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For many years, multi-scale models of chromatin domains, such as A/B compartments, sub-compartments, topologically associated domains (TADs), sub-TADs, and loops have been popular. However, existing methods can only identify structures at a single scale and cannot partition multi-scale structures. In this paper, we proposed a method (TORNADOES) for chromatin domain partitioning based on hypergraph clustering. First, we use a density clustering algorithm to identify TADs at different scales based on Hi-C data with different resolutions. Then, by combining ChIP-seq data features and TAD results at different scales, we generate a hypergraph based on these TADs. Finally, we partition the chromatin domain structure at different scales, including A/B, A1, A2, B1, B2, and B3 based on the Laplacian matrix feature of the hypergraph. Similarity comparison experiments and ChIP-seq signal enrichment analysis are performed on the A/B region and sub-TAD levels, respectively, demonstrating that our method can identify chromatin domains with distinct features and provide a deeper understanding of the organizational patterns and functional differences in TADs at the genomic hierarchical structure. Comparative analysis of multiple cell line data shows that TORNADOES can better classify different numbers and types of compartments by changing the factors ChIP-seq data and clustering number used to characterize TAD compared to other methods. Source code for the TORNADOES method can be found at https://github.com/ghaiyan/TORNADOES.

Many studies now indicate that brain disorders are associated with functional connectivity between brain regions, but the impact of interactions among functional connections on diseases remains to be explored. Moreover, existing models for analyzing the temporal and spatial attributes of fMRI data exhibit limitations in their efficacy. Currently, there is an absence of a comprehensive model capable of simultaneously capturing disease-related interactions within functional connectivity, fully leveraging the temporal and spatial characteristics of fMRI, and achieving superior diagnostic accuracy. To address these gaps, we propose a model to classify brain disorders that employs a novel algorithm and multimodal graph convolutional networks to process the temporal and spatial features of fMRI data separately, along with the demographic information. In the proposed model, a deep feature selection algorithm is employed to discover disease-related biomarkers and evaluate their impact on brain disorders. Remarkably, this approach has led to a classification accuracy of 80.66%, which surpasses the performance of graph neural networks by approximately 10%. Notably, our investigations have uncovered novel biomarkers linked to autism, with our findings corroborated by relevant literature. These achievements underscore the model's potential in revealing critical insights pertinent to autism, thereby facilitating groundbreaking experimental research in the field.

The development and improvement of electromagnetic underground metal target detection methods can be implemented by a framework that is experimental supporting, modular, and extensible. In this paper, we organize the components of electromagnetic underground metal target detection in a comprehensive, modular, and extensible framework. Furthermore, we present an open-source toolbox in Python called MicEMD (Modeling, Inversion, and Classification in ElectroMagnetic Detection, https://github.com/UndergroundDetection/MICEMD). The graphical user interface (GUI) and the library with a Python application programming interface (API) are contained in MicEMD. Included in MicEMD are staggered frequency-domain and time-domain electromagnetic forward modeling, least-squares inversion, and data-based classification methods at present. MicEMD’s capabilities are presented by two synthetic case studies. The first example shows the application of frequency-domain inversion. The second example shows the application of time-domain classification. It is anticipated that MicEMD offers a flexible tool in electromagnetic underground metal target detection.

Thermal interface materials (TIMs), as important materials for heat dissipation of insulated gate bipolar transistor (IGBT) modules, degrade under long-term thermal cycling, resulting in elevated junction temperatures and threatening the safe operation of IGBT modules. In consideration of the thermal coupling effect in IGBT modules, this paper proposes a three-dimensional thermal network model characterizing the TIM degradation. An IGBT module with the TIM is adopted to establish the finite element method (FEM) model, in which the TIM layer is divided into regular areas. The thermal network is established by simulating the heat transfer paths. The thermal impedance in each path is extracted according to the heat flow through its area considering the TIM degradation. Thereby, the impact of thermal coupling and TIM degradation on junction temperature of IGBT modules could be characterized. Furthermore, the proposed thermal network is verified by simulation from calculation accuracy of junction temperature and TIM temperature.

Topologically associated domains (TADs) play a pivotal role in disease detection. This study introduces a novel TADs recognition approach named TOAST, leveraging graph auto-encoders and clustering techniques. TOAST conceptualizes each genomic bin as a node of a graph and employs the Hi-C contact matrix as the graph's adjacency matrix. By employing graph auto-encoders, TOAST generates informative embeddings as features. Subsequently, the unsupervised clustering algorithm HDBSCAN is utilized to assign labels to each genomic bin, facilitating the identification of contiguous regions with the same label as TADs. Our experimental analysis of several simulated Hi-C data sets shows that TOAST can quickly and accurately identify TADs from different types of simulated Hi-C contact matrices, outperforming existing algorithms. We also determined the anchoring ratio of TAD boundaries by analyzing different TAD recognition algorithms, and obtained an average ratio of anchoring CTCF, SMC3, RAD21, POLR2A, H3K36me3, H3K9me3, H3K4me3, H3K4me1, Enhancer, and Promoters of 0.66, 0.47, 0.54, 0.27, 0.24, 0.12, 0.32, 0.41, 0.26, and 0.13, respectively. In conclusion, TOAST is a method that can quickly identify TAD boundary parameters that are easy to understand and have important biological significance. The TOAST web server can be accessed via http://223.223.185.189:4005/. The code of TOAST is available online at https://github.com/ghaiyan/TOAST.

Background: Small cell lung cancer (SCLC) is a highly malignant and aggressive neuroendocrine tumor. With the rise of immunotherapy, it has provided a new direction for SCLC. However, due to the lack of prognostic biomarkers, the median overall survival of SCLC is still to be improved. This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC. Methods: Gene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus (GEO) database, and tumor microenvironment (TME) infiltration profile data were obtained using CIBERSORT. The robust rank aggregation (RRA) method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes (DEGs) between normal and tumor tissue samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of the robust DEGs. Subsequently, protein–protein interaction networks and key modules were constructed by Cytoscape, and hub genes were selected from the whole network using the plugin cytoHubba. Survival analysis of hub genes was performed by Kaplan–Meier plotter in 18 patients with extensive-stage SCLC. Results: A total of 312 robust DEGs, including 55 upregulated and 257 downregulated genes, were screened from 129 SCLC tissue samples and 44 normal tissue samples. GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection, focal adhesion, complement and coagulation cascades, tumor necrosis factor (TNF) signaling pathway, and ECM-receptor interaction, which are closely associated with the development and progression of SCLC. Subsequently, three DEGs modules and six hub genes (ITGA10, DUSP12, PTGS2, FOS, TGFBR2, and ICAM1) were identified through screening with the Cytoscape plugins MCODE and cytoHubba, respectively. Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+ T cells were the predominant infiltrating immune cells in SCLC. In addition, Kaplan–Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential prognostic biomarker of SCLC. Conclusions: Hub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC, and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.

Background: The primary aim of this essay was to explore the best fitting model in remifentanil-propofol combined administrations during esophageal instrumentation (EI) from five distinct response surface models. The secondary aim was to combine the models to give appropriate effect-site drug concentrations (Ces) range with maximal comfort and safety. Methods: The Greco, reduced Greco, Minto, Scaled C50 Hierarchy and Fixed C50 Hierarchy models were constructed to fit four drug effects: loss of response to esophageal instrumentation (LREI), loss of response to esophageal instrumentation revised (LREIR), intolerable ventilatory depression (IVD) and respiratory compromise (RC). Models were tested by chi-square statistical test and evaluated with Akaike Information Criterion (AIC). Model prediction performance were measured by successful prediction rate (SPR) and three prediction errors. Results: The reduced Greco model was the best fitting model for LREI and RC, and the Minto model was the best fitting model for LREIR and IVD. The SPRs of reduced Greco model for LREI and RC were 81.76% and 79.81%. The SPRs of Minto model for LREIR and IVD were 80.32% and 80.12%. Overlay of the reduced Greco model for LREI and Minto model for IVD offered visual aid for guidance in drug administration. Conclusion: Using proper response surface model to fit different drug effects will describe the interactions between anesthetic drugs better. Combining response surface models to select the more reliable effect-site drug concentrations range can be used to guide clinical drug administration with greater safety and provide an improvement of anesthesia precision.

Objective: Blood-oxygen-level-dependent functional MRI allows to investigte neural activities and connectivity. While the non-human primate plays an essential role in neuroscience research, multimodal methods combining functional MRI with other neuroimaging and neuromodulation enable us to understand the brain network at multiple scales. Approach: In this study, a tight-fitting helmet-shape receive array with a single transmit loop for anesthetized macaque brain MRI at 7T was fabricated with four openings constructed in the coil housing to accommodate multimodal devices, and the coil performance was quantitatively evaluated and compared to a commercial knee coil. In addition, experiments over three macaques with infrared neural stimulation (INS), focused ultrasound stimulation (FUS), and transcranial direct current stimulation (tDCS) were conducted. Main results: The RF coil showed higher transmit efficiency, comparable homogeneity, improved SNR and enlarged signal coverage over the macaque brain. Infrared neural stimulation was applied to the amygdala in deep brain region, and activations in stimulation sites and connected sites were detected, with the connectivity consistent with anatomical information. Focused ultrasound stimulation was applied to the left visual cortex, and activations were acquired along the ultrasound traveling path, with all time course curves consistent with pre-designed paradigms. The existence of transcranial direct current stimulation electrodes brought no interference to the RF system, as evidenced through high-resolution MPRAGE structure images. Significance: This pilot study reveals the feasibility for brain investigation at multiple spatiotemporal scales, which may advance our understanding in dynamic brain networks.

Objective: N4-acetylcytidine (ac4C) acetylation can promote target gene expression through improved mRNA stability. To explore the role of ac4C acetylation in osteosarcoma, U2OS and MG63 cell lines were treated with the N-acetyltransferase 10 (NAT10) inhibitor Remodelin. Reverse transcription–polymerase chain reaction (RT-PCR) and Western blot were used to test the gene and protein expression efficiency. Methods: The proliferation rate of osteosarcoma cells was measured by a cell counting kit-8 (CCK8) assay. The cell cycle and apoptosis were analyzed by flow cytometry. The invasiveness of osteosarcoma cells was detected by a transwell invasion assay. The ac4C acetylation of target genes was screened by acetylated RNA immunoprecipitation and sequencing (acRIP-seq). Results: We found that when osteosarcoma cells were treated with Remodelin at the optimal concentration, their NAT10 expression and the cell proliferation was inhibited, the cells in the G1 phase increased (P

Summary: Here, we present a protocol for calculating the spatial density of regulatory chromatin interactions (SD-RCI) using Hi-C, ATAC-seq, and ChIP-seq datasets from the same cell line. We describe steps for selecting and preprocessing datasets, training and predicting a model to obtain regulatory chromatin interactions, and evaluating model performance. We then detail calculation of SD-RCI and visualization of the correlation between SD-RCI and gene expression. This protocol is applicable to Hi-C, ATAC-seq, and ChIP-seq data from the human cell line.For complete details on the use and execution of this protocol, please refer to Gong et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

In China, regional green spaces (RGSs) are green spaces outside urban built-up areas and have a wide distribution, large scale, and outstanding ecological function. RGSs have become increasingly important as urban clusters expand and increase in number. However, studies on RGSs are rare and it remains unclear what drives their distribution in the context of rapid urbanization. This study used integrated approaches to explore the distribution and driving factors of RGSs in the Nanjing metropolitan area (NMA) between 2000 and 2020, using Landsat image data. Spatiotemporal variations in the distribution of RGSs were obtained using the net change rate index and standard deviational ellipse. The driving factors were identified using Pearson correlation, ordinary least squares (OLS), and geographically weighted regression (GWR). We found that: (1) More RGSs are in the south of the NMA, and less in the north region. A “V” pattern of RGSs area change was observed, with substantial losses shifting from Yangtze River coastline to hilly and mountainous regions from 2000 to 2020. (2) The distribution of RGSs was affected by a combination of physical geographic, socioeconomic, and policy management factors. The functions of these influencing factors have pronounced spatiotemporal heterogeneity in direction or magnitude. Physical geographic factors including slope and annual precipitation exhibited the strongest correlation with RGSs distribution, and the regression coefficients showed relatively stable spatial performance. Among the socioeconomic factors, the distance from built-up areas and GDP played an important role. Policy management has played a guiding role, and the positive influence generated by dedicated financial expenditure and statutory green space area tends to increase and maintain a balance. The results of this study can help further understand the spatial distribution of RGSs and provide theoretical support for green space construction and ecological management of metropolitan areas during rapid urbanization.

With the development of Hi-C technology, the detection of topologically associated domains (TADs) boundaries plays an important role in exploring the relationship between gene structure and expression. However, a method that can identify accurate TAD boundaries from the Hi-C contact matrix with different resolutions is currently lacking. We proposed a method named CASPIAN that can identify chromatin TAD boundaries based on the spatial density clustering algorithm. CASPIAN requires few parameters to call TADs. This method is realized using the hierarchical density-based clustering method HDBSCAN, where the distance of pairwise bins is calculated based on three distance metrics (Euclidean, Manhattan, and Chebyshev distance metric) to adapt to the characteristics of the Hi-C contact matrix generated from simulation experiments or normalized methods. Our results show that, same as standard methods (e.g., Insulation Score, TopDom), CASPIAN can enrich factors related to promoting the gene expression, such as CTCF, H3K4me1, H3K4me3, RAD21, POLR2A, and SMC3. We also calculated the approximate proportion of various factors anchored at the TAD boundaries to observe the distribution of these factors surrounding the TAD boundaries. In conclusion, CASPIAN is an easy method to explore the relationship between transcription factors and TAD boundaries. CASPIAN is available online (https://gitee.com/ghaiyan/caspian).

It is challenging for most Historical Preservation Areas (HPA) to improve thermal comfort, especially under the contradiction of urban preservation. The study selected the Badaguan District as the object, which is the most famous HPA in Qingdao. Simulation is conducted by ENVI-met based on field measurements. Taking the current situation (green space 46.6% and building density 17.8%) as the basic case, we modeled two cases (reducing the green space to 29.1% and increasing the building density to 29.9% respectively according to the local standard). The results show that: (1) There are significant differences in air temperature and relative humidity among different streets; (2) District parameters including sky view factor (SVF) and Aspect Ratio(H/W) have different impact on microclimate; (3) Reducing the percentage of the green space(GS) to 29.1% (Case Ⅰ)and increasing the percentage of the building density(BD) to 29.9% (Case Ⅱ) both make the microclimate deteriorate. (4) By comparing the thermal comfort calendars of three cases, the thermal environment of the basic case is the best, and thermal environment of Case II is the worst especially at 12:00. The study can inform policy makers and urban designers to discover the balance between preservation of HPA and thermal environment improvement.

Background: Some Western medicine schools in China established standardized patient (SP) programs for medical education. However, SP programs are rarely applied to the education of traditional Chinese medicine (TCM). In this study, we evaluated the effectiveness of using standardized patient traditional Chinese medicine (SP-TCM) to improve clinical competency among TCM medical students. Methods: This study was a prospective, 2-group, parallel-training randomized trial over the course of 5 years. Data were collected from September 2016 to December 2020. Participants in each year were randomly allocated into the traditional-method training group or the SP-TCM training group (1:1) for a 3-month curriculum. Measurement of clinical competency among all trainees was based on a standardized examination composed of scores of medical record documentation, scores of TCM syndrome differentiation and therapeutic regimen, and checklist assessment from both SP-TCMs and TCM professionals. Feedback was collected using semi-constructive questionnaires from both groups. Results: Compared with those assigned to traditional-method training, those assigned to SP-TCM training demonstrated significantly greater post-training improvement in medical record documentation and TCM syndrome differentiation and therapeutic regimen. Moreover, SP-TCM trainees outscored those assigned to traditional training in the assessment for encounter performance given by independent SP-TCMs and TCM professionals. The SP-TCM method gained higher satisfaction of training efficacy and test performance than the traditional method. Conclusion: This SP-TCM program demonstrated great benefits for improving clinical competency among TCM medical students.

Rapid urbanization has brought a range of urban environmental problems that could be harmful to human thermal comfort and health. Urban parks can improve outdoor thermal comfort significantly because of its cooling island effect. However, outdoor thermal comfort is difficult to predict because of its complex nature and there was rare research focused on human physiological thermal responses in different green spaces of urban parks. In this study, thermal comfort of 52 participants were evaluated based on questionnaire survey and physiological parameters measurements including skin temperature (SKT), oxygen saturation (SaO2), heart rate (HR), heart rate variability (HRV) under different green spaces in parks, simultaneously with microclimate measurements. According to the regression analysis, physiological parameters were found having high accuracy in predicting human outdoor thermal comfort, with R2 were 0.803 for SKT, 0.830 for HRV, 0.767 for HR, 0.711 for SaO2 respectively. Results demonstrated that these four physiological parameters were sensitive to microclimate changes and human outdoor comfort thermal state can be captured by monitoring them with reasonable accuracy. This research can provide reference to evaluate outdoor thermal comfort by incorporating the physiological parameters, which can also provide a design basis for creating thermal comfortable outdoor spaces in urban parks.

With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.

Objectives: This study evaluated the associated biological effects of radio-frequency (RF) exposure at 16 T magnetic resonance imaging (MRI) on mice health. Material and methods: A total of 48 healthy 8-week-old male C57BL/6 mice were investigated. A 16 T high static magnetic field (HiSMF) was generated by a superconducting magnet, and a radiofrequency (RF) electromagnetic field for hydrogen resonance at 16 T (700 MHz) was transmitted via a homemade RF system. The mice were exposed inside the 16 T HiSMF with the 700 MHz RF field for 60 min, and the body weight, organ coefficients, histomorphology of major organs, and blood indices were analyzed for the basal state of the mice on day 0 and day 14. The Heat Shock Protein 70 (HSP70), cyclooxygenase 2 (COX2), and interleukin- 6 (IL-6) were used to evaluate the thermal effects on the brain. Locomotor activity, the open field test, tail suspension test, forced swimming test, and grip strength test were used to assess the behavioral characteristics of the mice. Results: The 16 T HiSMF with 700 MHz RF electromagnetic field exposure had no significant effects on body weight, organ coefficients, or histomorphology of major organs in the mice. On day 0, the expressions of HSP70 and COX2 in the brain were increased by 16 T HiSMF with 700 MHz RF electromagnetic field exposure. However, the expression of HSP70, COX2, and IL-6 had no significant difference compared with the sham group on day 14. Compared with the sham groups, the meancorpuscularvolume (MCV) on day 0 and the total protein (TP) on day 14 were increased significantly, whereas the other blood indices did not change significantly. The 16 T HiSMF with 700 MHz RF electromagnetic field exposure caused the mice to briefly circle tightly but had no effect on other behavioral indicators. Conclusions: In summary, 16 T HiSMF with 700 MHz RF electromagnetic field exposure for 60 min did not have severe effects on mice.

Spatiotemporal fusion has provided a feasible way to generate fractional vegetation cover (FVC) data with high spatial and temporal resolution. However, when the currently available spatiotemporal fusion methods are applied over agricultural regions, they usually underestimate high FVC values at the peak vegetation growth stage with medium FVC values as base data. This mainly results from inconsistencies in the temporal variations between fine- and coarse-resolution data if substantial temporal changes occur in vegetation. Therefore, a Spatial and Temporal Fusion method combining with Vegetation Growth Models (STF-VGM) was proposed to address this problem in this study, which incorporates vegetation growth models into the fusion process. Unlike other spatiotemporal fusion methods that mainly rely on changes in coarse-resolution data for prediction, STF-VGM fully utilizes available coarse- and fine-resolution time series data, including uncontaminated information in cloud/cloud shadow contaminated images. By establishing vegetation growth models with time series data, a conversion relationship between coarse- and fine-resolution FVC that changes along with the nonlinear vegetation change process can be extracted. STF-VGM makes prediction based on this variable relationship. A typical agricultural region located in the North China Plain was selected as the study area. The validation results indicated that the prediction accuracy for high FVC values was significantly improved using STF-VGM compared to the commonly used Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF) methods (STF-VGM: coefficient of determination (R2) = 0.9491, root mean square error (RMSE) = 0.0650, average difference (AD) = -0.0092; ESTARFM: R2 = 0.9341, RMSE = 0.1127, AD = -0.0631; FSDAF: R2 = 0.9224, RMSE = 0.1110, AD = -0.0599). The satisfactory performance of STF-VGM was also achieved in predicting FVC values at other vegetation growth stages (early growth stage: R2 = 0.8277, RMSE = 0.0440, AD = 0.0027; rapid growth stage: R2 = 0.9183, RMSE = 0.0844, AD = 0.0500). In addition, STF-VGM also has the potential to improve the spatiotemporal fusion accuracy of other vegetation parameters and vegetation indices, which will be evaluated in the future.

Although previous studies have assessed the relationships between visual space indicators and urban residents' psychological perceptions, systematic research on the relationship between the urban visual space and residents' psychological perceptions is still rare. The purpose of this study is to explore the correlation between the urban visual space and residents' psychological perceptions, analyze the influence of visual space indicators on residents' subjective perceptions, and focus on whether blue-green space is influenced by walkability, enclosure, openness, imageability, and traffic flow in the mechanism of its effect on urban residents' psychology. In this study, the Fully Convolutional Network (FCN-8s) is used to segment the street view images of Wuhan. An urban perception data set is obtained by coupling the human-machine adversarial framework and the Random Forest algorithm. Based on the street view data, we design seven kinds of visual space indicators. We conduct multiple linear regression analysis on urban residents' perceptions and the mediating effect analysis of blue-green space on residents' psychology using the above five visual space indicators as mediator. The results show that there is a strong relationship between the urban visual space indicators and residents' psychological perceptions, the visual space indicators such as greenness and enclosure have a strong influence on residents' psychological perceptions and significant mediating effects of some visual space indicators occur in the process of blue-green space influencing residents' psychology. This study comprehensively analyzes the relationship and mechanism between visual space indicators and residents’ psychological perception, and makes forward-looking work to further explore the complex mechanisms between visual space and residents’ psychology.

We have previously shown that INS-fMRI is a rapid method for mapping mesoscale brain networks in the macaque monkey brain. Focal stimulation of single cortical sites led to the activation of connected cortical locations, resulting in a global connectivity map. Here, we have extended this method for mapping brainwide networks following stimulation of single subcortical sites. As a testbed, we focused on the basal nucleus of the amygdala in the macaque monkey. We describe methods to target basal nucleus locations with submillimeter precision, pulse train stimulation methods, and statistical tests for assessing non-random nature of activations. Using these methods, we report that stimulation of precisely targeted loci in the basal nucleus produced sparse and specific activations in the brain. Activations were observed in the insular and sensory association cortices as well as activations in the cingulate cortex, consistent with known anatomical connections. What is new here is that the activations were focal and, in some cases, exhibited shifting topography with millimeter shifts in stimulation site. The precision of the method enables networks mapped from different nearby sites in the basal nucleus to be distinguished. While further investigation is needed to improve the sensitivity of this method, our analyses do support the reproducibility and non-random nature of some of the activations. We suggest that INS-fMRI is a promising method for mapping large-scale cortical and subcortical networks at high spatial resolution.

Abstracts: Functional magnetic resonance imaging (fMRI) in monkeys is important for bridging the gap between invasive animal brain studies and non-invasive human brain studies. To resolve the finer functional structure of the monkey brain, ultra-high-field (UHF) MR is essential, and high-performance, close-fitting RF receive coils are typically desired to fully leverage the intrinsic gains provided by UHF MRI. Moreover, static field (B0) inhomogeneity arising from the tissue susceptibility interface is more severe at UHF, presenting an obstacle to achieving high-resolution fMRI. B0 shim of the monkey head is challenging due to its smaller size and more complex sources of B0 offsets in multi-modal imaging tasks. In the present work, we have customized an array coil for lightly-anesthetized monkey fMRI in the 7T human scanner that combines RF and multi-coil (MC) B0 shim functionality (also referred to as AC/DC coils) to provide high imaging SNR and high-spatial-order, rapidly switchable B0-shim capability. Additional space was retained on the coil to render it compatible with monkey multi-modal imaging studies. Both MC global (whole-volume) and dynamic (slice-optimized) shim methods were tested and evaluated, and the benefits of MC shim for fMRI experiments was also studied. A minor reduction in RF coil performance was found after introducing additional B0 shim circuitry. However, the proposed RF coil provided higher image SNR and more uniform contrast compared to a commercially available coil for human knee imaging. Compared with static 2nd-order shim, the B0 inhomogeneity was reduced by 56.8%, and 95-percentile B0 offset was reduced to within 28.2 Hz through MC shim, versus 68.7 Hz with 2nd-order static shim. As a result, functional image quality could be improved, and brain activation can be better detected using the proposed AC/DC monkey coil.

Although the functional connectivity of patients with disorders of consciousness (DOC) has been widely examined, less is known about brain white matter connectivity. The aim of this study was to explore structural network alterations for the diagnosis and prognosis of patients with chronic DOC. Eleven DOC patients and 11 sex- and age-matched controls were included in the study. Participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 tesla (7 T). Graph-theoretical analysis and network-based statistics were used to analyze the group differences. Two patients were scanned twice for a longitudinal study to examine the relationship between connectome metrics and the patients' prognoses. Compared with healthy controls, DOC patients showed significantly elevated transitivity (p