Multiparametric cerebellar imaging and clinical phenotype in childhood ataxia telangiectasia

BackgroundAtaxia Telangiectasia (A-T) is an inherited multisystem disorder with cerebellar neurodegeneration. The relationships between imaging metrics of cerebellar health and neurological function across childhood in A-T are unknown, but may be important for determining timing and impact of therapeutic interventions.PurposeTo test the hypothesis that abnormalities of cerebellar structure, physiology and cellular health occur in childhood A-T and correlate with neurological disability, we performed multiparametric cerebellar MRI and establish associations with disease status in childhood A-T.MethodsProspective cross-sectional observational study. 22 young people (9 females / 13 males, age 6.6-17.8 years) with A-T and 24 matched healthy controls underwent 3-Tesla MRI with volumetric, diffusion and proton spectroscopic acquisitions. Participants with A-T underwent structured neurological assessment, and expression / activity of ataxia-telangiectasia mutated (ATM) kinase were recorded.ResultsAtaxia-telangiectasia participants had cerebellar volume loss (fractional total cerebellar volume: 5.3% vs 8.7%, P less than 0.0005, fractional 4th ventricular volumes: 0.19% vs 0.13%, P less than 0.0005), that progressed with age (fractional cerebellar volumes, r=-0.66, P=0.001), different from the control group (t=-4.88, P less than 0.0005). The relationship between cerebellar volume and age was similar for A-T participants with absent ATM kinase production and those producing non-functioning ATM kinase. Markers of cerebellar white matter injury were elevated in ataxia-telangiectasia vs controls (apparent diffusion coefficient: 0.89×10−3mm2s−1 vs 0.69×10−3mm2s−1, p less than 0.0005) and correlated (age-corrected) with neurometabolite ratios indicating impaired neuronal viability (N-acetylaspartate:creatine r=-0.70, P less than 0.001); gliosis (inositol:creatine r=0.50, P=0.018; combined glutamine/glutamate:creatine r=-0.55, P=0.008) and increased myelin turnover (choline:creatine r=0.68, P less than 0.001). Fractional 4th ventricular volume was the only variable retained in the regression model predicting neurological function (adjusted r2=0.29, P=0.015).ConclusionsQuantitative MRI demonstrates cerebellar abnormalities in children with A-T, providing non-invasive measures of progressive cerebellar injury and markers reflecting neurological status. These MRI metrics may be of value in determining timing and impact of interventions aimed at altering the natural history of A-T

Retrospective assessment of MRI-based volumetric changes of normal tissues in glioma patients following radio(chemo)therapy

In glioma patients, linac-based photon beam irradiation is a widely applied therapy, which achieves highly conformal target volume coverage, but is also known to cause side-effects to adjacent areas of healthy tissue. Apart from subjective measures, such as quality of life assessment and neurocognitive function tests, objective methods to quantify tissue damage are needed to assess this impact. Magnetic resonance imaging (MRI) is a well-established method for brain tumor diagnoses as well as assessing treatment response. In this study, we retrospectively assessed volumetric changes of gray matter (GM) and white matter (WM) in glioma patients following photon irradiation using a heterogeneous MRI-dataset obtained in routine clinical practice at different sites with imaging parameters and magnetic field strengths. We found a significant reduction in WM volume at one year (p=0.01) and two years (p=0.008) post radio(chemo)therapy whereas corresponding GM volumes did not change significantly (p=0.05 and p=0.11, respectively). More importantly, we also found large variations in the segmented tissue volumes caused by the heterogeneous MR data, thus potentially masking more subtle tissue changes over time. On the basis of these observations, we present suggestions regarding data acquisitions in future prospective MR studies to assess such volumetric changes

Classification of single voxel 1H spectra of brain tumours using LCModel

[EN] This study presents a novel method for the direct classification of H-1 single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS. Copyright (C) 2011 John Wiley & Sons, Ltd.FR was supported by grant C7809/A10342 as part of the Cancer Research-UK and Engineering and Physical Sciences Research Council Cancer Imaging Programme at the Children's Cancer and Leukaemia Group (CCLG), in association with the Medical Research Council and Department of Health (England). EF-G acknowledges funding by the Health Institute Carlos III through the RETICS Combiomed.Raschke, F.; Fuster García, E.; Opstad, KS.; Howe, F. (2012). Classification of single voxel 1H spectra of brain tumours using LCModel. NMR in Biomedicine. 25(2):322-331. doi:10.1002/nbm.1753S322331252García-Gómez, J. M., Luts, J., Julià-Sapé, M., Krooshof, P., Tortajada, S., Robledo, J. V., … Robles, M. (2008). Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magnetic Resonance Materials in Physics, Biology and Medicine, 22(1), 5-18. doi:10.1007/s10334-008-0146-yDevos, A., Lukas, L., Suykens, J. A. K., Vanhamme, L., Tate, A. R., Howe, F. A., … Van Huffel, S. (2004). Classification of brain tumours using short echo time 1H MR spectra. Journal of Magnetic Resonance, 170(1), 164-175. doi:10.1016/j.jmr.2004.06.010Opstad, K. S., Ladroue, C., Bell, B. A., Griffiths, J. R., & Howe, F. A. (2007). Linear discriminant analysis of brain tumour1H MR spectra: a comparison of classification using whole spectra versus metabolite quantification. NMR in Biomedicine, 20(8), 763-770. doi:10.1002/nbm.1147Tate, A. R., Underwood, J., Acosta, D. M., Julià-Sapé, M., Majós, C., Moreno-Torres, À., … Arús, C. (2006). Development of a decision support system for diagnosis and grading of brain tumours usingin vivo magnetic resonance single voxel spectra. NMR in Biomedicine, 19(4), 411-434. doi:10.1002/nbm.1016Davies, N. P., Wilson, M., Harris, L. M., Natarajan, K., Lateef, S., MacPherson, L., … Peet, A. C. (2008). Identification and characterisation of childhood cerebellar tumours byin vivoproton MRS. NMR in Biomedicine, 21(8), 908-918. doi:10.1002/nbm.1283Weis, J., Ring, P., Olofsson, T., Ortiz-Nieto, F., & Wikström, J. (2009). Short echo time MR spectroscopy of brain tumors: Grading of cerebral gliomas by correlation analysis of normalized spectral amplitudes. Journal of Magnetic Resonance Imaging, 31(1), 39-45. doi:10.1002/jmri.21991Laudadio, T., Mastronardi, N., Vanhamme, L., Van Hecke, P., & Van Huffel, S. (2002). Improved Lanczos Algorithms for Blackbox MRS Data Quantitation. Journal of Magnetic Resonance, 157(2), 292-297. doi:10.1006/jmre.2002.2593Hao, J., Zou, X., Wilson, M. P., Davies, N. P., Sun, Y., Peet, A. C., & Arvanitis, T. N. (2009). A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour1H magnetic resonance spectra. NMR in Biomedicine, 22(8), 809-818. doi:10.1002/nbm.1393Ladroue, C., Howe, F. A., Griffiths, J. R., & Tate, A. R. (2003). Independent component analysis for automated decomposition of in vivo magnetic resonance spectra. Magnetic Resonance in Medicine, 50(4), 697-703. doi:10.1002/mrm.10595Herminghaus, S., Dierks, T., Pilatus, U., Möller-Hartmann, W., Wittsack, J., Marquardt, G., … Zanella, F. E. (2003). Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. Journal of Neurosurgery, 98(1), 74-81. doi:10.3171/jns.2003.98.1.0074Laudadio, T., Selén, Y., Vanhamme, L., Stoica, P., Van Hecke, P., & Van Huffel, S. (2004). Subspace-based MRS data quantitation of multiplets using prior knowledge. Journal of Magnetic Resonance, 168(1), 53-65. doi:10.1016/j.jmr.2004.01.015Vanhamme, L., van den Boogaart, A., & Van Huffel, S. (1997). Improved Method for Accurate and Efficient Quantification of MRS Data with Use of Prior Knowledge. Journal of Magnetic Resonance, 129(1), 35-43. doi:10.1006/jmre.1997.1244Vanhamme, L., Van Huffel, S., Van Hecke, P., & van Ormondt, D. (1999). Time-Domain Quantification of Series of Biomedical Magnetic Resonance Spectroscopy Signals. Journal of Magnetic Resonance, 140(1), 120-130. doi:10.1006/jmre.1999.1835Provencher, S. W. (1993). Estimation of metabolite concentrations from localizedin vivo proton NMR spectra. Magnetic Resonance in Medicine, 30(6), 672-679. doi:10.1002/mrm.1910300604Pérez-Ruiz, A., Julià-Sapé, M., Mercadal, G., Olier, I., Majós, C., & Arús, C. (2010). The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses. BMC Bioinformatics, 11(1), 581. doi:10.1186/1471-2105-11-581Tate, A. R., Majós, C., Moreno, A., Howe, F. A., Griffiths, J. R., & Arús, C. (2002). Automated classification of short echo time in in vivo1H brain tumor spectra: A multicenter study. Magnetic Resonance in Medicine, 49(1), 29-36. doi:10.1002/mrm.10315Howe, F. A., Barton, S. J., Cudlip, S. A., Stubbs, M., Saunders, D. E., Murphy, M., … Griffiths, J. R. (2003). Metabolic profiles of human brain tumors using quantitative in vivo1H magnetic resonance spectroscopy. Magnetic Resonance in Medicine, 49(2), 223-232. doi:10.1002/mrm.10367Van der Graaf, M., Julià-Sapé, M., Howe, F. A., Ziegler, A., Majós, C., Moreno-Torres, A., … Heerschap, A. (2008). MRS quality assessment in a multicentre study on MRS-based classification of brain tumours. NMR in Biomedicine, 21(2), 148-158. doi:10.1002/nbm.1172Chen, L., Weng, Z., Goh, L., & Garland, M. (2002). An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization. Journal of Magnetic Resonance, 158(1-2), 164-168. doi:10.1016/s1090-7807(02)00069-1Stoyanova, R., & Brown, T. R. (2001). NMR spectral quantitation by principal component analysis. NMR in Biomedicine, 14(4), 271-277. doi:10.1002/nbm.700Shlens J A tutorial on principal component analysis 2009 http://www.snl.salk.edu/~shlens/pca.pdfKennard, R. W., & Stone, L. A. (1969). Computer Aided Design of Experiments. Technometrics, 11(1), 137. doi:10.2307/1266770Daszykowski, M., Walczak, B., & Massart, D. L. (2002). Representative subset selection. Analytica Chimica Acta, 468(1), 91-103. doi:10.1016/s0003-2670(02)00651-7Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., … Kleihues, P. (2007). The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathologica, 114(2), 97-109. doi:10.1007/s00401-007-0243-4Fuster-Garcia, E., Navarro, C., Vicente, J., Tortajada, S., García-Gómez, J. M., Sáez, C., … Robles, M. (2011). Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra. Magnetic Resonance Materials in Physics, Biology and Medicine, 24(1), 35-42. doi:10.1007/s10334-010-0241-

Targeted transcranial theta-burst stimulation alters fronto-insular network and prefrontal GABA

Repetitive transcranial magnetic stimulation (rTMS) has been used worldwide to treat depression. However, the exact physiological effects are not well understood. Pathophysiology of depression involves crucial limbic structures (e.g. insula), and it is still not clear if these structures can be modulated through neurostimulation of surface regions (e.g. dorsolateral prefrontal cortex, DLPFC), and whether rTMS-induced excitatory/inhibitory transmission alterations relate to fronto-limbic connectivity changes. Therefore, we sought proof-of-concept for neuromodulation of insula via prefrontal intermittent theta-burst stimulation (iTBS), and how these effects relate to GABAergic and glutamatergic systems. In 27 healthy controls, we employed a single-blind crossover randomised-controlled trial comparing placebo and real iTBS using resting-state functional MRI and magnetic resonance spectroscopy. Granger causal analysis was seeded from right anterior insula (rAI) to locate individualized left DLPFC rTMS targets. Effective connectivity coefficients within rAI and DLPFC were calculated, and levels of GABA/Glx, GABA/Cr and Glx/Cr in DLPFC and anterior cingulate voxels were also measured. ITBS significantly dampened fronto-insular connectivity and reduced GABA/Glx in both voxels. GABA/Glx had a significant mediating effect on iTBS-induced changes in DLPFC-to-rAI connectivity. We demonstrate modulation of the rAI using targeted iTBS through alterations of excitatory/inhibitory interactions, which may underlie therapeutic effects of rTMS, offering promise for rTMS treatment optimization

Trends of incidence and treatment strategies for operatively treated distal fibula fractures from 2005 to 2019: a nationwide register analysis

Introduction!#!Valid epidemiological data about distal fibular fractures and their treatment strategies are missing. Innovative osteosynthesis techniques were introduced and improved during the past 15 years. The aim of this study was to investigate the epidemiologic development and the implementation of new treatment strategies in a nationwide register in Germany over a period of 15 years.!##!Materials and methods!#!Data of the German Federal Statistical Office from 2005 until 2019 were screened. Adults with a fracture of the distal fibula were included. Data were separated for gender, age and treatment strategy.!##!Results!#!During the past 15 years, there was a steady annual incidence of distal fibula fractures of 74 ± 32 per 100,000 people without any significant changes (p = 0.436). 60.1% ± 0.6% of all fractures occurred in females. The annual incidence for male was nearly constant over the different age groups, whereas for female, there was a clear increase in incidence above the age of 40. Whereas 66% of fractures in between 20 and 30 years of age occurred in male, approximately 70% of fractures above the age of 60 occurred in females. The relative quantity of locking plates increased from 2% in 2005 to 34% in 2019. In 2019, only 1.02% of the patients were operated with an intramedullary nail.!##!Conclusions!#!Operatively treated distal fibular fractures revealed an age dependent increase in incidence in postmenopausal women compared to younger females. Regarding the treatment strategy, there was an increase in application of locking plates. The data implicate a typical fragility fracture related age and gender distribution for distal fibula fractures

The Glenolabral Articular Disruption Lesion Is a Biomechanical Risk Factor for Recurrent Shoulder Instability

PURPOSE: To investigate the biomechanical effect of a glenolabral articular disruption (GLAD) lesion on glenohumeral laxity. METHODS: Human cadaveric glenoids (n = 10) were excised of soft tissue, including the labrum to focus on the biomechanical effects of osteochondral surfaces. Glenohumeral dislocations were performed in a robotic test setup, while displacement forces and three-dimensional morphometric properties were measured. The stability ratio (SR), a biomechanical characteristic for glenohumeral stability, was used as an outcome parameter, as well as the path of least resistance, determined by a hybrid robot displacement. The impacts of chondral and bony defects were analyzed related to the intact glenoid. Statistical comparison of the defect states on SR and the path of least resistance was performed using repeated-measures ANOVA and Tukey’s post hoc test for multiple comparisons (P < .05). The relationship between concavity depth and SR was approximated in a nonlinear regression. RESULTS: The initial SR of the intact glenoid (28.3 ± 7.8%) decreased significantly by 4.7 ± 3% in case of a chondral defect (P = .002). An additional loss of 3.2 ± 2.3% was provoked by a 20% bony defect (P = .004). The path of least resistance was deflected significantly more inferiorly by a GLAD lesion (2.9 ± 1.8°, P = .002) and even more by a bony defect (2.5 ± 2.9°, P = .002). The nonlinear regression with concavity depth as predictor for the SR resulted in a high correlation coefficient (r = .81). CONCLUSIONS: Chondral integrity is an important contributor to the SR. Chondral defects as present in GLAD lesions may cause increased laxity, influence the humeral track on the glenoid during dislocation, and represent a biomechanical risk factor for a recurrent instability

Glenoid concavity has a higher impact on shoulder stability than the size of a bony defect

Purpose!#!Surgical treatment of shoulder instability caused by anterior glenoid bone loss is based on a critical threshold of the defect size. Recent studies indicate that the glenoid concavity is essential for glenohumeral stability. However, biomechanical proof of this principle is lacking. The aim of this study was to evaluate whether glenoid concavity allows a more precise assessment of glenohumeral stability than the defect size alone.!##!Methods!#!The stability ratio (SR) is a biomechanical estimate of glenohumeral stability. It is defined as the maximum dislocating force the joint can resist related to a medial compression force. This ratio was determined for 17 human cadaveric glenoids in a robotic test setup depending on osteochondral concavity and anterior defect size. Bony defects were created gradually, and a 3D measuring arm was used for morphometric measurements. The influence of defect size and concavity on the SR was examined using linear models. In addition, the morphometrical-based bony shoulder stability ratio (BSSR) was evaluated to prove its suitability for estimation of glenohumeral stability independent of defect size.!##!Results!#!Glenoid concavity is a significant predictor for the SR, while the defect size provides minor informative value. The linear model featured a high goodness of fit with a determination coefficient of R!##!Conclusion!#!Glenoid concavity is a crucial factor for the SR. Independent of the defect size, the computable BSSR is a precise biomechanical estimate of the measured SR. The inclusion of glenoid concavity has the potential to influence clinical decision-making for an improved and personalised treatment of glenohumeral instability with anterior glenoid bone loss