Dispersion Imaging Research Articles

Cytotoxicity effect of nanoparticles of Euphorbia antiquorum on breast cancer cell line

Euphorbia antiquorum was known for its various therapeutic values like anti-inflammatory, anti-diabetic activity etc. Preliminary phytochemicals screening of plant extract disclosed the existence of various important bioactive constituents in hexane, ethanol and aqueous extract of the plant. Aqueous extract of Euphorbia antiquorum was used for silver and copper nanoparticle synthesis. Synthesized silver and copper nanoparticle showed the absorbance line at 422 and 367 nm respectively. Fourier-transform infrared spectroscopy analysis of synthesized nanoparticles reported the presence of phenol groups, amines and esters. Shape and morphology of the synthesized nanoparticles were characterized through Field Emission Scanning Electron Microscopy which showed the spherical shaped nanoparticles. Energy dispersive X-ray spectroscopy images showed the composition of analysed nanoparticles. The antioxidant activity of synthesized silver and copper nanoparticles of Euphorbia antiquorum by hydrogen peroxide scavenging activity method using ascorbic acid as the standard showed effective scavenging activity and indicated the potentiality of the plant as a source of natural antioxidant. Cytotoxicity study of silver nanoparticles showed 73.22% of inhibition at the concentration of 1000 µg/ml whereas copper nanoparticles at the same concentration showed 68.85% of inhibition against breast cancer cell line.

High-precision Redshifts for Type Ia Supernovae with the Nancy Grace Roman Space Telescope P127 Prism

We present results from simulating slitless spectroscopic observations with the Nancy Grace Roman Space Telescope’s (Roman) Wide-Field Instrument P127 prism spanning from 0.75 to 1.8 μm. We quantify the efficiency of recovered Type Ia supernova (SN Ia) redshifts, as a function of P127 prism exposure time, to guide planning for future observing programs with the Roman prism. Generating the two-dimensional dispersed images and extracting one-dimensional spectra is done with the slitless spectroscopy package pyLINEAR along with custom-written software. From the analysis of 1698 simulated SN Ia P127 prism spectra, we show the efficiency of recovering SN redshifts to z ≲ 3.0, highlighting the exceptional sensitivity of the Roman P127 prism. Redshift recovery is assessed by setting a requirement of . We find that 3 hr exposures are sufficient for meeting this requirement, for ≳50% of the sample of mock SNe Ia at z ≈ 2 and within ±5 days of rest-frame maximum light in the optical. We also show that a 1 hr integration of Roman can achieve the same precision in completeness to a depth of 24.4 ± 0.06 AB mag (or z ≲ 1). Implications for cosmological studies with Roman P127 prism spectra of SNe Ia are also discussed.

Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment : Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning.

This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning algorithms. A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K‑nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t‑test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI patients. The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (pvalue range, < 0.001-0.042) and Viso values increased (pvalue range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores. NODDI combined with machine learning algorithms is apromising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

Comparisons between non-interferometric and interferometric passive surface wave imaging methods—towards linear receiver array

SUMMARYPassive seismic methods in highly populated urban areas have gained much attention from the geophysics and civil engineering communities. Linear arrays are usually deployed for passive surface wave investigations because of their high convenience, and passive surface wave imaging methods commonly used for linear arrays can be grouped as non-interferometric methods (e.g. passive multichannel analysis of surface wave, refraction microtremor) and interferometric methods (e.g. multichannel analysis of passive surface waves and spatial autocorrelation). It is well known that the seismic interferometry method is able to retrieve Green’s function between inter-station pairs based on passive seismic data and that is how interferometric methods work. Although non-interferometric methods are also popular and effective in near-surface seismic imaging, particularly in the geotechnical industry, there is no theoretical proof to clarify the accuracy and/or the bias of these methods. In this study, we use numerical derivations and simulations to demonstrate the underlying physics for both non-interferometric and interferometric methods, under two common noise source environments including a homogeneous source distribution and a dominant in-line source distribution. We also prove the strength of interferometric methods for accurate dispersion imaging over the non-interferometric methods, and provide a way to estimate the biases in non-interferometric measurements. Finally, we present comprehensive comparisons between different passive surface wave methods with three typical field examples considering various observation systems.

Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome.

Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.

White matter microstructure in habit and reward circuits in anorexia nervosa: Insights from a neurite orientation dispersion and density imaging study.

Behavioral features of anorexia nervosa (AN) suggest abnormalities in reward and habit. Neuroimaging evidence suggests morphometric and functional perturbations within these circuits, although fewer studies have assessed white matter characteristics in AN, and no studies to date have assessed white matter microstructure in AN. In this brain imaging study, 29 female adolescents with partially or fully weight-restored AN and 27 healthy controls, all between 10 and 19 years, underwent whole-brain multi-shell diffusion tensor imaging. Utilizing neurite orientation dispersion and density imaging methods, we investigated group differences in white matter neurite density, orientation dispersion, and myelin density in tracts between prominent nodes of the reward circuit (ventral tegmental area (VTA) to nucleus accumbens (NAcc)) and the habit circuit (sensory motor area [SMA] to putamen). Findings revealed reduced neurite (F=5.20, p=0.027) and myelin density (F=5.39, p=0.025) in the left VTA-NAcc tract, and reduced orientation dispersion in the left (F=7.00, p=0.011) and right (F=6.77, p=0.012) VTA-NAcc tract. There were no significant group differences in the SMA-putamen tract. Significant relationships, after corrections, were not evident between tract microstructure and reward responsiveness, compulsive behaviors, illness duration, or BMI. Adolescents with AN exhibit less dense, undermyelinated, and less dispersed white matter tracts connecting prominent reward system nodes, which could potentially signify underutilization of this part of the reward circuit. These results provide a detailed examination of white matter microstructure in tracts underlying instrumental behavioral phenotypes contributing to illness in AN.

A novel weight-drop seismic energy source for subsurface characterization

Multichannel Analysis of Surface Waves (MASW) studies for subsurface characterization in Central Ganga Basin of India, where thick cover of alluvium absorbs most of the vibrations generated by a sledgehammer source, give low quality seismic data at large offsets and generally provide imaging depth lesser than 30 m due to low quality of dispersion curves at lower frequencies. Therefore, a novel seismic energy source is designed and developed to produce large amplitude surface waves even at longer offsets and for deeper depths of imaging using dispersion analysis of surface waves. The developed source for seismic data acquisition is low-cost, repeatable, portable, and easy to operate with no environmental impact. Unlike the sledgehammer source where the quality of surface waves generated also depends on the person hitting the metal plate, the seismic data quality from the novel weight-drop source is independent of the operator.The developed source is an impact type where a free-falling mass accelerated due to gravitational pull hits onto a base plate coupled with the earth generating vibrations in the ground. The performance of the source was evaluated in a field trial. A receiver array of 24-channel cabled geophones with 3 m spacing was planted on the ground. Seismic data were acquired using the source at 5 m negative offset inline from the first geophone. Seismic data were also recorded with the sledgehammer as a source for comparison. Five shot gathers for each source were acquired with the same acquisition geometry and source position.Shot gathers were analyzed and compared using trace attributes, 1D and 2D Fourier spectra, signal-to-noise ratio, relative energy, and dispersion images. The study shows that the developed source produces low frequency high amplitude signal and better dispersion image, notably in the lower frequency range, in comparison with the sledgehammer source. Therefore, the developed source is adapted to investigate deeper subsurface velocity structure with dispersion curve from high amplitude surface wave analysis. Moreover, the repeatability study suggests that the repeat shot gathers acquired from the developed source have remarkably high degree of similarity. Therefore, seismic data acquisition with the developed source does not require repeat shots for vertical stacking as often done in case of a hammer source. Sound pressure level measurement indicates that the developed source has no adverse environmental impact on the operator. From the field trial of the developed source, it is convinced that the source is cost-effective, portable, easy to operate and handle and is environmentally friendly.

Shear Wave Dispersion Slope Measured with Shear Wave Dispersion Imaging Is Associated with Variceal Hemorrhage in Cirrhotic Patients.

Portal hypertension (PH), as the main consequence of cirrhosis, leads to the development of gastroesophageal varices (GEVs). Variceal hemorrhage (VH) caused by the rupture of GEVs is a life-threatening emergency. Thus, the prediction of VH risk is considerably important. Our pilot study aimed to identify the risk factors of variceal hemorrhage (VH) in cirrhosis. Cirrhotic patients were prospectively included and divided into two groups according to the presence or absence of VH. Conventional ultrasound and shear wave dispersion (SWD) imaging were conducted to detect the portal vein diameter, spleen diameter, ascites, liver stiffness (LS) and shear wave dispersion slope (SWDS). The laboratory tests were recorded, including platelets (PLT), alanine transaminase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL) and albumin (ALB). The risk factors of VH were screened using univariate analyses and identified using multivariate logistic regression. The ROC curves were used to assess diagnostic accuracy. Comparisons between AUCs were performed using the Delong method. Sixty-five patients with 22 VHs were finally included. The SWDS, spleen diameter and ascites were identified as independent risk factors for VH. The SWDS showed good performance for diagnosing VH (AUC = 0.768, 95% CI: 0.647-0.864), and sensitively identified 95.5% (95% CI: 77.2%-99.9%) of patients with VH. Including the three risk factors in multivariate logistic regression, we obtained a formula for diagnosing VH: -20.749 + 0.804 × SWDS + 0.449 × spleen diameter + 1.803 × ascites (no ascites = 0, ascites = 1). Comparison of AUCs revealed that the formula (AUC = 0.900, 95% CI: 0.800-0.961) performed better than LS, SWDS, and spleen diameter in diagnosing VH (p &lt; 0.001; p &lt; 0.05; p &lt; 0.05). SWDS is a sensitive parameter for assessing the risk of VH. Combining the SWDS, spleen diameter and ascites resulted in good diagnostic accuracy.

Effects of Arterial Stiffness on Cerebral WM Integrity in Older Adults: A Neurite Orientation Dispersion and Density Imaging and Magnetization Transfer Saturation Imaging Study.

Arterial stiffness is reported to be able to cause axonal demyelination or degeneration. The present study aimed to use advanced MR imaging techniques to examine the effect of arterial stiffness on the WM microstructure among older adults. Arterial stiffness was measured using the cardio-ankle vascular elasticity index (CAVI). The high-CAVI (mean CAVI ≥ 9 points) and the low-CAVI groups (mean CAVI < 9 points) were created. The neuronal fiber integrity of the WM was evaluated by neurite orientation dispersion and density imaging and magnetization transfer saturation imaging. Tract-Based Spatial Statistics and the tracts-of-interest analysis were performed. Specific WM regions (corpus callosum, internal capsule, anterior thalamic radiation, corona radiata, superior longitudinal fasciculus, forceps minor, and inferior fronto-occipital fasciculus) were selected in the tracts-of-interest analysis. In Tract-Based Spatial Statistics, the high-CAVI group showed a significantly lower myelin volume fraction value in the broad WM and significantly higher radial diffusivity and isotropic volume fraction values in the corpus callosum, forceps minor, inferior fronto-occipital fasciculus, internal capsule, corona radiata, and anterior thalamic radiation than the low-CAVI group. In tracts-of-interest analysis using multivariate linear regression, significant associations were found between the mean CAVI and radial diffusivity in the anterior thalamic radiation and the corona radiata; isotropic volume fraction in the anterior thalamic radiation and the corona radiata; and myelin volume fraction in the superior longitudinal fasciculus (P < .05). Additionally, partial correlation coefficients were observed for the significant associations of executive function with radial diffusivity and myelin volume fraction (P < .05). Arterial stiffness could be associated with demyelination rather than axonal degeneration.

Trimethylamine N-Oxide Reduces Neurite Density and Plaque Intensity in a Murine Model of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common aging-associated neurodegenerative disease; nevertheless, the etiology and progression of the disease is still incompletely understood. We have previously shown that the microbially-derived metabolite trimethylamine N-oxide (TMAO) is elevated in the cerebrospinal fluid (CSF) of individuals with cognitive impairment due to AD and positively correlates with increases in CSF biomarkers for tangle, plaque, and neuronal pathology. We assessed the direct impact of TMAO on AD progression. To do so, transgenic 5XFAD mice were supplemented with TMAO for 12 weeks. Neurite density was assessed through quantitative brain microstructure imaging with neurite orientation dispersion and density imaging magnetic resonance imaging (MRI). Label-free, quantitative proteomics was performed on cortex lysates from TMAO-treated and untreated animals. Amyloid-β plaques, astrocytes, and microglia were assessed by fluorescent immunohistochemistry and synaptic protein expression was quantified via western blot. Oral TMAO administration resulted in significantly reduced neurite density in several regions of the brain. Amyloid-β plaque mean intensity was reduced, while plaque count and size remained unaltered. Proteomics analysis revealed that TMAO treatment impacted the expression of 30 proteins (1.5-fold cut-off) in 5XFAD mice, including proteins known to influence neuronal health and amyloid-β precursor protein processing. TMAO treatment did not alter astrocyte and microglial response nor cortical synaptic protein expression. These data suggest that elevated plasma TMAO impacts AD pathology via reductions in neurite density.

Efficacy of shear wave dispersion imaging for viscoelastic assessment of the liver in acute graft-versus-host disease rats.

To investigate the feasibility of using shear wave dispersion (SWD) imaging to evaluate hepatic acute graft-versus-host disease (aGVHD) in a rat model. To establish an aGVHD model, 30 Wistar rats were subjected to bone marrow transplantation, 10 Fischer 344 rats were used as donors, and 6 Wistar rats were used as the control group. Each week, 6 rats were randomly chosen and divided into groups of 1 week (1 w) to 5 weeks (5 w). For each subgroup, the rats received a clinical index assessment and shear wave dispersion (SWD) examination with 2 quantitative values, shear wave (SW) speed and SWD slope. The histological characteristics were then used as the reference standard to divide the rats into the aGVHD group and the no aGVHD (nGVHD) group. In the 2 weeks (2 w) group, only SWD slope [median: 7.26, interquartile range (IQR): 7.04 to 7.31] showed a significant increase in the measured value (P<0.05). The value of the 3 weeks (3 w) group (median: 7.88, IQR: 7.84 to 8.49) significantly increased compared with the 2 w value (P<0.05). Although the value increased gradually from week 3 to week 5, it had no statistical significance (P>0.05). The SW speed [mean ± standard deviation (SD): 1.54±0.11, 95% confidence interval (CI): 1.48 to 1.59] and SWD slope (mean ± SD: 8.29±0.56, 95% CI: 7.99 to 8.59) of the aGVHD group were higher than those of the control group and the nGVHD group (P<0.001). The correlation of SWD slope with pathological grade was the highest (r=0.798, P<0.01), followed by SW speed (r=0.785, P<0.01), and the correlation of clinical index with pathological grade was the lowest (r=0.751, P<0.01). In addition, the area under the receiver operating characteristic (ROC) curve (AUC) value of aGVHD using the SWD slope was 0.844 (95% CI: 0.67 to 0.95, sensitivity: 93.75%, specificity: 78.57%), which was higher than the AUC of both SW speed and clinical index, and the difference was statistically significant compared to the AUC of the clinical index. The SWD slope could show significant abnormalities earlier than SW speed and clinical index and is also more consistent with the change in aGVHD severity level. The SWD slope may help in detecting hepatic aGVHD during ultrasound SWD examination.

Biodistribution and Toxicological Effects of Ultra-Small Pt Nanoparticles Deposited on Au Nanorods (Au@Pt NRs) in Mice with Intravenous Injection.

Pt-based nanostructures are one of the promising nanomaterials for being used in catalysts, sensors, and therapeutics. However, their impacts on the health and biological systems are not adequately understood yet. In this work, nanorods composed of ultrasmall platinum (Pt) nanoparticles deposited on the surface and gold nanorod as the core (Au@Pt NRs) were synthesized, and the distribution and toxic effects of Au@Pt NRs were investigated in C57BL/6 mice with intravenous injection by using atomic absorption spectroscopy (AAS), transmission electron microscope (TEM), hematoxylin-eosin (HE) staining and blood cell analyzer. At the time point of Day 1, Day 8 and Day 16 post injection of Au@Pt NRs (6 mg/kg of Pt atom), Au@Pt NRs were mainly accumulated in the liver and spleen. The energy dispersive spectrometer mapping images showed Au@Pt NRs experienced quick corrosion and Au released faster than Pt in the physiological environments. The catalase (CAT) activity in tissues increased slightly in the early stage of the Au@Pt NRs exposure and went down to the normal level. With HE staining, inflammatory cells infiltration could be seen in the tissues, while no significant influences were detected on the blood biochemistry and the function of liver and kidney. In conclusion, intravenously injected Au@Pt NRs mainly distributed in the liver and spleen with comparable levels, and did not exert any significant toxic effects on the organs' function within two weeks; meanwhile, Au@Pt NRs were able to degrade, which indicated acceptable safety to the mice and potentials of biomedical application.

Impact of deposition temperature on crystalline quality, oxygen vacancy, defect modulations and hetero-interfacial properties of RF sputtered deposited Ga2O3 thin films on Si substrate

In the current work, thin film (∼55 nm) of n-type Ga2O3 (n-Ga2O3) is deposited on silicon (p-Si) substrate by using radio-frequency (RF) sputtering technique with systematic substrate temperature variations (room temperature to 700 °C). The structural, optical properties and chemical states of elements of the deposited films are observed to depend significantly on the deposition temperatures. The chemical composition and oxidation states, optical properties, defect states and structural quality of the deposited films are investigated in detail by employing x-ray photoelectron spectroscopy, energy dispersive x-ray, spectroscopic ellipsometry, Raman, photoluminescence and atomic force microscopy images. X-ray diffraction result reveals a polycrystalline nature of monoclinic β-phase of Ga2O3 with (403) dominant plane. The work functions are calculated from the ultraviolet photo-electron spectroscopy for all the deposited films and Ga2O3/Si heterojunction properties are investigated by using current–voltage (I–V) and capacitance–voltage (C–V) measurements. Among all the fabricated heterojunctions, 600 °C deposited Ga2O3 film exhibits superior performance in terms of energy bandgap, work function, refractive index, barrier height, rectification ratio and effective carrier concentrations. The current transport mechanism is analysed using the appropriate energy band diagram of Ga2O3 and Si. Therefore, the study suggests that 600 °C deposition temperatures is the optimum temperature for developing a high quality Ga2O3 thin film on Si by using RF sputtering technique and corresponding Ga2O3 thin film/Si heterojunction can be a potential candidate for developing several electronic and optoelectronic devices.

Understanding surface wave modal content for high-resolution imaging of submarine sediments with distributed acoustic sensing

SUMMARY Ocean bottom distributed acoustic sensing (OBDAS) is emerging as a new measurement method providing dense, high-fidelity and broad-band seismic observations from fibre-optic cables deployed offshore. In this study, we focus on 35.7 km of a linear telecommunication cable located offshore the Sanriku region, Japan, and apply seismic interferometry to obtain a high-resolution 2-D shear wave velocity (VS) model below the cable. We first show that the processing steps applied to 13 d of continuous data prior to computing cross-correlation functions (CCFs) impact the modal content of surface waves. Continuous data pre-processed with 1-bit normalization allow us to retrieve dispersion images with high Scholte-wave energy between 0.5 and 5 Hz, whereas spatial aliasing dominates dispersion images above 3 Hz for non-1-bit CCFs. Moreover, the number of receiver channels considered to compute dispersion images also greatly affects the resolution of extracted surface-wave modes. To better understand the remarkably rich modal nature of OBDAS data (i.e. up to 30 higher modes in some regions), we simulate Scholte-wave dispersion curves for stepwise linear VS gradient media. For soft marine sediments, simulations confirm that a large number of modes can be generated in gradient media. Based on pre-processing and theoretical considerations, we extract surface wave dispersion curves from 1-bit CCFs spanning over 400 channels (i.e. ∼2 km) along the array and invert them to image the subsurface. The 2-D velocity profile generally exhibits slow shear wave velocities near the ocean floor that gradually increase with depth. Lateral variations are also observed. Flat bathymetry regions, where sediments tend to accumulate, reveal a larger number of Scholte-wave modes and lower shallow velocity layers than regions with steeper bathymetry. We also compare and discuss the velocity model with that from a previous study and finally discuss the combined effect of bathymetry and shallow VS layers on earthquake wavefields. Our results provide new constraints on the shallow submarine structure in the area and further demonstrate the potential of OBDAS for high-resolution offshore geophysical prospecting.

Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI.

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.

Phase-velocity and intercept-time scanning (PIS) technique for extracting experimental dispersion characteristics of surface waves

In active surface wave methods, the ability to extract the true dispersion trends from surface wave field data is critical for inferring accurate shear-wave velocity profiles. However, dispersion images such as those generated by the conventional wavefield transformation method are usually accompanied by numerical artifacts including side-lobes and aliasing, both of which decrease the dispersion image resolution and may lead to misinterpretation of dispersion modes. For example, the side lobes can be misinterpreted as higher or lower modes, which would introduce additional errors into the inverted stiffness profiles. A new experimental dispersion analysis method is presented to improve the resolution and sharpness of dispersion images and minimize the side lobes. The proposed method employs scanning of the phase-velocities and intercept-times of a series of harmonic signals obtained by Fourier transformation of raw multichannel data in the space-time (x-t) domain. Results obtained from synthetic and real field data demonstrate that the improved method can yield high-quality dispersion images compared to those of the conventional dispersion-imaging method. In addition, the improved method do not rely on the assumption that the impact point is the generation point of the Rayleigh waves, which is needed for the conventional method.

Optimal stacking of noise cross-correlation functions

SUMMARY Cross-correlations of ambient seismic noise are widely used for seismic velocity imaging, monitoring and ground motion analyses. A typical step in analysing noise cross-correlation functions (NCFs) is stacking short-term NCFs over longer time periods to increase the signal quality. Spurious NCFs could contaminate the stack, degrade its quality and limit its use. Many methods have been developed to improve the stacking of coherent waveforms, including earthquake waveforms, receiver functions and NCFs. This study systematically evaluates and compares the performance of eight stacking methods, including arithmetic mean or linear stacking, robust stacking, selective stacking, cluster stacking, phase-weighted stacking, time–frequency phase-weighted stacking, Nth-root stacking and averaging after applying an adaptive covariance filter. Our results demonstrate that, in most cases, all methods can retrieve clear ballistic or first arrivals. However, they yield significant differences in preserving the phase and amplitude information. This study provides a practical guide for choosing the optimal stacking method for specific research applications in ambient noise seismology. We evaluate the performance using multiple onshore and offshore seismic arrays in the Pacific Northwest region. We compare these stacking methods for NCFs calculated from raw ambient noise (referred to as Raw NCFs) and from ambient noise normalized using a one-bit clipping time normalization method (referred to as One-bit NCFs). We evaluate six metrics, including signal-to-noise ratios, phase dispersion images, convergence rate, temporal changes in the ballistic and coda waves, relative amplitude decays with distance and computational time. We show that robust stacking is the best choice for all applications (velocity tomography, monitoring and attenuation studies) using Raw NCFs. For applications using One-bit NCFs, all methods but phase-weighted and Nth-root stacking are good choices for seismic velocity tomography. Linear, robust and selective stacking methods are all equally appropriate choices when using One-bit NCFs for monitoring applications. For applications relying on accurate relative amplitudes, the linear, robust, selective and cluster stacking methods all perform well with One-bit NCFs. The evaluations in this study can be generalized to a broad range of time-series analysis that utilizes data coherence to perform ensemble stacking. Another contribution of this study is the accompanying open-source software package, StackMaster, which can be used for general purposes of time-series stacking.

Alterations of brain microstructures in a mouse model of prenatal opioid exposure detected by diffusion MRI

Growing opioid use among pregnant women is fueling a crisis of infants born with prenatal opioid exposure. A large body of research has been devoted to studying the management of opioid withdrawal during the neonatal period in these infants, but less substantive work has explored the long-term impact of prenatal opioid exposure on neurodevelopment. Using a translationally relevant mouse model of prenatal methadone exposure (PME), the aim of the study is to investigate the cerebral microstructural differences between the mice with PME and prenatal saline exposure (PSE). The brains of eight-week-old male offspring with either PME (n = 15) or PSE (n = 15) were imaged using high resolution in-vivo diffusion magnetic resonance imaging on a 9.4 Tesla small animal scanner. Brain microstructure was characterized using diffusion tensor imaging (DTI) and Bingham neurite orientation dispersion and density imaging (Bingham-NODDI). Voxel-based analysis (VBA) was performed using the calculated microstructural parametric maps. The VBA showed significant (p < 0.05) bilateral alterations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI) and dispersion anisotropy index (DAI) across several cortical and subcortical regions, compared to PSE. Particularly, in PME offspring, FA, MD and AD were significantly higher in the hippocampus, dorsal amygdala, thalamus, septal nuclei, dorsal striatum and nucleus accumbens. These DTI-based results suggest widespread bilateral microstructural alterations across cortical and subcortical regions in PME offspring. Consistent with the observations in DTI, Bingham-NODDI derived ODI exhibited significant reduction in PME offspring within the hippocampus, dorsal striatum and cortex. NODDI-based results further suggest reduction in dendritic arborization in PME offspring across multiple cortical and subcortical regions. To our best knowledge, this is the first study of prenatal opioid exposure to examine microstructural organization in vivo. Our findings demonstrate perturbed microstructural complexity in cortical and subcortical regions persisting into early adulthood which could interfere with critical neurodevelopmental processes in individuals with prenatal opioid exposure.

NODDI, diffusion tensor microstructural abnormalities and atrophy of brain white matter and gray matter contribute to cognitive impairment in multiple sclerosis.

Pathologically specific MRI measures may elucidate in-vivo the heterogeneous processes contributing to cognitive impairment in multiple sclerosis (MS). Using diffusion tensor and neurite orientation dispersion and density imaging (NODDI), we explored the contribution of focal lesions and normal-appearing (NA) tissue microstructural abnormalities to cognitive impairment in MS. One hundred and fifty-two MS patients underwent 3T brain MRI and a neuropsychological evaluation. Forty-eight healthy controls (HC) were also scanned. Fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICV_f) and orientation dispersion index (ODI) were assessed in cortical and white matter (WM) lesions, thalamus, NA cortex and NAWM. Predictors of cognitive impairment were identified using random forest. Fifty-two MS patients were cognitively impaired. Compared to cognitively preserved, impaired MS patients had higher WM lesion volume (LV), lower normalized brain volume (NBV), cortical volume (NCV), thalamic volume (NTV), and WM volume (p ≤ 0.021). They also showed lower NAWM FA, higher NAWM, NA cortex and thalamic MD, lower NAWM ICV_f, lower WM lesion ODI, and higher NAWM ODI (false discovery rate-p ≤ 0.026). Cortical lesion number and microstructural abnormalities were not significantly different. The best MRI predictors of cognitive impairment (relative importance) (out-of-bag area under the curve = 0.727) were NAWM FA (100%), NTV (96.0%), NBV (84.7%), thalamic MD (43.4%), NCV (40.6%), NA cortex MD (26.0%), WM LV (23.2%) and WM lesion ODI (17.9%). Our multiparametric MRI study including NODDI measures suggested that neuro-axonal damage and loss of microarchitecture integrity in focal WM lesions, NAWM, and GM contribute to cognitive impairment in MS.

Multi-compartment diffusion magnetic resonance imaging models link tract-related characteristics with working memory performance in healthy older adults.

Multi-compartment diffusion MRI metrics [such as metrics from free water elimination diffusion tensor imaging (FWE-DTI) and neurite orientation dispersion and density imaging (NODDI)] may reflect more specific underlying white-matter tract characteristics than traditional, single-compartment metrics [i.e., metrics from Diffusion Tensor Imaging (DTI)]. However, it remains unclear if multi-compartment metrics are more closely associated with age and/or cognitive performance than single-compartment metrics. Here we compared the associations of single-compartment [Fractional Anisotropy (FA)] and multi-compartment diffusion MRI metrics [FWE-DTI metrics: Free Water Eliminated Fractional Anisotropy (FWE-FA) and Free Water (FW); NODDI metrics: Intracellular Volume Fraction (ICVF), Orientation Dispersion Index (ODI), and CSF-Fraction] with both age and working memory performance. A functional magnetic resonance imaging (fMRI) guided, white matter tractography approach was employed to compute diffusion metrics within a network of tracts connecting functional regions involved in working memory. Ninety-nine healthy older adults (aged 60–85) performed an in-scanner working memory task while fMRI was performed and also underwent multi-shell diffusion acquisition. The network of white matter tracts connecting functionally-activated regions was identified using probabilistic tractography. Diffusion metrics were extracted from skeletonized white matter tracts connecting fMRI activation peaks. Diffusion metrics derived from both single and multi-compartment models were associated with age (ps ≤ 0.011 for FA, FWE-FA, ICVF and ODI). However, only multi-compartment metrics, specifically FWE-FA (p = 0.045) and ICVF (p = 0.020), were associated with working memory performance. Our results suggest that while most current diffusion metrics are sensitive to age, several multi-compartment metrics (i.e., FWE-FA and ICVF) appear more sensitive to cognitive performance in healthy older adults.