RESUMEN
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.
Asunto(s)
Encéfalo , Cognición , Conectoma , Imagen por Resonancia Magnética , Humanos , Conectoma/métodos , Femenino , Masculino , Imagen por Resonancia Magnética/métodos , Cognición/fisiología , Recién Nacido , Encéfalo/crecimiento & desarrollo , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Preescolar , Desarrollo del Lenguaje , Desarrollo Infantil/fisiologíaRESUMEN
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.
Asunto(s)
Conectoma , Sustancia Blanca , Humanos , Conectoma/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Encéfalo/diagnóstico por imagen , Imagen de Difusión por Resonancia Magnética/métodos , Sustancia Blanca/diagnóstico por imagenRESUMEN
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.
Asunto(s)
Cobalto , Técnicas Electroquímicas , Plomo , Plomo/análisis , Plomo/química , Cobalto/química , Cobalto/análisis , Técnicas Electroquímicas/métodos , Técnicas Electroquímicas/instrumentación , Límite de Detección , Hierro/química , Hierro/análisis , Estructuras Metalorgánicas/químicaRESUMEN
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.
Asunto(s)
Encéfalo , Procesamiento de Imagen Asistido por Computador , Humanos , Lactante , Procesamiento de Imagen Asistido por Computador/métodos , Encéfalo/diagnóstico por imagen , Redes Neurales de la Computación , Imagen por Resonancia Magnética/métodos , Sustancia GrisRESUMEN
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.
Asunto(s)
Encéfalo , Imagen por Resonancia Magnética , Adolescente , Mapeo Encefálico , Corteza Cerebral , Niño , Cognición , Humanos , Imagen por Resonancia Magnética/métodos , Vías NerviosasRESUMEN
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.
Asunto(s)
Encéfalo/anatomía & histología , Encéfalo/diagnóstico por imagen , Imagen de Difusión Tensora/métodos , Red Nerviosa/anatomía & histología , Red Nerviosa/diagnóstico por imagen , HumanosRESUMEN
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.
Asunto(s)
Conectoma , Encéfalo/diagnóstico por imagen , Cognición , Conectoma/métodos , Humanos , Individualidad , Imagen por Resonancia Magnética/métodosRESUMEN
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.
Asunto(s)
Corteza Cerebral/fisiología , Conectoma , Individualidad , Red Nerviosa/fisiología , Corteza Cerebral/diagnóstico por imagen , Corteza Cerebral/crecimiento & desarrollo , Femenino , Humanos , Recién Nacido , Imagen por Resonancia Magnética , Masculino , Red Nerviosa/diagnóstico por imagen , Red Nerviosa/crecimiento & desarrollo , Reproducibilidad de los Resultados , Descanso/fisiologíaRESUMEN
The default-mode network (DMN) is a set of functionally connected regions that play crucial roles in internal cognitive processing. Previous resting-state fMRI studies have demonstrated that the intrinsic functional organization of the DMN undergoes remarkable reconfigurations during childhood and adolescence. However, these studies have mainly focused on cross-sectional designs with small sample sizes, limiting the consistency and interpretations of the findings. Here, we used a large sample of longitudinal resting-state fMRI data comprising 305 typically developing children (6-12 years of age at baseline, 491 scans in total) and graph theoretical approaches to delineate the developmental trajectories of the functional architecture of the DMN. For each child, the DMN was constructed according to a prior parcellation with 32 brain nodes. We showed that the overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N = 61, 18-28 years of age). These increases were primarily located in the midline structures. Global and local network efficiency in the DMN also increased with age, indicating an enhanced capability in parallel information communication within the brain system. Based on the divergent developmental rates of nodal centrality, we identified three subclusters within the DMN, with the fastest rates in the cluster mainly comprising the anterior medial prefrontal cortex and posterior cingulate cortex. Together, our findings highlight the developmental patterns of the functional architecture in the DMN from childhood to adolescence, which has implications for the understanding of network mechanisms underlying the cognitive development of individuals.
Asunto(s)
Desarrollo del Adolescente , Encéfalo/diagnóstico por imagen , Desarrollo Infantil , Red en Modo Predeterminado/diagnóstico por imagen , Adolescente , Adulto , Encéfalo/crecimiento & desarrollo , Encéfalo/fisiología , Niño , Conectoma , Red en Modo Predeterminado/crecimiento & desarrollo , Red en Modo Predeterminado/fisiología , Femenino , Neuroimagen Funcional , Humanos , Procesamiento de Imagen Asistido por Computador , Estudios Longitudinales , Imagen por Resonancia Magnética , Masculino , Descanso , Adulto JovenRESUMEN
The median cleft of the mandible and lower lip is an extremely rare congenital maxillofacial deformity, and the therapeutic options are controversial. To evaluate the clinical characteristics and identify a better choice of treatment modes used among us and others, we reviewed 34 relevant literature and herein describe a 17-year follow-up of a case with a median cleft of the mandible and lower lip. Based on the literature and our case with good functional and aesthetical outcomes, we propose a prospective clinical treatment: Patients of Tessier 30 cleft associated with cleft of the mandible could undergo mandibular repair after puberty in conditions of a good occlusal relationship and normal maxillofacial development, even with mild masticatory dysfunction.
Asunto(s)
Labio Leporino , Labio Leporino/diagnóstico por imagen , Labio Leporino/cirugía , Estudios de Seguimiento , Humanos , Labio , Mandíbula/diagnóstico por imagen , Estudios ProspectivosRESUMEN
The human brain undergoes explosive growth during the prenatal period and the first few postnatal years, establishing an early infrastructure for the later development of behaviors and cognitions. Revealing the developmental rules during the early phase is essential for understanding the emergence of brain functions and the origin of developmental disorders. Graph-theoretical network modeling in combination with multiple neuroimaging probes provides an important research framework to explore the early development of the topological wiring and organizational paradigms of the brain. Here, we reviewed studies that employed neuroimaging and graph-theoretical modeling to investigate brain network development from approximately 20 gestational weeks to 2 years of age. Specifically, the structural and functional brain networks have evolved to highly efficient topological architectures in the early stage; where the structural network remains ahead and paves the way for the development of the functional network. The brain network develops in a heterogeneous order, from primary to higher-order systems and from a tendency of network segregation to network integration in the prenatal and postnatal periods. The early brain network topologies show abilities in predicting certain cognitive and behavior performance in later life, and their impairments are likely to continue into childhood and even adulthood. These macroscopic topological changes may be associated with possible microstructural maturations, such as axonal growth and myelinations. Collectively, this review provides a detailed delineation of the early changes in the baby brains in a graph-theoretical modeling framework, which opens up a new avenue for understanding the developmental principles of the connectome.
Asunto(s)
Encéfalo/embriología , Encéfalo/crecimiento & desarrollo , Modelos Teóricos , Red Nerviosa/embriología , Red Nerviosa/crecimiento & desarrollo , Conectoma/métodos , Femenino , Feto , Humanos , Lactante , Recién Nacido , Masculino , Modelos NeurológicosRESUMEN
In magnetic resonance (MR) imaging studies of child brain development, structural brain atlases usually serve as important references for the pediatric population, in which individual images are spatially normalized into a common or standard stereotactic space. However, the popular existing pediatric brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD) are mostly based on MR images obtained from Caucasian populations and thus are not ideal for the characterization of the brains of Chinese children due to neuroanatomical differences related to genetic and environmental factors. Here, we use an unbiased template construction algorithm to create a set of age-specific Chinese pediatric (CHN-PD) atlases based on high-quality T1-and T2-weighted MR images from 328 cognitively normal Chinese children aged 6-12 years. The CHN-PD brain atlases include asymmetric and symmetric templates, sex-specific templates and tissue probability templates, and contain multiple age-specific templates at one-year intervals. A direct comparison of the CHN-PD and NIH-PD atlases reveals dramatic anatomical differences mainly in the bilateral frontal and parietal regions. After applying the CHN-PD and NIH-PD atlases to two independent Chinese pediatric datasets (Nâ¯=â¯114 and Nâ¯=â¯71), we find that the CHN-PD atlases result in significantly higher accuracy than the NIH-PD atlases in both predicting "brain age" and guiding brain tissue segmentation. These results suggest that the CHN-PD brain atlases are necessary for studies of the typical and atypical development of the Chinese pediatric population. These CHN-PD atlases have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) website (https://www.nitrc.org/projects/chn-pd).
Asunto(s)
Atlas como Asunto , Encéfalo/anatomía & histología , Neuroimagen/métodos , Pueblo Asiatico , Encéfalo/diagnóstico por imagen , Niño , China , Femenino , Humanos , Imagen por Resonancia Magnética , MasculinoRESUMEN
During the 3rd trimester, large-scale neural circuits are formed in the human brain, resulting in a highly efficient and segregated connectome at birth. Despite recent findings identifying important preterm human brain network properties such as rich-club organization, how the structural network develops differentially across brain regions and among different types of connections in this period is not yet known. Here, using high resolution diffusion MRI of 77 preterm-born and full-term neonates scanned at 31.9-41.7 postmenstrual weeks (PMW), we constructed structural connectivity matrices and performed graph-theory-based analyses. Faster increases of nodal efficiency were mainly located at the brain hubs distributed in primary sensorimotor regions, superior-middle frontal, and precuneus regions during 31.9-41.7PMW. Higher rates of edge strength increases were found in the rich-club and within-module connections, compared to other connections. The edge strength of short-range connections increased faster than that of long-range connections. Nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed more rapid efficiency increases of the hub and rich-club connections as well as higher developmental rates of edge strength in short-range and within-module connections. These jointly underlie network segregation and differentiated emergence of brain functions.
Asunto(s)
Encéfalo/embriología , Red Nerviosa/embriología , Mapeo Encefálico/métodos , Imagen de Difusión por Resonancia Magnética , Femenino , Humanos , Recién Nacido , Recien Nacido Prematuro , MasculinoRESUMEN
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the six leading cancer by incidence worldwide. The 5-year survival rate of HNSCC patients remains less than 65% due to lack of symptoms in the early stage. Hence, biomarkers which can improve detection of HNSCC should improve clinical outcome. METHODS: Gene expression profiles (GSE6631, GSE58911) and the Cancer Genome Atlas (TCGA) HNSCC data were used for integrated bioinformatics analysis; the differentially expressed genes (DEGs) were then subjected to functional and pathway enrichment analysis, protein-protein interaction (PPI) network construction. Subsequently, module analysis of the PPI network was performed and overall survival (OS) analysis of hub genes in subnetwork was studied. Finally, immunohistochemistry was used to verify the selected markers. RESULTS: A total of 52 up-regulated and 80 down-regulated DEGs were identified, which were mainly associated with ECM-receptor interaction and focal adhesion signaling pathways. Importantly, a set of prognostic signatures including SERPINE1, PLAU and ACTA1 were screened from DEGs, which could predict OS in HNSCC patients from TCGA cohort. Experiment of clinical samples further successfully validated that these three signature genes were aberrantly expressed in the oral epithelial dysplasia and HNSCC, and correlated with aggressiveness of HNSCC patients. CONCLUSIONS: SERPINE1, PLAU and ACTA1 played important roles in regulating the initiation and progression of HNSCC, and could be identified as key biomarkers for precise diagnosis and prognosis of HNSCC, which will provide potential targets for clinical therapies.
Asunto(s)
Actinas/genética , Neoplasias de Cabeza y Cuello/genética , Proteínas de la Membrana/genética , Inhibidor 1 de Activador Plasminogénico/genética , Carcinoma de Células Escamosas de Cabeza y Cuello/genética , Actinas/metabolismo , Biomarcadores de Tumor/genética , Biología Computacional , Regulación Neoplásica de la Expresión Génica , Neoplasias de Cabeza y Cuello/mortalidad , Neoplasias de Cabeza y Cuello/patología , Humanos , Inmunohistoquímica , Estimación de Kaplan-Meier , Proteínas de la Membrana/metabolismo , Inhibidor 1 de Activador Plasminogénico/metabolismo , Pronóstico , Mapas de Interacción de Proteínas/genética , Reproducibilidad de los Resultados , Carcinoma de Células Escamosas de Cabeza y Cuello/mortalidad , Carcinoma de Células Escamosas de Cabeza y Cuello/patologíaRESUMEN
Purpose To determine whether individuals with subjective cognitive decline (SCD), which is defined by memory complaints with normal performance at objective neuropsychologic examinations, exhibit disruptions of white matter (WM) connectivity and topologic alterations of the brain structural connectome. Materials and Methods Diffusion-tensor magnetic resonance imaging and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 36 participants with SCD (21 women: mean age, 62.0 years ± 8.6 [standard deviation]; age range, 42-76 years; 15 men: mean age, 65.5 years ± 8.9; age range, 51-80 years) and 51 age-, sex-, and years of education-matched healthy control participants (33 women: mean age, 63.7 years ± 8.8; age range, 46-83 years; 18 men: mean age, 59.4 years ± 9.3; age range, 43-75 years). Individual WM networks were constructed for each participant, and the network properties between two groups were compared with a linear regression model. Results Graph theory analyses revealed that the participants with SCD had less global efficiency (P = .001) and local efficiency (P = .008) compared with the healthy control participants. Lower regional efficiency was mainly distributed in the bilateral prefrontal regions and left thalamus (P < .05, corrected). Furthermore, a disrupted subnetwork was observed that consisted of widespread anatomic connections (P < .05, corrected), which has the potential to discriminate individuals with SCD from control participants. Moreover, similar hub distributions and less connection strength between the hub regions (P = .023) were found in SCD. Importantly, diminished strength of the rich-club and local connections was correlated with the impaired memory performance in patients with SCD (rich-club connection: r = 0.43, P = .011; local connection: r = 0.36, P = .037). Conclusion This study demonstrated disrupted topologic efficiency of the brain's structural connectome in participants with SCD and provided potential connectome-based biomarkers for the early detection of cognitive impairment in elderly individuals. © RSNA, 2017 Online supplemental material is available for this article.
Asunto(s)
Disfunción Cognitiva/diagnóstico por imagen , Conectoma/métodos , Interpretación de Imagen Asistida por Computador/métodos , Sustancia Blanca/diagnóstico por imagen , Anciano , Imagen de Difusión Tensora/métodos , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Estudios ProspectivosRESUMEN
PURPOSE: This retrospective study aimed to identify the clinical characteristics of radiation-induced sarcoma of the head and neck (RISHN) that could help in the early diagnosis of this rare disease. MATERIALS AND METHODS: From August 1995 through October 2014, 15 cases of RISHN presenting at the authors' department and 323 cases in the literature were reviewed. RESULTS: The incidence of RISHN was higher in men than in women (male-to-female ratio, 2.4:1). The mean latency was long (9.3 yr), and the tumor often occurred in middle age (50.0 yr old). Osteosarcoma was the predominant pathologic diagnosis (34.1%). The prognosis of RISHN was poor. CONCLUSION: RISHN is a serious long-term complication of radiotherapy and its incidence has been increasing in recent years. Owing to the long latency period, its early diagnosis is difficult to make. RISHN should be considered when a patient who has undergone radiotherapy presents with a mass, pain, or trismus in the irradiated field.
Asunto(s)
Detección Precoz del Cáncer , Neoplasias Maxilomandibulares/diagnóstico , Neoplasias Inducidas por Radiación/diagnóstico , Sarcoma/diagnóstico , Adulto , Factores de Edad , Carcinoma/radioterapia , Niño , Femenino , Fibrosarcoma/diagnóstico , Estudios de Seguimiento , Humanos , Masculino , Persona de Mediana Edad , Neoplasias Nasofaríngeas/radioterapia , Neoplasias Primarias Secundarias/diagnóstico , Osteosarcoma/diagnóstico , Pronóstico , Dosificación Radioterapéutica , Estudios Retrospectivos , Factores SexualesRESUMEN
Lifespan is a dynamic process with remarkable changes in brain structure and function. Previous neuroimaging studies have indicated age-related microstructural changes in specific white matter tracts during development and aging. However, the age-related alterations in the topological architecture of the white matter structural connectome across the human lifespan remain largely unknown. Here, a cohort of 113 healthy individuals (ages 9-85) with both diffusion and structural MRI acquisitions were examined. For each participant, the high-resolution white matter structural networks were constructed by deterministic fiber tractography among 1024 parcellation units and were quantified with graph theoretical analyses. The global network properties, including network strength, cost, topological efficiency, and robustness, followed an inverted U-shaped trajectory with a peak age around the third decade. The brain areas with the most significantly nonlinear changes were located in the prefrontal and temporal cortices. Different brain regions exhibited heterogeneous trajectories: the posterior cingulate and lateral temporal cortices displayed prolonged maturation/degeneration compared with the prefrontal cortices. Rich-club organization was evident across the lifespan, whereas hub integration decreased linearly with age, especially accompanied by the loss of frontal hubs and their connections. Additionally, age-related changes in structural connections were predominantly located within and between the prefrontal and temporal modules. Finally, based on the graph metrics of structural connectome, accurate predictions of individual age were obtained (r = 0.77). Together, the data indicated a dynamic topological organization of the brain structural connectome across human lifespan, which may provide possible structural substrates underlying functional and cognitive changes with age.
Asunto(s)
Envejecimiento/fisiología , Encéfalo/crecimiento & desarrollo , Conectoma/métodos , Vías Nerviosas/crecimiento & desarrollo , Sustancia Blanca/crecimiento & desarrollo , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Encéfalo/anatomía & histología , Niño , Imagen de Difusión Tensora , Femenino , Giro del Cíngulo/anatomía & histología , Giro del Cíngulo/crecimiento & desarrollo , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Fibras Nerviosas , Vías Nerviosas/anatomía & histología , Corteza Prefrontal/anatomía & histología , Corteza Prefrontal/crecimiento & desarrollo , Reproducibilidad de los Resultados , Caracteres Sexuales , Lóbulo Temporal/anatomía & histología , Lóbulo Temporal/crecimiento & desarrollo , Sustancia Blanca/anatomía & histología , Adulto JovenRESUMEN
BACKGROUND: To investigate the test-retest (TRT) reliability of the diffusion measures in cerebral white matter obtained from the diffusion MRI dataset acquired with multiband acceleration. METHODS: With the multiband diffusion MRI dataset with two repeated scanning sessions, the TRT reliability of diffusion measures (fractional anisotropy [FA], mean diffusivity [MD], primary diffusivity [PD] and transverse diffusivity [TD]) was investigated through several fully automated analysis methods, including two voxel-level analyses (voxel-based analysis [VBA] and tract-based spatial statistics [TBSS]) and an atlas ROI-based analysis. The reproducibility was assessed by the intra-class correlation coefficient (ICC). RESULTS: Our results demonstrated moderate to high reproducibility (ICC > 0.4) of diffusion measures from the multiband EPI sequence with different analysis approaches. Across different measures, FA exhibited the highest reproducibility (mean ICC = 0.70), while MD showed the lowest reliability (mean ICC = 0.55) (P = 0.006). Additionally, ICCs varied across different tract ROIs: Commissural tracts showed higher reproducibility than other categories of tracts (projection, association and brainstem), while the brainstem tracts exhibited the poorest reliability (P = 0.004). CONCLUSION: Our findings suggest a potential utility of the multiband EPI sequence for exploring individual differences of cerebral white matter and provide reference for future white matter studies.
Asunto(s)
Encéfalo/anatomía & histología , Imagen de Difusión Tensora/métodos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Adulto , Femenino , Humanos , Aprendizaje Automático , Masculino , Persona de Mediana Edad , Variaciones Dependientes del Observador , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Procesamiento de Señales Asistido por ComputadorRESUMEN
With the increasing consumption of organic solvents in chemical and pharmaceutical industries, the environment pollution of volatile organic compounds (VOCs) has become an urgent problem. Therefore, the rapid-visual detection method is of great significance in the analysis of VOCs. Based on the fluorescence quenching/enhancement mechanism of carbon quantum dots (CQDs), with the help of carboxymethyl cellulose membrane with porous and large specific surface area structure, a series of green CQDs@carboxymethyl cellulose composite film (CQDs@CMC composite film) was prepared in this study. In the typical-targeted pollutants (toluene) detection application, a fluorescence spectroscopy method was established which could achieve the high sensitivity and strong specificity detection. The mainly conclusions were as follows: The fluorescence spectrometric detection method for toluene: A kind of hydrophobic Lmi/Bet CQDs@CMC composite film was prepared and characterized with imidazole/betaine CQDs and porous carboxymethyl cellulose composite film as raw materials. The toluene detection performance was studied, and the recognition mechanism was explored. The results showed that toluene enhanced the fluorescence of Lmi/Bet CQDs@CMC composite film. The fluorescence intensity of composite films was proportional to toluene concentration when the toluene concentration ranged from 200 to 2200 mg/L. The detection limit of toluene was 1.169 mg/L, which provides a theoretical basis for the detection of toluene by fluorescence spectrometry.
Asunto(s)
Carbono , Carboximetilcelulosa de Sodio , Puntos Cuánticos , Tolueno , Puntos Cuánticos/química , Carboximetilcelulosa de Sodio/química , Tolueno/química , Tolueno/análisis , Carbono/química , Espectrometría de Fluorescencia/métodos , Gases/análisis , Gases/química , Límite de Detección , Compuestos Orgánicos Volátiles/análisis , Compuestos Orgánicos Volátiles/químicaRESUMEN
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.