Prefrontal cortex involvement in selective letter generation: A functional magnetic resonance imaging study

Cerebral responses to alternating periods of a control task and a selective letter generation paradigm were investigated with functional Magnetic Resonance Imaging (fMRI). Subjects selectively generated letters from four designated sets of six letters from the English language alphabet, with the instruction that they were not to produce letters in alphabetical order either forward or backward, repeat or alternate letters. Performance during this condition was compared with that of a control condition in which subjects recited the same letters in alphabetical order. Analyses revealed significant and extensive foci of activation in a number of cerebral regions including mid-dorsolateral frontal cortex, inferior frontal gyrus, precuneus, supramarginal gyrus, and cerebellum during the selective letter generation condition. These findings are discussed with respect to recent positron emission tomography (PET) and fMRI studies of verbal working memory and encoding/retrieval in episodic memory.

Assessment of dynamic susceptibility contrast cerebral blood flow response to amphetamine challenge: A human pharmacological magnetic resonance imaging study at 1.5 and 4 T

Pharmacological MRI (phMRI) techniques can be used to monitor the neurophysiological effects of central nervous system (CNS) active drugs. In this study, we investigated whether dynamic susceptibility contrast (DSC) perfusion imaging employing the use of superparamagnetic iron oxide nanoparticles (Resovist) could be used to measure hemodynamic response to d-amphetamine challenge in human subjects at both 1.5 and 4 T. Significant changes in cerebral blood flow (CBF) were found in focal regions associated with the nigrostriatal circuit and mesolimbic and mesocortical dopaminergic pathways. More significant CBF responses were found at higher field strength, mainly within striatal structures. The results from this study indicate that DSC perfusion imaging using Resovist can be used to assess the efficacy of CNS-active drugs and may play a role in the development of novel psychiatric therapies at the preclinical level. © 2005 Wiley-Liss, Inc.

Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment

Background: Magnetic resonance diffusion tensor imaging (DTI) shows promise in the early detection of microstructural pathophysiological changes in the brain. Objectives: To measure microstructural differences in the brains of participants with amnestic mild cognitive impairment (MCI) compared with an age-matched control group using an optimised DTI technique with fully automated image analysis tools and to investigate the correlation between diffusivity measurements and neuropsychological performance scores across groups. Methods: 34 participants (17 participants with MCI, 17 healthy elderly adults) underwent magnetic resonance imaging (MRI)-based DTI. To control for the effects of anatomical variation, diffusion images of all participants were registered to standard anatomical space. Significant statistical differences in diffusivity measurements between the two groups were determined on a pixel-by-pixel basis using gaussian random field theory. Results: Significantly raised mean diffusivity measurements (p<0.001) were observed in the left and right entorhinal cortices (BA28), posterior occipital-parietal cortex (BA18 and BA19), right parietal supramarginal gyrus (BA40) and right frontal precentral gyri (BA4 and BA6) in participants with MCI. With respect to fractional anisotropy, participants with MCI had significantly reduced measurements (p<0.001) in the limbic parahippocampal subgyral white matter, right thalamus and left posterior cingulate. Pearson's correlation coefficients calculated across all participants showed significant correlations between neuropsychological assessment scores and regional measurements of mean diffusivity and fractional anisotropy. Conclusions: DTI-based diffusivity measures may offer a sensitive method of detecting subtle microstructural brain changes associated with preclinical Alzheimer's disease.

Tuning the resonance properties of 2D carbon nanotube networks towards a mechanical resonator

The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purposes. Based on large-scale molecular dynamics simulations, we have assessed the applicability of a new class of carbon nanomaterials for nanoresonator usage, i.e. the single-wall carbon nanotube (SWNT) network. It is found that SWNT networks inherit excellent mechanical properties from the constituent SWNTs, possessing a high natural frequency. However, although a high quality factor is suggested from the simulation results, it is hard to obtain an unambiguous Q-factor due to the existence of vibration modes in addition to the dominant mode. The nonlinearities resulting from these extra vibration modes are found to exist uniformly under various testing conditions including different initial actuations and temperatures. Further testing shows that these modes can be effectively suppressed through the introduction of axial strain, leading to an extremely high quality factor in the order of 109 estimated from the SWNT network with 2% tensile strain. Additional studies indicate that the carbon rings connecting the SWNTs can also be used to alter the vibrational properties of the resulting network. This study suggests that the SWNT network can be a good candidate for applications as nanoresonators.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Bayesian parametric bootstrap for models with intractable likelihoods

In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods. The approach is most appealing when the semi-automatic approximate Bayesian computation (ABC) summary statistics are selected. After a pilot run of ABC, the likelihood-free parametric bootstrap approach requires very few model simulations to produce an approximate posterior, which can be a useful approximation in its own right. An alternative is to use this approximation as a proposal distribution in ABC algorithms to make them more efficient. In this paper, the parametric bootstrap approximation is used to form the initial importance distribution for the sequential Monte Carlo and the ABC importance and rejection sampling algorithms. The new approach is illustrated through a simulation study of the univariate g-and- k quantile distribution, and is used to infer parameter values of a stochastic model describing expanding melanoma cell colonies.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Parametric study on cement-based soft-hard-soft (SHS) multi-layer composite pavement against blast load

An innovative cement-based soft-hard-soft (SHS) multi-layer composite has been developed for protective infrastructures. Such composite consists of three layers including asphalt concrete (AC), high strength concrete (HSC), and engineered cementitious composites (ECC). A three dimensional benchmark numerical model for this SHS composite as pavement under blast load was established using LSDYNA and validated by field blast test. Parametric studies were carried out to investigate the influence of a few key parameters including thickness and strength of HSC and ECC layers, interface properties, soil conditions on the blast resistance of the composite. The outcomes of this study also enabled the establishment of a damage pattern chart for protective pavement design and rapid repair after blast load. Efficient methods to further improve the blast resistance of the SHS multi-layer pavement system were also recommended.

Spinal inflammation in the absence of sacroiliac joint inflammation on magnetic resonance imaging in patients with active nonradiographic axial spondyloarthritis

Objective: To evaluate the presence of spinal inflammation with and without sacroiliac (SI) joint inflammation on magnetic resonance imaging (MRI) in patients with active nonradiographic axial spondyloarthritis (SpA), and to compare the disease characteristics of these subgroups. Methods: ABILITY-1 is a multicenter, randomized, controlled trial of adalimumab versus placebo in patients with nonradiographic axial SpA classified using the Assessment of SpondyloArthritis international Society axial SpA criteria. Baseline MRIs were centrally scored independently by 2 readers using the Spondyloarthritis Research Consortium of Canada (SPARCC) method for the SI joints and the SPARCC 6-discovertebral unit method for the spine. Positive evidence of inflammation on MRI was defined as a SPARCC score of >2 for either the SI joints or the spine. Results: Among patients with baseline SPARCC scores, 40% had an SI joint score of >2 and 52% had a spine score of >2. Forty-nine percent of patients with baseline SI joint scores of <2, and 58% of those with baseline SI joint scores of >2, had a spine score of >2. Comparison of baseline disease characteristics by baseline SI joint and spine scores showed that a greater proportion of patients in the subgroup with a baseline SPARCC score of >2 for both SI joints and spine were male, and patients with spine and SI joint scores of <2 were younger and had shorter symptom duration. SPARCC spine scores correlated with baseline symptom duration, and SI joint scores correlated negatively with the baseline Bath Ankylosing Spondylitis Disease Activity Index, but neither correlated with the baseline Ankylosing Spondylitis Disease Activity Score, total back pain, the patient's global assessment of disease activity, the Bath Ankylosing Spondylitis Functional Index, morning stiffness, nocturnal pain, or C-reactive protein level. Conclusion: Assessment by experienced readers showed that spinal inflammation on MRI might be observed in half of patients with nonradiographic axial SpA without SI joint inflammation.

Graphene quantum dots and the resonance light scattering technique for trace analysis of phenol in different water samples

A novel, highly selective resonance light scattering (RLS) method was researched and developed for the analysis of phenol in different types of industrial water. An important aspect of the method involved the use of graphene quantum dots (GQDs), which were initially obtained from the pyrolysis of citric acid dissolved in aqueous solutions. The GQDs in the presence of horseradish peroxidase (HRP) and H2O2 were found to react quantitatively with phenol such that the RLS spectral band (310 nm) was quantitatively enhanced as a consequence of the interaction between the GQDs and the quinone formed in the above reaction. It was demonstrated that the novel analytical method had better selectivity and sensitivity for the determination of phenol in water as compared to other analytical methods found in the literature. Thus, trace amounts of phenol were detected over the linear ranges of 6.00×10−8–2.16×10−6 M and 2.40×10−6–2.88×10−5 M with a detection limit of 2.20×10−8 M. In addition, three different spiked waste water samples and two untreated lake water samples were analysed for phenol. Satisfactory results were obtained with the use of the novel, sensitive and rapid RLS method.

Multimodal polymer nanoparticles with combined 19F magnetic resonance and optical detection for tunable, targeted, multimodal imagingin vivo

Understanding the complex nature of diseased tissue in vivo requires development of more advanced nanomedicines, where synthesis of multifunctional polymers combines imaging multimodality with a biocompatible, tunable, and functional nanomaterial carrier. Here we describe the development of polymeric nanoparticles for multimodal imaging of disease states in vivo. The nanoparticle design utilizes the abundant functionality and tunable physicochemical properties of synthetically robust polymeric systems to facilitate targeted imaging of tumors in mice. For the first time, high-resolution 19F/1H magnetic resonance imaging is combined with sensitive and versatile fluorescence imaging in a polymeric material for in vivo detection of tumors. We highlight how control over the chemistry during synthesis allows manipulation of nanoparticle size and function and can lead to very high targeting efficiency to B16 melanoma cells, both in vitro and in vivo. Importantly, the combination of imaging modalities within a polymeric nanoparticle provides information on the tumor mass across various size scales in vivo, from millimeters down to tens of micrometers.

Utility of magnetic resonance imaging-based finite element analysis for the biomechanical stress analysis of hemorrhagic and non-hemorrhagic carotid plaques

Background: Biomechanical stress analysis has been used for plaque vulnerability assessment. The presence of plaque hemorrhage (PH) is a feature of plaque vulnerability and is associated with thromboembolic ischemic events. The purpose of the present study was to use finite element analysis (FEA) to compare the stress profiles of hemorrhagic and non-hemorrhagic profiles. Methods and Results: Forty-five consecutive patients who had suffered a cerebrovascular ischemic event with an underlying carotid artery disease underwent high-resolution magnetic resonance imaging (MRI) of their symptomatic carotid artery in a 1.5-T MRI system. Axial images were manually segmented for various plaque components and used for FEA. Maximum critical stress (M-CstressSL) for each slice was determined. Within a plaque, the maximum M-CstressSL for each slice of a plaque was selected to represent the maximum critical stress of that plaque (M-CstressPL) and used to compare hemorrhagic and non-hemorrhagic plaques. A total of 62% of plaques had hemorrhage. It was observed that plaques with hemorrhage had significantly higher stress (M-CstressPL) than plaques without PH (median [interquartile range]: 315 kPa [247-434] vs. 200 kPa [171-282], P=0.003). Conclusions: Hemorrhagic plaques have higher biomechanical stresses than non-hemorrhagic plaques. MRI-based FEA seems to have the potential to assess plaque vulnerability.

Association between biomechanical structural stresses of atherosclerotic carotid plaques and subsequent ischaemic cerebrovascular events - A longitudinal in vivo magnetic resonance imaging-based finite element study

Background: High-resolution magnetic resonance (MR) imaging has been used for MR imaging-based structural stress analysis of atherosclerotic plaques. The biomechanical stress profile of stable plaques has been observed to differ from that of unstable plaques; however, the role that structural stresses play in determining plaque vulnerability remains speculative. Methods: A total of 61 patients with previous history of symptomatic carotid artery disease underwent carotid plaque MR imaging. Plaque components of the index artery such as fibrous tissue, lipid content and plaque haemorrhage (PH) were delineated and used for finite element analysis-based maximum structural stress (M-C Stress) quantification. These patients were followed up for 2 years. The clinical end point was occurrence of an ischaemic cerebrovascular event. The association of the time to the clinical end point with plaque morphology and M-C Stress was analysed. Results: During a median follow-up duration of 514 days, 20% of patients (n=12) experienced an ischaemic event in the territory of the index carotid artery. Cox regression analysis indicated that M-C Stress (hazard ratio (HR): 12.98 (95% confidence interval (CI): 1.32-26.67, pZ0.02), fibrous cap (FC) disruption (HR: 7.39 (95% CI: 1.61e33.82), p Z 0.009) and PH (HR: 5.85 (95% CI: 1.27e26.77), p Z 0.02) are associated with the development of subsequent cerebrovascular events. Plaques associated with future events had higher M-C Stress than those which had remained asymptomatic (median (interquartile range, IQR): 330 kPa (229e494) vs. 254 kPa (166-290), p Z0.04). Conclusions: High biomechanical structural stresses, in addition to FC rupture and PH, are associated with subsequent cerebrovascular events.

The ATHEROMA (Atorvastatin Therapy: Effects on Reduction of Macrophage Activity) Study. Evaluation using ultrasmall superparamagnetic iron oxide-enhanced magnetic resonance imaging in carotid disease

Objectives: The aim of this study was to evaluate the effects of low-dose (10 mg) and high-dose (80 mg) atorvastatin on carotid plaque inflammation as determined by ultrasmall superparamagnetic iron oxide (USPIO)-enhanced carotid magnetic resonance imaging (MRI). The hypothesis was that treatment with 80 mg atorvastatin would demonstrate quantifiable changes in USPIO-enhanced MRI-defined inflammation within the first 3 months of therapy. Background: Preliminary studies indicate that USPIO-enhanced MRI can identify macrophage infiltration in human carotid atheroma in vivo and hence may be a surrogate marker of plaque inflammation. Methods: Forty-seven patients with carotid stenosis >40% on duplex ultrasonography and who demonstrated intraplaque accumulation of USPIO on MRI at baseline were randomly assigned in a balanced, double-blind manner to either 10 or 80 mg atorvastatin daily for 12 weeks. Baseline statin therapy was equivalent to 10 mg of atorvastatin or less. The primary end point was change from baseline in signal intensity (ΔSI) on USPIO-enhanced MRI in carotid plaque at 6 and 12 weeks. Results: Twenty patients completed 12 weeks of treatment in each group. A significant reduction from baseline in USPIO-defined inflammation was observed in the 80-mg group at both 6 weeks (ΔSI 0.13; p = 0.0003) and at 12 weeks (ΔSI 0.20; p < 0.0001). No difference was observed with the low-dose regimen. The 80-mg atorvastatin dose significantly reduced total cholesterol by 15% (p = 0.0003) and low-density lipoprotein cholesterol by 29% (p = 0.0001) at 12 weeks. Conclusions: Aggressive lipid-lowering therapy over a 3-month period is associated with significant reduction in USPIO-defined inflammation. USPIO-enhanced MRI methodology may be a useful imaging biomarker for the screening and assessment of therapeutic response to "anti-inflammatory" interventions in patients with atherosclerotic lesions. (Effects of Atorvastatin on Macrophage Activity and Plaque Inflammation Using Magnetic Resonance Imaging [ATHEROMA]; NCT00368589).

Study of reproducibility of human arterial plaque reconstruction and its effects on stress analysis based on multispectral in vivo magnetic resonance imaging

Purpose: To quantify the uncertainties of carotid plaque morphology reconstruction based on patient-specific multispectral in vivo magnetic resonance imaging (MRI) and their impacts on the plaque stress analysis. Materials and Methods: In this study, three independent investigators were invited to reconstruct the carotid bifurcation with plaque based on MR images from two subjects to study the geometry reconstruction reproducibility. Finite element stress analyses were performed on the carotid bifurcations, as well as the models with artificially modified plaque geometries to mimic the image segmentation uncertainties, to study the impacts of the uncertainties to the stress prediction. Results: Plaque reconstruction reproducibility was generally high in the study. The uncertainties among interobservers are around one or the subpixel level. It also shows that the predicted stress is relatively less sensitive to the arterial wall segmentation uncertainties, and more affected by the accuracy of lipid region definition. For a model with lipid core region artificially increased by adding one pixel on the lipid region boundary, it will significantly increase the maximum Von Mises Stress in fibrous cap (>100%) compared with the baseline model for all subjects. Conclusion: The current in vivo MRI in the carotid plaque could provide useful and reliable information for plaque morphology. The accuracy of stress analysis based on plaque geometry is subject to MRI quality. The improved resolution/quality in plaque imaging with newly developed MRI protocols would generate more realistic stress predictions.