Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis

Rare Variant of Vastus Medialis Detected in vivo by Ultrasound and Confirmed by High-resolution MRI.

[Purpose] This report describes an unusual incidental finding during ultrasound investigation of the vastus medialis muscle. Volunteers underwent ultrasound scanning as part of an on-going investigation into the architecture of the vastus medialis muscle. [Subjects and Methods] The distal thighs of forty-one subjects were scanned using the Philips iU22 US system. An unusual muscle morphology was detected bilaterally in one subject, who then underwent a 3T Magnetic Resonance Imaging (MRI) scan in order to further investigate the muscle morphology. The subject in question was a 32 year-old female who suffers from recurrent bilateral patellar dislocations. [Results] The MRI scan confirmed the ultrasound findings, and indicated the presence of the vastus medialis in two layers, with the VML continuing deep, separate from the VMO. [Conclusion] Although this rare variant has been been reported in previous cadaveric studies, we believe this to be the first report in the literature of this morphology in vivo. The biomechanical implications of this muscle arrangement are unknown, but it may not be without significance that this individual suffers from recurrent patellar dislocations

Reduced anterior cingulate grey matter volume in painful hand osteoarthritis

Objective Increasing evidence supports the role of central sensitisation in osteoarthritis (OA) pain. In this study, we used neuroimaging to compare pain-processing regions of the brain in participants with and without hand OA. We then assessed for volumetric changes in these brain regions following treatment with centrally acting analgesics. Methods Participants with hand OA (n = 28) underwent T1-weighted MRI of the brain before and after 12 weeks of treatment with pregabalin, duloxetine or placebo. Grey matter volume in the anterior cingulate cortex (ACC), insular cortex and thalamus was compared to non-OA control subjects (n = 11) using FreeSurfer regional volumetric analysis and voxel-based morphometry, and evaluated for differences pre- and post-treatment. Results Relative to non-OA controls, hand OA participants had areas of reduced grey matter volume in the ACC at baseline (p = 0.007). Regional volumetric differences in the ACC persisted after 13 weeks’ treatment with pregabalin or duloxetine (p = 0.004) with no significant differences between treatment cohorts, despite improvements in NRS pain scores for pregabalin (p = 0.005) and duloxetine (p = 0.050). The ACC grey matter changes persisted despite a significant improvement in pain in the pregabalin and duloxetine groups vs. placebo. No structural differences were observed in the insular cortex or thalamus at baseline or following treatment. Conclusion Our study found evidence of reduced ACC grey matter volume in participants with hand arthritis that persisted after treatment with centrally acting analgesics pregabalin and duloxetine, respectively. The sustained changes observed in the ACC in our study could reflect the relatively short duration of treatment, or that the differences observed are irreversible volume changes due to chronic pain that are established over time

Can MRI T1 be used to detect early changes in 5xFAD Alzheimer’s mouse brain?

In the present study, we have tested whether MRI T1 relaxation time is a sensitive marker to detect early stages of amyloidosis and gliosis in the young 5xFAD transgenic mouse, a well-established animal model for Alzheimer's disease.5xFAD and wild-type mice were imaged in a 4.7 T Varian horizontal bore MRI system to generate T1 quantitative maps using the spin-echo multi-slice sequence. Following immunostaining for glial fibrillary acidic protein, Iba-1, and amyloid-β, T1 and area fraction of staining were quantified in the posterior parietal and primary somatosensory cortex and corpus callosum.In comparison with age-matched wild-type mice, we observed first signs of amyloidosis in 2.5-month-old 5xFAD mice, and development of gliosis in 5-month-old 5xFAD mice. In contrast, MRI T1 relaxation times of young, i.e., 2.5- and 5-month-old, 5xFAD mice were not significantly different to those of age-matched wild-type controls. Furthermore, although disease progression was detectable by increased amyloid-β load in the brain of 5-month-old 5xFAD mice compared with 2.5-month-old 5xFAD mice, MRI T1 relaxation time did not change.In summary, our data suggest that MRI T1 relaxation time is neither a sensitive measure of disease onset nor progression at early stages in the 5xFAD mouse transgenic mouse model

Comparison of structural magnetic resonance imaging findings between neuropsychiatric systemic lupus erythematosus and systemic lupus erythematosus patients: A systematic review and meta-analysis

Introduction: Neuropsychiatric systemic lupus erythematosus is often clinically challenging to diagnose, treat and monitor. Although brain magnetic resonance imaging is frequently performed before lumbar puncture in neuropsychiatric systemic lupus erythematosus, it is not clear from the literature whether specific brain magnetic resonance imaging findings are associated with distinct clinical features of neuropsychiatric systemic lupus erythematosus. Methods: We conducted a systematic review and meta-analysis on published studies of neuropsychiatric systemic lupus erythematosus including brain magnetic resonance imaging and the 1999 American College of Rheumatology-defined clinical neuropsychiatric systemic lupus erythematosus syndromes to determine their relationship. Pooled prevalence and risk ratio for distinct neuropsychiatric systemic lupus erythematosus associations were determined with 95% confidence intervals. Results: Of 821 studies screened, 21 fulfilled inclusion criteria. A total of 818 participants were evaluated (91% female) with 1064 neuropsychiatric systemic lupus erythematosus episodes assessed. Neuropsychiatric systemic lupus erythematosus features included headache (24%), seizures (19%), cerebrovascular disease (18%), cognitive dysfunction (15%) and acute confusional state (14%). Normal magnetic resonance imaging was significant for anxiety disorder (risk ratio: 9.00; 95% confidence interval: 2.40, 33.79), autonomic disorder (risk ratio: 7.00; 95% confidence interval: 0.51, 96.06) and plexopathy (risk ratio: 5.00; 95% confidence interval: 0.81, 31.00). Highest risk ratio of neuropsychiatric systemic lupus erythematosus syndrome with abnormal magnetic resonance imaging was observed for cerebrovascular disease (risk ratio: 0.15; 95% confidence interval: 0.10, 0.24) and demyelination (risk ratio: 0.11; 95% confidence interval: 0.02, 0.72). Conclusion: Normal magnetic resonance imaging in neuropsychiatric systemic lupus erythematosus was the most significant correlate from our meta-analysis for psychological symptoms including anxiety and peripheral nerve features of autonomic disorder and plexopathy. The main abnormal brain magnetic resonance imaging correlates included cerebrovascular disease and demyelination. Brain magnetic resonance imaging correlates poorly with neuropsychiatric systemic lupus erythematosus features, and specific clinical symptoms should be the main determinants of performing magnetic resonance imaging rather than presence of neuropsychiatric systemic lupus erythematosus per se

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

BACKGROUND: There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. METHODS: DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. RESULTS: Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. CONCLUSIONS: D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning

Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition.

Purpose Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient D1,2, in mm2 s−1 and a fractional exponent, α, defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between b = 0 and 5000 s mm−2. The effects of varying maximum b-value (bmax), number of b-value shells, and the effects of Rician noise were investigated. Results QDTI measures showed bmax dependence, most significantly for α in white matter, which monotonically decreased with higher bmax leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of D1,2 and underestimation of α in white matter, and overestimation of D1,2 and α anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at bmax = 5000 s mm−2, and 4 b-value shells at bmax = 3960 s mm−2, providing minimal bias in D1,2 and α compared to the MbR. Conclusion A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures

Haar expectations of ratios of random characteristic polynomials

We compute Haar ensemble averages of ratios of random characteristic polynomials for the classical Lie groups K = O(N), SO(N), and USp(N). To that end, we start from the Clifford-Weyl algebera in its canonical realization on the complex of holomorphic differential forms for a C-vector space V. From it we construct the Fock representation of an orthosymplectic Lie superalgebra osp associated to V. Particular attention is paid to defining Howe's oscillator semigroup and the representation that partially exponentiates the Lie algebra representation of sp in osp. In the process, by pushing the semigroup representation to its boundary and arguing by continuity, we provide a construction of the Shale-Weil-Segal representation of the metaplectic group. To deal with a product of n ratios of characteristic polynomials, we let V = C^n \otimes C^N where C^N is equipped with its standard K-representation, and focus on the subspace of K-equivariant forms. By Howe duality, this is a highest-weight irreducible representation of the centralizer g of Lie(K) in osp. We identify the K-Haar expectation of n ratios with the character of this g-representation, which we show to be uniquely determined by analyticity, Weyl group invariance, certain weight constraints and a system of differential equations coming from the Laplace-Casimir invariants of g. We find an explicit solution to the problem posed by all these conditions. In this way we prove that the said Haar expectations are expressed by a Weyl-type character formula for all integers N \ge 1. This completes earlier work by Conrey, Farmer, and Zirnbauer for the case of U(N).Comment: LaTeX, 70 pages, Complex Analysis and its Synergies (2016) 2:

The effect of pregabalin or duloxetine on arthritis pain: a clinical and mechanistic study in people with hand osteoarthritis (vol 10, pg 2437, 2017)

Sofat N, Harrison A, Russell MD, et al. J Pain Res. 2017;10:2437–2449.On page 2443, Table 3, Placebo column, NRS section, the difference was reported as: –0.9 (–0.2 to 0.2). This is incorrect, and it should read as follows: –0.9 (–2.0 to 0.2).Read the original articl