Mapping the human connectome at multiple scales with diffusion spectrum MRI.

The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

Cerebrospinal fluid in the diagnosis of multiple sclerosis: a consensus report.

The Committee of the European Concerted Action for Multiple Sclerosis (Charcot Foundation) organised five workshops to discuss CSF analytical standards in the diagnosis of multiple sclerosis. This consensus report from 12 European countries summarises the results of those workshops. It is hoped that neurologists will confer with their colleagues in clinical chemistry to arrange the best possible local practice. The most sensitive method for the detection of oligoclonal immunoglobulin bands is isoelectric focusing. The same amounts of IgG in parallel CSF and serum samples are used and oligoclonal bands are revealed with IgG specific antibody staining. All laboratories performing isoelectric focusing should check their technique at least annually using "blind" standards for the five different CSF and serum patterns. Quantitative measurements of IgG production in the CNS are less sensitive than isoelectric focusing. The preferred method for detection of blood-CSF barrier dysfunction is the albumin quotient. The CSF albumin or total protein concentrations are less satisfactory. These results must be interpreted with reference to the age of the patient and the local method of determination. Cells should be counted. The normal value is no more than 4 cells/microliters. Among evolving optional tests, measurement of the combined local synthesis of antibodies against measles, rubella, and/or varicella zoster could represent a significant advance if it offers higher specificity (not sensitivity) for identifying chronic rather than acute inflammation. Other tests that may have useful correlations with clinical indices include those for oligoclonal free light chains, IgM, IgA, or myelin basic protein concentrations.

Effect of multiple oral doses of androgenic anabolic steroids on endurance performance and serum indices of physical stress in healthy male subjects.

Anabolic androgenic steroids (AAS) are doping agents that are mostly used for improvement of strength and muscle hypertrophy. In some sports, athletes reported that the intake of AAS is associated with a better recovery, a higher training load capacity and therefore an increase in physical and mental performances. The purpose of this study was to evaluate, the effect of multiple doses of AAS on different physiological parameters that could indirectly relate the physical state of athletes during a hard endurance training program. In a double blind settings, three groups (n = 9, 8 and 8) were orally administered placebo, testosterone undecanoate or 19-norandrostenedione, 12 times during 1 month. Serum biomarkers (creatine kinase, ASAT and urea), serum hormone profiles (testosterone, cortisol and LH) and urinary catecholamines (noradrenalin, adrenalin and dopamine) were evaluated during the treatment. Running performance was assessed before and after the intervention phase by means of a standardized treadmill test. None of the measured biochemical variables showed significant impact of AAS on physical stress level. Data from exercise testing on submaximal and maximal level did not reveal any performance differences between the three groups or their response to the treatment. In the present study, no effect of multiple oral doses of AAS on endurance performance or bioserum recovery markers was found.

Simultaneous identification of multiple mammalian species from mixed forensic samples based on mtDNA control region length polymorphism

Molecular species identification in mixed or contaminated biological material has always been problematic. We developed a simple and accurate method for mammal DNA identification in mixtures, based on interspecific mitochondrial DNA control region length polymorphism. Contrary to other published methods dealing with species mixtures, our protocol requires a single universal primer pair and amplification step, and is not based on a pre-defined panel of species. This protocol has been routinely employed by our laboratory for species identification in dozens of human and animal forensic caseworks. Six representative forensic caseworks involving the specific identification of mixed animal samples are reported in this paper, in order to demonstrate the applicability and usefulness of the method.

First consumption ever of multiple substances: applying an expert-based taxonomy to a Swiss national sample of adolescents.

The use of multiple legal and illegal substances by adolescents is a growing concern in all countries, but since no consensus about a taxonomy did emerge yet, it is difficult to understand the different patterns of consumption and to implement tailored prevention and treatment programs directed towards specific subgroups of the adolescent population. Using data from a Swiss survey on adolescent health, we analyzed the age at which ten legal and illegal substances were consumed for the first time ever by applying a method combining the strength of both automatic clustering and use of substance experts. Results were then compared to 30 socio-economic factors to establish the usefulness of and to validate our taxonomy. We also analyzed the succession of substance first use for each group. The final taxonomy consists of eight groups ranging from non-consumers to heavy drug addicts. All but four socio-economic factors were significantly associated with the taxonomy, the strongest associations being observed with health, behavior, and sexuality factors. Numerous factors influence adolescents in their decision to first try substances or to use them on a regular basis, and no factor alone can be considered as an absolute marker of problematic behavior regarding substance use. Different processes of experimentation with substances are associated with different behaviors, therefore focusing on only one substance or only one factor is not efficient. Prevention and treatment programs can then be tailored to address specific issues related to different youth subgroups.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Inversion of multiple intersecting high-resolution crosshole GPR profiles for hydrological characterization at the Boise Hydrogeophysical Research Site

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling.

Automated segmentation of multiple red blood cells with digital holographic microscopy.

We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.

Critical evaluation of outcome scales to assess outcome after lateral ankle ligament repair

Introduction: Several scores are commonly used to evaluate patients' postoperative satisfaction after lateral ankle ligament repair, including: AOFAS, FAAM, CAIT and CAIS. Comparing published studies in the literature is difficult, as the same patient can have markedly different results depending on which scoring system is used. The current study aims to address this gap in the literature by developing a system to compare these tests, to allow better analysis and comparison of published studies. Patients and methods: This is a retrospective cohort study of 47 patients following lateral ankle ligament repair using a modified Broström-Gould technique. All patients were operated between 2005 and 2010 by a single surgeon and followed the same post operative rehabilitation protocol. Six patients were excluded from the study because of concomitant surgery. Patients were assessed by an independent observer. We used the Pearson correlation coefficient to analyse the concordance of the scores, as well as scatter plots to assess the linear relationship between them. Results: A linear distribution between the scores was found when the results were analysed using scatter plots. We were thus able to use the Pearson correlation coefficient to evaluate the relationship between each of the different postoperative scores. The correlation was found to be above 0.5 in all cases except for the comparison between the CAIT and the FAAM for the activities of daily living (0.39). We were, therefore, able to compare the results obtained and assess the relative concordance of the scoring systems. The results showed that the more specific the scale is, the worst the score is and inversely. So the CAIT and the CAIS appeared to be more severe than the AOFAS and the FAAM measuring the activities of daily living. The sports subscale of the FAAM demonstrated intermediate results. Conclusion: This study outlines a system to compare different postoperative scores commonly used to evaluate outcome after ankle stabilization surgery. The impact of this study is that it makes comparison of published studies easier, even though they use a variety of different clinical scores, thus facilitating better outcome analysis of operative techniques.

Epidemiology of multiple congenital anomalies in Europe: a EUROCAT population-based registry study.

BACKGROUND: This study describes the prevalence, associated anomalies, and demographic characteristics of cases of multiple congenital anomalies (MCA) in 19 population-based European registries (EUROCAT) covering 959,446 births in 2004 and 2010. METHODS: EUROCAT implemented a computer algorithm for classification of congenital anomaly cases followed by manual review of potential MCA cases by geneticists. MCA cases are defined as cases with two or more major anomalies of different organ systems, excluding sequences, chromosomal and monogenic syndromes. RESULTS: The combination of an epidemiological and clinical approach for classification of cases has improved the quality and accuracy of the MCA data. Total prevalence of MCA cases was 15.8 per 10,000 births. Fetal deaths and termination of pregnancy were significantly more frequent in MCA cases compared with isolated cases (p < 0.001) and MCA cases were more frequently prenatally diagnosed (p < 0.001). Live born infants with MCA were more often born preterm (p < 0.01) and with birth weight < 2500 grams (p < 0.01). Respiratory and ear, face, and neck anomalies were the most likely to occur with other anomalies (34% and 32%) and congenital heart defects and limb anomalies were the least likely to occur with other anomalies (13%) (p < 0.01). However, due to their high prevalence, congenital heart defects were present in half of all MCA cases. Among males with MCA, the frequency of genital anomalies was significantly greater than the frequency of genital anomalies among females with MCA (p < 0.001). CONCLUSION: Although rare, MCA cases are an important public health issue, because of their severity. The EUROCAT database of MCA cases will allow future investigation on the epidemiology of these conditions and related clinical and diagnostic problems.

First use of multiple substances: Identification of meaningful patterns

Context: Understanding the process through which adolescents and young adults are trying legal and illegal substances is a crucial point for the development of tailored prevention and treatment programs. However, patterns of substance first use can be very complex when multiple substances are considered, requiring reduction into a few meaningful number of categories. Data: We used data from a survey on adolescent and young adult health conducted in 2002 in Switzerland. Answers from 2212 subjects aged 19 and 20 were included. The first consumption ever of 10 substances (tobacco, cannabis, medicine to get high, sniff (volatile substances, and inhalants), ecstasy, GHB, LSD, cocaine, methadone, and heroin) was considered for a grand total of 516 different patterns. Methods: In a first step, automatic clustering was used to decrease the number of patterns to 50. Then, two groups of substance use experts, three social field workers, and three toxicologists and health professionals, were asked to reduce them into a maximum of 10 meaningful categories. Results: Classifications obtained through our methodology are of practical interest by revealing associations invisible to purely automatic algorithms. The article includes a detailed analysis of both final classifications, and a discussion on the advantages and limitations of our approach.