BACKGROUND: Pulmonary hypertension is a heterogeneous disease and a significant portion of patients at risk for it have available computed tomography (CT) imaging. Advanced automated processing techniques could be leveraged to for early detection, screening and development of quantitative phenotypes. Pruning and vascular tortuosity have been previously described in pulmonary arterial hypertension (PAH) but the extent of these phenomena in arterial versus venous pulmonary vasculature and in exercise pulmonary hypertension (ePH) have not been described.
RESEARCH QUESTION: What are the arterial and venous manifestations of pruning and vascular tortuosity using CT imaging in PAH and do they also occur in ePH?
STUDY DESIGN AND METHODS: A cohort of patients with PAH, ePH and controls with available CT angiograms were retrospectively identified to examine the differential arterial and venous presence of pruning and tortuosity in patients with precapillary pulmonary hypertension not confounded by lung or thromboembolic disease The pulmonary vasculature was reconstructed, an AI method was used to separate arteries and veins and used to compute arterial and venous vascular volumes and tortuosity.
RESULTS: 42 PAH, 12 ePH, 37 controls were identified. There was relatively lower arterial small vessel volume in subjects with PAH (PAH: 14.7(11.7-16.2) p<0.0001 vs controls 16.9(15.6-19.2)) and venous small vessel volume in subjects with PAH and ePH (PAH: 8.0(6.5-9.6) p<0.0001, ePH:7.8(7.5-11.4) p=0.004 vs control 11.5(10.6-12.2)). Higher large arterial volume, however, was only observed in the pulmonary arteries (PAH: 17.1(13.6-23.4) p<0.0001 vs controls 11.4(8.1-15.4)). Similarly, tortuosity was higher in the pulmonary arteries in PAH (PAH: 3.5(3.3-3.6) p=0.0002, vs control 3.2(3.2-3.3).
INTERPRETATION: Lower small distal pulmonary vascular volume, higher proximal arterial volume and higher arterial tortuosity are observed and can be quantified using automated techniques from clinically acquired CT scans of patients with exercise and resting pulmonary arterial hypertension.
Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or 'shell'), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its 'isotropic part'. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
PURPOSE: To develop a time-division multiplexing echo-planar imaging (TDM-EPI) sequence for approximately two- to threefold acceleration when acquiring joint relaxation-diffusion MRI data with multiple TEs.
METHODS: The proposed TDM-EPI sequence interleaves excitation and data collection for up to 3 separate slices at different TEs and uses echo-shifting gradients to disentangle the overlapping echo signals during the readout period. By properly arranging the sequence event blocks for each slice and adjusting the echo-shifting gradients, diffusion-weighted images from separate slices can be acquired. Therefore, we present 2 variants of the sequence. A single-TE TDM-EPI is presented to demonstrate the concept. Next, a multi-TE TDM-EPI is presented to highlight the advantages of the TDM approach for relaxation-diffusion imaging. These sequences were evaluated on a 3 Tesla scanner with a water phantom and in vivo human brain data.
RESULTS: The single-TE TDM-EPI sequence can simultaneously acquire 2 slices with a maximum b value of 3000 s/mm2 and 2.5 mm isotropic resolution using interleaved readout windows with TE ≈ 78 ms. With the same b value and resolution, the multi-TE TDM-EPI sequence can simultaneously acquire 2 or 3 separate slices using interleaved readout sections with shortest TE ≈ 70 ms and ΔTE ≈ 30 ms. Phantom and in vivo experiments have shown that the proposed TDM-EPI sequences can provide similar image quality and diffusion measures as conventional EPI readouts with multiple echoes but can reduce the overall relaxation-diffusion protocol scan time by approximately two- to threefold.
CONCLUSION: TDM-EPI is a novel approach to acquire diffusion imaging data at multiple TEs. This enables a significant reduction in acquisition time for relaxation-diffusion MRI experiments but without compromising image quality and diffusion measurements, thus removing a significant barrier to the adoption of relaxation-diffusion MRI in clinical research studies of neurological and mental disorders.
OBJECTIVES: Brain white matter (WM) microstructural changes evaluated by diffusion MRI were well documented in patients with systemic lupus erythematosus (SLE). Yet, conventional diffusion tensor imaging technique fails to differentiate WM changes that originate from tissue alterations from those due to increased extracellular free water (FW) related to neuroinflammation, microvascular disruption, atrophy, or other extracellular processes. Here, we sought to delineate changes in WM tissue microstructure and extracellular FW volume and examine their relationships with neurocognitive function in SLE patients.
METHODS: Twenty SLE patients (16 females, aged 36.0±10.6) without clinically-overt neuropsychiatric manifestation and 61 healthy controls (HC) (29 females, aged 29.2±9.4) underwent diffusion MRI and computerized neuropsychological assessments cross-sectionally. The FW imaging method was applied to compare microstructural tissue changes and extracellular FW volume of the brain WM between SLE patients and HC. Association between extracellular FW changes and neurocognitive performance was studied.
RESULTS: SLE patients had higher WM extracellular FW compared to HC (family-wise-error-corrected p < 0.05) while no group difference was found in FW-corrected tissue compartment and structural connectivity metrics. Extracellular FW increases in SLE patients were associated with poorer neurocognitive performance that probed sustained attention (p = 0.022) and higher cumulative glucocorticoid dose (p = 0.0041). Such findings remained robust after controlling for age, gender, IQ, and total WM volume.
CONCLUSIONS: The association between WM extracellular FW increases and reduced neurocognitive performance suggest possible microvascular degradation and/or neuroinflammation in SLE patients with clinically-inactive disease. The mechanistic impact of cumulative glucocorticoids on WM FW deserves further evaluation.
BACKGROUND: Exposure to repetitive head impacts (RHI) is associated with an increased risk of later-life neurobehavioral dysregulation and neurodegenerative disease. The underlying pathomechanisms are largely unknown.
PURPOSE: To investigate whether RHI exposure is associated with later-life corpus callosum (CC) microstructure and whether CC microstructure is associated with plasma total tau and neuropsychological/neuropsychiatric functioning.
STUDY TYPE: Retrospective cohort study.
POPULATION: Seventy-five former professional American football players (age 55.2 ± 8.0 years) with cognitive, behavioral, and mood symptoms.
FIELD STRENGTH/SEQUENCE: Diffusion-weighted echo-planar MRI at 3 T.
ASSESSMENT: Subjects underwent diffusion MRI, venous puncture, neuropsychological testing, and completed self-report measures of neurobehavioral dysregulation. RHI exposure was assessed using the Cumulative Head Impact Index (CHII). Diffusion MRI measures of CC microstructure (i.e., free-water corrected fractional anisotropy (FA), trace, radial diffusivity (RD), and axial diffusivity (AD)) were extracted from seven segments of the CC (CC1-7), using a tractography clustering algorithm. Neuropsychological tests were selected: Trail Making Test Part A (TMT-A) and Part B (TMT-B), Controlled Oral Word Association Test (COWAT), Stroop Interference Test, and the Behavioral Regulation Index (BRI) from the Behavior Rating Inventory of Executive Function, Adult version (BRIEF-A).
STATISTICAL TESTS: Diffusion MRI metrics were tested for associations with RHI exposure, plasma total tau, neuropsychological performance, and neurobehavioral dysregulation using generalized linear models for repeated measures.
RESULTS: RHI exposure was associated with increased AD of CC1 (correlation coefficient (r) = 0.32, P < 0.05) and with increased plasma total tau (r = 0.34, P < 0.05). AD of the anterior CC1 was associated with increased plasma total tau (CC1: r = 0.30, P < 0.05; CC2: r = 0.29, P < 0.05). Higher trace, AD, and RD of CC1 were associated with better performance (P < 0.05) in TMT-A (trace, r = 0.33; AD, r = 0.31; and RD, r = 0.28) and TMT-B (trace, r = 0.31; RD, r = 0.34). Higher FA and AD of CC2 were associated with better performance (P < 0.05) in TMT-A (FA, r = 0.36; AD, r = 0.28), TMT-B (FA, r = 0.36; AD, r = 0.27), COWAT (FA, r = 0.36; AD, r = 0.32), and BRI (AD, r = 0.29).
DATA CONCLUSION: These results suggest an association among RHI exposure, CC microstructure, plasma total tau, and clinical functioning in former professional American football players.
LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 1.
BACKGROUND: White matter hyperintensities (WMHs) are one of the hallmarks of cerebral small vessel disease (CSVD), but the pathological mechanisms underlying WMHs remain unclear. Recent studies suggest that extracellular fluid (ECF) is increased in brain regions with WMHs. It has been hypothesized that ECF accumulation may have detrimental effects on white matter microstructure. To test this hypothesis, we used cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) as a unique CSVD model to investigate the relationships between ECF and fiber microstructural changes in WMHs.
METHODS: Thirty-eight CADASIL patients underwent 3.0 T MRI with multi-model sequences. Parameters of free water (FW) and apparent fiber density (AFD) obtained from diffusion-weighted imaging (b = 0 and 1000 s/mm2) were respectively used to quantify the ECF and fiber density. WMHs were split into four subregions with four levels of FW using quartiles (FWq1 to FWq4) for each participant. We analyzed the relationships between FW and AFD in each subregion of WMHs. Additionally, we tested whether FW of WMHs were associated with other accompanied CSVD imaging markers including lacunes and microbleeds.
RESULTS: We found an inverse correlation between FW and AFD in WMHs. Subregions of WMHs with high-level of FW (FWq3 and FWq4) were accompanied with decreased AFD and with changes in FW-corrected diffusion tensor imaging parameters. Furthermore, FW was also independently associated with lacunes and microbleeds.
CONCLUSIONS: Our study demonstrated that increased ECF was associated with WM degeneration and the occurrence of lacunes and microbleeds, providing important new insights into the role of ECF in CADASIL pathology. Improving ECF drainage might become a therapeutic strategy in future.
Background: Clinical outcomes in high-grade glioma (HGG) have remained relatively unchanged over the last 3 decades with only modest increases in overall survival. Despite the validation of biomarkers to classify treatment response, most newly diagnosed (ND) patients receive the same treatment regimen. This study aimed to determine whether a prospective functional assay that provides a direct, live tumor cell-based drug response prediction specific for each patient could accurately predict clinical drug response prior to treatment.
Methods: A modified 3D cell culture assay was validated to establish baseline parameters including drug concentrations, timing, and reproducibility. Live tumor tissue from HGG patients were tested in the assay to establish response parameters. Clinical correlation was determined between prospective ex vivo response and clinical response in ND HGG patients enrolled in 3D-PREDICT (ClinicalTrials.gov Identifier: NCT03561207). Clinical case studies were examined for relapsed HGG patients enrolled on 3D-PREDICT, prospectively assayed for ex vivo drug response, and monitored for follow-up.
Results: Absent biomarker stratification, the test accurately predicted clinical response/nonresponse to temozolomide in 17/20 (85%, P = .007) ND patients within 7 days of their surgery, prior to treatment initiation. Test-predicted responders had a median overall survival post-surgery of 11.6 months compared to 5.9 months for test-predicted nonresponders (P = .0376). Case studies provided examples of the clinical utility of the assay predictions and their impact upon treatment decisions resulting in positive clinical outcomes.
Conclusion: This study both validates the developed assay analytically and clinically and provides case studies of its implementation in clinical practice.
Objective: Sexual dimorphism has been investigated in schizophrenia, although sex-specific differences among individuals who are at clinical high-risk (CHR) for developing psychosis have been inconclusive. This study aims to characterize sexual dimorphism of language areas in the brain by investigating the asymmetry of four white matter tracts relevant to verbal working memory in CHR patients compared to healthy controls (HC). HC typically show a leftward asymmetry of these tracts. Moreover, structural abnormalities in asymmetry and verbal working memory dysfunctions have been associated with neurodevelopmental abnormalities and are considered core features of schizophrenia. Methods: Twenty-nine subjects with CHR (17 female/12 male) for developing psychosis and twenty-one HC (11 female/10 male) matched for age, sex, and education were included in the study. Two-tensor unscented Kalman filter tractography, followed by an automated, atlas-guided fiber clustering approach, were used to identify four fiber tracts related to verbal working memory: the superior longitudinal fasciculi (SLF) I, II and III, and the superior occipitofrontal fasciculus (SOFF). Using fractional anisotropy (FA) of tissue as the primary measure, we calculated the laterality index for each tract. Results: There was a significantly greater right>left asymmetry of the SLF-III in CHR females compared to HC females, but no hemispheric difference between CHR vs. HC males. Moreover, the laterality index of SLF-III for CHR females correlated negatively with Backward Digit Span performance, suggesting a greater rightward asymmetry was associated with poorer working memory functioning. Conclusion: This study suggests increased rightward asymmetry of the SLF-III in CHR females. This finding of sexual dimorphism in white matter asymmetry in a language-related area of the brain in CHR highlights the need for a deeper understanding of the role of sex in the high-risk state. Future work investigating early sex-specific pathophysiological mechanisms, may lead to the development of novel personalized treatment strategies aimed at preventing transition to a more chronic and difficult-to-treat disorder.
INTRODUCTION: The current framework for investigating respiratory diseases is based on defining lung health as the absence of lung disease. In order to develop a comprehensive approach to prevent the development of lung disease, there is a need to evaluate the full spectrum of lung health spanning from ideal to impaired lung health. The American Lung Association (ALA) Lung Health Cohort is a new, population-based, cohort study focused primarily on characterising lung health in members of the millennial generation without diagnosed severe respiratory disease. Participants will be enrolled for the baseline study visit starting in 2021, and funding will be sought to support future study exams as part of a longitudinal cohort study. This study will be crucial for developing a novel paradigm of lung health throughout the adult life course.
METHODS AND ANALYSIS: This study will leverage the existing infrastructure of the ALA Airways Clinical Research Centers network to enrol 4000 participants between ages 25 and 35 years old at 39 sites across the USA between April 2021 and December 2024. Study procedures will include physical assessment, spirometry, chest CT scan, accelerometry and collection of nasal epithelial lining fluid, nasal epithelial cells, blood and urine. Participants will complete questionnaires about their sociodemographic characteristics, home address histories and exposures, work history and exposure, medical histories, lung health and health behaviours and activity.
ETHICS AND DISSEMINATION: The study was approved by the Johns Hopkins Medicine Institutional Review Board. Findings will be disseminated to the scientific community through peer-reviewed journals and at professional conferences. The lay public will receive scientific findings directly through the ALA infrastructure including the official public website. Deidentified datasets will be deposited to BioLINCC, and deidentified biospecimens may be made available to qualified investigators along with a limited-use datasets.
In the preparation of an Al-Ti-C grain refiner under an ultrasonic field, the mechanism of the wetting behaviour between Al and C was systematically investigated. The results demonstrated that the wetting behaviour was mainly dependent on the wetting of the Al melt on graphite under the ultrasonic field (physical wetting) and the formation and mass transfer of TiC (reactive wetting). The diffusion of Ti atoms and their adsorption around the graphite could contribute to the wetting of Al-C. TiC particles were formed under the high temperature caused by the cavitation effect, and they detached from the interface due to the sound pressure, which resulted in consistently sufficient contact on the wetting interface. Moreover, the wetting and spreading behaviour of the Al melt on graphite under an ultrasonic field were numerically simulated, strongly manifesting that the ultrasonic field could facilitate the wetting of the Al-C interface.
In this work, we leverage the Laplacian eigenbasis of voxel-wise white matter (WM) graphs derived from diffusion-weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.
PURPOSE: Tensor-valued diffusion encoding provides more specific information than conventional diffusion-weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa).
METHODS: Seventeen patients with histologically proven PCa were enrolled. DWI of the prostate was performed with linear and spherical tensor encoding using a maximal b-value of 1.5 ms/µm2 and a voxel size of 3 × 3 × 4 mm3 . The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MKI ), and the anisotropic kurtosis (MKA ). Regions of interest were placed in MR-defined cancerous tissues, as well as in apparently healthy tissues in the peripheral and transitional zones (PZs and TZs).
RESULTS: DWI with linear and spherical encoding yielded different image contrasts at high b-values, which enabled the estimation of MKA and MKI . Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P < .05), higher MKI (P < 10-5 ), and lower MKA (P < .05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10-3 ) and higher MKA (P < 10-3 ). No significant differences were found between cancers of different Gleason scores, possibly because of the limited sample size.
CONCLUSION: Tensor-valued diffusion encoding enabled mapping of MKA and MKI in the prostate. The elevated MKI in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.
Diffusion MRI uses magnetic field gradients to sensitize the signal to the random motion of spins. In addition to the prescribed gradient waveforms, background field gradients contribute to the diffusion weighting and thereby cause an error in the measured signal and consequent parameterization. The most prominent contribution to the error comes from so-called 'cross-terms.' In this work we present a novel gradient waveform design that enables diffusion encoding that cancels such cross-terms and yields a more accurate measurement. This is achieved by numerical optimization that maximizes encoding efficiency with a simultaneous constraint on the 'cross-term sensitivity' (c = 0). We found that the optimized cross-term-compensated waveforms were superior to previous cross-term-compensated designs for a wide range of waveform types that yield linear, planar, and spherical b-tensor encoding. The efficacy of the proposed design was also demonstrated in practical experiments using a clinical MRI system. The sensitivity to cross-terms was evaluated in a water phantom with a folded surface which provoked strong internal field gradients. In every comparison, the cross-term-compensated waveforms were robust to the effects of background gradients, whereas conventional designs were not. We also propose a method to measure background gradients from diffusion-weighted data, and show that cross-term-compensated waveforms produce parameters that are markedly less dependent on the background compared to non-compensated designs. Finally, we also used simulations to show that the proposed cross-term compensation was robust to background gradients in the interval 0 to 3 mT/m, whereas non-compensated designs were impacted in terms of a severe signal and parameter bias. In conclusion, we have proposed and demonstrated a waveform design that yields efficient cross-term compensation and facilitates accurate diffusion MRI in the presence of static background gradients regardless of their amplitude and direction. The optimization framework is compatible with arbitrary spin-echo sequence timing and RF events, b-tensor shapes, suppression of concomitant gradient effects and motion encoding, and is shared in open source.
Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.
OBJECTIVES: The objectives of this exploratory study were to investigate the feasibility of multidimensional diffusion magnetic resonance imaging (MddMRI) in assessing diffusion heterogeneity at both a macroscopic and microscopic level in prostate cancer (PCa).
MATERIALS AND METHODS: Informed consent was obtained from 46 subjects who underwent 3.0-T prostate multiparametric MRI, complemented with a prototype spin echo-based MddMRI sequence in this institutional review board-approved study. Prostate cancer tumors and comparative normal tissue from each patient were contoured on both apparent diffusion coefficient and MddMRI-derived mean diffusivity (MD) maps (from which microscopic diffusion heterogeneity [MKi] and microscopic diffusion anisotropy were derived) using 3D Slicer. The discriminative ability of MddMRI-derived parameters to differentiate PCa from normal tissue was determined using the Friedman test. To determine if tumor diffusion heterogeneity is similar on macroscopic and microscopic scales, the linear association between SD of MD and mean MKi was estimated using robust regression (bisquare weighting). Hypothesis testing was 2 tailed; P values less than 0.05 were considered statistically significant.
RESULTS: All MddMRI-derived parameters could distinguish tumor from normal tissue in the fixed-effects analysis (P < 0.0001). Tumor MKi was higher (P < 0.05) compared with normal tissue (median, 0.40; interquartile range, 0.29-0.52 vs 0.20-0.18; 0.25), as was tumor microscopic diffusion anisotropy (0.55; 0.36-0.81 vs 0.20-0.15; 0.28). The MKi could not be predicted (no significant association) by SD of MD. There was a significant correlation between tumor volume and SD of MD (R2 = 0.50, slope = 0.008 μm2/ms per millimeter, P < 0.001) but not between tumor volume and MKi.
CONCLUSIONS: This explorative study demonstrates that MddMRI provides novel information on MKi and microscopic anisotropy, which differ from measures at the macroscopic level. MddMRI has the potential to characterize tumor tissue heterogeneity at different spatial scales.
Q-space trajectory imaging (QTI) enables the estimation of useful scalar measures indicative of the local tissue structure. This is accomplished by employing generalized gradient waveforms for diffusion sensitization alongside a diffusion tensor distribution (DTD) model. The first two moments of the underlying DTD are made available by acquisitions at low diffusion sensitivity (b-values). Here, we show that three independent conditions have to be fulfilled by the mean and covariance tensors associated with distributions of symmetric positive semidefinite tensors. We introduce an estimation framework utilizing semi-definite programming (SDP) to guarantee that these conditions are met. Applying the framework on simulated signal profiles for diffusion tensors distributed according to non-central Wishart distributions demonstrates the improved noise resilience of QTI+ over the commonly employed estimation methods. Our findings on a human brain data set also reveal pronounced improvements, especially so for acquisition protocols featuring few number of volumes. Our method's robustness to noise is expected to not only improve the accuracy of the estimates, but also enable a meaningful interpretation of contrast in the derived scalar maps. The technique's performance on shorter acquisitions could make it feasible in routine clinical practice.
Transient receptor potential vanilloid 4 (TRPV4) is a Ca2+-permeable non-selective cation channel that is involved in the development of neuropathic pain. P2X7 receptor (P2X7) belongs to a class of ATP-gated nonselective cation channels that plays an important role in neuropathic pain. Nevertheless, little is known about the interaction between them for neuropathic pain. In this paper, we investigated role of TRPV4-P2X7 pathway in neuropathic pain. We evaluated the effect of TRPV4-P2X7 pathway on neuropathic pain in a chronic compression of the dorsal root ganglion (DRG) (hereafter termed CCD) model. We analyzed the effect of P2X7 on mechanical and thermal hyperalgesia mediated by TRPV4 in CCD. Furthermore, we assessed the effect of TRPV4 on the expression of P2X7 and the release of IL-1β and IL-6 in DRG after CCD. We found that intraperitoneal injection of TRPV4 agonist GSK-1016790A led to a significant increase of mechanical and thermal hyperalgesia in CCD, which was partially suppressed by P2X7 blockade with antagonist Brilliant Blue G (BBG). Then, we further noticed that GSK-1016790A injection increased the P2X7 expression of CCD, which was decreased by TRPV4 blockade with antagonist RN-1734 and HC-067047. Furthermore, we also discovered that the expressions of IL-1β and IL-6 were upregulated by GSK-1016790A injection but reduced by RN-1734 and HC-067047. Our results provide evidence that P2X7 contributes to development of neuropathic pain mediated by TRPV4 in the CCD model, which may be the basis for treatment of neuropathic pain relief.
The 22q11.2 deletion syndrome (22q11DS) is a developmental genetic syndrome associated with a 30% risk for developing schizophrenia. Lateral ventricles and subcortical structures are abnormal in this syndrome as well as in schizophrenia. Here, we investigated whether these structures are related in young adults with 22q11DS with and without prodromal symptoms (PS) for schizophrenia and whether abnormalities in volumes are associated with global functioning. MR images were acquired on a 3T scanner from 51 individuals with 22q11DS and 30 healthy controls (mean age: 21±2 years). Correlations were performed to evaluate the relationship between ventricular and subcortical volumes, with Global Assessment of Functioning (GAF) and Premorbid Adjustment Scale (PAS) in each group. Lateral ventricular volumes correlated negatively with subcortical volumes in individuals with 22q11DS. In individuals with 22q11DS with PS only, GAF correlated positively with volumes of the lateral ventricles and negatively with subcortical volumes. PAS correlated negatively with lateral ventricle volumes, and positively with volumes of subcortical structures. The results suggest a common neurodevelopmental mechanism related to the growth of these brain structures. Further, the ratio between the volumes and clinical measures could potentially be used to characterize individuals with 22q11DS and those from the general population for the risk of the development of schizophrenia.