Move More for Life Study, which aims to assess the relative efficacy of two promising theory-based, print interventions designed to promote regular physical activity amongst breast cancer survivors.Due to early detection and advances in treatment, the number of women surviving breast cancer is increasing. Whilst there are many positive aspects of improved survival, breast cancer survival is associated with many long-term health and psychosocial sequelae. Engaging in regular physical activity post-diagnosis can reduce this burden. Despite this evidence, the majority of breast cancer survivors do not engage in regular physical activity. The challenge is to provide breast cancer survivors with appealing and effective physical activity support in a sustainable and cost-effective way. This article describes the protocol for the Breast cancer survivors were recruited from across Australia. Participants will be randomised into one of three groups: (1) A tailored-print intervention group, (2) a targeted-print intervention group, or (3) a standard recommendation control group. Participants in the tailored-print intervention group will receive 3 tailored newsletters in the mail over a three month period. Participants in the targeted-print group will receive a previously developed physical activity guidebook designed specifically for breast cancer survivors immediately after baseline. Participants in the standard recommendation control will receive a brochure detailing the physical activity guidelines for Australian adults. All participants will be assessed at baseline, and at 4 and 10 months post-baseline. Intervention efficacy for changing the primary outcomes  and secondary outcomes  will be assessed. Mediation and moderation analyses will also be conducted.Given the growing number of cancer survivors, distance-based behaviour change programs addressing physical activity have the potential to make a significant public health impact.Australian New Zealand Clinical Trials Registry (ANZCTR) identifier: ACTRN12611001061921 Due to earlier detection and advances in treatment, more and more women are surviving breast cancer each year. Whilst One promising cancer recovery strategy is the promotion of regular physical activity (PA),10. EvidThere is also new evidence that addressing the pattern of activity is important, with unique metabolic consequences associated with prolonged sedentary behaviour, regardless of total activity time,17. DespWhilst over 70 PA intervention studies have been conducted with cancer survivors, the majority have been atheoretical face-to-face programs conducted during the treatment phase-26. WhilThe purpose of this study is to evaluate the relative efficacy of two promising distance-based approaches (targeted and tailored print interventions) for promoting PA among post-treatment breast cancer survivors compared to a standard recommendation control group. In targeted-print interventions, irrelevant information is reduced by providing individuals with materials targeted to a particular subgroup they belong to . In tailSome theories of information processing, such as The Elaboration Likelihood Model, suggestThis study is a nationally-based, three-arm randomised controlled trial (RCT), testing the relative efficacy of two distance-based PA interventions (tailored and targeted print) compared to a standard recommendation control group. Participants will complete data collection at baseline, 4 months and 10 months. Ethics approval was obtained from the University of Newcastle Human Research Ethics Committee (H-2010-11-3). The RE-AIM framework will serFemale breast cancer survivors who are over the age of 18 and who have finished “active” cancer treatment , who can read and write in English were eligible to participate in the study. All potential participants were required to complete a physical activity readiness questionnaire , to scrParticipants were recruited using convenience sampling methods from a range of sources across Australia. Specifically, this involved (1) asking organisations  and health professionals  with direct contact with breast cancer survivors to disseminate information about the study on behalf of the research team; (2) promoting the study at events potentially relevant to eligible participants (e.g. breast cancer forums); and (3) snowballing recruitment .The randomisation sequence will be generated by a statistician (PM) using SAS 9.2 statistical software. An equal number of participants will be randomised to each group (1:1:1) using a randomised block design, with a block size of six, to ensure the study groups are balanced,40.Participants, identified only by their ID number, will be randomised by an administrative staff member into groups upon receipt of their baseline survey. All project team members will be blinded to this process and participant details will remain de-identified until participant allocation is completed. Participant blinding is not possible due to the difference in delivery schedule of the two interventions.The study’s primary analysis will be the comparison of self-reported PA behaviour (i.e. mins/week of aerobic exercise and sessions/week of resistance training) between the three groups, from baseline to the 4 month time-point. Assuming a small-moderate correlation (r = 0.4) between baseline and post-intervention, to detect a mean difference of 0.5 standard deviation between study groups  for the main dependent outcome  at post-intervention the requAs a secondary consideration, we also ensured that this sample size would be adequate to detect a clinically significant change in step counts per day , and 10 months post-baseline (7 month post tailored-intervention follow-up). At each of these time-points, participants will be asked to wear a pedometer for seven days and complete a step count diary.The primary outcome variables, minutes of PA (aerobic) per week and average number of sessions/exercises per week (resistance) will be assessed using an adapted version of the vAverage daily step counts will be PA type, frequency and duration measured by 8 items from the adapted version of the GLTEQ,46 will Sedentary behaviour is measured using a validated five item scale asking about time spent sitting (hours and minutes) each day during the week and on the weekend in the following situations (a) while travelling to and from places; (b) while at work; (c) while watching television; (d) while using a computer from home; and (e) in leisure time not including watching television .Quality of life is measured using version 4 of the internationally validated 37-item FACT-Breast measurement system (FACT-B). The FACFatigue is measured using the validated 13-item FACIT  Fatigue scale, which assesses self-reported tiredness, weakness and difficulty conducting usual activities.Hypothesized social cognitive mediators of PA behaviour are assessed using previously published, validated instruments where possible. Some items were adapted to make them more appropriate for use in this study. The adaptations were based on our own qualitative research and formative research in the field (e.g.) and werOutcome expectations is measured using 5 general items from the validated exercise pros subscale with 6-aOutcome expectancies will be assessed by asking participants to rate how important each of the outcome expectations are to them  on a 3-point scale . This scale has been utilised and tested in prior research,58.Task self-efficacy will be assessed using 4-items developed and evalBarrier self-efficacy will be assessed using 12-items based on previous scales used in chronic disease populations  how much they agree with each of the statements: I know how to warm up and cool down before/after an exercise session; I have a good idea of what type of PA to do to gain health benefits; I have a good idea of how hard I should engage in PA to gain health benefits; I have a good idea of how much PA I should do to gain health benefits; I have the skills I need to engage in aerobic physical activities; and I have the skills I need to engage in resistance-based physical activities.Social support is assessed using the 15-item social support for exercise habits scale[. Participants are asked to rate how often during the past four months their friends and family (separately) supported them/discouraged them to exercise in a variety of ways. Response options range from 1 (none) to 5 (very often). An example item is: “During the past four months, my friends gave me encouragement to stick with my exercise program”. The perceived built environment will be assessed using an adapted version of the 7-item IPAQ environmental module[ts scale. Particil module. ParticiSelf-regulation will be assessed using a 12-item scale developed for use among older adults[r adults. The iter adults. An examAction planning will be assessed using 4-items developed by Rise et al.[e et al. and adape et al., participants are asked five questions about their health status  and nine questions about their cancer diagnosis .Participant evaluation of the intervention materials will be measured using 15 multiple choice items and one open-ended question, included in the immediate post intervention follow-up questionnaire. The 15 multiple choice items were purpose-designed by the research team and are based on the Elaboration Likelihood Model (ELM), which iPotential participants were asked to contact the project co-ordinator to express their interest in participating in the study. Potential participants were then provided with an information statement and a consent form and asked to return it to the project team within two weeks. Information was resent at two weeks if no response was received.Participants will be asked to complete a pen-and-paper questionnaire, wear a pedometer for seven days and complete a pen-and-paper step count diary at baseline, four and ten months from baseline Figure. ParticiUpon receipt of the baseline questionnaire, an administrative assistant will allocate participants using the ID number written on the questionnaire into one of three groups using the randomly generated allocation sequence provided by the statistician. Participants will be sent intervention or standard recommendation materials within three weeks of allocation.Participants in the tailored-print intervention group will be sent additional intervention materials at 6 weeks and 12 weeks post baseline and update cards (3-item update card), 4 weeks and 8 weeks post base-line and asked to return them to the research team using a reply paid envelope within 7 days. Participants in the standard recommendation group will receive one tailored newsletter and a pdf version of the targeted guidebook after completion of the10 month follow-up survey .Participants randomised into this group will receive a copy of a theory-based exercise guidebook developed specifically for promoting physical activity among breast cancer survivors. This guidebook was developed for use and evaluated in a previous study and has Participants randomised into this group will be mailed three Social Cognitive Theory-based,69 compuNewsletter 1 (N1) will include information on the Australian PA guidelines for cancer survivors (non-tailored), tailored feedback on PA behaviour  relative to the guidelines, information about the beneficial outcomes of PA, safety advice and an action planning activity. An activity planner and exercise illustrations (stretches and resistance training exercises) will also be included.Newsletter 2 (N2) will include expert advice from a behaviour change expert (non-tailored), feedback on PA performance  relative to N1, a testimonial illustrating success, advice on eliciting social support and an action planning activity.Newsletter 3 (N3) will include expert advice from an exercise physiologist (non-tailored), tailored feedback on PA performance  relative to N2 and N1, advice on restructuring the physical environment, information about available support services and an action planning activity. See Additional filehttp://www.healthyactive.gov.au.Participants randomised into this condition will receive the “An active way to better health” brochure published by the Australian government, detailing the national physical activity guidelines for adults. The guiAnalyses will be conducted according to the intention to treat principal, as outlined by White et al.. Namely,This study will test the relative efficacy of two theory-based PA behaviour change interventions. In doing so, this study will address a seminal research question in distance-based patient-centred care – is tailoring or targeting health education messages a more efficacious approach to health behaviour change in the PA domain? Furthermore, this study will be one of the first to promote a pattern of PA that addresses the metabolic consequences of unbroken sedentary behaviours and the advantages of completing both aerobic and resistance-training exercises. The limitations reported in previous research will be addressed by examining adherence after the intervention period and by utilising an objective measure of PA behaviour . Finally, this study will add to the behaviour change literature by addressing the paucity of knowledge surrounding determinants of PA behaviour change among cancer survivors and potential mediators of intervention effects.The authors declare that they have no competing interests.ELJ and CES conceived the study. CES, AG and RCP obtained the funding. All authors provided input into the study design. CES was primarily responsible for intervention design and recruitment, with significant input from ELJ and RCP. CES, ELJ and RCP were responsible for drafting the manuscript. PM provided statistical guidance and support and drafted the statistical analyses section of the manuscript. All authors critically evaluated the article for content and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/172/prepubTable S1. Operationalisation of SCT constructs for the Move More for Life intervention.Click here for file


Percutaneous cardiac interventions are currently performed under X-ray guidance. MR-guided cardiac interventions have been previously described but mainly in animals. Their translation into humans has been limited by the lack of MR-compatible and safe equipment, such as MR guidewires with mechanical characteristics similar to standard guidewires.We are presenting the initial results of the first-in-man clinical trial on MR-guided interventions in patients with congenital heart disease.Ethics and UK regulatory authority approval were obtained prior to commencement of the trial. Inclusion criteria included patients above 2 years of age with great arterial valve stenosis, branch pulmonary artery or aortic arch stenosis and patients that needed balloon dilation of existing stents.Seven patients aged 3 - 64 years have been recruited so far. Five patients have undergone successful interventions for pulmonary valve stenosis (n=4) and native aortic coarctation (n=1). One patient with left pulmonary artery stent underwent right heart catheterisation with the aid of the new MR-wire, but the gradient across the stent was only 5mmHg, hence no intervention was required. The last patient (8 year old child) with severe aortic stenosis had an unsuccessful attempt at ballooning the aortic valve, due to the inability of turning the wedge catheter into the ascending aorta, as the balloon of the catheter kept being pushed back by the aortic stenosis jet. Catheter manipulations were monitored with real time MRI sequence with interactive modification of imaging plane and slice position. Temporal resolution was 11-12 frames/sec. Median procedure and catheterisation times were 180 and 110 min, respectively. All patients were discharged home the day after the procedure with > 50% reduction of the stenosis gradient and no procedural complications.Initial results of the first-in-man clinical trial on MR-guided cardiac interventions are encouraging that with the availability of the new MR compatible and safe guidewire, certain percutaneous cardiac interventions will become feasible to perform solely under MR-guidance in the future.


We analyzed the response of human glioma cells to West Nile virus infection by investigating host transcriptional changes. Changes in expression of 23 genes showed similarities to those in other neurodegenerative diseases. These changes may be useful as potential biomarkers and elucidate novel mechanisms behind the neuropathology of infection with this virus. Flaviviridae, is the etiologic agent of West Nile fever. Since WNV is neurotropic, severe human meningoencephalitis is a common complication of infection and results in a considerable number of deaths. The medulla of the brainstem in the central nervous system (CNS) is the primary target of WNV (West Nile virus (WNV), a member of the family WNV replicates in a wide variety of cell types, and studies have traditionally been carried out in Vero (green monkey kidney) and C6 (mosquito) cells. However, little work has been done with CNS cells. We conducted a global transcriptional analysis of human glioblastoma cell response to infection with WNV during peak virus production to determine the crucial virus-host interactions that take place during a severe neuroinvasive attack and identify putative mechanisms involved in WNV pathogenesis. The factors governing the development of neurologic disease, host immune response, patterns of clinical features, and outcomes are poorly understood in those infected with neurotropic flaviviruses . This demonstrated the highly susceptible nature of the neuroglial cells to WNV infection. Batches of cells were infected for microarray experiments, and a quantitative polymerase chain reaction was used to verify the reproducibility of the changes in gene expression.Most laboratory studies of WNV infections have been carried out in animal cell lines or human cell lines of non-CNS origins. In this study, human glioblastoma (A172) cells were found to be a useful laboratory model for investigating WNV infections. A172 (human glioblastoma) cells were maintained at 37°C in Dulbecco modified Eagle medium  supplemented with 10% fetal calf serum. Confluent monolayers of A172 cells were infected with the Sarafend strain of WNV at a multiplicity of infection of 1. Twenty-four hours after infection, cells showed signs of cytopathic effects (cell-rounding) and produced high virus titer . The CyScribe first-strand cDNA labeling kit  was used to incorporate fluorescent Cy3-dCTP or Cy5-dCTP (Amersham Biosciences) into cDNA probes. The probes were subsequently purified by using CyScribe GFX purification columns (Amersham Biosciences). Equal amounts of labeled cDNA probes (≈25 pmol) were combined for microarray hybridizations. Human 1A microarrays  were used, and hybridizations were performed on a Lucidea SlidePro Hybridizer (Amersham Biosciences). The microarray experiment was carried out in triplicate: 1 of the microarrays was with a dye-swap labeling to prevent skew in the results due to bias in CyDye incorporation.http://linus.nci.nih.gov/BRB-ArrayTools.html), and normalized by using the Lowess method. A stringent lower limit threshold was set at 3 standard deviations of the pixel intensities of the negative control spots, and images were screened for changes in expression values of at least 2-fold. The differentially regulated genes were separately uploaded into EASE (http://sps.nus.edu.sg/~kohweele/awn_genes.htm).Analyses of the scanned microarray images were performed with BRB ArrayTools version 3.1 . From this set of genes, a group of 23 genes that may provide the molecular basis for the observed pathogenesis in the A172 cells was identified . RNA was reverse-transcribed by using SuperScript III , and a real-time PCR was carried out with Platinum SYBR Green (Invitrogen). A negative template control that contained all SYBR green reagents except DNA was performed in parallel on the iCycler iQ . The results corroborated the microarray data, thereby verifying the accuracy of the statistical analysis (A quantitative reverse transcription–polymerase chain reaction (qRT-PCR) was carried out to ensure an independent assessment of the microarray results. Genes for the qRT-PCR were selected to represent the broad spectrum of identified functional classes from the microarrays. The hypoxanthine guanine phosphoribosyltransferase gene was used as an internal control  or type-I IFN pathways (An example of this activation was finding that the HLA-C gene coding for the major histocompatibility complex class I (MHC-I) antigens was upregulated in the A172 cells. Peptides derived from endogenous intracellular proteins are generally bound by the MHC-I molecules for presentation, thus paving the way for cell cytotoxicity in cellular immunity. In mice, the targeted killing of WNV-infected cells by CD8+ T cells may result in the severe neurologic disease often observed in WNV infections  was observed to be upregulated in WNV-infected A172 cells. Increased production of INDO by glial cells causes neuronal injury in neuroinflammatory diseases , nuclear factor of κ light-chain gene (NFKBIA), TNF receptor-associated factor (TRAF1), and spermidine/spermine N1-acetyltransferase (SAT). This highly conserved process of cellular self-destruction serves to limit the spread of WNV , cytochrome c oxidase (COX5B/COX6B), and various genes of the ATP synthase complex . Decreased energy production from the downregulation of these genes is known to cause severe neurodegeneration (In summary, this global transcriptional study showed a complex network of WNV-induced A172 cell interactions during infection. The examination of glial A172 cell response has provided insights into the molecular mechanisms behind the observed neuronal pathology in WNV encephalitis.


Patients with type 2 diabetes have 2–4 times greater risk for cardiovascular morbidity and mortality than those without, and this is even further aggravated if they also suffer from hypertension. Unfortunately, less than one third of hypertensive diabetic patients meet blood pressure targets, and more than half fail to achieve target HbA1c values. Thus, appropriate blood pressure and glucose control are of utmost importance. Since treatment sometimes fails in clinical practice while clinical trials generally suggest good efficacy, data from daily clinical practice, especially with regard to the use of newly developed anti-diabetic and anti-hypertensive compounds in unselected patient populations, are essential. The DIALOGUE registry aims to close this important gap by evaluating different treatment approaches in hypertensive type 2 diabetic patients with respect to their effectiveness and tolerability and their impact on outcomes. In addition, DIALOGUE is the first registry to determine treatment success based on the new individualized treatment targets recommended by the ADA and the EASD.DIALOGUE is a prospective observational German multicentre registry and will enrol 10,000 patients with both diabetes and hypertension in up to 700 sites. After a baseline visit, further documentations are scheduled at 6, 12 and 24 months. There are two co-primary objectives referring to the most recent guidelines for the treatment of diabetes and hypertension: 1) individual HbA1c goal achievement with respect to anti-diabetic pharmacotherapy and 2) individual blood pressure goal achievement with different antihypertensive treatments. Among the secondary objectives the rate of major cardio-vascular and cerebro-vascular events (MACCE) and the rate of hospitalizations are the most important.The registry will be able to gain insights into the reasons for the obvious gap between the demonstrated efficacy and safety of anti-diabetic and anti-hypertensive drugs in clinical trials and their real world balance of effectiveness and safety. There is an increasing prevalence of type-2 diabetes, which is attributable to a growing population, an increase in life expectancy, increased diagnostic efforts, and a reduced diabetes attributable risk due to recent advances in diabetes treatment . HyperteA number of studies have shown improved glycaemic control to be able to delay the onset and also to halt the progression of micro-vascular complications such as diabetic retinopathy and nephropathy. This has also been shown for neuropathic secondary disorders ,6. For mAn exploratory analysis of the United Kingdom Prospective Diabetes Study (UKPDS) data showed that the risk of both micro- and macro-vascular complications of type 2 diabetes (T2D) was strongly associated with the mean systolic blood pressure and that blood pressure lowering by 10 mmHg led to a 15 % reduction in the risk for death related to diabetes .In the Steno-2 study, intensified therapy of the modifiable risk factors in patients with T2D and microalbuminuria was compared to standard treatment. In addition to lifestyle changes and diet modifications, all patients in this group received ACE-inhibitors (ACEI) or angiotensin receptor blockers (ARB) irrespective of baseline BP values and a vitamin-mineral supplement. The target limits for HbA1c, fasting cholesterol and triglycerides and blood pressure were much stricter than in the control group. This multi-factorial approach led to significant reductions in both micro- and macro-vascular event rates ,10.Thus, in diabetic patients with hypertension, appropriate blood pressure control as well as glucose control is important. Current guidelines ,12 recomDuring recent years new medications and fixed dose combinations have been developed for the treatment of T2D and hypertension. For achieving glycaemic control, a number of oral therapeutic options are available, using different approaches. There are agents that increase insulin secretion, such that improve insulin action and also substances delaying carbohydrate absorption. Unfortunately, most of them – with the exception of metformin – are associated with weight gain. Hypoglycaemia and in some cases gastrointestinal side effects and oedema are other possible disadvantages. Another issue is the failure to achieve adequate control of postprandial blood glucose and also long term glycaemic control . In receTo achieve adequate blood pressure control, an even larger number of therapy options exist. As diabetic patients frequently also have impaired renal function associated with microalbuminuria, substances affecting the renin-angiotensin system, which are known to display renal benefits independent of blood pressure reduction, appear to be particularly beneficial -23. ACEITreatment recommendations by current guidelines are mainly based on evidence from randomised controlled trials. However, these trials reflect only selected patient populations defined by detailed in- and exclusion criteria of these trials. Patients in daily practice are usually older and suffer from more co-morbidities as compared to those in clinical trials ,27. AvaiThe purpose of the registry is to evaluate various therapy regimes of anti-diabetic (including incretin-based and exclusively non-incretin-based therapies) and anti-hypertensive treatments (including RAAS-inhibitors and exclusively non-RAAS-inhibitors) as well as their combinations, patient reported outcomes and treatment success in hypertensive T2D patients. It is the first prospective registry to determine treatment success based on the new individualized treatment targets of the ADA and the EASD .DIALOGUE is a prospective observational national multicentre registry with a follow-up of 24 months and will enrol 10,000 patients with both T2D and hypertension from up to 700 sites in Germany. Data are recorded at baseline and will be prospectively documented during follow up visits at 6, 12 and 24 months.Verband forschender Arzneimittelhersteller (VFA).This registry is conducted in accordance with the ethical principles that have their origin in the Declaration of Helsinki and adhere to the principles of Good Epidemiology Practice (GEP), and applicable regulatory requirements. The protocol of this registry was approved by the ethics committee of the Ruhr University Bochum, Germany. Patients that being enrolled into this registry will provide written informed consent. DIALOGUE has further been registered in the database of the The two co-primary objectives are: 1) documentation of individual HbA1c goal achievement with respect to anti-diabetic pharmacotherapy and 2) documentation of individual blood pressure goal achievement with different anti-hypertensive treatments.Secondary objectives are (1) to document major cardio-vascular and cerebro-vascular events (MACCE) during 2 year follow-up; (2) to document hospitalizations during 2 year follow-up; (3) to assess the proportion of patients reaching blood glucose target values without experiencing the following adverse effects: peripheral oedema or proven hypoglycaemic events or discontinuation due to gastrointestinal events or significant weight gain (>5 %); (4) to describe patient characteristics in patients with diabetes mellitus and hypertension in clinical practice in the overall registry population; (5) to document anti-diabetic and anti-hypertensive therapy and its impact on treatment target achievements in diverse subject populations, which have to be pre-specified by the scientific committee ; (6) to verify the applicability of and the adherence to the current guidelines for the treatment of diabetes and hypertension in clinical practice; (7) to document utilisation patterns of drugs used for the treatment of diabetes as well as hypertension in clinical practice; (8) To evaluate adverse cardio-vascular events as well as diabetes-related micro-vascular and macro-vascular events; (9) to evaluate the glycaemic profiles of the participants with regards to differences in anti-diabetic treatment patterns; (10) to evaluate the blood pressure profiles; (11) To evaluate co-morbid disease conditions; (12) to evaluate the change in BMI over the course of the study; (13) to evaluate the proportion of patients with hypoglycaemic events over the course of the follow-up; (14) to evaluate cardio-vascular risk by using validated cardio-vascular risk scores such as the EURO Score; (15) to evaluate health status (EQ-5D); (16) to determine costs associated with the treatment and disease related complications; (17) to document treatment persistence over time, change in treatments / dosing during a follow-up of two years ; (18) to document patient reported outcome (PRO).Institut für Herzinfarktforschung to be representative for the ambulatory treatment of diabetes and hypertension in Germany. For this purpose, a representative cross-section of different types of centres including diabetologists and primary care physicians will be built. The sampling strategy will thus provide a representative dataset for the description of oral anti-diabetic treatment patterns in Germany.The registry will be performed in primary care and diabetes centres in Germany, with a planned participation of up to 700 sites. Centres will be selected from a database maintained at the Inclusion criteria are as follows: 1) Age: ≥18 years 2) Diagnosed type 2 diabetes mellitus and manifest hypertension (comorbidity) 3) Antidiabetic therapy presently on oral mono- or dual combination therapy  4) The treating physician considers blood glucose lowering medication to be not adequate and/or not safe/tolerable 5) The physician adds another oral drug / switches drug treatment to achieve glycaemic control 6) Written informed consent for participation obtained from the subject.exclusion criteria apply: 1.) Current participation in any randomised controlled trial. 2) Patients not under regular supervision of the treating physician for the duration of the study 3) Use of GLP-1-analogues or insulin before enrolment 4) Patients treated with aliskiren in a dual renin angiotensin aldosterone (RAAS) blockade 5) Pregnancy 6) Diabetes secondary to malnutrition, infection or surgery 7) Maturity onset diabetes of the young 8) Known cancer.Patients will not be eligible for inclusion if any of the following Patients will be enrolled according to a pre-specified ratio based on their treatment, which is not pre-determined by the study protocol but based on the physician’s decision Figure . “IncretPatient enrolment has started in July 2012. It is estimated that the first visit of the last patient enrolled will take place in early 2013, the last visit of the last patient is planned for early 2015. The Clinical Study Report will then be published in April of the following year. Figure Table There are three strategies for data quality checks: validations that occur at the time of data entry , a second, more sophisticated quality control program that runs as a prelude to the creation of the analysis data set and on-site data monitoring.Front-end data checks are advantageous because mistakes are caught and corrected at the time of entry – a system that is efficient for data collectors. Certain data elements can be required, while other variables may allow for missing values. Additionally, parameters will be defined to allow entry of only those records that meet inclusion criteria.Prior to the creation of the analytic dataset, more extensive quality control processes are performed. These checks, programmed in SAS, include parent–child edits, consistency edits, and data transformations that will facilitate analyses.Source documentation and data accuracy will be verified by site visits in randomly selected 2% of the sites.All variables collected in the eCRF as well as the data obtained from the quality of life assessments and all derived parameters will be used in the statistical analysis. Binary, categorical, and ordinal parameters will be summarised by means of absolute and percentage numbers within the various categories . Numerical data will be summarised by means of standard statistics . In addition, adequate graphs  may be presented to summarise the results for some parameters. Time-to-event variables will be analysed via a Cox proportional hazard regression model presenting hazard ratios and the corresponding 95% confidence intervals. In addition Kaplan-Meier curves will be presented for these variables. Two-sided 95%-CI will be presented for important parameters, but should be interpreted in an exploratory descriptive way. Further multivariable analyses will be performed according to the statistical analysis plan (SAP). Formal statistical tests will not be performed within the statistical analysis. A report including descriptive statistics of all documented parameters will be generated for the overall patient population. Depending on the variable(s) of interest, additional selection criteria for patients  considered in specific analyses may be used, if considered useful during the statistical analysis. Details on the selection criteria used will be given in the SAP and in the statistical section of the report. The statistical analysis will be performed using SAS .There are a number of epidemiological studies, which documented (among other aspects) the treatment of type-2 diabetes in Germany. Table and hypertension and the effect of two matching treatment approaches on target goal achievement as to the more recent guidelines which suggest more individualized treatment targets [Most of these studies however either had a detailed look on the prevalence of type-2 diabetes in primary care practice (HYDRA and DETECT), the co-morbidity burden (DETECT), the costs , or on self-monitoring of blood glucose (ROSSO). Some were also retrospective in design . Similar to DIALOGUE the effect of a tailored intervention on target achievement was investigated in DUTY and DIG. The added value of DIALOGUE is, that it will enrol almost 10,000 patients exclusively with the co-morbid disease constellation diabetes targets ,42,43 batargets ,45.DIALOGUE is the first registry that focuses at the evaluation of T2D patients who also suffer from hypertension, addressing both achievement of glycaemic and anti-hypertensive goals considering the individualized treatment targets as recommended by recent guidelines. As the combination of both diseases is a major factor in the development of vascular complications, data obtained will help to explain the gap between the inadequate glycaemic and blood pressure control in the “real world setting” despite the demonstrated efficacy and safety of anti-diabetic and anti-hypertensive drugs in clinical trials.MACCE: Major Cardiovascular and Cerebrovascular Events; MAGE: Mean Amplitude of Glycaemic Excursions; T2D: Type-2 Diabetes Mellitus; UKPDS: United Kingdom Prospective Diabetes Study; ACEI: ACE-inhibitor; ARB: Antiotensin Receptor Blocker; RAAS: Renin Angiotensin Aldosterone System.Anselm K Gitt (AKG), Roland E. Schmieder (RES), Peter Bramlage (PB), and Diethelm Tschöpe (DT) have received research support and honoraria for lectures from a number of pharmaceutical companies producing anti-diabetic drugs including Novartis the sponsor of this study. Eve Duetting (ED) is employee of the sponsor. Steffen Schneider (StS) has no potential conflict of interest to disclose.AKG, ED, RES and DT designed the registry. StS is responsible for the analysis of data. PB drafted the manuscript based on the protocol and all other authors revised the article for important intellectual content. All authors have finally approved the version to be published.DIALOGUE study group: Sibel Avsar (Ludwigshafen), Peter Bramlage (Mahlow), Eva Duetting (Nürnberg), Anselm K. Gitt (Ludwigshafen), Alexander Neumer (Ludwigshafen), Taoufik Ouarrak (Ludwigshafen), Roland E. Schmieder (Erlangen), Steffen Schneider (Ludwigshafen), Diethelm Tschöpe (Bad Oeynhausen).


Here, we review the present state of the art with respect to cardiac disease models already generated using IPSC technology and which have been  characterized. Human iPSC (hiPSC) models have been described for the cardiac arrhythmia syndromes, including LQT1, LQT2, LQT3-Brugada Syndrome, LQT8/Timothy syndrome and catecholaminergic polymorphic ventricular tachycardia (CPVT). In most cases, the hiPSC-derived cardiomyoctes recapitulate the disease phenotype and have already provided opportunities for novel insight into cardiac pathophysiology. It is expected that the lines will be useful in the development of pharmacological agents for the management of these disorders.Cardiac arrhythmias are a major cause of morbidity and mortality. In younger patients, the majority of sudden cardiac deaths have an underlying Mendelian genetic cause. Over the last 15 years, enormous progress has been made in identifying the distinct clinical phenotypes and in studying the basic cellular and genetic mechanisms associated with the primary Mendelian (monogenic) arrhythmia syndromes. Investigation of the electrophysiological consequences of an ion channel mutation is ideally done in the native cardiomyocyte (CM) environment. However, the majority of such studies so far have relied on heterologous expression systems in which single ion channel genes are expressed in non-cardiac cells. In some cases, transgenic mouse models have been generated, but these also have significant shortcomings, primarily related to species differences. The discovery that somatic cells can be reprogrammed to pluripotency as induced pluripotent stem cells (iPSC) has generated much interest since it presents an opportunity to generate patient- and disease-specific cell lines from which normal and diseased human CMs can be obtained These genetically diverse human model systems can be studied  Cardiac arrhythmias can be life threatening and are a major cause of morbidity and mortality in developed nations , Brugada Syndrome (BrS), and catecholaminergic polymorphic ventricular tachycardia (CPVT)  cells, and Chinese Hamster Ovary (CHO) cells  of human and mouse  environment. However, obtaining ventricular cardiac biopsies from patients is a highly invasive procedure and not without significant risk. Consequently the majority of functional studies on specific mutations associated with the Mendelian rhythm disorders have relied on heterologous expression systems, primarily The discovery of somatic cell reprogramming to generate induced pluripotent stem cells (iPSC) ] spontaneous differentiation to cells of the three germ layers occurs. CMs originate from the mesodermal germ layer, so that CM differentiation first requires efficient differentiation toward the mesodermal lineage. Directed differentiation toward the cardiac lineage is mainly achieved by one of the following strategies: (1) The first involves the formation of EBs in the presence of growth factors and repressors known to influence heart development  ranging from 9 to 40 V/s which is slow compared to those non-spontaneously beating hiPSC-CM with a dV/dtmax of 115 V/s, and native ventricular CMs with a dV/dtmax of 215–234 V/s , is longer in spontaneous active cells (313–495 ms) compared to non-spontaneously active hiPSC-CMs (173 ms) and native freshly isolated CMs (213–351 ms)  is responsible for the AP upstroke in ventricular CMs i) and Ca+ ([Ca2+]i)  via ryanodine receptors (RyRs). This phenomenon is referred to as “Ca2+−induced Ca2+ release” (CICR) , junctin (Jun), triadin (TRDN), Na+/Ca2+ exchanger (NCX), calsequestrin (CASQ2), L-type Ca2+ channel (Cav1.2), inositol-1,4,5-trisphosphate receptor (IP3R2) and PLN are expressed in hiPSC-CM , which is typical for t-tubule deficient cells . The delayed rectifier currents were studied in ventricular-like myocytes. In hiPSC-CMs derived from the LQT1 patient (LQT1-iPSC-CM), IKs peak and tail current densities were reduced by approximately 75%, and IKr conductance was unaffected. APs of atrial-like and ventricular-like hiPSC-CMs were significantly prolonged in LQT1-iPSC-CMs compared to control (WT-iPSC-CMs). Adaptation of the AP duration to higher pacing frequencies and the response to isoproterenol were impaired in LQT- iPSC-CM. EADs were elicited in response to isoproterenol (a β-adrenergic agonist) in 6 out of 9 LQT1-iPSC-CM and never in WT-iPSC-CM. When propranolol (a non-selective β-blocker) was applied the effect of isoproterenol was blunted. These data are in line with observations in LQT1 patients as these patients suffer from arrhythmias during increased heart rates caused by emotional stress or exercise. The data is also in line with the beneficial effects of β-blockers in suppressing arrythmias in these patients , which are afterdepolarizations after and AP   compared to control iPSC-CMs (~39 bpm). Isoproterenol induced DADs, oscillatory arrhythmic prepotentials  and increased [Ca2+]i.Novak et al.  studied in vitro in the hiPSC-CM system the disease phenotype of patients with Mendelian cardiac rhythm disorders. Furthermore, different studies have shown that LQT-iPSC-CMs of symptomatic patients show a more severe cellular phenotype than those obtained from asymptomatic patients with the same mutation  by 1-ethyl-2-benzimidazolinone (Kleger et al., Another difficulty is the purity of the population of hiPSC-CMs acquired. With the current techniques it is not possible to acquire a pure population of CMs, the fraction of CMs obtained may vary from 1% to ~50% of the total cells (Dambrot et al., In summary, iPSC-CM models recapitulate the phenotype of patients with cardiac arrhythmia syndromes. However, the interpretation of electrophysiological data derived from these cells, should be done with caution, since hiPSC-CMs present immature phenotypes and do not recapitulate all the electrical characteristics of an adult CM. Thus, studies addressing the maturity and purity of the hiPSC-CMs acquired are needed, as well as studies to characterize the electrophysiological and pharmacological characteristcs in more detail. Because of this, there are still limitations for the use of this model system in studies on cardiac rhythm disorders, especially if the disease causing mutation is not known. So far, hiPSC-CMs have only been applied as models to the study of disorders for which mutations in particular genes have been identified. Future studies are likely to demonstrate the potential of these cell systems in pointing us to pathophysiological mechanisms for those cases for which no gene mutations are yet known. Another future application of hiPSC-CMs is the use in cardiac safety pharmacology and the development of new drugs. hiPSC-CMs provide researchers with CMs of human origin which are better-suited than CMs of animal origin or heterologous cell systems. Furthermore, hiPSC-CMs provide us with the opportunity to test drugs in disease-specific CMs instead of healthy CMs. Thus, in future studying hiPSC models might lead to novel insights in pathophysiology, improve understanding of genotype-phenotype relationships and could be used in the development and testing of pharmacological agents to treat human cardiac disease.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution. Here, we constructed the first human PDZ domain-ligand interaction network (PDZNet) together with binding motif sequences and interaction strengths of ligands. PDZNet includes 1,213 interactions between 97 human PDZ proteins and 591 ligands that connect most PDZ protein-mediated interactions (98%) in a large single network via shared ligands. We examined the rewiring of PDZ domain-ligand interactions throughout eukaryotic evolution by tracing changes in the C-terminal binding motif sequences of the PDZ ligands. We found that interaction rewiring by sequence mutation frequently occurred throughout evolution, largely contributing to the growth of PDZNet. The rewiring of PDZ domain-ligand interactions provided an effective means of functional innovations in nervous system development. Our findings provide empirical evidence for a network evolution model that highlights the rewiring of interactions as a mechanism for the development of new protein functions. PDZNet will be a valuable resource to further characterize the organization of the PDZ domain-mediated signaling proteome. Rewiring of interactions is a powerful tool for the evolution of organism complexity. Rewiring among preexisting proteins provides a simple mechanism for the development of new signaling circuits by redirecting information flows without a gain or loss of genes. Particularly, interactions mediated by short linear motifs can be easily changed by mutations during evolution, resulting in a rewiring of interactions. However, how interaction rewiring of linear motif interactions facilitates the emergence of new protein function during evolution is poorly understood. Here, we systematically investigated the rewiring of interactions mediated by PDZ domains, which are one of the most commonly found peptide recognition modules. We found that PDZ domain-ligand interactions are frequently rewired by C-terminal sequence mutations in PDZ ligands during evolution. Especially, rewiring of PDZ domain-ligand interactions was involved in neuronal function development, occurring concurrently with the emergence of vertebrates and suggesting that reorganization of signaling pathways by rewiring PDZ domain-ligand interactions significantly contributed to the evolution of nervous systems in vertebrates. Our findings highlight the rewiring of interactions as an effective means for functional innovation, providing new insight into eukaryotic evolution, which has not been fully explained by only the expansion of protein families. PDZ domains are linear motif-mediated protein-protein interaction modules. PDZ domain-ligand interactions have been greatly expanded in metazoans and are widely used to assemble signaling complexes, including those found in neuronal synapses Systematic analysis of interaction rewiring will provide new insights into eukaryotic evolution, which is not fully explained via only the expansion of protein families. Recently, it was suggested that rewiring of interactions is an important mechanism for the evolution of biological systems. Network comparison studies showed that protein interactions frequently change after gene duplication Structural information of interacting cellular components  would provide a more complete picture of a cell and help elucidate the evolutionary principle of the protein interaction network In this work, we attempted the first systematic investigation of interaction rewiring in the PDZ domain-ligand interaction network and its role in eukaryotic evolution. We constructed a comprehensive human PDZ domain-ligand interaction network and traced the changes in interaction rewiring during evolution. We developed position weight matrices (PWMs) of human PDZ domains from the experimental data of PDZ domain-ligand interactions. The binding motif information of PDZNet helped to elucidate the changes in PDZ domain-ligand interactions. We found that PDZ domain-ligand interactions are frequently rewired throughout evolution via mutations of C-terminal PDZ ligand sequences. Particularly, interaction rewiring occurred concurrently with emergence of vertebrates whose rewired interactions were largely involved in neuronal signaling, suggesting that nervous system evolution might be achieved by the interaction rewiring of signaling components, such as PDZ protein-ligand interactions. Furthermore, the broad specificity of PDZ domains contributes to interaction rewiring by increasing the chance of acquiring PDZ binding motifs by sequence mutations. Our findings will prompt a new approach for the study of eukaryotic evolution by considering the rewiring of interactions as a major evolutionary process of domain-ligand interactions.To elucidate how PDZ domain-ligand interactions have evolved, an accurate and detailed understanding of their interactions is essential. Furthermore, a network approach is useful to understand how evolution of PDZ domain-ligand interactions contributed to eukaryotic evolution, because protein functions may not be encoded in an individual protein but rather be encoded in the relationships between proteins in a protein-protein interaction network in vivo PDZ domain-ligand interactions (PDZBase) We developed a quantitative model of PDZ domain binding strengths from the experimental data of PDZ domain-ligand interactions, including interactions between 81 PDZ domains and 217 peptides from a protein array 2 = 0.76) and ERBIN (R2 = 0.85) PDZ domain-peptide interactions. Moreover, in vivo binding affinities of PSD-95_1 (the first PDZ domain of PSD-95) with its ligands correlated well with its binding scores from the PWMs  of the known PDZ ligands were found in the top 10 percentile of binding scores  the introduction of new PDZ ligands by gene duplication of existing partners, or (2) the g motifs . To examNext, we examined the sequence evolution of the binding motifs of human PDZ ligands and discovered that a large portion of PDZ ligands acquired their binding motifs via sequence mutations. We examined the C-terminal sequences of PDZ ligands in each PDZ domain-ligand interaction pair across 16 representative species. We found that nearly one-third of human PDZ ligands gained their PDZ domain interactions by C-terminal mutations during evolution . For exaEscherichia coli to humans and examined the changes in the C-terminal sequences of PDZ ligands and the binding specificities of PDZ domains  changed Ile to Thr and another mutation (C→T) changed the codon for Gln to a stop codon  that describe the number of hydrogen bond donors Our goal was to predict the specificities of a PDZ domain without knowledge of its structure. As such, a method to extract pocket residues from the sequences of PDZ domains was designed. To identify the positions of pocket residues within the PDZ domain sequence, an MSA was constructed, and the known structure of the PSD-95_1 domain was referenced. We performed a multiple alignment of the PDZ domain sequences using a HMM a, i) is the affinity contribution of amino acid a at the ith position, a,if is the frequency of amino acid a at the ith position in the collected set, and a,ip is the background frequency, defined as the probability of observing amino acid a at the ith position in any ligand protein. The constant k was empirically determined to be 1.921, so that PDZNet includes all experimentally confirmed PDZ domain-ligand interactions as positive binding scores.To estimate the PDZ domain-ligand binding affinity, we adopted an information theory-based PWM method that is widely used to estimate protein-DNA binding affinities i,iS) is the affinity contribution of the amino acid iS at the ith position in the matrix and iS is the amino acid at the ith position of the peptide.A PWM was used to calculate the binding score of a potential interaction partner with a given sequence by summing the corresponding amino acids for the affinity contribution of each position. The binding score of each peptide was calculated according to the following formula:Affinity values of the 5,257 peptides against both the SNA1 and ERBIN PDZ domains were obtained from Wiedemann et al. th∼100th percentile of the binding score; To evaluate the performance of our method, we measured the ability to identify the 217 known binding partners of 145 PDZ domains in the PDZBase We compiled human protein interactions from a total of 22 existing protein interaction databases: the Bio-molecular Interaction Network Database (BIND), the Human Protein Reference Database (HPRD), the Molecular Interaction database (MINT), DIP, IntAct, BioGRID, Reactome, the Protein-Protein Interaction Database (PPID), BioVerse, CCS-HI1, the comprehensive resource of mammalian protein complexes (CORUM), IntNetDB, the Mammalian Protein-Protein Interaction Database (MIPS), the Online Predicted Human Interaction Database (OPHID), Ottowa, PC/Ataxia, Sager, Transcriptome, Complexex, Unilever, protein-protein interaction database for PDZ-domains (PDZBase), and a protein interaction dataset from the literature We collected all physical interactions mediated by the PDZ proteins from the integrated PPI network. This PDZ protein-mediated interaction set may have some interactions that are mediated by interaction domains other than PDZ domains, because many PDZ proteins have various domains other than PDZ domains. Therefore, we removed such interactions that were connected by domain-domain interactions rather than PDZ domain-ligand interactions. First, we confirmed that PDZ domain-mediated interactions are rarely augmented by other interaction domains. We found that domain-domain interactions are not present in the experimentally confirmed PDZ protein-ligand interactions from the PDZBase We also removed interactions that could be mediated by other peptide-binding domains, such as SH3 and WW domains, rather than PDZ domains. We searched the known peptide-binding motifs and removed interactions mediated by peptide-binding domains that had low binding scores. The cut-off binding score was set to the lowest binding score of the experimentally confirmed PDZ domain-peptide interactions from the PDZBase i and j, be in a common tree with humans, and species i is more distant from humans. If a human PDZ interaction is absent in species i and present in species j, we define the interaction as rewired. Thus, the rewiring occurred during the time interval between the emergence of species i and j. We also consider that all proteins in species i could be rewired to PDZ proteins in species j. Thus, we define the rewiring rate as the following:j is the number of rewired interaction found in species j; t is the divergence time from human; pall,i is the number of proteins orthologous to human protein in species i; and ppdz,j is the number of proteins orthologous to human PDZ proteins. Divergent time was obtained from the timetree (http://www.timetree.net).Let two species, To analyze the interactions between orthologous PDZ domains, we calculated the binding scores of the C-terminal sequences of orthologous PDZ ligands and the predicted PWMs with orthologous PDZ domain sequences.We examined whether particular protein functions were enriched for protein categories that were defined based on the time of protein emergence and PDZ-binding motif acquisition. We systematically classified PDZ ligands into two categories: (1) proteins arose in invertebrates and acquired PDZ domain interaction sites in vertebrates; (2) proteins arose and acquired PDZ domain interaction sites in invertebrates; we then analyzed the overrepresented functional terms of each group. We used DAVID p values for over- or under-representation of the disease-associated genes in PDZNet were obtained using a hypergeometric distribution. We independently calculated the probability of the disease-associated genes in each class.Mutations of PDZNet proteins were mapped to genetic diseases using disease-gene association databases from OMIM. The OMIM database lists gene-disease associations between 2,929 disease types defined by Morbid Map (MM) and 1,777 genes associated with particular disease types. Disease types were further categorized into 1,340 distinct diseases by joining disease subtypes into a single disease if similar disease names were used. These disease types were further classified into 20 disease classes based on the physiological system affected We created a user-friendly web service that provides a PWM and rank list of interaction candidates of a given PDZ domain sequence . The autFigure S1Comparisons of the quantitative model- and phage display data-derived PWMs of MAGI1_2, DLG1_2, and PTN13_2.(PDF)Click here for additional data file.Figure S2Distribution of the interaction partners of 97 human PDZ proteins. The maximum number of ligands per PDZ protein is 102. The average interaction partner of the human PDZ protein is 12.(PDF)Click here for additional data file.Figure S3Network representation of domain-level interactions in PDZNet (best viewed by magnification in a PDF viewer). Domain numbers are presented on the right side of the PDZ protein names with a delimiter (‘_’). The network is composed of 2,643 interactions between 190 PDZ domains and 593 ligands.(PDF)Click here for additional data file.Figure S4Dendrogram of PDZ domains based on the identity of pocket residues. Domain numbers are presented on the right side of the PDZ protein names with a delimiter (‘_’).(PDF)Click here for additional data file.Figure S5Relationship between specificity determining residue (SDR) identity and PWM similarity. Each point represents an orthologous PDZ domain pair.(PDF)Click here for additional data file.Figure S6Phylogenetic profile of human PDZ proteins and ligands across 13 fully sequenced species. The presence (yellow) and absence (black) of orthologs for the 104 PDZ proteins and 554 PDZ ligands are presented.(PDF)Click here for additional data file.Figure S7Multiple sequence alignment (MSA) of EXOC4 orthologs. The MSA was generated using Muscle with default options. C-terminal PDZ binding motifs are shown in bold.(PDF)Click here for additional data file.Figure S8Amino acid preference patterns of human PDZ domain pockets. (A–D) Clustering of amino acid preference profiles of 241 human PDZ domain pockets is shown.(PDF)Click here for additional data file.Figure S9Alternative expression of SAP97 ligands across three human tissues. The protein expression levels of SAP97 PDZ protein and its 13 ligands were compared across brain, bone, and epidermis. Protein expression was measured by quantitative mass spectrometry (PDF)Click here for additional data file.Figure S10Macaca mulatta PBK protein. The binding motif is highlighted in the PWM of SAP97_1 (right). Mutations in the PDZ-binding motif are shown in the alignment of DNA sequences (bottom). (B) A DNA segment insertion generated a PDZ-binding motif in the C-terminal amino acids of the Oryzias latipes EXOC4 protein. The binding motif is highlighted in the PWM of SAP102_1 (right). The inserted DNA segment is shown in the alignment of DNA sequences (bottom).Types of DNA modifications that gain PDZ-binding motifs. (A) A point mutation generated a PDZ-binding motif in the C-terminal amino acids of the (PDF)Click here for additional data file.Figure S11Mutation effects of the C-terminal GluR7 sequence. (A) C-terminal sequences and binding scores of wild-type and mutation forms of GluR7. (B) The PWM of the PICK1 PDZ domain. Four C-terminal residues of wild-type GluR7 are highlighted.(PDF)Click here for additional data file.Figure S12left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of proteins . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S13left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of proteins . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S14left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of proteins . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S15left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of interactions . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S16left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of interactions . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S17left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of interactions . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S18Discriminating power of selectivity axes. Each boxplot shows distributions of binders and non-binders of an amino acid, which are presented at the top of the plot. Binders are PDZ domain pockets that prefer the amino acid, and non-binders are those domain pockets that do not prefer the amino acid. The vertical axis corresponds to an axis of a selectivity space. Fisher's score (FS) is presented at the top of each plot, indicating the discriminating power of the selectivity axes.(PDF)Click here for additional data file.Figure S19Procedure for extracting pocket residues. (A) Schematic drawing of the PSD-95_1 domain structure. (B) Position of each pocket residue on the structure. (C) The MSA of three representative PDZ domains was constructed using the hidden Markov model that was optimized for the PDZ domain. By adjusting the secondary structural profile on the MSA, the positions of pocket residues were identified. Gray boxes indicate the positions of pocket residues.(PDF)Click here for additional data file.Figure S20Fraction of domain-domain interactions according to the binding scores of all PDZ protein-mediated interactions. The PDZ protein-mediated interactions were binned based on binding score. The fraction of domain-domain interactions were measured for each bin.(PDF)Click here for additional data file.Figure S21Flow chart of web server and a sample output. The web server takes a query PDZ domain sequence and a species name. The outputs are pocket residues, a PWM of the query PDZ domain, and a genome-wide rank list of proteins from the species chosen by the user.(PDF)Click here for additional data file.Table S1Position weight matrices (PWMs) for 515 human PDZ domains. For resource purposes, homologous PDZ domains are included in the list.(XLS)Click here for additional data file.Table S2in vivo partners derived from various species.Validation of PWMs on (XLS)Click here for additional data file.Table S3PDZ domain-ligand interactions in PDZNet.(XLS)Click here for additional data file.Table S4C-terminal sequences of human PDZ ligand orthologs.(XLS)Click here for additional data file.Table S5Experimental evidence of human PDZ domain-ligand interactions that emerged via sequence mutations. ‘−’ indicates the absence of an ortholog(XLS)Click here for additional data file.Table S6Over-represented gene ontology (GO) terms of PDZNet proteins based on the time point of acquiring PDZ domain interaction sites.(XLS)Click here for additional data file.Table S7Disease classes associated with mutations of PDZNet components.(XLS)Click here for additional data file.Table S8A morbid map of PDZNet components with the classification of genetic diseases.(XLS)Click here for additional data file.


Most studies investigating disability outcomes following injury have examined hospitalised patients. It is not known whether variables associated with disability outcomes are similar for injured people who are not hospitalised.This paper compares the prevalence of disability 24 months after injury for participants in the Prospective Outcomes of Injury Study who were hospitalised and those non-hospitalised, and also seeks to identify pre-injury and injury-related predictors of disability among hospitalised and non-hospitalised participants.Participants, aged 18–64 years, were recruited from an injury claims register managed by New Zealand’s no-fault injury compensation insurer after referral by health care professionals. A wide range of pre-injury socio-demographic, health and psychosocial characteristics were collected, as well as injury-related characteristics; outcome is assessed using the WHODAS. Multivariable models estimating relative risks of disability for hospitalised and non-hospitalised participants were developed using Poisson regression methods.Of 2856 participants, analyses were restricted to 2184 (76%) participants for whom both pre-injury and 24 month WHODAS data were available. Of these, 25% were hospitalised. In both hospitalised and non-hospitalised groups, 13% experience disability (WHODAS≥10) 24 months after injury; higher than pre-injury (5%). Of 28 predictor variables, seven independently placed injured participants in the hospitalised group at increased risk of disability 24 months after injury; eight in the non-hospitalised. Only four predictors  were common to both the hospitalised and non-hospitalised groups. There is some evidence to suggest that among the hospitalised group, Māori have higher risk of disability relative to non-Māori.At 24 months considerable disability is borne, equally, by hospitalised and non-hospitalised groups. However, predictors of disability are not necessarily consistent between the hospitalised and non-hospitalised groups, suggesting caution in generalising results from one group to the other. Injuries are responsible for significant health burdens in terms of premature mortality from fatal injuries and disability to injury survivors. Recently, the Global Burden of Disease report from the World Health Organisation (WHO) revealed that reductions in the disability-adjusted life year (DALY) burden associated with communicable, maternal, neonatal, and nutritional disorders have been achieved, but that similar gains have not been demonstrated for injury We have previously reported the prevalence of disability outcomes three months after injury for participants in the Prospective Outcomes of Injury Study (POIS) underway in New Zealand In this paper, we investigate whether the difference in risk factors observed three months after injury remains when longer-term outcomes are considered. POIS was developed with careful attention paid to the principles of the Treaty of Waitangi, a treaty of cession signed by representatives of Māori  and the British Crown in 1840 This paper compares the prevalence of disability 24 months after injury for POIS participants who were hospitalised and those who were non-hospitalised, especially for participants reporting Māori or Pacific ethnicity. The paper also seeks to identify predictors of participants’ disability in terms of their pre-injury socio-demographic, disability, health and psychosocial and injury-related characteristics, to help inform future development of appropriate interventions.The study was undertaken following approval from the New Zealand Health and Disability Multi-Region Ethics Committee (MEC/07/07/093). Following feedback from participants in the pilot study and to be inclusive of all people (including those with poor vision or limited literacy), and with the approval of the Ethics Committee, all participants granted oral consent to participate after receiving comprehensive information about the study. Oral consent was documented by interviewers, and all participants received copies of the consent form.The design of POIS and the main characteristics of participants have been described previously Disability outcome was measured using the brief WHO Disability Assessment Schedule (WHODAS II 12-item) Data from POIS participants were probabilistically linked to a national database  to identify participants admitted to hospital or treated at an Emergency Department for at least three hours, within seven days of the injury event, as described previously Explanatory variables were grouped according to pre-injury socio-demographic, pre-injury health and psychosocial, and injury-related, characteristics. These items have been described before, and are therefore only briefly summarised here Participants reported socio-demographic characteristics at the time of the first interview including: age, sex, ethnicity, highest educational qualification and living arrangements, based on questions from the 2006 New Zealand Census www.facit.org) asked participants if they found “comfort in faith and spiritual beliefs” Overall pre-injury ‘general health’ was rated on a five-point scale  Pre-injury ‘family involvement’ was assessed by asking whether family played a ‘very large’, ‘large’, ‘small’ or ‘very small’ part in participants’ lives Pre-injury levels of physical activity were assessed by asking the number of days in the seven-day period prior to injury they had engaged in either 30 minutes of moderate activity  or 15 minutes of vigorous activity At the three month interview, participants were asked to report whether the injury cause was intentional (assault); whether, at the time of injury, they felt the injury was a threat to their life; and a threat of severe longer-term disability. For each of these separate variables, those responding ‘yes’ or ‘maybe/possibly’ were grouped together and compared to those responding ‘no’. Information about post-injury access to healthcare services was obtained by asking people at the three month interview if they had trouble getting to or contacting health services; ‘yes’ and ‘mixed’ were grouped together and compared to those who said ‘no’.Twelve injury type (injury region/nature) variables, based on the International Classification of Diseases (ICD-10) injury mortality diagnosis matrix and the Barell injury diagnosis matrix, were developed using ACC data to describe the injured body region and nature of the injury Chi-squared tests were used to compare proportions hospitalised and non-hospitalised according to each explanatory variable. Proportions with a WHODAS score≥10 24 months after injury are presented with 95% confidence intervals (95%CI).To identify possible predictors of disability 24 months after injury we used Poisson models with robust standard errors All variables retained in either of the independent models for the hospitalised and non-hospitalised groups were then consistently retained in two further models, allowing us to present relative risks consistently for both groups. This was done to allow readers to ascertain whether or not a variable which is (say) marginally non-significant in the hospitalised group, may have an effect in the same direction as a significant finding in the non-hospitalised group. The final multivariable models  include all participants with non-missing responses for the retained variables (apart from the three mentioned above with an ‘undisclosed’ category). Model fit was assessed using deviance goodness-of-fit test.Missingness in our complete case analysis is unlikely be ‘missing completely at random’ Stata 12.1 was used for analyses Of 2856 participants in the three month interview, 2256 (79%) participated in the 24 month interview. As some were missing pre-injury or 24 month WHODAS disability outcome scores, the analyses are restricted to the 2184 (76%) participants for whom both pre-injury and 24 month follow-up WHODAS data were available. Of these, 548 (25%) were hospitalised within seven days of the injury event; the remainder were classified as non-hospitalised (n = 1636).Similar proportions in both the hospitalised and non-hospitalised groups, were experiencing disability (WHODAS≥10) at 24 months . There were no substantial differences in 24 month disability according to age or sex for either the hospitalised or non-hospitalised groups . For othMāori in the hospitalised group were at 70% increased risk of disability compared to non-Māori, whilst taking account of a range of pre-injury and injury-related variables in the modelling, although the 95%CI included 1 . There was weak evidence (p = 0.02) to suggest risk of disability differed by age for those in the non-hospitalised group. There was no evidence to suggest that risk of disability differed for those within the separate categories of sex, Pacific ethnicity or living arrangements. Highest educational qualifications, working for pay and household income were not retained in the final models.Having a pre-injury WHODAS≥10 increased the risk of post-injury disability (24 months after injury) for both the hospitalised  and non-hospitalised  groups. Having two or more chronic conditions pre-injury, compared to having none, also independently predicted an increased risk of disability 24 months after injury among the hospitalised ; and non-hospitalised  groups. Not being optimistic pre-injury increased the risk of disability among the hospitalised group ; whereas having a depressive-type episode pre-injury increased the risk of disability among the non-hospitalised group . Having a BMI≥30 independently predicted an increased risk of disability compared to those with BMI<30 in both the hospitalised and non-hospitalised groups . There was weak evidence that cigarette smoking was associated with an increased risk of disability among the hospitalised and non-hospitalised groups . There was no evidence to suggest a relationship existed between sense of community and risk of disability. General health, self-efficacy, comfort in faith or spiritual beliefs, family involvement, social relationships, physical activity, sleep, and alcohol and recreational drug use were not retained in the final models.Intentional injury cause (assault) increased the risk of disability among the non-hospitalised group only . Perceived threat of longer-term disability at the time of injury increased the risk of disability among the hospitalised group only . Having trouble accessing healthcare services independently predicted an increased risk of disability for both the hospitalised and non-hospitalised groups . Perceived threat to life at the time of the injury event was not retained in the final models.There was no evidence to suggest a relationship existed between anatomical severity of injury and risk of disability for either the hospitalised or non-hospitalised group. For the hospitalised group head/neck superficial injury predicted risk of disability ; whereas intracranial injury  and spine sprain or dislocation  predicted an increased risk of disability 24 months later among the non-hospitalised group. In the hospitalised group, those with a lower extremity open wound were at decreased risk of disability at 24 months compared to those with other injuries . There was insufficient evidence for the remaining injury types independently predicting risk of disability.Our focus was on comparing hospitalised and non-hospitalised groups within a cohort comprising participants with a range of injury types, across a wide range of possible pre-injury and injury-related predictors of disability 24 months after injury , 4, 5, 6Multivariable analyses indicate that pre-injury disability exposes injured participants to increased risk of disability 24 months later, regardless of whether or not they were hospitalised. Data from a large survey in the United States found that people with pre-existing disability face barriers to access to services The analytic approach in this paper differs somewhat from the earlier three month analyses As in our earlier paper, several factors were associated with disability outcome in one group or the other, but not in both Studies of injury outcome, including for people with a range of injury types, are being undertaken in different countries. As previously mentioned, finding studies directly comparable with ours is problematic due to the use of different outcome measures, variable follow-up rates, and different methods and recruitment strategies Strengths of our investigation include being able to recruit both those with an injury resulting in treatment at hospital and those not receiving hospital treatment, using a measure of outcome specifically developed to measure disability (WHODAS), being able to recruit participants with ‘all-injury’ types, and having a wide range of pre-injury and injury-related factors to include in our analyses. It is also a strength of our study that we were able to interview participants independently of ACC , thereby reducing the likelihood of perverse incentives leading to an exaggeration of poor outcome (e.g. in some litigious insurance systems participants may feel the impact of the injury needs to be sustained until their case has reached court or otherwise been resolved). In fact, possibly the very existence of New Zealand’s no-fault insurer reduces such incentives. Regardless, our participants were all interviewed independently of the insurer, and knew that their results were confidential to the university research team.Our limitations include asking participants to recall a number of pre-injury states three months after the injury; although subsequent analyses of our cohort suggest that recall bias is likely to be minor, at worst Our multivariable model presented in This study reports relationships between a wide range of pre-injury and injury-related variables and risk of longer-term (24 month) disability. Certain pre-injury and injury-related variables independently predict longer-term disability, but only four  are common to both the hospitalised and non-hospitalised groups. As our earlier paper suggested, our results indicate that it may be unwise to generalise from results about predictors of risk for hospitalised patients to injured non-hospitalised groups


Functional connectivity measures applied to magnetoencephalography (MEG) data have the capacity to elucidate neuronal networks. However, the task-related modulation of these measures is essential to identifying the functional relevance of the identified network. In this study, we provide evidence for the efficacy of measuring “state-related”  changes in MEG functional connectivity for revealing a sensorimotor network. We investigate changes in functional connectivity, measured as cortico-cortical coherence (CCC), between rest blocks and the performance of a visually directed motor task in a healthy cohort. Task-positive changes in CCC were interpreted in the context of any concomitant modulations in spectral power. Task-related increases in whole-head CCC relative to the resting state were identified between areas established as part of the sensorimotor network as well as frontal eye fields and prefrontal cortices, predominantly in the beta and gamma frequency bands. This study provides evidence for the use of MEG to identify task-specific functionally connected sensorimotor networks in a non-invasive, patient friendly manner. Coordinated activity of brain regions is essential for integrating multiple information streams into a task-specific strategy for response. Thus, the capacity of the human brain to dynamically involve brain areas in different networks to facilitate neural communication is essential The most common non-invasive neuroimaging techniques for FC analysis are functional magnetic resonance imaging (fMRI), electro- and magneto-encephalography (EEG/MEG). MEG has several advantages for FC analysis over EEG and fMRI. Whole-head MEG systems provide higher spatial resolution than EEG, as the magnetic fields detected using MEG are less impacted by the varying conductivities of tissues within the head than the electric potentials detected using EEG. In comparison to fMRI, MEG records a direct correlate of neural activity with high temporal resolution, while the blood oxygen level dependent (BOLD) response is a slower, indirect measure of neural activity The goal underlying FC analysis is to quantify the synchrony between ‘nodes’ in the brain to define a neural network. The underlying assumption is that synchronized activity in spatially distinct neural populations is indicative of neural communication between these areas. Co-activation of both areas by a third neural population is a plausible alternative explanation. In MEG and EEG, synchrony between nodes is often expressed as cortico-cortical coherence (CCC), which measures the consistency over time of the phase difference between two signals as a function of frequency state-related), wherein the “condition” is the task as a whole, enabling comparative FC analysis to be performed over the course of minutes. For example, reliable changes in FC between rest states  have been shown using mutual information as a measure of neuronal communication While it is well established that MEG-related FC analysis can identify synchrony during a single task state Given the emerging importance of task modulation in elucidating patterns of network connectivity, the purpose of the present study was to provide further validation of this state-related approach to imaging functionally relevant neural networks. To this end, a group of healthy participants underwent MEG scans while at rest and during the performance of a visually cued bilateral motor task. This experimental design provided MEG data during two states, for which existing resting state fMRI literature would predict an increase in synchrony for node-pairs in the SM network during the task state. We compared CCC of anatomically prescribed node-pairs between rest and the performance of a motor task. We hypothesized that the pattern of CCC across the whole-brain would be significantly different between the task state and rest state. Further, we hypothesized that this change would manifest as increased CCC during task performance in functionally relevant nodes of the SM network – specifically, bilateral primary motor and somatosensory cortices (SI/MI), pre-motor cortices (PMC), supplementary motor area (SMA), thalamus and cerebellum (CB). This result would verify the efficacy of the FC approach, which enables the non-invasive, patient friendly investigation of the dynamic role of neural networks in brain function.Eight healthy right-handed adults  participated in the study. Handedness was quantified based on the Edinburgh Handedness Inventory Eight study blocks were completed with each participant. The first and last blocks were “rest blocks” that provided maximal contrast to the performance of the motor task. In the rest blocks, participants were asked to relax with their eyes closed. Resting block duration was 6 minutes. The second and sixth blocks (“test blocks”) required the participant to perform a visually cued bilateral gripping task that involved controlling the movement of a cursor towards a target (described below). In the third, fourth and fifth blocks, participants practiced the bilateral gripping task at a self-paced rate (“training blocks”). The seventh block involved repetition of the test block with a different target location (“flip block”). Total duration of the motor task blocks varied across participants owing to variability in response time. On average, motor task block duration was approximately 7 minutes. Total time for the MEG session was approximately 1 hour. Only the “rest” and “test” blocks  were utilized for the purposes of the present paper.During the test blocks, participants performed a visually cued bilateral gripping task with visual feedback. The goal of the visuomotor task was to accurately move a cursor towards a target located a fixed distance  and angle  from the initial cursor position. Subjects held two rubber bulbs (inflation bulbs from a sphygmomanometer), one each in the left and right hand, which controlled vertical and horizontal movement of the cursor, respectively. Cursor movement was achieved by gripping the bulbs. The initial change in air pressure generated when a bulb was squeezed was transmitted through pneumatic tubing to a corresponding pressure sensor  that converted air pressure to an output voltage. The output voltages corresponding to the left and right hand were translated into horizontal and vertical components of the cursor movement.The applied pressure was determined from a 300 ms synchronous sampling epoch of the output voltage from the left and right bulb , 100 ms after the onset of the gripping action. Consequently, the cursor moved along the prescribed trajectory, and the participant received visual feedback regarding the proximity of the cursor to the target . A 3–4 second rest period  occurred before the start of each trial. Participants performed 50 trials of the task during the test blocks.Before the session, head position indicator (HPI) coils were placed on the participant's forehead (x2) and in front of the ears. The positions of the coils, three anatomical landmarks (nasion and left/right pre-auricular points), and a 150-point head shape were digitized using a Polhemus digitization device . Self-adhering Ag/AgCl electrodes  were placed on the anterior aspect of the participant's left and right forearm in a bi-polar configuration (inter-electrode distance of 2 cm) to monitor muscle activity. The electrodes were positioned to record electromyography (EMG) of the long flexors of the digits  during task performance.MEG, EMG and pressure bulb output voltages were collected during the six task and two rest blocks while the subject was seated in the MEG scanner. The MEG system acquired data from a single magnetometer and two orthogonal planar gradiometers at 102 locations covering the whole head . All data were acquired continuously on the same electronics at a sampling rate of 1500 Hz with an inline low-pass filter at 500 Hz for MEG, EMG, and pressure bulb output voltage. Head position indicator coils were activated during all blocks to continuously monitor head movement.Whole-head anatomical images were acquired using 3D T1-weighted scanning sequences on either a 1.5T  or 4T  MRI scanner, based on availability. Co-registration of MEG and MRI coordinate frames was achieved by identifying the nasion and left/right pre-auricular points on the MRI image using the MRILab graphical user interface . The head shape digitization supplied both constraint for, and verification of, the co-registration. Anatomical MRIs were skull-stripped Seventy-two seed cortical brain areas, as well as eight seed locations in the CB, were selected as nodes to establish a shared coordinate-independent framework for all subjects Continuous HPI data were analyzed to ensure that the head position was stable over the duration of each rest and task MEG block. Temporal signal-space separation a priori frequency range of interest) were used in further analysis. Magnitude-squared CCC for each node-pair and frequency bin was calculated using the complex Fourier transform data, as described elsewhere The virtual electrode data at each node were used to calculate the coherence along all node-pairs for each task and rest block as follows. For each block, complex Fourier transforms of the virtual electrode data were calculated on 1.0 s segments every 0.5 s, after applying a Hanning window to each segment. Each Fourier transform contained 256 frequency bins, and generated an estimate of source amplitude and phase over the data segment. Only 51 frequency bins between 4 and 64 Hz . Mean-centered partial-least squares (PLS) analysis th percentile of all BSR values for the current LV. For each node-pair, the largest (if any) significant positive or negative BSR was determined in the frequency bands defined by the following separation of cortical rhythms; i.e., theta (4–8 Hz), alpha (8–16 Hz), beta (16–32 Hz), and gamma (32–64 Hz).Latent variables that showed significant differences across the group were investigated further to reveal which node-pairs and frequency bins reliably expressed the inter-block difference. For each LV, a bootstrap approach with 512 iterations estimated the reliability of the inter-block difference in CCC at each frequency/node-pair combination across the group as a bootstrap ratio (BSR) For each frequency band and LV, we grouped node-pairs with supra-threshold positive and negative BSRs separately. We calculated the node degree – defined as the number of node-pairs that connect each node to the rest of the network – for each node in the resultant positive and negative networks th percentile of all BSR values for the current LV. The interpretation of all CCC results, in terms of functional connectivity, was completed in the context of concomitant modulations in spectral power identified herein.Measures of phase synchrony are sensitive to changes in the signal-to-noise ratio of the signals of interest p<0.005). Thus, the first LV reveals that FC is modulated between performance of the motor task and rest in this subject group.Two blocks of MEG data during performance of the task (“test1” and “test2”) and two blocks of resting MEG data (“rest1” and “rest2”) were included in the FC analysis. Inter-block changes in CCC between estimates of brain activity at the 80 anatomically prescribed nodes were tested for statistically significant differences from the null hypothesis . The PLS approach revealed one significant LV across the group that differentiated CCC between “task” and “rest” blocks (th percentile) BSRs reliably showed “task-positive” increases in connectivity. Task-positive increases in CCC occurred mainly in the beta (16–32 Hz) and gamma (32–64 Hz) frequency bands.We further investigated the spectral and spatial pattern of the task-related change in connectivity. The BSRs for the first LV were calculated to evaluate the reliability of the task-related difference across the group between each node-pair and at each frequency bin. Thus, for the first LV, node-pairs and frequency bins with positive supra-threshold  in the beta  and gammNodes of the task-positive network with degree of three or greater  are listed in p<0.005). Thus, both spectral power and CCC expressed the same inter-block change. The BSRs for this LV at each node and at each frequency bin – expressing the reliability of the task-related power difference across the group– is shown in The PLS approach applied to the virtual electrode power spectra identified a single inter-block contrast (LV), which differentiated “test” from “rest” blocks , motor planning (PMC/SMA/CB), executive control (PFC) and vision (FEF). Task-positive connectivity manifested as greater CCC mainly in the beta and gamma frequency ranges. The nodes of this task-positive network include many components of the SM network defined based on current resting state While functional connectivity maps can be generated based on fMRI data, the neurovascular response is non-specific about the underlying neural mechanisms. Existing electrophysiological literature provides strong evidence for the predominant role of synchrony in beta Combining the mean-centered PLS approach with CCC calculated for MEG beamformer source estimates at anatomically based nodes provided an ideal framework for testing our hypothesis regarding task-related changes in FC. This approach is gaining traction in the FC neuroimaging field for electrophysiological data, and has been applied in an event-related experimental design, requiring the repetitive presentation of a stimulus or performance of a task One of the challenges in performing FC analysis in MEG (and EEG) is source estimation owing to the reduced spatial resolution of these techniques relative to fMRI. Multiple source estimation techniques, such as the beamformer spatial filter (employed here) and minimum norm estimation, face the challenge that it can be difficult to determine if synchrony between nodes is actually due to two distinct signals being generated in the brain. An alternative explanation is that the source analysis technique has not completely spatially resolved the brain activity at the two distinct locations. In this case, the source estimates contain traces of the same MEG sensor signals – termed “cross-talk” – that can lead to spuriously high levels of coherence between the locations An additional challenge with FC analysis in general is the requirement to calculate the measures over minutes of data. Integrating over time is a necessity in FC analysis given the poor signal-to-noise ratio of the acquired data. This is an inherent weakness of many neuroimaging measures. For example, event-related analysis will average data across numerous stimulus repetitions over the course of minutes. The underlying assumption is that the brain activity is consistent across the measurement interval. Subjects in the current study were actively engaged in the visuomotor task for the majority of the task block. Thus, the increased FC observed during the task block is likely indicative of changes in neural synchrony that support the performance of the task.The results of this study support the use of MEG-based FC analyses to image task-related changes in neural networks. Further work however is necessary to validate this approach in other experimental paradigms, including those examining cognitive function. In light of several recent studies that show altered patterns of network connectivity in patients with neurological deficits before