EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.

Non-pharmacological interventions for chronic pain in people with spinal cord injury.

BACKGROUND: Chronic pain is frequent in persons living with spinal cord injury (SCI). Conventionally, the pain is treated pharmacologically, yet long-term pain medication is often refractory and associated with side effects. Non-pharmacological interventions are frequently advocated, although the benefit and harm profiles of these treatments are not well established, in part because of methodological weaknesses of available studies. OBJECTIVES: To critically appraise and synthesise available research evidence on the effects of non-pharmacological interventions for the treatment of chronic neuropathic and nociceptive pain in people living with SCI. SEARCH METHODS: The search was run on the 1st March 2011. We searched the Cochrane Injuries Group's Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), four other databases and clinical trials registers. In addition, we manually searched the proceedings of three major scientific conferences on SCI. We updated this search in November 2014 but these results have not yet been incorporated. SELECTION CRITERIA: Randomised controlled trials of any intervention not involving intake of medication or other active substances to treat chronic pain in people with SCI. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed risk of bias in the included studies. The primary outcome was any measure of pain intensity or pain relief. Secondary outcomes included adverse events, anxiety, depression and quality of life. When possible, meta-analyses were performed to calculate standardised mean differences for each type of intervention. MAIN RESULTS: We identified 16 trials involving a total of 616 participants. Eight different types of interventions were studied. Eight trials investigated the effects of electrical brain stimulation (transcranial direct current stimulation (tDCS) and cranial electrotherapy stimulation (CES); five trials) or repetitive transcranial magnetic stimulation (rTMS; three trials). Interventions in the remaining studies included exercise programmes (three trials); acupuncture (two trials); self-hypnosis (one trial); transcutaneous electrical nerve stimulation (TENS) (one trial); and a cognitive behavioural programme (one trial). None of the included trials were considered to have low overall risk of bias. Twelve studies had high overall risk of bias, and in four studies risk of bias was unclear. The overall quality of the included studies was weak. Their validity was impaired by methodological weaknesses such as inappropriate choice of control groups. An additional search in November 2014 identified more recent studies that will be included in an update of this review.For tDCS the pooled mean difference between intervention and control groups in pain scores on an 11-point visual analogue scale (VAS) (0-10) was a reduction of -1.90 units (95% confidence interval (CI) -3.48 to -0.33; P value 0.02) in the short term and of -1.87 (95% CI -3.30 to -0.45; P value 0.01) in the mid term. Exercise programmes led to mean reductions in chronic shoulder pain of -1.9 score points for the Short Form (SF)-36 item for pain experience (95% CI -3.4 to -0.4; P value 0.01) and -2.8 pain VAS units (95% CI -3.77 to -1.83; P value < 0.00001); this represented the largest observed treatment effects in the included studies. Trials using rTMS, CES, acupuncture, self-hypnosis, TENS or a cognitive behavioural programme provided no evidence that these interventions reduce chronic pain. Ten trials examined study endpoints other than pain, including anxiety, depression and quality of life, but available data were too scarce for firm conclusions to be drawn. In four trials no side effects were reported with study interventions. Five trials reported transient mild side effects. Overall, a paucity of evidence was found on any serious or long-lasting side effects of the interventions. AUTHORS' CONCLUSIONS: Evidence is insufficient to suggest that non-pharmacological treatments are effective in reducing chronic pain in people living with SCI. The benefits and harms of commonly used non-pharmacological pain treatments should be investigated in randomised controlled trials with adequate sample size and study methodology.

What spatial data do we need to develop global mammal conservation strategies?

Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.

Association Study of Common Genetic Variants and HIV-1 Acquisition in 6,300 Infected Cases and 7,200 Controls.

Multiple genome-wide association studies (GWAS) have been performed in HIV-1 infected individuals, identifying common genetic influences on viral control and disease course. Similarly, common genetic correlates of acquisition of HIV-1 after exposure have been interrogated using GWAS, although in generally small samples. Under the auspices of the International Collaboration for the Genomics of HIV, we have combined the genome-wide single nucleotide polymorphism (SNP) data collected by 25 cohorts, studies, or institutions on HIV-1 infected individuals and compared them to carefully matched population-level data sets (a list of all collaborators appears in Note S1 in Text S1). After imputation using the 1,000 Genomes Project reference panel, we tested approximately 8 million common DNA variants (SNPs and indels) for association with HIV-1 acquisition in 6,334 infected patients and 7,247 population samples of European ancestry. Initial association testing identified the SNP rs4418214, the C allele of which is known to tag the HLA-B*57:01 and B*27:05 alleles, as genome-wide significant (p = 3.6×10(-11)). However, restricting analysis to individuals with a known date of seroconversion suggested that this association was due to the frailty bias in studies of lethal diseases. Further analyses including testing recessive genetic models, testing for bulk effects of non-genome-wide significant variants, stratifying by sexual or parenteral transmission risk and testing previously reported associations showed no evidence for genetic influence on HIV-1 acquisition (with the exception of CCR5Δ32 homozygosity). Thus, these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size.

Folate intake and the risk of oral cavity and pharyngeal cancer: A pooled analysis within the International Head and Neck Cancer Epidemiology Consortium.

There are suggestions of an inverse association between folate intake and serum folate levels and the risk of oral cavity and pharyngeal cancers (OPCs), but most studies are limited in sample size, with only few reporting information on the source of dietary folate. Our study aims to investigate the association between folate intake and the risk of OPC within the International Head and Neck Cancer Epidemiology (INHANCE) Consortium. We analyzed pooled individual-level data from ten case-control studies participating in the INHANCE consortium, including 5,127 cases and 13,249 controls. Odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were estimated for the associations between total folate intake (natural, fortification and supplementation) and natural folate only, and OPC risk. We found an inverse association between total folate intake and overall OPC risk (the adjusted OR for the highest vs. the lowest quintile was 0.65, 95% CI: 0.43-0.99), with a stronger association for oral cavity (OR = 0.57, 95% CI: 0.43-0.75). A similar inverse association, though somewhat weaker, was observed for folate intake from natural sources only in oral cavity cancer (OR = 0.64, 95% CI: 0.45-0.91). The highest OPC risk was observed in heavy alcohol drinkers with low folate intake as compared to never/light drinkers with high folate (OR = 4.05, 95% CI: 3.43-4.79); the attributable proportion (AP) owing to interaction was 11.1% (95% CI: 1.4-20.8%). Lastly, we reported an OR of 2.73 (95% CI:2.34-3.19) for those ever tobacco users with low folate intake, compared with nevere tobacco users and high folate intake (AP of interaction =10.6%, 95% CI: 0.41-20.8%). Our project of a large pool of case-control studies supports a protective effect of total folate intake on OPC risk.

Effectiveness of cladribine therapy in patients with pulmonary Langerhans cell histiocytosis.

BackgroundPulmonary Langerhans cell histiocytosis (PLCH) is a rare disorder characterised by granulomatous proliferation of CD1a-positive histiocytes forming granulomas within lung parenchyma, in strong association with tobacco smoking, and which may result in chronic respiratory failure. Smoking cessation is considered to be critical in management, but has variable effects on outcome. No drug therapy has been validated. Cladribine (chlorodeoxyadenosine, 2-CDA) down-regulates histiocyte proliferation and has been successful in curbing multi-system Langerhans cell histiocytosis and isolated PLCH.Methods and patientsWe retrospectively studied 5 patients (aged 37¿55 years, 3 females) with PLCH who received 3 to 4 courses of cladribine therapy as a single agent (0.1 mg/kg per day for 5 consecutive days at monthly intervals). One patient was treated twice because of relapse at 1 year. Progressive pulmonary disease with obstructive ventilatory pattern despite smoking cessation and/or corticosteroid therapy were indications for treatment. Patients were administered oral trimethoprim/sulfamethoxazole and valaciclovir to prevent opportunistic infections. They gave written consent to receive off-label cladribine in the absence of validated treatment.ResultsFunctional class dyspnea improved with cladribine therapy in 4 out of 5 cases, and forced expiratory volume in 1 second (FEV1) increased in all cases by a mean of 387 ml (100¿920 ml), contrasting with a steady decline prior to treatment. Chest high-resolution computed tomography (HRCT) features improved with cladribine therapy in 4 patients. Hemodynamic improvement was observed in 1 patient with pre-capillary pulmonary hypertension. The results suggested a greater treatment effect in subjects with nodular lung lesions and/or thick-walled cysts on chest HRCT, with diffuse hypermetabolism of lung lesions on positron emission tomography (PET)-scan, and with progressive disease despite smoking cessation. Infectious pneumonia developed in 1 patient, with later grade 4 neutrocytopenia but without infection.DiscussionData interpretation was limited by the retrospective, uncontrolled study design and small sample size.ConclusionCladribine as a single agent may be effective therapy in patients with progressive PLCH.

Swain: Stata module to correct the SEM chi-square overidentification test in small sample sizes or complex models. Statistical Software Components S457617, Boston College Department of Economics.

Swain corrects the chi-square overidentification test (i.e., likelihood ratio test of fit) for structural equation models whethr with or without latent variables. The chi-square statistic is asymptotically correct; however, it does not behave as expected in small samples and/or when the model is complex (cf. Herzog, Boomsma, & Reinecke, 2007). Thus, particularly in situations where the ratio of sample size (n) to the number of parameters estimated (p) is relatively small (i.e., the p to n ratio is large), the chi-square test will tend to overreject correctly specified models. To obtain a closer approximation to the distribution of the chi-square statistic, Swain (1975) developed a correction; this scaling factor, which converges to 1 asymptotically, is multiplied with the chi-square statistic. The correction better approximates the chi-square distribution resulting in more appropriate Type 1 reject error rates (see Herzog & Boomsma, 2009; Herzog, et al., 2007).

Designing phase III or IV trials for vaccines: choosing between individual or cluster randomised trial designs.

The choice of design between individual randomisation, cluster or pseudo-cluster randomisation is often made difficult. Clear methodological guidelines have been given for trials in general practice, but not for vaccine trials. This article proposes a decisional flow-chart to choose the most adapted design for evaluating the effectiveness of a vaccine in large-scale studies. Six criteria have been identified: importance of herd immunity or herd protection, ability to delimit epidemiological units, homogeneity of transmission probability across sub-populations, population's acceptability of randomisation, availability of logistical resources, and estimated sample size. This easy to use decisional method could help sponsors, trial steering committees and ethical committees adopt the most suitable design.

Genes mirror geography within Europe.

Understanding the genetic structure of human populations is of fundamental interest to medical, forensic and anthropological sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation and suggest the potential to use large samples to uncover variation among closely spaced populations. Here we characterize genetic variation in a sample of 3,000 European individuals genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, the results are relevant to the prospects of genetic ancestry testing; an individual's DNA can be used to infer their geographic origin with surprising accuracy-often to within a few hundred kilometres.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Which is the optimal sampling strategy for habitat suitability modelling

Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling. This paper compares four such strategies, namely, 'random', 'regular', 'proportional-stratified' and 'equal -stratified'- to investigate (1) how they affect prediction accuracy and (2) how sensitive they are to sample size. In order to compare them, a virtual species approach (Ecol. Model. 145 (2001) 111) in a real landscape, based on reliable data, was chosen. The distribution of the virtual species was sampled 300 times using each of the four strategies in four sample sizes. The sampled data were then fed into a GLM to make two types of prediction: (1) habitat suitability and (2) presence/ absence. Comparing the predictions to the known distribution of the virtual species allows model accuracy to be assessed. Habitat suitability predictions were assessed by Pearson's correlation coefficient and presence/absence predictions by Cohen's K agreement coefficient. The results show the 'regular' and 'equal-stratified' sampling strategies to be the most accurate and most robust. We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design'

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Host genetic determinants of spontaneous hepatitis C clearance

Acute infection with the hepatitis C virus (HCV) induces a wide range of innate and adaptive immune responses. A total of 20-50% of acutely HCV-infected individuals permanently control the virus, referred to as 'spontaneous hepatitis C clearance', while the infection progresses to chronic hepatitis C in the majority of cases. Numerous studies have examined host genetic determinants of hepatitis C infection outcome and revealed the influence of genetic polymorphisms of human leukocyte antigens, killer immunoglobulin-like receptors, chemokines, interleukins and interferon-stimulated genes on spontaneous hepatitis C clearance. However, most genetic associations were not confirmed in independent cohorts, revealed opposing results in diverse populations or were limited by varying definitions of hepatitis C outcomes or small sample size. Coordinated efforts are needed in the search for key genetic determinants of spontaneous hepatitis C clearance that include well-conducted candidate genetic and genome-wide association studies, direct sequencing and follow-up functional studies.

Pediatric morphea (localized scleroderma): review of 136 patients.

BACKGROUND: Morphea is an autoimmune inflammatory sclerosing disorder that may cause permanent functional disability and disfigurement. OBJECTIVES: We sought to determine the clinical features of morphea in a large pediatric cohort. METHODS: We conducted a retrospective chart review of 136 pediatric patients with morphea from one center, 1989 to 2006. RESULTS: Most children showed linear morphea, with a disproportionately high number of Caucasian and female patients. Two patients with rapidly progressing generalized or extensive linear morphea and arthralgias developed restrictive pulmonary disease. Initial oral corticosteroid treatment and long-term methotrexate administration stabilized and/or led to disease improvement in most patients with aggressive disease. LIMITATIONS: Retrospective analysis, relatively small sample size, and risk of a selected referral population to the single site are limitations. CONCLUSIONS: These data suggest an increased prevalence of morphea in Caucasian girls, and support methotrexate as treatment for problematic forms. Visceral manifestations rarely occur; the presence of progressive problematic cutaneous disease and arthralgias should trigger closer patient monitoring.