High b-value Diffusion-weighted Imaging: A Sensitive Method to Reveal White Matter Changes in Schizophrenia

Background: b-value is the parameter characterizing the intensity of the diffusion weighting during image acquisition. Data acquisition is usually performed with low b value (b~1000 s/mm2). Evidence shows that high b-values (b>2000 s/mm2) are more sensitive to the slow diffusion compartment (SDC) and maybe more sensitive in detecting white matter (WM) anomalies in schizophrenia.Methods: 12 male patients with schizophrenia (mean age 35 +/-3 years) and 16 healthy male controls matched for age were scanned with a low b-value (1000 s/mm2) and a high b-value (4000 s/mm2) protocol. Apparent diffusion coefficient (ADC) is a measure of the average diffusion distance of water molecules per time unit (mm2/s). ADC maps were generated for all individuals. 8 region of interests (frontal and parietal region bilaterally, centrum semi-ovale bilaterally and anterior and posterior corpus callosum) were manually traced blind to diagnosis.Results: ADC measures acquired with high b-value imaging were more sensitive in detecting differences between schizophrenia patients and healthy controls than low b-value imaging with a gain in significance by a factor of 20- 100 times despite the lower image Signal-to-noise ratio (SNR). Increased ADC was identified in patient's WM (p=0.00015) with major contributions from left and right centrum semi-ovale and to a lesser extent right parietal region.Conclusions: Our results may be related to the sensitivity of high b-value imaging to the SDC believed to reflect mainly the intra-axonal and myelin bound water pool. High b-value imaging might be more sensitive and specific to WM anomalies in schizophrenia than low b-value imaging

Athletic injuries of the extensor carpi ulnaris subsheath: MRI findings and utility of gadolinium-enhanced fat-saturated T1-weighted sequences with wrist pronation and supination.

OBJECTIVE: To report the magnetic resonance imaging (MRI) findings in athletic injuries of the extensor carpi ulnaris (ECU) subsheath, assessing the utility of gadolinium-enhanced (Gd) fat-saturated (FS) T1-weighted sequences with wrist pronation and supination. METHODS: Sixteen patients (13 male, three female; mean age 30.3 years) with athletic injuries of the ECU subsheath sustained between January 2003 and June 2009 were included in this retrospective study. Initial and follow-up 1.5-T wrist MRIs were performed with transverse T1-weighted and STIR sequences in pronation, and Gd FS T1-weighted sequences with wrist pronation and supination. Two radiologists assessed the type of injury (A to C), ECU tendon stability, associated lesions and rated pulse sequences using a three-point scale: 1=poor, 2=good and 3=excellent. RESULTS: Gd-enhanced FS T1-weighted transverse sequences in supination (2.63) and pronation (2.56) were most valuable, compared with STIR (2.19) and T1-weighted (1.94). Nine type A, one type B and six type C injuries were found. There were trends towards diminution in size, signal intensity and enhancement of associated pouches on follow-up MRI and tendon stabilisation within the ulnar groove. CONCLUSION: Gd-enhanced FS T1-weighted sequences with wrist pronation and supination are most valuable in assessing and follow-up athletic injuries of the ECU subsheath on 1.5-T MRI.

Looking for Validity or Testing It? The Perils of Stepwise Regression, Extreme-Scores Analysis, Heteroscedasticity, and Measurement Error

When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.

Adapting football to the child: an application of the logistic regression model in observational mewthodology.

Logistic regression is included into the analysis techniques which are valid for observationalmethodology. However, its presence at the heart of thismethodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child"s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played.

Hepatic hemangiomas : factors associated with T2 shine-through effect on diffusion-weighted MR sequences

Objectifs: Déterminer la fréquence et les facteurs prédictifs de l'effet T2 shine-through dans l'hémangiome hépatique. Matériels et méthodes: Entre janvier 2010 et novembre 2011, l'imagerie par résonance magnétique du foie de 149 patients avec 388 hémangiomes hépatiques a été revue rétrospectivement. Les caractéristiques lésionnelles: la taille, la localisation, le signal et l'aspect en T1, T2 et en diffusion, l'effet T2 shine-through, le coefficient apparent de diffusion des hémangiomes hépatiques et du foie et type de rehaussement ont été évalués. Résultats: L'effet T2 shine-through était observé dans 204/388 (52.6%) des hémangiomes hépatiques et 100 (67.1%) patients. L'ADC moyen des hémangiomas avec T2 shine-through effect était significativement plus bas que les hémangiomas sans T2 shine-through effect (2.0 +/- 0.48 vs 2.38 +/- 0.45 10"3 mm2/s, P < .0001). L'analyse multivariée retrouvait comme facteurs indépendants de la présence d'un effet T2 shine-through un hypersignal sur les images fat- suppressed T2-weighted fast spin-echo, les hémangiomes avec un rehaussement classique et retardé, et l'ADC du foie. Conclusion: Le T2 shine-through effect est fréquemment observé dans les hémangiomes hépatiques et dépend des caractéristiques lésionnelles. Sa présence ne remet pas en question le diagnostic lorsque les signes IRM typiques sont présents.

The Uncertain generalized probabilistic weighted average and its application in the theory of expertons

A new model for dealing with decision making under risk by considering subjective and objective information in the same formulation is here presented. The uncertain probabilistic weighted average (UPWA) is also presented. Its main advantage is that it unifies the probability and the weighted average in the same formulation and considering the degree of importance that each case has in the analysis. Moreover, it is able to deal with uncertain environments represented in the form of interval numbers. We study some of its main properties and particular cases. The applicability of the UPWA is also studied and it is seen that it is very broad because all the previous studies that use the probability or the weighted average can be revised with this new approach. Focus is placed on a multi-person decision making problem regarding the selection of strategies by using the theory of expertons.

An integer-based score to predict functional outcome in acute ischemic stroke: The ASTRAL score.

OBJECTIVE: To develop and validate a simple, integer-based score to predict functional outcome in acute ischemic stroke (AIS) using variables readily available after emergency room admission. METHODS: Logistic regression was performed in the derivation cohort of previously independent patients with AIS (Acute Stroke Registry and Analysis of Lausanne [ASTRAL]) to identify predictors of unfavorable outcome (3-month modified Rankin Scale score >2). An integer-based point-scoring system for each covariate of the fitted multivariate model was generated by their β-coefficients; the overall score was calculated as the sum of the weighted scores. The model was validated internally using a 2-fold cross-validation technique and externally in 2 independent cohorts (Athens and Vienna Stroke Registries). RESULTS: Age (A), severity of stroke (S) measured by admission NIH Stroke Scale score, stroke onset to admission time (T), range of visual fields (R), acute glucose (A), and level of consciousness (L) were identified as independent predictors of unfavorable outcome in 1,645 patients in ASTRAL. Their β-coefficients were multiplied by 4 and rounded to the closest integer to generate the score. The area under the receiver operating characteristic curve (AUC) of the score in the ASTRAL cohort was 0.850. The score was well calibrated in the derivation (p = 0.43) and validation cohorts (0.22 [Athens, n = 1,659] and 0.49 [Vienna, n = 653]). AUCs were 0.937 (Athens), 0.771 (Vienna), and 0.902 (when pooled). An ASTRAL score of 31 indicates a 50% likelihood of unfavorable outcome. CONCLUSIONS: The ASTRAL score is a simple integer-based score to predict functional outcome using 6 readily available items at hospital admission. It performed well in double external validation and may be a useful tool for clinical practice and stroke research.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Evaluation of a multicomponent worksite health promotion program for cardiovascular risk factors-correcting for the regression towards the mean effect.

BACKGROUND: We assessed the impact of a multicomponent worksite health promotion program for0 reducing cardiovascular risk factors (CVRF) with short intervention, adjusting for regression towards the mean (RTM) affecting such nonexperimental study without control group. METHODS: A cohort of 4,198 workers (aged 42 +/- 10 years, range 16-76 years, 27% women) were analyzed at 3.7-year interval and stratified by each CVRF risk category (low/medium/high blood pressure [BP], total cholesterol [TC], body mass index [BMI], and smoking) with RTM and secular trend adjustments. Intervention consisted of 15 min CVRF screening and individualized counseling by health professionals to medium- and high-risk individuals, with eventual physician referral. RESULTS: High-risk groups participants improved diastolic BP (-3.4 mm Hg [95%CI: -5.1, -1.7]) in 190 hypertensive patients, TC (-0.58 mmol/l [-0.71, -0.44]) in 693 hypercholesterolemic patients, and smoking (-3.1 cig/day [-3.9, -2.3]) in 808 smokers, while systolic BP changes reflected RTM. Low-risk individuals without counseling deteriorated TC and BMI. Body weight increased uniformly in all risk groups (+0.35 kg/year). CONCLUSIONS: In real-world conditions, short intervention program participants in high-risk groups for diastolic BP, TC, and smoking improved their CVRF, whereas low-risk TC and BMI groups deteriorated. Future programs may include specific advises to low-risk groups to maintain a favorable CVRF profile.

Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond.

The complex structural organization of the white matter of the brain can be depicted in vivo in great detail with advanced diffusion magnetic resonance (MR) imaging schemes. Diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique-the mapping of apparent diffusion coefficient values-to the more complex, such as diffusion tensor imaging, q-ball imaging, diffusion spectrum imaging, and tractography. The type of structural information obtained differs according to the technique used. To fully understand how diffusion MR imaging works, it is helpful to be familiar with the physical principles of water diffusion in the brain and the conceptual basis of each imaging technique. Knowledge of the technique-specific requirements with regard to hardware and acquisition time, as well as the advantages, limitations, and potential interpretation pitfalls of each technique, is especially useful.

Validation of tissue modelization and classification techniques in T1-weighted MR brain images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods.

Techniques for Estimating Flood-Frequency Discharges for Streams in Iowa, HR-395A, 2001

A statewide study was conducted to develop regression equations for estimating flood-frequency discharges for ungaged stream sites in Iowa. Thirty-eight selected basin characteristics were quantified and flood-frequency analyses were computed for 291 streamflow-gaging stations in Iowa and adjacent States. A generalized-skew-coefficient analysis was conducted to determine whether generalized skew coefficients could be improved for Iowa. Station skew coefficients were computed for 239 gaging stations in Iowa and adjacent States, and an isoline map of generalized-skew-coefficient values was developed for Iowa using variogram modeling and kriging methods. The skew map provided the lowest mean square error for the generalized-skew- coefficient analysis and was used to revise generalized skew coefficients for flood-frequency analyses for gaging stations in Iowa. Regional regression analysis, using generalized least-squares regression and data from 241 gaging stations, was used to develop equations for three hydrologic regions defined for the State. The regression equations can be used to estimate flood discharges that have recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for ungaged stream sites in Iowa. One-variable equations were developed for each of the three regions and multi-variable equations were developed for two of the regions. Two sets of equations are presented for two of the regions because one-variable equations are considered easy for users to apply and the predictive accuracies of multi-variable equations are greater. Standard error of prediction for the one-variable equations ranges from about 34 to 45 percent and for the multi-variable equations range from about 31 to 42 percent. A region-of-influence regression method was also investigated for estimating flood-frequency discharges for ungaged stream sites in Iowa. A comparison of regional and region-of-influence regression methods, based on ease of application and root mean square errors, determined the regional regression method to be the better estimation method for Iowa. Techniques for estimating flood-frequency discharges for streams in Iowa are presented for determining ( 1) regional regression estimates for ungaged sites on ungaged streams; (2) weighted estimates for gaged sites; and (3) weighted estimates for ungaged sites on gaged streams. The technique for determining regional regression estimates for ungaged sites on ungaged streams requires determining which of four possible examples applies to the location of the stream site and its basin. Illustrations for determining which example applies to an ungaged stream site and for applying both the one-variable and multi-variable regression equations are provided for the estimation techniques.

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

Improving T2 -weighted imaging at high field through the use of kT -points.

PURPOSE: At high magnetic field strengths (B0 ≥ 3 T), the shorter radiofrequency wavelength produces an inhomogeneous distribution of the transmit magnetic field. This can lead to variable contrast across the brain which is particularly pronounced in T2 -weighted imaging that requires multiple radiofrequency pulses. To obtain T2 -weighted images with uniform contrast throughout the whole brain at 7 T, short (2-3 ms) 3D tailored radiofrequency pulses (kT -points) were integrated into a 3D variable flip angle turbo spin echo sequence. METHODS: The excitation and refocusing "hard" pulses of a variable flip angle turbo spin echo sequence were replaced with kT -point pulses. Spatially resolved extended phase graph simulations and in vivo acquisitions at 7 T, utilizing both single channel and parallel-transmit systems, were used to test different kT -point configurations. RESULTS: Simulations indicated that an extended optimized k-space trajectory ensured a more homogeneous signal throughout images. In vivo experiments showed that high quality T2 -weighted brain images with uniform signal and contrast were obtained at 7 T by using the proposed methodology. CONCLUSION: This work demonstrates that T2 -weighted images devoid of artifacts resulting from B1 (+) inhomogeneity can be obtained at high field through the optimization of extended kT -point pulses. Magn Reson Med 71:1478-1488, 2014. © 2013 Wiley Periodicals, Inc.