the genetic signatures underlying robust hubs remain unknown. Here, we conduct a worldwide harmonized meta-connectomic analysis by pooling resting-state functional MRI data of 5212 healthy young adults across 61 independent cohorts. We identify highly consistent and reproducible connectome hubs in heteromodal and unimodal regions both across cohorts and across individuals, with the greatest effects observed in lateral parietal cortex. These hubs show heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using post-mortem transcriptome datasets, we show that as compared to non-hubs, connectome hubs have a spatiotemporally distinctive transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and metabolic processes. These results highlight the robustness of macroscopic connectome hubs and their potential cellular and molecular underpinnings, which markedly furthers our understanding of how connectome hubs emerge in development, support complex cognition in health, and are involved in disease.

Fusobacterium nucleatum: a new player in regulation of cancer development and therapeutic response.

A dysbiosis in microbial diversity or functionality can promote disease development. Emerging preclinical and clinical evidence emphasizes the interplay between microbiota and both disease evolution and the treatment response of different cancers. One bacterium that has garnered much attention in a few cancer microbiota studies is Fusobacterium nucleaum (Fn). To provide updated knowledge of the functional role of Fn in cancer prevention and management, this review summarizes the relationship among Fn, cancer, and chemoimmunotherapy response, with the potential mechanisms of action also intensively discussed, which will benefit the development of strategies to prevent or treat cancer via Fn-based therapeutic interventions.

Structural insight into the individual variability architecture of the functional brain connectome.

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

MIR600HG sponges miR-125a-5p to regulate glycometabolism and cisplatin resistance of oral squamous cell carcinoma cells via mediating RNF44.

There is increasing evidence that dysregulated long non-coding RNA (lncRNA) is implicated in tumorigenesis and progression. We aim to explore the role of lncRNA MIR600HG in glycometabolism and cisplatin (DDP) resistance of oral squamous cell carcinoma (OSCC) cells via regulating microRNA-125a-5p (miR-125a-5p) and RING finger 44 (RNF44). Expression of MIR600HG, miR-125a-5p, and RNF44 in OSCC clinical samples, cell lines, and DDP-resistant OSCC cells (SCC-9/DDP) was determined. In SCC-9 cells, proliferation, IC50 value of DDP, migration, invasion, and apoptosis were detected; in SCC-9/DDP cells, proliferation, IC50 value of DDP, apoptosis, glucose consumption, and production of lactic acid and ATP were evaluated. The interaction of MR600HG, miR-125a-5p, and RNF44 was verified. MIR600HG and RNF44 were upregulated while miR-125a-5p was downregulated in OSCC tissues and cell lines, and also in SCC-9/DDP cells. In SCC-9 cells, MIR600HG overexpression improved cell growth, metastasis, and inhibited cell susceptibility to DDP; in SCC-9/DDP cells, silencing of MIR600HG promoted apoptosis, improved DDP sensitivity, and inhibited cell glycolysis. Downregulation of miR-125a-5p showed the opposite effect to downregulation of MIR600HG. MIR600HG bound to miR-125a-5p and miR-125a-5p targeted RNF44. Downregulation of miR-125a-5p reversed the improvement of DDP sensitivity and the inhibition of cell glycolysis by downregulated MIR600HG on SCC-9/DDP cells. Downregulating RNF44 reversed the promotion of DDP resistance and cell glycolysis of SCC-9/DDP cells mediated by downregulation of miR-125a-5p. Collectively, our study addresses that MIR600HG downregulation elevates miR-125a-5p and reduces RNF44 expression, thereby improving DDP sensitivity and inhibiting glycolysis in DDP-resistant OSCC cells.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence.

Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

Nitrogen-rich carbon quantum dots (N-CQDs) based on natural deep eutectic solvents: Simultaneous detection and treatment of trace Co2+ under saline conditions.

Trace Co2+, when present in large quantities, is harmful to the environment and therefore cannot be ignored. Inductively coupled plasma mass spectrometry (ICP-MS) is a standard method used to detect metal ions, however, detecting trace Co2+ under high saline conditions can be challenging. Similarly, existing Co2+ treatment methods are prone to secondary pollution and have high energy consumption. Therefore, it is necessary to find an efficient and non-polluting method for Co2+ detection and treatment. This study successfully synthesized nitrogen-rich carbon quantum dots (N-CQDs) based on natural deep eutectic solvents (NADES) using a one-step solvothermal method. The prepared N-CQDs exhibited excellent fluorescence and high salt tolerance. The simultaneous detection and treatment of trace Co2+ in water under high salinity conditions were achieved for the first time. The response of the N-CQDs to Co2+ under saline condition was linear in the range of 5-250 µM with a limit of detection (LOD) of 1.2269 µM. Feasibility of practical application was assessed by quantitative detection of Co2+ in real water samples. Furthermore, the N-CQDs can treat Co2+, and the removal rate was 99.98%.

Oral Microbiota Analysis of Tissue Pairs and Saliva Samples From Patients With Oral Squamous Cell Carcinoma - A Pilot Study.

Oral microbiota dysbiosis is associated with the occurrence and progression of oral cancer. To investigate the association between the microbiota and risk of oral squamous cell carcinoma (OSCC), we identified the microbial composition of paired tumor (TT)/normal paracancerous tissues (NPT) and saliva (TS) samples in OSCC patients through 16S rRNA gene sequencing. A total of 22 phyla, 321 genera, and 869 species were identified in the oral samples. Paired comparisons revealed significant differences between TT, NPT, and TS groups, with the genus Filifactor significantly enriched in TT. The phylum Actinobacteria; genus Veillonella; and species Granulicatella adiacens, Streptococcus sanguinis, and Veillonella rogosae were significantly enriched in NPT, while the phylum Bacteroidetes; genera Capnocytophaga, Haemophilus, and Prevotella; and seven species, including Capnocytophaga sp., Haemophilus sp., and Neisseria sp., were significantly enriched in TS. In TTs, the abundance of Prevotella intermedia was profoundly higher in the gingiva, while Capnocytophaga gingivalis and Rothia mucilaginosa were enriched in the lining mucosa and tongue. Increasing in abundance from the early tumor stage to the late stage, Solobacterium moorei in TT and Campylobacter sp. strain HMT 044 in TS were positively correlated with OSCC development, suggesting that bacteria were selected by different microenvironments. The correlation between 11 microbial species and 17 pathway abundances was revealed, indicating the potential function of low-abundance bacteria. Overall, our analysis revealed that multiple oral bacterial taxa are associated with a subsequent risk of OSCC and may be used as biomarkers for risk prediction and intervention in oral cancers.

Individual Uniqueness in the Neonatal Functional Connectome.

The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30 mm) and middle-range (30-60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.