Search

Objective: Determine the appropriate soymilk concentration and slurry depth to provide a theoretical reference for the efficient and high-quality production of yuba. Methods: The effect of soymilk solids concentration and slurry depth on the yield, film-forming rate, nutrient contents, mechanical properties, cooking loss rate, and rehydration ratio were investigated. Results: The optimal yield solids of yuba (34.68%) and highest nutritional value occurs when the soymilk solids concentration is 6% and the soymilk slurry depth is 6 cm, which presents 51.05% protein, 23.42% fats, and it also has good tensile strength and rehydration qualities, with respective measurements of 3.74 MPa and 3.80 g/g. Conclusion: Considering various factors such as yield, film formation rate and overall food quality, it is recommended to employ processing conditions with a soymilk solids concentration of 6% and a slurry depth of 6 cm for the efficient and high-quality production of yuba.

[Objective] To investigate the impact of soil aggregate structure on the formation of preferential pathways in the gully system of the dry-hot valley of the Jinsha River to enhance our understanding of soil moisture and solute transport in the gully system. [Methods] We selected different sections (i.e., catchment area, gully head, gully bed, and gully bottom) of both active and stable gullies in the dry-hot valley of the Jinsha River as our research subjects. We used staining tracers and morphometric image analysis techniques to extract the preferential pathways from the soil level profile. RDA (Redundancy Analysis), gray correlation, and coupled coordination were used to explore the relationship between soil aggregate structure and preferential pathways. [Results] (1) The stability of soil aggregates in stable gullies were higher than those in active gullies. Except for specific soil layers, the contents of mechanically stable aggregates (>0.25 mm, DR0.25), water-stable aggregates (>0.25 mm, WR0.25) and average mass diameter (MWD) in active gully were significantly lower than those in the stable gully (p10 mm served as primary channel. The Simpson index of active gullies exhibited an increase by 3.12% compared to stable gullies, indicating a higher richness of preferential pathways across different diameter classes. (3) The formation and distribution of preferential pathways were affected by the stability of soil aggregates, and MWD and WR0.25 were strongly correlated with the number of preferential pathways and had the most significant influence (pcatchment area>gully bottom>gully bed. [Conclusion] Soil aggregate structure is an of the important driving factors for the formation of preferential paths, and the distribution of preferential paths has significant impact on the development of gully systems in dry-hot valleys.

Cultivated meat, as an innovative substitute for conventional meat, is widely recognized as an effective solution to challenges such as meat supply shortages, environmental impacts, and animal welfare concerns. Based on the production technology used, cultivated meat can be categorized into three types: plant-based meat, cell-cultured meat, and microbial protein meat. This review explores the production processes and sensory quality enhancement techniques for these three types of cultivated meat, analyzes their respective technological advantages and challenges, and provides a forecast on the future development trends of cultivated meat products.

Objective: This study aimed to investigate the effects of drying methods on passion fruit peels. Methods: Passion fruit peels were treated with microwave vacuum freeze-drying, vacuum freeze-drying, heat pump drying, and far-infrared radiation drying. The drying characteristics, rehydration ratio, color difference, dietary fiber, functional properties, microstructure, total phenolic content, total flavonoid content, and antioxidant properties were measured and determined respectively. Results: Heat pump-dried passion fruit peels had poor rehydration ratio and functional properties. Microwave vacuum freeze drying reduced the drying time by 53% when compared tp far-infrared radiation drying, and the microwave vacuum freeze-dried treated passion fruit peels had a higher rehydration ratio (7.45), total phenolic content (13.45 mg GAE/g DW) and antioxidant activity (DPPH was 38.53%, FRAP was 1.14 mmol/L). Additionally, the passion fruit peels were vacuum freeze-dried with treatment to increase their water holding capacity [(21.64±0.21) g/g], oil holding capacity [(6.09±0.71) g/g], swelling [(25.00±1.00) g/g], and total flavonoid content (7.84 mg RT/g DW). Different drying techniques led to higher rehydration ratios, and they also significantly differed (P＜0.05) in terms of dietary fiber, functional properties, total phenolic content, total flavonoid content and antioxidant activity of the peels. Conclusion: The quality of passion fruit peel can be greatly affected by the drying process, and microwave vacuum freeze-drying is the most preferred technique.

Shelling rate and kernel breaking rate are the two key indicators of shelled materials, and mechanical shelling is one of the important methods to improve these key indicators. In this paper, the common mechanical shelling methods, mechanical shelling types, mechanical shelling principles and development status of shelling materials were analyzed. The advantages and disadvantages of mechanical hulling equipment were summarized, and suggestions were made for the problems existing in the current application of mechanical hulling.

Background: This study aimed to develop a time-efficient method of acquiring simultaneous, dual-slice MR spectroscopic imaging (MRSI) for the evaluation of brain metabolism. Methods: Adaptive Hadamard-encoded pulses were developed and integrated with atlas-based automatic prescription. The excitation profiles were evaluated via simulation, phantom and volunteer experiments. The feasibility of γ-aminobutyric acid (GABA)-edited dual-slice MRSI was also assessed. Results: The signal between slices in the dual-band MRSI was less than 1% of the slice profiles. Data from a homemade phantom containing separate, interfacing compartments of creatine and acetate solutions demonstrated ~0.4% acetate signal contamination relative to the amplitude in the excited creatine compartment. The normalized signal-to-noise ratios from atlas-based acquisitions in volunteers were found to be comparable between dual-slice, Hadamard-encoded MRSI and 3D acquisitions. The mean and standard deviation of the coefficients of variation for NAA/Cho from the repeated volunteer scans were 8.2% ± 0.8% and 10.1% ± 3.7% in the top and bottom slices, respectively. GABA-edited, dual-slice MRSI demonstrated simultaneous detection of signals from GABA and coedited macromolecules (GABA+) from both superior grey and deep grey regions of volunteers. Conclusion: This study demonstrated a fully automated dual-slice MRSI acquisition using atlas-based automatic prescription and adaptive Hadamard-encoded pulses.

This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.

Objective： This study aimed to obtain suitable turbulence model for simulation infrared combined spouted bed numerical valued. Methods: Two equation models (SST k-ε and Realizable k-ε) were used for numerical simulation to obtain the performance parameters of the infrared spouted bed and the velocity, temperature and particle concentration distribution of the flow field in the drying chamber under different working conditions. The particle drying experiment of the infrared spouted bed was carried out. Results: When under the working conditions of air inlet velocity of infrared spouted bed drying chamber of 8 m/s and a temperature of 50 ℃, the values obtained by Realizable k-ε simulation are similar to those obtained by SST k-ε. The particle volume fraction distribution, velocity distribution and temperature distribution in the fluid domain of the drying chamber obtained by numerical simulation with the two turbulence models were similar, and the overall effect is good, which showed that the application of the two turbulence models to the equipment is feasible. The turbulence model of Realizable k-ε converged faster, but the stability of SST k-ε was better. Conclusion: The Realizable k-ε turbulence model is more reasonable to describe three-dimensional flow field of infrared spouted bed.

Hyperpolarized (HP) 13C Magnetic Resonance Imaging (MRI) was applied for the first time to image and quantify the uptake and metabolism of [2–13C]pyruvate in the human brain to provide new metabolic information on cerebral energy metabolism. HP [2–13C]pyruvate was injected intravenously and imaged in 5 healthy human volunteer exams with whole brain coverage in a 1-minute acquisition using a specialized spectral-spatial multi-slice echoplanar imaging (EPI) pulse sequence to acquire 13C-labeled volumetric and dynamic images of [2–13C]pyruvate and downstream metabolites [5–13C]glutamate and [2–13C]lactate. Metabolic ratios and apparent conversion rates of pyruvate-to-lactate (kPL) and pyruvate-to-glutamate (kPG) were quantified to investigate simultaneously glycolytic and oxidative metabolism in a single injection.

This study aimed to implement a multimodal 1H/HP-13C imaging protocol to augment the serial monitoring of patients with glioma, while simultaneously pursuing methods for improving the robustness of HP-13C metabolic data. A total of 100 1H/HP [1-13C]-pyruvate MR examinations (104 HP-13C datasets) were acquired from 42 patients according to the comprehensive multimodal glioma imaging protocol. Serial data coverage, accuracy of frequency reference, and acquisition delay were evaluated using a mixed-effects model to account for multiple exams per patient. Serial atlas-based HP-13C MRI demonstrated consistency in volumetric coverage measured by inter-exam dice coefficients (0.977 ± 0.008, mean ± SD; four patients/11 exams). The atlas-derived prescription provided significantly improved data quality compared to manually prescribed acquisitions (n = 26/78; p = 0.04). The water-based method for referencing [1-13C]-pyruvate center frequency significantly reduced off-resonance excitation relative to the coil-embedded [13C]-urea phantom (4.1 ± 3.7 Hz vs. 9.9 ± 10.7 Hz; p = 0.0007). Significantly improved capture of tracer inflow was achieved with the 2-s versus 5-s HP-13C MRI acquisition delay (p = 0.007). This study demonstrated the implementation of a comprehensive multimodal 1H/HP-13C MR protocol emphasizing the monitoring of steady-state/dynamic metabolism in patients with glioma.

PurposeWhile 3D MR spectroscopic imaging (MRSI) provides valuable spatial metabolic information, one of the hurdles for clinical translation is its interpretation, with voxel-wise quality control (QC) as an essential and the most time-consuming step. This work evaluates the accuracy of machine learning (ML) models for automated QC filtering of individual spectra from 3D healthy control and patient datasets.MethodsA total of 53 3D MRSI datasets from prior studies (30 neurological diseases, 13 brain tumors, and 10 healthy controls) were included in the study. Three ML models were evaluated: a random forest classifier (RF), a convolutional neural network (CNN), and an inception CNN (ICNN) along with two hybrid models: CNN + RF, ICNN + RF. QC labels used for training were determined manually through consensus of two MRSI experts. Normalized and cropped real-valued spectra was used as input. A cross-validation approach was used to separate datasets into training/validation/testing sets of aggregated voxels.ResultsAll models achieved a minimum AUC of 0.964 and accuracy of 0.910. In datasets from neurological disease and controls, the CNN model produced the highest AUC (0.982), while the RF model achieved the highest AUC in patients with brain tumors (0.976). Within tumor lesions, which typically exhibit abnormal metabolism, the CNN AUC was 0.973 while that of the RF was 0.969. Data quality inference times were on the order of seconds for an entire 3D dataset, offering drastic time reduction compared to manual labeling.ConclusionML methods accurately and rapidly performed automated QC. Results in tumors highlights the applicability to a variety of metabolic conditions.

Background: Dynamic hyperpolarized (HP)-13C MRI has enabled real-time, non-invasive assessment of Warburg-related metabolic dysregulation in glioma using a [1-13C]pyruvate tracer that undergoes conversion to [1-13C]lactate and [13C]bicarbonate. Using a multi-parametric 1H/HP-13C imaging approach, we investigated dynamic and steady-state metabolism, together with physiological parameters, in high-grade gliomas to characterize active tumor. Methods: Multi-parametric 1H/HP-13C MRI data were acquired from fifteen patients with progressive/treatment-naïve glioblastoma [prog/TN GBM, IDH-wildtype (n = 11)], progressive astrocytoma, IDH-mutant, grade 4 (G4AIDH+, n = 2) and GBM manifesting treatment effects (n = 2). Voxel-wise regional analysis of the cohort with prog/TN GBM assessed imaging heterogeneity across contrast-enhancing/non-enhancing lesions (CEL/NEL) and normal-appearing white matter (NAWM) using a mixed effects model. To enable cross-nucleus parameter association, normalized perfusion, diffusion, and dynamic/steady-state (HP-13C/spectroscopic) metabolic data were collectively examined at the 13C resolution. Prog/TN GBM were similarly compared against progressive G4AIDH+ and treatment effects. Results: Regional analysis of Prog/TN GBM metabolism revealed statistically significant heterogeneity in 1H choline-to-N-acetylaspartate index (CNI)max, [1-13C]lactate, modified [1-13C]lactate-to-[1-13C]pyruvate ratio (CELval > NELval > NAWMval); [1-13C]lactate-to-[13C]bicarbonate ratio (CELval > NELval/NAWMval); and 1H-lactate (CELval/NELval > NAWMundetected). Significant associations were found between normalized perfusion (cerebral blood volume, nCBV; peak height, nPH) and levels of [1-13C]pyruvate and [1-13C]lactate, as well as between CNImax and levels of [1-13C]pyruvate, [1-13C]lactate and modified ratio. GBM, by comparison to G4AIDH+, displayed lower perfusion %-recovery and modeled rate constants for [1-13C]pyruvate-to-[1-13C]lactate conversion (kPL), and higher 1H-lactate and [1-13C]pyruvate levels, while having higher nCBV, %-recovery, kPL, [1-13C]pyruvate-to-[1-13C]lactate and modified ratios relative to treatment effects. Conclusions: GBM consistently displayed aberrant, Warburg-related metabolism and regional heterogeneity detectable by novel HP-13C/1H imaging techniques.

Objective: This study aimed to develop the application of egg yolk powder in the field of feed, functional food and biopharmaceuticals. Methods: Using fresh eggs as raw materials, yolk powder was prepared by spray drying, and the effects of different parameters on the content of yolk antibody (IgY), protein structure and functional properties of egg yolk powder were analyzed. Results: Considering the antibody content, physicochemical properties and functional properties of egg yolk powder comprehensively, when the inlet air temperature was 140 ℃ and the feed flow rate was 600 mL/h, the IgY content of egg yolk powder was 17.71 mg/g, and the solubility was 61.97% respectively. The powdery rate and water content was 36.46% and 3.8% respectively, and the surface hydrophobicity, water holding capacity and oil holding capacity was 106.67 μg, 3.63 g/g and 3.03 g/g respectively. The emulsifying property was 27.15%, and the emulsifying stability was 24.48%. The overall quality isoptimum. Conclusion: Changing the spray drying parameters can keep the structure of egg yolk powder intact and improve the physicochemical and functional properties of egg yolk powder, as well as ensure the high IgY content.

Background: Real-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma. Methods: Dynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global–local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared. Results: Both denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4–5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD). Conclusion: Post-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

Neonatal brain injury may impact brain development and lead to lifelong functional impairments. Hypoxic-ischemic encephalopathy (HIE) and congenital heart disease (CHD) are two common causes of neonatal brain injury differing in timing and mechanism. Maturation of whole-brain neural networks can be quantified during development using diffusion magnetic resonance imaging (dMRI) in combination with graph theory metrics. DMRI of 35 subjects with CHD and 62 subjects with HIE were compared to understand differences in the effects of HIE and CHD on the development of network topological parameters and functional outcomes. CHD newborns had worse 12–18 month language (P

In this study, the root exudates of wetland plants, Pistia stratiotes, black algae, and Cyperus alternifolius, exposed to six phosphorus concentration gradients (0, 0.2, 1, 5, 10, and 20 mg/L) were characterized. The experimental seedlings were cultivated in Hoagland solutions, which were then extracted, decompressed, and concentrated with CH2Cl2; subsequently, a gas chromatography-mass spectrometry (GC-MS) analysis was performed to study the root exudates effects under different phosphorus concentrations. Results showed the existence of several organic compounds, such as alkanes, esters, alcohols, amines, benzene, and acids (phthalic acid, cycloheptasiloxane, benzoic acid, and cyclopentasiloxane) in the root exudates of the wetland plants. The relative contents of phthalate, benzene dicarboxylic acid, and cyclohexasiloxane in the root exudates first increased, and then decreased, with the change in phosphorus concentration. The relative contents of three compounds in Pistia were the highest at 1 mg/L of phosphorus, and the lowest relative contents of phthalic acid and benzene dicarboxylic acid were observed at 20 mg/L of phosphorus. However, the relative content of cyclohexasiloxane was the lowest in the absence of P stress. In black algae, the relative contents of the three compounds were 36.66, 16.24, and 14.61%, respectively. The relative content of cyclohexasiloxane in the black algae first decreased and then increased, with its lowest relative content occurring at 5 mg/L of phosphorus and the highest at 10 mg/L of phosphorus. In Cyperus alternifolius, the highest relative concentrations of the four compounds: phthalic acid, dimethyl phthalate, octadecane, and diphenyl sulfone in Cyperus were observed at 5 mg/L phosphorus and the lowest at 10 mg/L phosphorus.

Background: Urea, the end product of protein metabolism, has been considered to have negligible toxicity for a long time. Our previous study showed a depression phenotype in urea transporter (UT) B knockout mice, which suggests that abnormal urea metabolism may cause depression. The purpose of this study was to determine if urea accumulation in brain is a key factor causing depression using clinical data and animal models. Methods: A meta-analysis was used to identify the relationship between depression and chronic diseases. Functional Magnetic Resonance Imaging (fMRI) brain scans and common biochemical indexes were compared between the patients and healthy controls. We used behavioural tests, electrophysiology, and molecular profiling techniques to investigate the functional role and molecular basis in mouse models. Findings: After performing a meta-analysis, we targeted the relevance between chronic kidney disease (CKD) and depression. In a CKD mouse model and a patient cohort, depression was induced by impairing the medial prefrontal cortex. The enlarged cohort suggested that urea was responsible for depression. In mice, urea was sufficient to induce depression, interrupt long-term potentiation (LTP) and cause loss of synapses in several models. The mTORC1-S6K pathway inhibition was necessary for the effect of urea. Lastly, we identified that the hydrolysate of urea, cyanate, was also involved in this pathophysiology. Interpretation: These data indicate that urea accumulation in brain is an independent factor causing depression, bypassing the psychosocial stress. Urea or cyanate carbamylates mTOR to inhibit the mTORC1-S6K dependent dendritic protein synthesis, inducing impairment of synaptic plasticity in mPFC and depression-like behaviour. CKD patients may be able to attenuate depression only by strict management of blood urea. Keywords: Urea, Depression, LTP, Carbamylation, mTOR

Introduction MRI and MR spectroscopy (MRS) provide early biomarkers of brain injury and treatment response in neonates with hypoxic-ischaemic encephalopathy). Still, there are challenges to incorporating neuroimaging biomarkers into multisite randomised controlled trials. In this paper, we provide the rationale for incorporating MRI and MRS biomarkers into the multisite, phase III high-dose erythropoietin for asphyxia and encephalopathy (HEAL) Trial, the MRI/S protocol and describe the strategies used for harmonisation across multiple MRI platforms.Methods and analysis Neonates with moderate or severe encephalopathy enrolled in the multisite HEAL trial undergo MRI and MRS between 96 and 144 hours of age using standardised neuroimaging protocols. MRI and MRS data are processed centrally and used to determine a brain injury score and quantitative measures of lactate and n-acetylaspartate. Harmonisation is achieved through standardisation—thereby reducing intrasite and intersite variance, real-time quality assurance monitoring and phantom scans.Ethics and dissemination IRB approval was obtained at each participating site and written consent obtained from parents prior to participation in HEAL. Additional oversight is provided by an National Institutes of Health-appointed data safety monitoring board and medical monitor.Trial registration number NCT02811263; Pre-result.

Soil moisture, stable hydrogen, and oxygen isotopes were sampled and determined in a demonstration area of soil and moisture conservation at the Laocheng Town of Yuanmou County in Chuxiong Prefecture, Yunnan of three land use types: Leucaena Benth artificial forest, Heteropogon contortus grass field, and farmland. The characteristics of stable hydrogen and oxygen isotopes of soil moisture in these different land use types at different soil depths were analyzed to investigate the regularities in the quantitative formation of soil moisture balance. In terms of forest land, we found that the variable coefficient of hydrogen isotopes in the 0-20 cm soil layer was the smallest, but decreased with depth under 20 cm. While in grassland, the variable coefficient in 80-100 cm was the largest, and decreased with depth above 80 cm. As for farmland, the variable coefficient in the top 20 cm was the largest, followed by 40-60 cm, and the medium 20-40 cm was the smallest. The soil moisture hydrogen isotope values of three land use type were different at surface layer, but prone to be consistent in each type. Along the soil depth in forest land, the hydrogen isotope increased first and then decreased, while increased in the end, and the maximum appeared in 80-100 cm. In grassland, the hydrogen isotope increased initially as the forest land but then decreased continuously, so the maximum was found at 20-40 cm. And in grassland, the hydrogen isotope of all depths were higher than which of forest land and farmland. In same land use type, the hydrogen isotope of soil moisture changed significantly at the surface, and the variation of hydrogen isotopes was obviously decreased along the depth. Our findings could provide reference data which would contribute to the assessment of regional groundwater resources in the dry-hot valley of Yuanmou in this study.

The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.

Mindfulness-based approaches show promise to improve emotional health in youth and may help treat and prevent adolescent depression and anxiety. However, there is a fundamental gap in understanding the neural reorganization that takes place as a result of such interventions. The Training for Awareness, Resilience, and Action (TARA) program, initially developed for depressed adolescents, uses a framework drawn from neuroscience, mindfulness, yoga, and modern psychotherapeutic techniques to promote emotional health. The goal of this study was to assess the effects of the TARA training on emotional health and structural white matter brain networks in healthy youth. We analyzed data from 23 adolescents who underwent the 12-week TARA training in a controlled within-subject study design and whose brain networks were assessed using diffusion MRI connectomics. Compared to the control time period, adolescents showed a significant decrease in anxiety symptoms with TARA (Cohen’s d = -0.961, p = 0.006); moreover, the node strength of the Right Amygdala decreased significantly after TARA (Cohen’s d = -1.026, p = 0.004). Post-hoc analyses indicated that anxiety at baseline before TARA was positively correlated with Right Amygdala node strength (r = 0.672, p = 0.001). While change in Right Amygdala node strength with TARA was not correlated with change in anxiety (r = 0.146, p = 0.51), it was associated with change in depression subscale of Anhedonia / Negative Affect (r = 0.575, p = 0.004, exploratory analysis), possibly due to overlapping constructs captured in our anxiety and depression scales. Our results suggest that increased structural connectivity of Right Amygdala may underlie increased anxiety in adolescents and be lowered through anxiety-reducing training such as TARA. The results of this study contribute to our understanding of the neural mechanisms of TARA and may facilitate neuroscience-based prevention and treatment of adolescent anxiety and depression.

Background Impairments in fetal oxygen delivery have been implicated in brain dysmaturation seen in congenital heart disease (CHD), suggesting a role for in utero transplacental oxygen therapy. We applied a novel imaging tool to quantify fetal cerebral oxygenation by measuring T2* decay. We compared T2* in fetuses with CHD with controls with a focus on cardiovascular physiologies (transposition or left‐sided obstruction) and described the effect of brief administration of maternal hyperoxia on T2* decay. Methods and Results This is a prospective study performed on pregnant mothers with a prenatal diagnosis of CHD compared with controls in the third trimester. Participants underwent a fetal brain magnetic resonance imaging scan including a T2* sequence before and after maternal hyperoxia. Comparisons were made between control and CHD fetuses including subgroup analyses by cardiac physiology. Forty‐four mothers (CHD=24, control=20) participated. Fetuses with CHD had lower total brain volume (238.2 mm3, 95% CI, 224.6–251.9) compared with controls (262.4 mm3, 95% CI, 245.0–279.8, P=0.04). T2* decay time was faster in CHD compared with controls (beta=−14.4, 95% CI, −23.3 to −5.6, P=0.002). The magnitude of change in T2* with maternal hyperoxia was higher in fetuses with transposition compared with controls (increase of 8.4 ms, 95% CI, 0.5–14.3, P=0.01), though between‐subject variability was noted. Conclusions Cerebral tissue oxygenation is lower in fetuses with complex CHD. There was variability in the response to maternal hyperoxia by CHD subgroup that can be tested in future larger studies. Cardiovascular physiology is critical when designing neuroprotective clinical trials in the fetus with CHD.

Increases in depressive and suicide-related symptoms among United States adolescents have been recently linked to increased use of smartphones. Understanding of the brain mechanisms that underlie the potential smartphone dependence may help develop interventions to address this important problem. In this exploratory study, we investigated the neural mechanisms underlying potential smartphone dependence in a sample of 19 adolescent volunteers who completed self-assessments of their smartphone dependence, depressive symptoms, and sleep problems. All 19 adolescents underwent diffusion MRI that allowed for assessment of white matter structural connectivity within the framework of connectomics. Based on previous literature on the neurobiology of addiction, we hypothesized a disruption of network centrality of three nodes in the mesolimbic network: Nucleus Accumbens, anterior cingulate cortex, and amygdala. Our results showed positive correlations between the node centrality of the right amygdala and self-reported smartphone dependence, between smartphone dependence and sleep problems, and between sleep problems and depressive symptoms. A higher phone dependence was observed in females compared to males. Supported by these results, we propose a model of how smartphone dependence can be linked to aberrations in brain networks, sex, sleep disturbances, and depression in adolescents.

Meditation has shown to benefit a wide range of conditions and symptoms, but the neural mechanisms underlying the practice remain unclear. Magnetic resonance imaging (MRI) studies have investigated the structural brain changes due to the practice by examining volume, density, or cortical thickness changes. However, these studies have focused on adults; meditation’s structural effects on the adolescent brain remain understudied. In this study, we investigated how meditation training affects the structure of the adolescent brain by scanning a group of 38 adolescents (16.48 ± 1.29 years) before and after participating in a 12-week meditation training. Subjects underwent Training for Awareness, Resilience, and Action (TARA), a program that mainly incorporates elements from mindfulness meditation and yoga-based practices. A subset of the adolescents also received an additional control scan 12 weeks before TARA. We conducted voxel-based morphometry (VBM) to assess gray matter volume changes pre- to post-training and during the control period. Subjects showed significant gray matter (GM) volume decreases in the left posterior insula and to a lesser extent in the left thalamus and left putamen after meditation training. There were no significant changes during the control period. Our results support previous findings that meditation affects regions associated with physical and emotional awareness. However, our results are different from previous morphometric studies in which meditation was associated with structural increases. We posit that this discrepancy may be due to the differences between the adolescent brain and the adult brain.

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

Background: Hyperpolarized carbon-13 (HP-13C) MRI is a non-invasive imaging technique for probing brain metabolism, which may improve clinical cancer surveillance. This work aimed to characterize the consistency of serial HP-13C imaging in patients undergoing treatment for brain tumors and determine whether there is evidence of aberrant metabolism in the tumor lesion compared to normal-appearing tissue. Methods: Serial dynamic HP [1-13C]pyruvate MRI was performed on 3 healthy volunteers (6 total examinations) and 5 patients (21 total examinations) with diffuse infiltrating glioma during their course of treatment, using a frequency-selective echo-planar imaging (EPI) sequence. HP-13C imaging at routine clinical timepoints overlapped treatment, including radiotherapy (RT), temozolomide (TMZ) chemotherapy, and anti-angiogenic/investigational agents. Apparent rate constants for [1-13C]pyruvate conversion to [1-13C]lactate (kPL) and [13C]bicarbonate (kPB) were simultaneously quantified based on an inputless kinetic model within normal-appearing white matter (NAWM) and anatomic lesions defined from 1H MRI. The inter/intra-subject consistency of kPL-NAWM and kPB-NAWM was measured in terms of the coefficient of variation (CV). Results: When excluding scans following anti-angiogenic therapy, patient values of kPL-NAWM and kPB-NAWM were 0.020 s−1 ± 23.8% and 0.0058 s−1 ± 27.7% (mean ± CV) across 17 HP-13C MRIs, with intra-patient serial kPL-NAWM/kPB-NAWM CVs ranging 6.8–16.6%/10.6–40.7%. In 4/5 patients, these values (0.018 s−1 ± 13.4% and 0.0058 s−1 ± 24.4%; n = 13) were more similar to those from healthy volunteers (0.018 s−1 ± 5.0% and 0.0043 s−1 ± 12.6%; n = 6) (mean ± CV). The anti-angiogenic agent bevacizumab was associated with global elevations in apparent rate constants, with maximum kPL-NAWM in 2 patients reaching 0.047 ± 0.001 and 0.047 ± 0.003 s−1 (±model error). In 3 patients with progressive disease, anatomic lesions showed elevated kPL relative to kPL-NAWM of 0.024 ± 0.001 s−1 (±model error) in the absence of gadolinium enhancement, and 0.032 ± 0.008, 0.040 ± 0.003 and 0.041 ± 0.009 s−1 with gadolinium enhancement. The lesion kPB in patients was reduced to unquantifiable values compared to kPB-NAWM. Conclusion: Serial measures of HP [1-13C]pyruvate metabolism displayed consistency in the NAWM of healthy volunteers and patients. Both kPL and kPB were globally elevated following bevacizumab treatment, while progressive disease demonstrated elevated kPL in gadolinium-enhancing and non-enhancing lesions. Larger prospective studies with homogeneous patient populations are planned to evaluate metabolic changes following treatment.

The dry-hot valley region counts as one of the most eco-sensitive zones in China, the issue of soil erosion is critical in regional ecological environment, soil mechanical property is one of the primary factors confining the occurrence of erosion, and it is attached crucial significance to in ascertaining the characteristics and principal factors of soil mechanics, and how to prevent and control soil erosion in arid red soil area of dry-hot valley. Through monitoring field location and directly shearing, the soil mechanical characteristics and the primary influencing factors of the mass erosion environment in the basin were ascertained. As the result indicates: (1) The soil moisture content, cohesion and internal friction angle are evidently correlated with each other abiding by power function, the relationship among soil cohesion, internal friction angle and volume moisture content goes as:

Abstract Background Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. Results In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. Conclusion The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE). However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6months, and assessed neurodevelopmental outcomes at 30months. All subjects underwent T1-weighted imaging at 3T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM) to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III) at 30months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6months are correlated with language outcome at 30months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes. Keywords: Neonatal encephalopathy, Hypoxic-ischemic encephalopathy, Language, Deformation based morphometry

Background Tube‐assisted feeding in infancy is common in patients with single‐ventricle physiology (SVP). Postnatal brain development is delayed, and injury is common, in patients with SVP. The role of brain findings in feeding outcomes remains unclear. We sought to determine the association between neonatal perioperative brain injury and postnatal brain maturation with feeding‐tube dependency in patients with SVP at neonatal discharge and just before the stage‐2 palliation. Methods and Results We evaluated a cohort of 48 term neonates with SVP who underwent pre‐ and postoperative brain magnetic resonance imaging. Perioperative brain injury and microstructural brain development were measured with diffusion tensor imaging including fractional anisotropy in white matter and apparent diffusion coefficient in gray matter. The primary outcome was defined as being 100% orally fed (binary). Of the patients 79% (38/48) were tube fed at hospital discharge, and 27% (12/45) were tube fed before stage‐2 palliation. Perioperative brain injury did not differ by group. Orally fed patients had a faster rate of decrease in apparent diffusion coefficient (3%, 95% CI 1.7% to 4.6%, P

Purpose: Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT. Methods: We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation. Results: The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus. Conclusions: Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression. Keywords: Machine learning, Adolescent depression, Diffusion MRI, Connectomics, Brain network, CBT

Background: To discuss the experience of diagnosis and treatment of ovarian cyst in infants. Materials and Methods: A retrospective review was conducted on 20 infants who suffered from ovarian cyst. Results: There were no dysplasia ovarian was found in children which were preoperatively diagnosed simplex cyst. Within thirteen children preoperatively detected mixed cystic-solid lesion, six cases ovarian cysts disappeared and two cases underwent poor blood supply in the following time. Conclusion: Adverse effects for ovarian cyst in infants can be prevented by agressive surgical intervention. Harmful effects of ovarian cyst can be prevented by positive surgical intervention despite the diagnostic difficulties in children with clinical symptoms of this condition.

Recent technological progress in the multiband echo planer imaging (MB EPI) technique enables accelerated MR diffusion weighted imaging (DWI) and allows whole brain, multi-b-value diffusion imaging to be acquired within a clinically feasible time. However, its applications at 7 T have been limited due to B1 field inhomogeneity and increased susceptibility artifact. It is an ongoing debate whether DWI at 7 T can be performed properly in patients, and a systematic SNR comparison for multiband spin-echo EPI between 3 T and 7 T has not been methodically studied. The goal of this study was to use MB EPI at 7 T in order to obtain 90-directional multi-shell DWI within a clinically feasible acquisition time for patients with glioma. This study included an SNR comparison between 3 T and 7 T, and the application of B1 mapping and distortion correction procedures for reducing the impact of variations in B0 and B1. The optimized multiband sequence was applied in 20 patients with glioma to generate both DTI and NODDI maps for comparison of values in tumor and normal appearing white matter (NAWM). Our SNR analysis showed that MB EPI at 7 T was comparable to that at 3 T, and the data quality acquired in patients was clinically acceptable. NODDI maps provided unique contrast within the T2 lesion that was not seen in anatomical images or DTI maps. Such contrast may reflect the complexity of tissue compositions associated with disease progression and treatment effects. The ability to consistently obtain high quality diffusion data at 7 T will contribute towards the implementation of a comprehensive brain MRI examination at ultra-high field.

In a multimodal volume coil, only one mode can generate homogeneous Radiofrequency (RF) field for Magnetic Resonance Imaging. The existence of other modes may increase the volume coil design difficulties and potentially decreases coil performance. In this study, we introduce common-mode resonator technique to high and ultrahigh field volume coil designs to reduce the resonant mode while maintain the homogeneity of the RF field. To investigate the design method, the common-mode resonator was realized by using a microstrip line which was split along the central to become a pair of parallel transmission lines within which common-mode currents exist. Eight common-mode resonators were placed equidistantly along the circumference of a low loss dielectric cylinder to form a volume coil. Theoretical analysis and comparison between the 16-strut common-mode volume coil and a conventional 16-strut volume coil in terms of RF field homogeneity and efficiency was performed using Finite-Difference Time-Domain (FDTD) method at 298.2 MHz. MR imaging experiments were performed by using a prototype of the common-mode volume coil on a whole body 7 Tesla scanner. FDTD simulation results showed the reduced number of resonant modes of the common-mode volume coil over the conventional volume coil, while the RF field homogeneity of the two type volume coils was kept at the same level. MR imaging of a water phantom and a kiwi fruit showing the feasibility of the proposed method for simplifying the volume coil design is also presented.

The solid-earth-tide models IERS1992 and IERS2003 are used to analyze some GPS-baseline, vertical-component and zenith-tropospheric-delay data from the Crustal Movement GPS Continuous Observation Network of Shandong and IGS stations. The results show that the differences between the baselines computed with the different models are at sub-millimeter level, and the differences in vertical component is direct proportional to station latitude. Also the amplitude of ZTD differences is about 0.6-1.0 mm, which is 6%-8% of the amplitude of solid-earth-tide differences. Although these effects are quite small, to analyze non-tidal deformation correctly, we should still use a single standard for processing GPS data.

MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage). In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a "reference brain" was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method's reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy.

Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopathy. In this manuscript we use diffusion tractography to construct structural networks for a cohort of patients with neonatal encephalopathy. We systematically map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds. Our algorithm offers a novel tool to evaluate neonates at risk for developing neurological deficit. The described approach can be applied to any brain pathology that affects structural connectivity.

Improved understanding of how the human brain is "wired" on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free "baby connectome" framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information--an important step toward a fully network-driven registration and analysis of brain connectivity.

By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research thinking concerning extreme learning machine into the economics classification area so as to fulfill the purpose of computerizing the speedy but effective evaluation of massive financial statements of listed companies pertain to different classes

Defining the structural and functional connectivity of the human brain (the human "connectome") is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores.