The behaviours of non-suicidal self-injury (NSSI) and deliberate self-harm (DSH) are prevalent among adolescents, and an increase of rates in recent years has been postulated. There is a lack of studies to support this postulation, and comparing prevalence across studies and nations is complicated due to substantial differences in the methodology and nomenclature of existing research.We conducted a systematic review of current (2005 - 2011) empirical studies reporting on the prevalence of NSSI and DSH in adolescent samples across the globe.Fifty-two studies fulfilling the inclusion criteria were obtained for analysis. No statistically significant differences were found between NSSI (18.0% SD = 7.3) and DSH (16.1% SD = 11.6) studies. Assessment using single item questions led to lower prevalence rates than assessment with specific behaviour checklists. Mean prevalence rates have not increased in the past five years, suggesting stabilization.NSSI and DSH have a comparable prevalence in studies with adolescents from different countries. The field would benefit from adopting a common approach to assessment to aide cross-cultural study and comparisons. Self-injurious behaviours among adolescents are eliciting increased attention and concern around the world. Research indicates that self-injury tends to first occur during adolescence  is assocand without suicidal intent that have non-fatal outcomes. This term tends to be used predominantly within European countries and in Australia. In contrast, many studies published by researchers within Canada and the United States have employed the term Non-suicidal self-injury  and different classification systems for self-injury. As noted by experts in the field -14 severctioned; ), which ctioned;  found thctioned; ) in ordeDue to difficulties with agreeing upon a shared definition of self-injury, only a few studies ,18,19 haThe lack of cross-nation comparisons is a striking deficit in the study of self-injury because it precludes drawing conclusions that could inform international policies and efforts to prevent these behaviours among adolescents. Most salient to this concern, however, may be that the DSM-5 is proposing a non-suicidal self-injury disorder  that is The purpose of the current study was to attempt to address some of the limitations in the existing literature with regards to the lack of studies comparing the prevalence of NSSI and DSH across countries. We aimed to draw a global perspective by including studies with different terminology  and different methodology . The inclusion of these variables permitted us to examine potential sources of bias/error across studies by comparing average prevalence rates according to definition (NSSI vs. DSH), time frame assessed , and assessment procedure . A secondary aim of the study was to examine whether, within shared definitions , the prevalence of self-injury has increased or stabilized since an increase in the phenomenon of self-injury has been frequently mentioned in the literature. Yet, a recent five-year cohort study of adolescents in the U.S. found the prevalence of NSSI to be rather stable . We wantTo obtain articles for the current study, we conducted electronic searches within the scholarly database search engines of Medline, PsycInfo, PsycArticles, JSTOR, Academic Search Complete, Social Sciences Citation Index, EBSCO, and PubMed. The search terms: "self-injury, non-suicidal self-injury, NSSI, deliberate self-harm, DSH, self-harm, self-mutilation, parasuicide, prevalence, rates, adolescent, and adolescence" were used to locate articles. We restricted the search to peer reviewed, empirical articles published between January 1, 2005 and December 1, 2011. In a second step, we reviewed the reference lists of identified studies as well as those of recent reviews of self-injury  for NSSI behaviour and 16.1% (SD = 11.6) for DSH was observed. The difference in mean prevalence was not statistically significant, t(18) = 1.07, p > .30, between the two definitional groups. This finding indicates that average rates for NSSI among community samples are comparable to rates of DSH within community samples.Tables SD = 4.5) whereas those using multiple item or behaviour checklists found an average prevalence of 23.6% (SD = 8.3), which represents a significantly higher rate relative to single item assessments, t (14) = 5.00, p < .01. A similar pattern is found within the DSH studies, with single item assessments reporting an average prevalence estimate of 12.2% (SD = 5.6) compared to a prevalence of 31.4% (SD = 14.9) found for behavioural check-list surveys. The difference in prevalence between these two DSH assessment modalities is statistically significant, t(6) = 3.17, p < .03, indicating that the type of assessment tools used are contributing potential bias to estimates of self-injury within adolescent populations.Another characteristic that appears salient in the current data is that a majority of studies utilize single item assessments for self-injury, regardless if the definition is NSSI or DSH. The assessment format used appears to contribute to very different estimates of the prevalence of self-injury. Among studies of NSSI, those using a single item (dichotomous Yes/No response) found an average lifetime prevalence of 12.5%  did not significantly differ from DSH rates  estimated with multi-item measures, t (5) = 1.29, p > .25. The same finding emerged when comparing the single item assessment of lifetime NSSI and DSH, t (12) = 0.24, p > .80.Given the apparent influence of assessment on lifetime prevalence estimates, we re-ran our analyses comparing the mean lifetime prevalence rates between NSSI and DSH by assessment method. The results confirmed that while behaviour based assessments yield higher prevalence estimates than single item assessments, the mean prevalence of NSSI within multi-item assessments (SD = 11.9). However, the studies that used self-report inventories where specific behaviours were presented to participants, an average 12-month prevalence of 28.4% (SD = 8.6) was reported. This is in sharp contrast to a 12-month NSSI prevalence of 9.6% (SD = 4.40) when a single item assessment was used, t(3) = 4.36, p <.03. Among the studies examining DSH, an average 12-month prevalence of 9.5% (SD = 5.7) is estimated. While our search results only found two studies assessing 12-month prevalence of DSH with a behavioural scale (vs. single item), the prevalence estimates that included multiple-item behaviour based measures yielded a slightly higher average 12-month prevalence of 11.1% (SD = 8.4) than the single item assessments mean of 8.5% (SD = 5.3).With regard to the time-frame in which self-injury is assessed, it appears that prevalence estimates again fluctuate and are strongly influenced by the assessment method. The average 12-month prevalence for NSSI was 19.0% , for both NSSI and DSH. Due to limited numbers of prevalence studies, publication years 2005 and 2006 were combined so an average rate could be calculated. Results are presented in Figure 1, for boThe data appears to support the conclusion that while average prevalence rates are slightly higher among studies published in 2011 compared to 2005, within the past five years the percentage of adolescents reporting NSSI or DSH is relatively consistent and stable. Thus, it appears the global lifetime prevalence of self-injury among community adolescents may have stabilized.The current study fills a gap in the existing literature by providing an empirical summary of the prevalence of self-injury within adolescent samples across multiple nations and countries. The literature on self-injury has been largely divided into two camps: those who study deliberate self-harm and those who study non-suicidal self-injury. This division has led to difficulties with advancing the field because different definitions and assessment frameworks are used within each nomenclature. The current study helps to bridge the current division by demonstrating that DSH and NSSI have very comparable prevalence estimates within adolescent samples across different countries, which suggests the studies are likely measuring similar phenomena. This finding may also increase comfort with the potential cultural validity of the behavioural criteria being considered by the DSM-5 for a diagnosis of non-suicidal self-injury disorder. Given the many shared features between suicidal self-injurious acts and self-injurious acts without suicidal intent  the finOne of the primary conclusions from our analysis of the prevalence estimates over time is that while at high levels, the percent of adolescents engaging in self-injury appears to have stabilized. Researchers and clinicians have been suggesting that the rates of self-injury are increasing. As the behaviour gained attention both within the clinical literature and media, the production of social/empirical media pertaining to self-injury did significantly increase  which coThe current results show that the way in which self-injury is measured has a significant impact on the rates identified. For example, prevalence estimates for NSSI are close to doubled when behavioural check-list measures are used compared to single item questions; and DSH rates using behavioural-based assessment are nearly three-times higher than single item assessments. While it is hard to know if single item assessments are under-estimating the prevalence or if the behavioural assessments are inflating rates, an assessment bias clearly exists. Future work in this area is needed to carefully examine how adolescents understand and interpret the questions being asked of them to ensure they are thinking of potential self-injurious behaviours in the same way experts in this field are. It is well established that how questions are asked of participants has a strong influence on responses. For example, Ross and Heath  reportedTo ensure the validity of our assessments and to avoid unintentionally inflating the estimated scope or severity of self-injury among adolescents, the field of self-injury needs to decide on best-practice assessment processes. Doing so would help to ensure consistency in research, aide with comparisons across studies, reduce artificially elevating concerns about self-injury, and could avoid over-pathologizing adolescents who endorse occasional, mild self-injury. Another option would be for the field to move beyond single-frequency endorsements of self-injury and consider including only those adolescents reporting repetitive acts of a self-injurious behaviour. This approach to assessment and categorization may also help move the field forward in terms of understanding truly pathological self-injury warranting a DSM-5 diagnosis from less pathological self-injury that may represent experimentation and sub-clinical level syndromes. Based on the observations from the current study, we recommend a gold-standard assessment process that would include a single item assessment that if endorsed positively, would be followed up by an interview process to ensure the participant is understanding the behaviour in the same way the field, or researcher, is defining the self-injurious nature of the behaviour.While the current study fills a void in the self-injury literature, its limitations need to be considered and replication of this study within future years is strongly encouraged. One limitation is that with the proliferation of research on NSSI/DSH it is likely some studies were missed and therefore not included in the current results. Also, we limited studies to those that expressed establishing prevalence as one of the purposes of the study. We recognize most research on self-injury report the percentage of adolescents engaging in the behaviour and thus, not every study conducted on self-injury in adolescents was included. Our decision to exclude studies that did not include assessing prevalence as a study aim may lead to some error in our current results. Another limitation is that we only included studies written in English, so published studies on the prevalence of NSSI/DSH in non-English text journals  were noThe field is currently benefiting from the growth of larger scale, epidemiological surveys of self-injury compared to the convenience samples that typified early prevalence estimates. While the prevalence of adolescents endorsing lifetime rates of self-injury is largely comparable across sample sizes, this growth of large scale studies should contribute to enhancing the field's ability to accurately assess the true prevalence of self-injury as well as improve analysis of trends. Where the field needs to focus efforts is on establishing a consistent standard for assessing self-injury within adolescent samples to ensure construct validity between researchers-adolescent participants. With the increased social and professional attention given to self-injury in recent years the field needs to be cautious not to over-pathologize behaviours. To aide this mission, an agreed upon nomenclature for self-injury  and assessment process that is acceptable across countries is required. At the moment, the current study supports the ability to reliably compare findings of DSH and NSSI with respect to prevalence and trends in the behaviour across adolescent groups and between countries. Given that prevalence seems to have stabilized, the next step is for researchers to start examining specific cultural influences on self-injury so that universal features of the behaviour can be used to inform interventions and culturally-specific variations can inform prevention efforts.Comparing rates from community samples reported between 2005 and 2011, the prevalence of NSSI and DSH seems to be very similar, thus adding validity to the proposed DSM 5 diagnosis. Prevalence rates seem to have stabilized within the last years, showing no further increase of NSSI and DSH.Jennifer Muehlenkamp, Laurence Claes and Lindsey Havertape declare that they have no competing interests. Paul L. Plener has received travel funding from Lundbeck pharmaceuticals.All authors were involved in the literature search and in drafting the manuscript. JJM and LH were involved in running the statistical analysis. All authors read and approved the final manuscript.


Subsequent ZnO growths on substrates annealed above 1,200°C resulted in a high density of nanosheets and nanowires, whereas lower temperatures led to low nanostructure densities. Separate Au film annealing experiments at 700°C showed little variation in the size and density of the Au catalyst droplets with substrate annealing temperature. The observed variation in the density of nanostructures is attributed to the number of surface nucleation sites on the substrate, leading to a competition between nucleation promoted by the Au catalyst and surface nucleation sites on the rougher surfaces annealed below 1,200°C.The effects of thermal substrate pretreatment on the growth of Au-catalyzed ZnO nanostructures by pulsed laser deposition are investigated. C-plane sapphire substrates are annealed  Fabriposition . Moreoveposition -16. Therposition ,18.prior to catalyst metalization can significantly impact catalyst-assisted nanostructure growth. This is demonstrated for ZnO nanostructures grown on c-plane sapphire substrates by pulsed laser deposition (PLD) using gold as a catalyst.In this report, we show how thermal annealing of the substrate 2. The substrates were heated to 700°C in a 0.5 mbar ambient of 5% oxygen/95% argon and ZnO was deposited for 30 min. Prior to growth, the "epi-ready" c-plane sapphire substrates  were annealed in oxygen at 1,000, 1,200 and 1,400°C for 1 h, followed by deposition of a 1-nm-thin layer of Au using e-beam evaporation. No further annealing of the Au layer took place before introduction into the PLD chamber.The nanostructures were grown by ablation from a raster-scanned ZnO target using a 248-nm KrF excimer laser at 10 Hz repetition rate and a fluency of ~1.33 J/cmIn order to investigate the effects of substrate annealing on the formation of Au catalyst droplets, separate experiments were carried out in the PLD chamber with a Au layer only. C-plane sapphire substrates coated with a 1-nm-thin film of Au were annealed for 5 min at the same growth temperature and ambient as for the growth of ZnO nanostructures.The clean and Au-coated substrates were examined using atomic force microscopy (AFM), and the PLD-grown ZnO nanostructures were studied with scanning electron microscopy (SEM).The size and density of the catalyst droplets resulting from the Au layer annealing experiments were determined using the image processing software "ImageJ" . The anaThe topography of the as-received and annealed c-plane sapphire substrates and their characteristic parameters are shown in Figure Figure It has been reported that substrate pretreatment and morphology may significantly influence the size and density of Au catalyst droplets promoting nanostructure growth ,21. In oIn the ideal scenario of catalyst-assisted nanostructure growth, the growth species are all incorporated into the nanostructure lattice via the catalyst-nanostructure interface. These species can reach the catalyst droplet either by direct impingement from the vapor or by surface diffusion from the substrate. In reality, however, the catalyst-nanostructure interface competes with nucleation at low-energy surface sites such as pits, craters and grain boundaries, as well as other surface defects abundant in rough surfaces -24. At tFrom Figure Furthermore, it is obvious from Figure In this study, we have shown that thermal annealing of the substrate prior to the Au catalyst deposition affects the density of ZnO nanostructures grown on c-plane sapphire. However, this substrate annealing does not seem to have a significant impact on the nanostructure morphology or the size and location of the Au catalyst droplets. The observed difference in nanostructure size and density can be explained by the competition between nucleation at the Au-ZnO interface and nucleation at low-energy surface sites associated with defects on rough substrate surfaces. The atomically flat surfaces obtained by high-temperature annealing promote formation of high densities of ZnO nanostructures through a significant reduction in surface nucleation sites, thus demonstrating the importance of smooth surfaces for catalyst-assisted nanostructure growth. We believe these findings will help improve control and understanding of catalyst-assisted nanostructure growth, also beyond the ZnO material system.The authors declare that they have no competing interests.CW participated in the acquisition of scanning electron microscopy images, in the analysis and in the interpretation of the data and drafted the manuscript. DS carried out the growth of the zinc oxide nanostructures and the acquisition of atomic force microscopic and scanning electron microscopic data. He also contributed to data analysis. CL conceived and designed the study and participated in the analysis and interpretation of the data. JG participated in the coordination of the study and helped to draft the manuscript. HW participated in conceiving, designing and coordinating the study and helped in the writing process of the manuscript.


The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC , to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes  and also identified many new candidates . Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at  MicroRNAs (miRNAs) are a class of small (∼22 nt) regulatory RNAs, which play important roles in many essential biological and physiological processes, such as embryo development, cancer progression and immune response. About 1,400 miRNAs have been identified in human and more than 30% known protein-coding genes are potentially regulated by evolutionary conserved miRNAs To infer the perturbed miRNA regulatory network, one popular strategy is to analyze miRNA target gene set enrichments in differentially expressed gene signatures. This includes many developed methods, such as gene set analysis by hyper-geometric test ; GSEA  Cancer is a multi-stage and mixed process, usually involving many hierarchically organized sub-processes regulated at multiple scales In this study, we proposed a new strategy to infer the onco-miRs and their perturbed regulatory networks. This strategy takes consideration of the multi-scale and hierarchically organized regulatory structures in the differentially expressed genes using gene co-expression information, and fine-tunes the scales in the gene co-expression hierarchy to analyze the miRNA target gene set enrichment. Our method, named as miRHiC , can infer the perturbed miRNA regulatory networks in cancer by analyzing the enrichments of miRNA target gene sets in the hierarchical gene co-expression signatures. These gene signatures were established by hierarchical gene co-expression clustering, one common way to separate the mixed signals in gene expression profiles at different correlation levels. In miRHiC, the miRNA target gene set is not required to be enriched in the whole set of the differentially expressed genes but within any signature at the fine scale of the gene co-expression hierarchy. Besides the higher sensitivity for inferring the onco-miR candidates, another advantage of considering the gene co-expression information is to reduce the noises of inferring the corresponding perturbed target genes: the “scattered” differentially expressed genes with little expression pattern similarity to other genes, which are more likely to be “false” miRNA targets due to expression noises K levels: all miRNA-target pairs were sorted according to their context scores in decreasing order (the pairs ranked on the top have the lowest regulation strength) and the discretized score for the miRNA-target pair with rank r was defined as: s = 1+b[rK/N]. It means the first 1/K miRNA-target pairs have lowest score 1, while the last 1/K pairs have highest score 1+b(K-1). According to ref.K is set as 5 and b as 3 in this study.miRNAs and their target genes (the miRNAs from the same family are merged as a single item) were extracted from TargetScan database (v6.2) The control miRNA target gene sets were generated by bipartite graph based random permutation of the miRNA-target pairs with the same discretized scores but keeping the sizes of all target gene sets. This kind of stringent permutation procedure can generate the control miRNA target gene sets which preserve the statistical properties much better than the randomization without restriction We test miRHiC on two large-scale cancer gene expression datasets downloaded from NCBI GEO database: 1) lung cancer (LUC) dataset, GSE19804 including 60 paired cancer and para-cancer samples; and 2) hepatocellular cancer (HCC) dataset, GSE22058 including 96 paired cancer and para-cancer samples. To avoid the noises in lowly expressed genes, we only kept the genes whose expression values rank at top 10,000 in at least 30% samples in each dataset. Then, the differentially expressed genes were identified with p-value <0.0001 using t-test . We identified 3,397 and 5,699 differentially expressed genes for LUC and HCC datasets, respectively.miRHiC was proposed to infer the perturbed miRNA regulatory networks in cancer by incorporating the hierarchically organized co-expression information of the differentially expressed genes: firstly, the hierarchical gene co-expression signatures were established by clustering the differentially expressed genes based on pairwise gene co-expression correlations; then the miRNA target gene set enrichment was analyzed across the hierarchical co-expression signatures; and finally, a permutation test was used to estimate the statistical significance of the enrichment .Firstly, average linkage hierarchical clustering is implemented to cluster the differentially expressed genes based on their pairwise co-expression correlations. To reduce the noises caused by poorly correlated genes, the hierarchical clustering is stopped if the gene co-expression correlation is too low: we used the correlation with z-score 0.52 as the cutoff in this study . This cutoff shows few influences on the results: for the LUC dataset, when the z-score cutoff changed from 0.3 to 0.9 by step 0.1, the hierarchical clustering was stopped at almost the same place. Then, we extracted the gene co-expression signatures (stable gene co-expression clusters) at different correlation scales by traversing the co-expression hierarchy from leaf to root (the correlation is decreasing and the size of signatures is increasing when traversing the hierarchy from leaf to root). The details of the signature extraction algorithm are given in user manual via miRHiC website.j-th gene co-expression signature in the hierarchy, we can find the overlapped genes between the signature (denoted as jS) and the i-th miRNA target gene set (denoted as iT), and then calculate the raw enrichment score by summing the discretized TargetScan scores (see the details of the score discretization in the above section) of the overlapped genes for i-th miRNA:For the ijp for this enrichment was estimated by examining the enrichment scores ijES(r) of 10,000 size-matched random control miRNA target gene sets:The p-value j = 1, 2, …), the P-score iP for the i-th miRNA was calculated as the p-value of the most significant enrichment:After getting the enrichments in all hierarchical gene co-expression signatures . It cannot be directly used to measure the statistical significance of enrichment. Again, we used permutation test to estimate the statistical significance of the P-score: the P-scores iP(r) of 10,000 size-matched control miRNA target gene sets were calculated according to above steps; and the empirical p-value ip for the P-score iP was calculated as:The ip was used to measure the statistical significance of miRNA target gene set enrichment across the whole hierarchical gene co-expression signatures. To correct the multiple test, fdrtool was used to calculate the q-values according to the empirical p-values The empirical p-value miRHiC was compared with Gene Set Enrichment Analysis (GSEA) and gene set analysis by hyper-geometric test (HG-test). GSEA is a widely used method for inferring the perturbed gene sets by taking the continuous values and the rank information of differential gene expressions GSEA and HG-test use different computational models to measure gene set enrichments with miRHiC. For directly testing the advantage of using the hierarchical gene co-expression information, we used the differentially expressed genes as the only signature and ran miRHiC on it. For clear presentation, we named this approach as miRDeG .k-means clustering is another commonly used algorithm to generate gene co-expression signatures. The algorithm can partition all differentially expressed genes into k non-overlapped clusters. Unlike hierarchical clustering, k-means is hard to exclude the poorly correlated genes by setting any threshold. In the comparison, we used k-means (k is set to 5 or 10) to get the gene co-expression signatures. Then we run the same procedure to analyze the miRNA target enrichments in the generated signatures with different k. We named this approach as miRKM (miRKM5 and miRKM10) in the below section.Except hierarchical clustering, To demonstrate that miRHiC did not have the problem of over-estimating the statistical significances, we generated 100 size-matched control target gene sets for each miRNA, and then calculated distributions of the empirical p-values for their enrichments in the hierarchical gene co-expression signatures using miRHiC. If miRHiC has no bias for estimating the empirical p-values, the p-values of these control miRNA target gene sets should be uniformly distributed between 0∼1. As expected, results showed that the empirical p-values are uniformly distributed . AnothermiRHiC can infer onco-miRs and their perturbed target regulatory networks by analyzing the miRNA target gene set enrichment in hierarchical gene co-expression signatures in cancer. On the two large-scale gene expression datasets of lung cancer (LUC) and hepatocellular cancer (HCC), miRHiC inferred 9 and 8 perturbed miRNAs or onco-miRs, respectively, with q-value <0.1. Under the same q-value cutoff, the three compared methods, GSEA, HG-test and miRDeG did not infer any candidate. Although miRKM inferred some candidates , these numbers are still less than miRHiC and most of the miRKM inferences are covered by miRHiC. The details results are provided in By looking at the targeted signatures of the inferred onco-miRs, we found that they have different levels of gene co-expressions in the hierarchies . The funThe onco-miRs inferred in multiple cancers may play more important roles in cancer initiation and development. Two miRNAs, miR-125 and miR-149 were inferred by miRHiC in both types of cancers. For the inferred perturbed miR-125 regulatory networks, there are only three common targets , which suggests that miR-125 may regulate different pathways in the two types of cancers. While for miR-149, its perturbed regulatory networks show significant target overlapping with a shared sub-network including 14 common targets. And the 14 targets are consistently over-expressed in cancer tissues .MiR-149 is a mammal conserved miRNA. A few studies show that miR-149 genetic polymorphisms are associated with the risk of cancer Analyzing miRNA target gene set enrichment in differentially expressed genes of large-scale gene expression profiles can greatly advance our understandings of the perturbed miRNA regulations. But due to the complexity of cancer transcriptome, it is challenging to infer the perturbed miRNA regulations by simply analyzing miRNA target gene set enrichment in the whole differentially expressed genes. In this study, we developed miRHiC to infer the perturbed miRNA regulatory networks in cancer by incorporating the hierarchical gene co-expression information into miRNA target gene set enrichment analysis. Results showed that miRHiC have much higher sensitivity for the inferences than the commonly used methods, such as HG-test, GSEA and miRDeG (FAME), of which all do not use the hierarchical gene co-expression information. Over 50% of the inferred onco-miRs have extensive literature supports and the gene co-expressions signatures targeted by these miRNAs are significantly related to multiple hallmarks of cancer. Recent studies also show that gene co-expressions can provide important information to identify the “real” target genes of miRNAs in the corresponding biological process The lengths of 3′-UTRs are strongly correlated with the number of targeted miRNAs and the context scores. The enriched signatures may significantly be biased to the ones with longer 3′-UTRs. When using hyper-geometry test to analyze the enrichments of miRNA target gene sets, we found that the signatures targeted by the inferred miRNAs have much longer average lengths of 3′-UTRs. However, like FAME miRHiC provides a general strategy to analyze miRNA regulations using hierarchical signatures. Different hierarchical clustering methods can be used to get the hierarchical gene co-expression signatures. Besides the gene co-expression, the functional and regulatory interactions between genes  can further be integrated to establish the hierarchical gene signatures. We will continuously test miRHiC strategy using different kinds of implementations.To get better control sets of the miRNA target gene sets, miRHiC used the bi-partite graph based permutation. But this permutation method is time-consuming. Also, the computational burden is high for calculating the empirical p-values in a nested manner across the hierarchical gene co-expression signatures. We plan to develop faster algorithm to reduce the redundant calculations for estimating the p-values in future.Table S1The detailed results of miRHiC, GSEA, HG-test, miRDeG and miRKM.(XLSX)Click here for additional data file.


The placental syncytiotrophoblast is the major source of maternal plasma corticotropin-releasing hormone (CRH) in the second half of pregnancy. Placental CRH exerts multiple functions in the maternal organism: It induces the adrenal secretion of cortisol via the stimulation of adrenocorticotropic hormone, regulates the timing of birth via its actions in the myometrium and inhibits the invasion of extravillous trophoblast cells in vitro. However, the auto- and paracrine actions of CRH on the syncytiotrophoblast itself are unknown. Intrauterine growth restriction (IUGR) is accompanied by an increase in placental CRH, which could be of pathophysiological relevance for the dysregulation in syncytialisation seen in IUGR placentas.We aimed to determine the effect of CRH on isolated primary trophoblastic cells in vitro. After CRH stimulation the trophoblast syncytialisation rate was monitored via syncytin-1 gene expression and beta-hCG (beta-human chorionic gonadotropine) ELISA in culture supernatant. The expression of the IUGR marker genes leptin and 11beta-hydroxysteroid dehydrogenase 2 (11beta-HSD2) was measured continuously over a period of 72 h. We hypothesized that CRH might attenuate syncytialisation, induce leptin, and reduce 11beta-HSD2 expression in primary villous trophoblasts, which are known features of IUGR.CRH did not influence the differentiation of isolated trophoblasts into functional syncytium as determined by beta-hCG secretion, albeit inducing syncytin-1 expression. Following syncytialisation, CRH treatment significantly increased leptin and 11beta-HSD2 expression, as well as leptin secretion into culture supernatant after 48 h.The relevance of CRH for placental physiology is underlined by the present in vitro study. The induction of leptin and 11beta-HSD2 in the syncytiotrophoblast by CRH might promote fetal nutrient supply and placental corticosteroid metabolism in the phase before labour induction. As part of the neuroendocrine system, the hypothalamo-pituitary-adrenal axis controls a wide range of body functions in humans. Hypothalamic corticotropin-releasing hormone (CRH) acts via its two receptors CRH-R1 and CRH-R2 to control stress reaction, autonomic functions, behavioural response, appetite, metabolism and the immune system.Since its discovery in placental extracts in 1982 ,4. CRH aAlthough it is known that the syncytiotrophoblast is a major source of CRH in the second half of pregnancy, the role of CRH on the process of cytotrophoblastic syncytialisation and on endocrine hormone regulation in the syncytiotrophoblast is unknown so far.To further investigate local actions of CRH on trophoblast function, we sought to determine its influence on the syncytialisation of isolated primary villous trophoblastic cells and on the expression of leptin and 11β-HSD2. We found that CRH induced leptin and 11β-HSD2 expression, without affecting syncytialisation of trophoblastic cells.th percentile according to Voigt et al. CRH to purified syncytiotrophoblast [The finding that leptin expression increases with the progression of trophoblast syncytialisation in both groups is in line with results from Ashworth et al. , who shophoblast . This coTo address the functional relevance of our findings studies with CRH antagonists, such as CRH antagonists antalarmin (CRH-R1) and antisauvagine (CRH-R2) would be needed.We found that CRH (2 μg/ml) dose-dependently induced CRH -R1α and –R2 gene expression at 48 h   and CRH In this respect it is noteworthy, that the major fraction of placental leptin and CRH is secreted into the maternal circulation ,61. NeveIn IUGR, a condition characterised by increased fetal serum CRH levels , we founWhite et al.  showed tIn summary, our data indicate that CRH stimulation induces leptin secretion in the human syncytiotrophoblast in an auto-/paracrine fashion. Similarly, CRH induced 11ß-HSD2 expression. This suggests a short-loop feedback of CRH-induced leptin on CRH action at the feto-maternal interface. Such a putative cross-talk could play an essential role in the regulation of syncytiotrophoblast nutrient supply and cortisol metabolism, besides possible further implications for myometrial contractility, placental bed perfusion and the timing of birth. Furthermore CRH-induced 11ß-HSD2 might locally determine placental corticosteroid metabolism and thereby the passage of placental CRH-triggered maternal cortisol via the syncytium to the fetus. This would protect the fetus from detrimental elevated maternal glucocorticoid exposure. The underlying mechanism and the functional role of the interaction of CRH with leptin and 11ß-HSD2 at the syncytiotrophoblast remain to be determined.The authors declare that they have no competing interests.FBF contributed to conception and design of the study, analysed and interpreted the data and drafted the manuscript. MR performed the cell culture experiments, including RT-PCRs. GV performed ELISA analysis. RO contributed to acquisition and analysis of the data, AH contributed to interpretation of data and was involved in drafting the manuscript, MR and CM-C were involved in the analysis of data and critically revised the manuscript for important intellectual content, RS contributed to the acquisition and analysis of data, WR critically revised the manuscript for important intellectual content, JD contributed to the conception and design of the study and critically revised the manuscript for important intellectual content. All authors have given the final approval of the version to be published.


We use data from rural Nepal and South India to compare the prevalence of small-for-gestational-age (SGA) and neonatal mortality risk associated with SGA using different birth-weight-for-gestation reference populations.th percentile cut points published. Those reference populations were then applied to populations from two community-based studies to determine SGA prevalence and its relative risk of neonatal mortality.We identified 46 reference populations in low-, middle-, and high-income countries, of which 26 met the inclusion criteria of being commonly cited and having numeric 10The prevalence of SGA ranged from 10.5% to 72.5% in Nepal, and 12.0% to 78.4% in India, depending on the reference population. Females had higher rates of SGA than males using reference populations that were not sex specific. SGA prevalence was lowest when using reference populations from low-income countries. Infants who were both preterm and SGA had much higher mortality risk than those who were term and appropriate-for-gestational-age. Risk ratios for those who are both preterm and SGA ranged from 7.34–17.98 in Nepal and 5.29–11.98 in India, depending on the reference population.These results demonstrate the value of a common birth-weight-for-gestation reference population that will facilitate comparisons of SGA prevalence and mortality risk across research studies. Low birth weight (LBW) has long been recognized as an important risk factor for infant mortality, indirectly accounting for 60–80% of the 3.1 million neonatal deaths th percentile cut offs of each reference population to two community-based populations in rural southern Nepal and Tamil Nadu, India, and estimated the prevalence of SGA and its association with neonatal mortality using gestational age, birth weight, and mortality data.There are many different birth-weight-for-gestation reference populations, some of which have been used extensively to calculate the prevalence of SGA in the literature. In order to compare the prevalence of SGA across different studies, we wished to better understand what impact the choice of a reference population would have on SGA prevalence, and whether this choice would impact the estimate of mortality risk associated with being born SGA. To address this question, we conducted a literature review to identify all birth-weight-for-gestation reference populations that have been cited frequently. We then applied the 10th percentile cutoffs were provided since we would have had to interpolate the 10th percentile cut points imprecisely in these cases. We used Web of Science  and Google Scholar to identify the number of times the paper was cited. Only those cited more than ten times in either Web of Science or Google Scholar were used in the analysis, with the understanding that the publication may not necessarily have been cited as a reference population  investigators) We used data from two population-based randomized trials of neonatal interventions to calculate the prevalence of SGA using the reference populations described above. The Nepal Newborn Washing Study (NCT00109616) enrolled all infants born alive in 30 Village Development Committees of Sarlahi District in rural southern Nepal  from September 2002 through January 2006 The Nepali and Indian populations were similar with respect to many characteristics . Facilitth percentile of each reference population for a specific gestational age. The range of gestational ages for which weight cutoffs was available varied widely by reference population , risk ratios (RR) and 95% confidence intervals were calculated for term-SGA, preterm-SGA, and preterm-appropriate-for-gestational-age (AGA), all with term-AGA as the reference. AGA is defined as birth weight being above the 10th percentile of the reference population at a specific gestational age. Forest plots of these RRs and confidence intervals were used to display the range of these estimates across the different reference populations.We also conducted the above analyses using Mikolajczyk et al. 's global reference, which produces birth weight percentiles adaptable to each local population from gestational ages 24 to 41 and is not sex-specific th percentile birth weights, or provided only growth curves. Another ten were excluded, as they were cited less than ten times, leaving a total of 26 reference populations A total of 46 reference populations were identified. Ten were excluded from analysis because they did not provide the 10The prevalence of SGA ranged from 10.5% to 72.5% in Nepal, and 12.0% to 78.4% in India, depending on the reference population used to define SGA  and 3. IThe RR of neonatal mortality among SGA and/or preterm infants also varied by reference population  with Mikolajczyk et al. 's global reference distribution. This produced a prevalence of 14.5%   for Nepal and 11.0%  (lowest) for India. The prevalences were similar when limiting the data to those within the gestational age range that has specific birth-weight cut-offs available (24–41 weeks gestation). In both cases, the term-SGA and preterm-SGA RRs were among the three highest when compared to the other reference distributions, while the preterm-AGA RRs were around the median value  are intended to provide an optimal growth population by considering those below the 10th percentile as exhibiting poor growth. The use of a local reference population may also document progress over time as well as tracking morbidity and mortality risk. Mikolajczyk et al's global reference distribution attempts to identify the lowest 10% within each population. The reference distribution that adapts to the local distribution may successfully identify babies at highest risk in that particular population, but is merely descriptive. It fails to acknowledge that a large percent of the population beyond the lowest 10% also have increased risk of mortality or morbidities when compared to an ideally nourished reference, and does not comment on how the fetus should be growing.th percentile cut-off across 13 different reference populations from high-income countries Goldenberg et al examined the issue of variation in reference populations by comparing the weights that defined the 10Our use of two large, population-based data sets from two different areas of South Asia provided similar results. Hence it is likely these results are generalizable throughout South Asia. By comparing SGA prevalence generated from different reference populations within the same data set, we removed the variability associated with study differences, which would normally be present when comparing SGA prevalence in the published literature.One limitation of this analysis was the use of dates of LMP by the two studies to estimate gestational age. LMP estimates tend to shift the gestational age distribution to the right In both of these studies, birth weight was measured with reasonable accuracy because they were research studies. Data collectors who were study employees were trained to follow a standard protocol using accurate scales that were calibrated regularly throughout the study. There was likely some inter-observer variability, but we believe this to be a minor source of misclassification. Table S1 in Another limitation is the range of gestational ages for which weight cut-offs were provided. For the gestational ages lower or higher than the bounds provided by the reference distribution, we used the 10th percentile cut-off of the closest gestational age. By doing so, we may be overestimating SGA prevalence in the highest gestational ages while underestimating SGA prevalence in the lower gestational ages.It should be noted that our estimates of SGA prevalence and neonatal mortality risk are biased by the exclusion of infants who were missing birth weight or who were weighed beyond 72 hours after delivery. Exclusion of infants who died before being weighed will tend to reduce the prevalence of preterm and SGA since these infants likely died because they had one or both these conditions. However, exclusion of infants weighed after 72 hours of age would likely bias the prevalence of preterm and SGA in the opposite direction, since they would be less likely to be preterm and/or SGA if they survived to 72 hours or beyond. Neither of these biases should impact the estimates of variation by reference population since the comparisons use the same cohort of infants.Finally, live and stillbirths could have been misclassified in both the trials and reference populations, perhaps more so in these two studies and in low- than high-income reference populations. This misclassification could have altered the estimates of RR but would not alter the comparison of RR across reference populations within each of the trials.SGA prevalence and health risks can be assessed. Prior literature will need to be re-evaluated against these new standards, as was done for child growth These results demonstrate the importance of reaching agreement on the appropriate reference population that should be used in future analyses where the primary purpose is to compare SGA prevalence across populations and to estimate global and regional SGA attributable burden. Local fetal growth references may still be useful when considering growth of individual infants in resource limited settings. As has been done with child growth standards File S1Tables S1–S4 and Figures S1–S3. Table S1. Comparison of Small-for-Gestational-Age Definitions: Reference Populations. * LMP  =  Date of last menstrual period; gestational age calculated using the period between date of birth and LMP. Text in italics and gray: population 10th percentile birth weight not available or only given as growth curves. Table S2. Risk ratios for Neonatal Mortality by Preterm and/or Small-for-Gestational-Age: Southern Nepal. Bolded: p<0.05; Italicized: 0.05≤p<0.10. * Preterm defined as gestational age≤37 weeks. ** SGA  defined as birth weight below the 10th percentile for gestational age. Note: Percentages may not add up to 100.0% due to rounding. Table S3. Risk ratios for Neonatal Mortality by Preterm and/or Small-for-Gestational-Age: South India. * Preterm defined as gestational age ≤37 weeks. ** SGA  defined as birth weight below the 10th percentile for gestational age. Note: Percentages may not add up to 100.0% due to rounding. Table S4. Small-for-Gestational-Age prevalence and risk ratio of neonatal mortality, using Mikolajczyk et al. 's global reference birth weight distribution. *Content in the parentheses represent its ranking relative to prevalence/RRs reported for the other reference distributions. Figure S1 in File S1. Risk ratios for Term-Small-for-Gestational-Age, Neonatal Mortality: South India . Figure S2 in File S1. Risk ratios for Preterm-Appropriate-for-Gestational-Age, Neonatal Mortality: South India . Figure S3 in File S1. Risk ratios for Preterm-Small-for-Gestational-Age, Neonatal Mortality: South India .(PDF)Click here for additional data file.


To compare the cortical dynamics of different oculomotor tasks, EEG and eye movements were recorded in 21 volunteers. Using a comprehensive approach, subjects were asked to perform saccadic tasks, which included a saccadic eye movement to a peripheral target (prosaccadic), a movement to the opposite side (antisaccadic), or maintain the gaze fixed (no-go). In mixed trials, prosaccadic, antisaccadic and no-go tasks were indicated by a color square (S1) present for 1900–2500 ms (instructive period). S1 disappeared for 370 ms (gap) and a black dot at 8 deg at right or left indicated the beginning of the task. Reaction times, amplitude of eye movements and number of errors were greatest in antisaccadic tasks, suggesting a greater difficulty. The EEG showed a contingent negativity variation (CNV) that increased progressively along the instructive period and suddenly during the gap: higher in antisaccadic, followed by prosaccadic and no-go tasks. Principal component analysis (PCA) disentangled fronto-central and occipital CNV-related and fronto-central gap-related components. The instructive period was characterized by fronto-central and occipital beta desynchronization (ERD) higher in antisaccadic than in no-go and parieto-occipital alpha synchronization higher in no-go than in antisaccadic tasks. During the gap, parieto-occipital beta and alpha ERD were higher in antisaccadic compared to no-go. The gap was further characterized by a fronto-central increase of inter-trial coherence in theta: highest during antisaccadic, followed by prosaccadic and no-go tasks. This phase locking in theta was also accompanied by theta ERS, which was significantly higher in antisaccadic than in the other two tasks. In PCA of spectral power two main components had dynamics similar to those extracted from voltage data, suggesting cross-frequency coupling. These results suggest that the more difficult saccadic tasks are associated with top-down control mediated by frontal cortex, while simpler tasks rely more on bottom-up control mediated by posterior cortices. In antisaccadic tasks, when a visual target appears on one side of the screen, subjects must move their eyes to the opposite side. The antisaccadic task allows evaluation of the capacity to inhibit reflexive saccades and produce voluntary saccades A number of approaches have been taken to explore differences in the preparation and execution of saccades during prosaccadic and antisaccadic tasks, including recording the activity of single neurons, functional magnetic resonance imaging (fMRI) and lesion studies. Single-neuron recordings in the superior colliculus (SC) and the frontal eye field (FEF) during the instruction period have shown increased activity of fixation-related neurons and decreased activity of saccade-related neurons during antisaccadic compared to prosaccadic trials. This activity pattern explains the longer reaction times (RT) on antisaccadic trials By fMRI, the preparatory period has been previously related with a higher activity in FEF, supplementary eye field (SEF), dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), supplementary motor area (SMA) and intraparietal sulcus during the antisaccadic in comparison with prosaccadic tasks Although considerable effort has been made to describe prosaccadic and antisaccadic tasks processing during preparatory and response periods, technical characteristics of the fMRI impose important limitations on the study of temporal dynamics. EEG-derived measures such as ERPs and time-frequency analysis provide higher temporal resolution. It is well known from EEG studies that when antisaccades or prosaccades are preceded by a cue indicating the type of task, a contingent negative variation (CNV) appears during the preparatory period. This CNV has a higher amplitude at fronto-central regions during antisaccadic than during prosaccadic tasks Another technique to examine cortical dynamics associated with the processing of stimulus-response is event-related frequency analysis. Major frequency changes have been observed in the EEG during preparatory periods for motor responses in which a decrease of spectral power or event-related desynchronization (ERD) of the mu rhythm occurs An additional complexity in this kind of experiments is when the antisaccadic and prosaccadic tasks are accompanied by a gap period -a temporal interval between switching off the central fixation point and switching on the peripheral target- which induces a decrease in RT During the gap period, a frontal gap-related negativity has been found in the ERP In addition to the antisaccadic and prosaccadic tasks, the present experiment included a no-go task, in which the subjects must ignore the imperative stimulus and inhibit their response. No-go task could be useful as a control during the preparatory period because no movement is prepared but memory load is similar to the other tasks.The aim of the present study was to identify differences in cortical activities during the instruction and gap periods in antisaccadic, prosaccadic and no-go tasks. Comparisons of saccadic parameters and RT suggested that antisaccades were more difficult and required higher attention to be performed than prosaccades and no-go. In parallel, during the instructive and gap periods, a higher frontal activity was found in antisaccadic than in prosaccadic and no-go tasks. This increased frontal activation was strongest during the gap period indicating that a higher top-down control must be expected in the more demanding tasks, particularly at the moment in which a precise timing is required.Twenty-one subjects  aged between 21 and 29 years (mean = 23.5±2.8 years) were recruited from the University of Seville student community, and gave their written and informed consent to participate. They reported having no neurological diseases and normal or corrected-to-normal vision. Experiments were approved by the Ethics Committee of the University of Seville (10/24/2007) and adhered to the principles of the Declaration of Helsinki (1964).Participants were seated 68 cm in front of a CRT monitor on which the stimuli were displayed. They were asked to avoid head movements during the experimental session. Each session consisted of 240 trials of randomly intermixed prosaccadic, antisaccadic and no-go tasks . In each trial, the subject maintained a fixed gaze on a colored square (0.6×0.6°) (S1) at the center of a white background screen . The colVisual stimuli were generated using Eevoke software  on a PC with a CRT monitor (refresh rate of 80 Hz and resolution of 1024×768). Trigger information from Eevoke was sent to the EEG recording system. Eye movements in the horizontal plane were recorded by infrared video-oculography . This system sampled the position of both eyes at 400 Hz. Data from eye positions were automatically calculated by the EyeTracking software  and the position of the eyes were digital-to-analog converted (2 ms delay) and recorded throughout the EEG amplifier as external signals. EEG activities were collected from 64 scalp tin electrodes mounted in a cap using the International 10–20 system. Voltages from electrodes were measured with respect to an average reference. Impedance was maintained below 5 KOhms for each electrode. EEG signals and eye movements were recorded through a full-band DC amplifier  at a sampling rate of 1024 Hz.Saccadic eye movements were automatically detected by a script programmed in Matlab 2008a  using eye velocity threshold RTs were calculated for prosaccadic and antisaccadic tasks for rightward and leftward-directed saccades. Mean RTs were compared per saccadic directions and tasks. Errors between antisaccadic and prosaccadic tasks were compared for each type of error. Statistical analysis comparisons of saccadic eye movement parameters, errors and RTs in the different tasks were carried out by one-way analysis of variance. When the normality condition failed, Wilcoxon test was applied; when homogeneity of variance was not achieved, a student t-test for related measures was performed.EEG recordings were analyzed with EEGLAB rev.10.0.0.0b toolbox A time-frequency EEG analysis was performed trial-by-trial using Hanning-windowed sinusoidal wavelets at 1.5 cycles (lowest) to 7.5 cycles (highest). Changes in event-related dynamics of the EEG spectral power were studied using the event-related spectral perturbation (ERSP) index In parallel, a principal component analysis (PCA) was applied to ERP and ERSP data. The PCA allows extraction of the total variance of the data as a few components that can be identified as sources of variance in the empirical data The PCA of ERP data was carried out on a matrix containing time columns and rows for voltage, electrodes, subjects and conditions. The matrix was similar for the PCA of the ERSP, replacing voltage with ERSP frequencies (3 to 30 in 0.5 hertz bin). The topographies of the components were obtained by averaging the component scores for the extracted components. For the ERSP matrix, only loading factors were taken into account. Given the high complexity of the component scores, analysis of those results would require a separate study.Specific applications of the PCA method that are relevant to understanding the dynamics of CNV and brain rhythms during the instructive and gap period include the following:PCA analysis in the voltage domain would permit separation of brain dynamics during the gap period from those of the previous instructive period.The topographical representation of the component scores in the time domain would help to disentangle the relative contribution of different components in the instructive period (namely the CNV) and the gap-related negativity.In the time-frequency domain, PCA would identify similarities, if any, between the dynamic of the components extracted by time frequency analysis and of those obtained from the voltage domain. In case of similarities, a frequency-coupling between voltage and the power of brain oscillations could be argued.F = 19.59, p<0.01). Main differences were due to errors in direction in antisaccadic (11.4±7.7%) and prosaccadic (2.3±2.8%) tasks   of correct responses, although different tasks showed different degrees of difficulty. The percentages of total errors during the experiments were 19.2±11.6, 13.2±10.1 and 3.9±4.6% for antisaccadic, prosaccadic and no-go tasks, respectively  . Other mon test) . Finallyon test) .F = 42.90, p<0.01) (t(20) = −3.19, p<0.01 tasks. Taken together, the differences in number and type of errors and in response time suggest that differences between the three tasks could be explained by a higher degree of difficulty to prepare and execute antisaccades compared to prosaccades, and the same for prosaccades compared to no-go responses. Furthermore, the greatest amplitude in antisaccadic compared to prosaccadic tasks could suggest excessive motor preparation, which could also be related to the higher difficulty of the antisaccadic task. p<0.01) . MoreoveDuring the instructive period, a negative potential was slowly developing on the scalp in all three tasks. This negativity, which was maximal at FCz, increased steadily until the beginning of the gap period. The negativity during both the instructive and gap periods displayed a very similar fronto-central and posterior topography . The firDuring the gap period, differences in scalp voltages were located fronto-centrally and were significant for the comparisons between the three tasks , bottom.The similitude in scalp voltage distribution during the instructive and gap periods was appealing. As the gap induces deep changes in response time, it seems reasonable to expect more differences between these two periods. To test the possibility that some signals during the instructive period would continue during the gap period and mask gap-related signals, a spatiotemporal decomposition of the ERP by PCA was carried out . The PCAAs the PCA suggested the existence of linear negative potential during the instructive period that continued during the gap, the slope of the trend line was corrected for each subject and electrode and the voltage map recalculated. The slope value of the ERP average was calculated for each subject in each electrode during the time period from −1800 to −300 ms. With these slopes, a theoretical line was generated and subtracted from the ERP average in the whole window (from −1800 to 0 ms), generating the corrected averaged signal. The baseline was calculated at the beginning of the gap period and the inter-subject grand average recalculated. The These results indicate that: i) during the instructive period, all three tasks induce a central negativity that intensifies sharply during the gap period in fronto-central electrodes; ii) this fronto-central negativity is greater in antisaccadic than prosaccadic and in prosaccadic than in no-go tasks; and iii) the occipital negativity is established during the instructive period and although it continues during the gap it is not intensified.Brain dynamic activities were also evaluated by ERSP and ITC analyses during all three tasks, revealing differential activities at the fronto-central and parieto-occipital cortex. To disclose spectral differences between the tasks, a paired comparative analysis was performed . During In the alpha band, the parieto-occipital ERS observed during the instructive period switched to an ERD during the gap. This ERD was greater in antisaccadic than in no-go tasks (p<0.05) . SimilarA PCA was applied to ERSP data obtained from wavelet analysis. This frequency-spatio-temporal decomposition yielded two main components, explaining 74% and 7% of the total variance. The first PCA component  displayeComplex behavioral paradigms that incorporate different types of tasks, such as prosaccadic, antisaccadic and no-go conditions, and include a gap period between the extinction of the cue and the target offer a very useful approach to the study of cortical dynamics because they include motor and sensory preparation, working memory, attention and executive motor processes. Analysis of behavior, ERP, ERSP, ITC and topographical analysis would reveal the brain signal computations specifically related to each of these processes. The present report refers only to the instructive and the gap periods.It is well known that antisaccadic responses show an increased latency and number of errors compared to prosaccadic response In these experiments, the preparatory period included an instructive period –in which a cue color indicating the type of task was present– and a gap period –in which the cue disappeared 370 ms before the appearance of S2. During these preparation times, different processes such as rule retention by working memory, timing, and sensory and motor preparation must be expected to be active. However, each process probably develops at different times and can change its relative importance depending on the type and difficulty of the task.In ERP analysis, the instructive period was characterized by a progressive slow negative potential, located in the fronto-central and posterior regions of the scalp. This negative potential can be considered a preparatory CNV. The frontal component probably corresponds to the classical motor preparation subcomponent of CNV During the instructive period, the negativity in frontal sites was significantly higher in antisaccadic tasks, followed by prosaccadic and no-go tasks. A reverse pattern was found in posterior sites, with higher amplitude in prosaccadic and no-go than in antisaccadic tasks. An increased CNV in central and pre-central regions in antisaccadic versus prosaccadic tasks has been previously described During the gap period, a sudden increase in negativity occurred at fronto-central and parieto-occipital regions. The PCA results can disentangle this sharp increase in negativity during the gap from the slow negativity during the instructive period. The PCA showed two main components, one with fronto-central and parieto-occipital distributions which would be related to the CNV during the instructive period and extended into the gap period, and a fronto-central component whose temporal dynamics and topography explain the variance associated with gap-related negativity The continuation of CNV activity during the gap period explains why there is a parieto-occipital voltage activity very similar to that observed during the instructive period. However, fronto-central negativity during the gap was greater than during the instructive period, indicating extra fronto-central activity during the gap period.Voltage activities during the instructive and gap periods in antisaccadic and prosaccadic tasks would have sources in areas described in fMRI for general and motor related preparation. The fronto-central localization strongly suggests that the RT facilitation attributed to the gap compared to the overlap paradigms is probably due to motor preparation, as suggested by previous studies using behavioral and ERP data The gap-related negativity was roughly located over the SMA and/or dorsal ACC. This increased negativity in the SMA could explain the increased activity in the superior colliculus observed during the gap period During the instructive period, there was an increase in alpha power in the parieto-occipital cortex (significantly lower in antisaccadic than no-go tasks) and a decrease in beta power in the fronto-central and parieto-occipital . Decreases in ERS in the alpha band has been related to complex tasks and effort in attention In the present experiments, there was a broadband theta, alpha and beta ERD in parieto-occipital sites during the gap period. Broadband power reduction has also been demonstrated in the late phase of the CNV Comparison of the loading factors of the PCA extracted from voltage and ERSP during the instructive and gap periods presented very similar temporal dynamics. This indicates a functional linkage between voltage and spectral power, as previously suggested by the broadband decrease in power that occurs during the CNV period Present results suggest that when a cue indicates tasks of increased difficulty, sensory-motor preparation includes increased fronto-central CNV and beta ERD and decreased alpha ERS parieto-occipital activity with respect to tasks with lesser cognitive demands. On the other hand, the presence of a gap before the target acts as a pre-saccadic trigger signal producing a fronto-central negativity mainly related with theta ERS and ITC. This gap-related negativity, which was accompanied by beta and alpha ERD, was probably related to top-down control of the cognitive and sensory-motor processes needed to produce an accurate response.


Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning.We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN.These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. Motor skills in our daily lives could be acquired through execution or imagery learning Motor task-related changes measured from functional activities were mostly identified in primary motor cortex (M1), supplementary motor area (SMA), premotor area (PMA) for execution learning and visual cortex  for motor imagery learning Spontaneous activity measured with blood-oxygen level-dependent (BOLD) in resting state appeared intrinsic and coherent in several distributed networks Using functional connectivity analysis, several fMRI studies have explored motor skill learning from resting state. Albert and his colleagues identified several motor-related resting-state networks (RSNs) and further revealed increased network strength of these RSNs after learning To address this issue, we performed a fMRI investigation on motor imagery learning from resting state and investigated resting-state functional connectivity before and after the two-week consecutive learning. Based on previous studies, we first identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments of connectivity and network strengths  Imaging Center for Brain Research, National Key Laboratory of Cognitive Neuroscience. All of the subjects gave written informed consent according to the guidelines set by the MRI Center of Beijing Normal University.Thirty-two right hand-dominant college students participated in the study, and several subjects subsequently dropped out due to health concerns or malfunctions of the equipment. At last, fourteen participants were included in the experimental group  and twelve participants were included in the control group . Participants with histories of neurological disorders, psychiatric disorders, experience with typewriters, or any experience learning to play musical instruments were excluded. All participants passed the Edinburgh Handedness Inventory, the Movement Imagery Questionnaire and the Vividness of Movement Imagery Questionnaire The experimental procedure included both resting- and task-state scans to allow for comparisons between the two states in the future. Therefore, the overall experimental procedure included a pre-rest scan, pre-task scans, an motor imagery learning period  or a no-learning period (control group), a post-rest scan and post-task scans, and the present study mainly focused on the pre-rest and post-rest scans.Outside the scanner, all the participants were instructed that from their index to little finger, each of the four fingers of their right hand represented a single digit number: one, two, three, and four. Thereafter, they went through a short period exercise to learn the 4 Hz rhythm with a metronome.Before the learning period, the pre-rest scan, lasting for 10 minute, was used to establish a baseline condition for the examination. Subjects were instructed to keep their eyes closed, relax their minds and remain as motionless as possible. The pre-task scans, which included a motor execution and an imagery scan, were then performed. The two 4.5 min sessions (execution and imagery) were separated by a 5-min inter-session rest period. Each session consisted of four 30-s periods of executing/imagining the motor sequence, and these periods were interspersed with five 30-s rest periods. The assignment of scan order was counterbalanced across subjects. In each scanning session, a sequential finger movement task with the press sequence 4–2–3–1–3–4–2 was used. Subjects attempted to execute or imagine this sequence with their right hands at a self-paced rate of 4 Hz when PUSH was displayed on the screen and relaxed when REST was displayed on the screen. These indications were displayed to the subject via a mirror mounted on the head coil that reflected visually presented indications on a semi-transparent screen at the end of the scanner bore. Cushions inside the head coil were used to reduce head movement. The tapping sequence was performed with a four-button response pad, and the response pad was connected to a computer running the E-prime program  to record the responses. The participants were kept in the scanner for the whole procedure.During the learning period, 14 motor imagery practice sessions were performed over 14 consecutive days to guarantee sufficient learning. Participants in the control group did not attend any learning sessions over the 14 days. Participants in the experimental group were trained under the supervision of the experimenter, and a cardboard box covered their right hands to prevent visual feedback. Participants were instructed to provide a qualitative description of their performance. The contents of the qualitative description were based on the Movement Imagery Questionnaire and included seven rating levels  Brain scans were performed at the MRI Center of Beijing Normal University using a 3.0-T Siemens whole-body MRI scanner. A single-shot T2*-weighted, gradient-echo, EPI sequence was used for functional imaging acquisition with the following parameters: TR/TE/flip angle = 3000 ms/40 ms/90°, acquisition matrix = 64×64; field of view (FOV) = 240 mm, and slice thickness = 5 mm with no inter-slice gap. Thirty-two axial slices parallel to the AC-PC line were obtained in an interleaved order to cover the entire cerebrum and cerebellum.http://www.fil.ion.ucl.ac.uk/spm). Then, the group ICA, which includes dimension reduction by principle component analysis (PCA), ICA decomposition, and back reconstruction, was carried out with GIFT for circuitry exploration (http://icatb.sourceforce.net).Functional images from the pre- and post-resting scans were preprocessed for each subject. The time series were first realigned before being spatially normalized into standard stereotaxic space , re-sliced to 3×3×4 mm voxels, and smoothed with an 8×8×8 full-width-at-half-maximum (FWHM) Gaussian kernel using SPM8 software . Then, z-maps of each component were divided into four conditions for further analysis according to pre-/post-rest scans of the experimental/control group. In the subsequent analysis, connectivity strength and network strength were measured as in previous studies p<0.05. The correction was determined from a Monte Carlo simulation and required a voxel-wise threshold of p<0.005 within a minimum 3D cluster of 41 contiguous significant voxels . Subsequently, the average z value of each RSN was measured as network strength. A repeated measures ANOVA model was employed to assess the changes in network strength according to the contrasts between the pre- and post-rest scans for the experimental/control group.The preprocessed resting-state data from the pre- and post-rest scans were combined into a single group ICA analysis for each group . Prior to the ICA analyses, two-step principal component analysis (PCA) was used to reduce the dimensionality of data, and the number of ICA components was set to 38 for both groups to preserve, on average, at least 99.99% of the original variance. Next, the data were decomposed by group ICA t(24) <2.06 and p>0.05, see details in The consistency between the baselines of the experimental and control group were also tested. The resting-state data from the pre-scan for both groups were combined into a single group ICA. Prior to ICA, the number of ICA components was set to 38 to preserve, on average, at least 99.99% of the original variance, and the sensory and cognitive components were identified visually and confirmed via templates based on an independent dataset The RSNs identified as the components of group ICA analysis were shown in F = 6.17, p<0.05, corrected).Depending on the assessment of network strength, learning-induced alterations during resting state were manifested in the cognitive networks but not in the sensory networks. The DMN, as one of the cognitive RSNs, showed a significant decrease in network strength after learning . Such a Interestingly, according to the assessment of connectivity strength, only sensory RSNs, specifically the LVN and SMN exhibited significant alterations of connectivity strength during resting state after learning . In the The present study stressed resting-state functional connectivity before and after a two-week period of motor imagery learning. Based on previous explorations, we first identified eight sensory and cognitive RSNs corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments of connectivity and network strengths before and after the learning. Our results revealed greater connectivity strengths in fusiform gyrus and precuneus of the sensory RSNs, LVN and SMN after learning, which may be associated with the sensory processing of learning in the resting state The sensory RSNs, LVN and SMN of the visual and sensory-motor brain systems exhibited significant variances in connectivity strength after learning. Increased connectivity strength was identified in right fusiform of the LVN and left precuneus of the SMN. The LVN was suggested to be involved in visual processing during resting state, and one potential role of LVN may be associated with memory consolidation The SMN recruited cortical areas which exhibited significant activations in most sensory-motor tasks, and the spontaneous activities observed in the SMN during resting state have been suggested to subserve coordinated processing in motor tasks In the present study, cognitive RSNs specifically the DMN showed significant alterations of network strength after learning. Although the function of the DMN is currently unclear, it still has been considered as one of the critical RSNs Previous investigations implied that neural plasticity underlying motor skill learning mostly focused on a single brain system  In summary, we assessed resting-state functional connectivity before and after two weeks of motor imagery learning. Cognitive and sensory RSNs in multiple brain systems exhibited alterations at functional connectivity level after motor imagery learning. These alterations, manifested as decreased network strength in the DMN and the increased connectivity strength in fusiform gyrus and precuneus of the LVN and SMN, suggested that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems. Such modulation may be associated with the functional reorganizations in resting state, further providing new insights into the therapeutic benefits of motor imagery learning. Nevertheless, as an exploratory investigation, the current study has some limitations. For example, the number of subjects participated in this study is low, thus these findings of this study should be further validated with a relatively large number of subjects. Moreover, the functional interactions between regions in each RSN were not fully examined owing to the methodological restriction in our analyses. Future studies stressing these issues will be helpful in understanding the neural plasticity underlying motor imagery learning.Table S1Regions significantly recruited in each RSN for experimental group. The RSNs identified through a single independent component analyses on the combined data of pre- and post-rest scans, were first converted to z-maps for each participant and were further tested to determine group t-maps for the experimental group, p<0.005, FDR corrected.(PDF)Click here for additional data file.Figure S1Baseline tests on the network strength of DMN between the experimental and control groups. (a) The group mean value of experiment/control group; (b) The individual value of experiment/control group.(PDF)Click here for additional data file.


Recording of slow spontaneous fluctuations at rest using functional magnetic resonance imaging (fMRI) allows distinct long-range cortical networks to be identified. The neuronal basis of connectivity as assessed by resting-state fMRI still needs to be fully clarified, considering that these signals are an indirect measure of neuronal activity, reflecting slow local variations in de-oxyhaemoglobin concentration. Here, we combined fMRI with multifocal transcranial magnetic stimulation (TMS), a technique that allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections. We investigated whether the physiological properties of parieto-frontal circuits mapped with short-latency multifocal TMS at rest may have some relationship with the resting-state fMRI measures of specific resting-state functional networks (RSNs). Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network, i.e., the attention system believed to be involved in reorientation of attention. We conclude that resting-state fMRI ongoing slow fluctuations likely reflect the interaction of underlying physiological cortico-cortical connections. In the human brain complex networks rather than isolated cortical areas sub-serve specific brain functions, such as, for instance, movement, memory, or attention In humans, simultaneous recording of electroencephalography (EEG) and fMRI at rest has returned significant correlations between the variations of band power of different cortical rhythms and BOLD fluctuations within specific brain networks A unique opportunity to challenge the neurophysiological characteristics of functional connectivity of the brain at rest is provided by multifocal transcranial magnetic stimulation (TMS) Here, we aimed to investigate whether the physiological properties of these circuits may have a relationship with the resting-state fMRI measures, hypothesizing that BOLD fluctuations reflect underlying neurophysiological interactions. Therefore, we used correlation analyses to verify whether, across subjects, specific associations exist between connectivity in parieto-frontal circuits assessed by multifocal TMS, and BOLD signal changes in well-known RSNs whose nodes overlap with the sites of TMS stimulation.In total, 19 healthy volunteers  took part in this study. The participants are a subsample of those enrolled for a previous study, in which we combined TMS and diffusion imaging data 2, slice thickness:1 mm, total scan time:12 min); (3) T2* weighted echo planar imaging (EPI) sensitised to blood oxygenation level dependent imaging (BOLD) contrast  for resting state fMRI. BOLD echo planar images were collected during rest for a 7 min and 20 s period, resulting in a total of 220 volumes. During this acquisition, subjects were instructed to keep their eyes closed, not to think of anything in particular, and not to fall asleep.All subjects underwent an MRI examination, obtained at 3 T , including the following sequences: (1) dual echo turbo spin echo ; (2) three-dimensional modified driven equilibrium Fourier transform (MDEFT) scan  to reduce the effect of low frequency drift and high frequency physiological noise.Data were pre-processed using Statistical Parametric Mapping , and in-house software implemented in Matlab . For each subject, the first four volumes of the fMRI series were discarded to allow for T1 equilibration effects. The pre-processing steps included correction for head motion, compensation for slice-dependent time shifts, normalization to the EPI template in MNI coordinates provided with SPM8, and smoothing with a 3D Gaussian Kernel with 8mmhttp://icatb.sourceforge.net/) to identify, on a subject by subject basis, regions belonging to previously identified resting-state networks (RSNs). ICA is a data-driven approach to extract independently distributed spatial patterns from fMRI data. In simple words, ICA unmixes a number of spatial “sources” from the whole data set, identifying the temporal pattern associated with each of them. In the case of resting-state fMRI the sources correspond to spatially independent brain regions, each associated with a specific time-course. ICA analysis was set to identify 20 independent components. Briefly, GIFT first concatenates the individual data across time, and then produces a computation of subject specific components and time courses. For all subjects grouped together, the toolbox performed the analysis in three steps: 1) data reduction, 2) application of the FastICA algorithm, and 3) back-reconstruction for each individual subject We employed independent component analysis (ICA)  was applied over the hand motor areas of left M1 and was defined as the point in which stimulation evoked the largest motor evoked potentials (MEPs) from the contralateral first dorsal interosseous (FDI) muscle. Electromyographic (EMG) traces were recorded bilaterally from the FDI muscles using 9-mm diameter, Ag–AgCl surface cup electrodes. The active electrode was placed over the belly muscle, while the reference electrode was located over the metacarpophalangeal joint of the index finger. Responses were amplified using a Digitimer D360 amplifier  through filters set at 20 Hz and 2 kHz with a sampling rate of 5 kHz, then recorded by a computer using SIGNAL software . The test stimulator for M1 was connected to a small custom-made figure-of-eight-shaped coil . The intensity of the TS was adjusted to evoke a MEP of approximately 1 mV peak to peak in the relaxed contralateral FDI muscles. To best activate the ipsilateral PPC-M1 facilitatory functional connection, a conditioning stimulus (CS1) applied over the PPC preceded the M1 TS by 5 ms, at an intensity of 90% of the ipsilateral resting motor threshold (RMT) , which wCONTRA) 10 ms before delivery of the PPC pulse ipsilateral to M1 (PPCIPSI), and therefore 15 ms before the M1 TS a posteriori into the Montreal Neurological Institute (MNI) coordinate system and averaged IPSI + TS; PPCCONTRA + PPCIPSI + TS. Fifteen responses were collected for each condition. The inter-trial interval was 5 sec (±10%). Measurements were obtained for each individual trial. For correlation analyses, IPSI-M1 functional connectivityintra-hemispheric PPC was measured as the percentage of the mean peak-to-peak amplitude size of the MEP obtained by TS of M1 in isolation CONTRA-PPCIPSI-M1 functional connectivityInter-hemispheric PPC was measured as the percentage of the mean peak-to-peak amplitude size of the MEP obtained by PPCIPSI + TS of M1 To investigate the functional inter-hemispheric connectivity between the PPC areas, a third TMS pulse (CS2) was applied over the contralateral PPC  in order to verify the changes in the MEP amplitude following bifocal or trifocal TMS. P values <0.05 were considered as statistically significant. A significant main effect in the ANOVA was followed by post hoc paired t test analysis. The Greenhouse-Geisser correction for non-spherical data was used.A within-subject ANOVA was applied on mean MEPs amplitude with condition as main factor , PPCIPSI-M1 functional connectivity assessed by TMS (as defined above) was used as a regressor; in the second model the inter-hemispheric PPCCONTRA -PPCIPSI-M1 functional connectivity (as defined above) was used as a regressor. T contrasts were used to test the hypotheses of either positive or negative correlations.The across-subject correlation between fMRI and TMS data was assessed using a random-effect analysis in SPM8. Two models were set up for each of the 3 selected RSNs . In the first model, intra-hemisperic PPCP-values were considered as statistically significant if inferior to 0.05, after false discovery rate  = 16.04; p = 0.00001; t = −2.12; p = 0.03), which implies the activation of intra-hemispheric facilitatory functional connectivity The integration of TMS and fMRI data revealed a remarkable correlation across subjects between functional connectivity (both intra- and inter-hemispheric) as measured by multifocal TMS, and the degree of correlation of spontaneous activity within the DAN In order to exclude the possibility that age and gender might have affected these results, we repeated the analysis, adding age and gender as covariates of no-interest. The results were consistent with those of the original analysis, although the statistical significance was reduced  due to the reduced number of degrees of freedom. A figure summarising the results of this analysis is provided on-line as rd order polynomial fit, realignment parameters, and the signal averaged over whole brain voxels. The mean timeseries from the clusters found to be significantly associated with the DAN in the main analysis (left and right PPC and left premotor cortex) were extracted from the resulting images. The correlation coefficient between each pair of regions was estimated subject by subject using Pearson's formula. The resulting values  were correlated across subject with the intra- and inter-hemispheric connectivity assessed using TMS.In order to further explore our hypothesis, we performed some further correlation analysis between the fMRI and the TMS data. Every subject's functional data underwent an additional step of pre-processing, consisting of the removal of potential sources of spurious variance, including: global temporal drift using a 3No significant results were found. Correlation coefficients and p values are shown in a priori selection of target regions. The fact that no correlation was found between TMS measures, and connectivity within other RSNs such as the DMN, despite the anatomical proximity of its posterior parietal nodes with those of the DAN, confirms the reliability of our findings, which are highly specific to the DAN.Our data indicate that the correlation structure of hemodynamic signals recorded by resting-state fMRI is tightly related to the physiological interactions tested by methods based on non-invasive cortical stimulation. Crucially, we report here a strong overlap between the cortical areas selected for the TMS, and the resting-state areas of BOLD signal correlation within the DAN, but not with other RSNs, namely the DMN and SMN. It should be highlighted that the fMRI analysis was performed in a completely data-driven fashion, and did not require any The DAN is supposed to be involved in voluntary visual attentional control trough a large-scale distributed network formed by the frontal, parietal and visual cortices Several previous studies have demonstrated that the fMRI RSNs signals correlate with EEG signals, suggesting that the different RSNs assemble through synchronization of electric activity as measured by EEG EEG/fMRI investigations, reporting an association between slow hemodynamic changes and faster electrical oscillations (up to 80 Hz), indicate a link between the ongoing connections that can be detected at different temporal scales. Notably, studies based on micro-state EEG provide evidence for a further association between slow resting state fMRI oscillations and neural activity in the scale of hundreds-milliseconds In this perspective, our findings reinforce the notion that the RSNs emerging from slow brain fluctuations are based on the neural activity of specific cortical substrates that operate at different time scales. These areas, belonging to the same network, might communicate with each other within different temporal scales, ranging from few seconds  to few millisecond .A crucial issue that still remains to be clarified concerns how the activity of these connections actually contributes to specific mental activities In the present study, although the correlations of physiological interaction and fMRI functional connectivity were strongly significant, they were in the reversed direction than predicted: the lower the Z values of functional connectivity, the higher the facilitation induced by ipsilateral PPC stimulation in the bi-focal condition and the lower the inhibition induced by contralateral PPC stimulation in the tri-focal condition. With this regards, it has to be noted that these physiological interactions involve complex polysynaptic pathways mediated by inhibitory interneurons that could easily reverse the relationship between the physiological activity and the functional connectivity. Therefore it is difficult to provide a solid hypothesis to explain such apparent discrepancy.It is also worth commenting on the inconsistency between the results of ICA and of the seed-based analyses. While strong correlations were found between TMS and DAN when using ICA decomposition, we were unable to demonstrate associations between TMS data and RS functional connectivity estimated by correlation analysis see . AlthougA limitation of the current study is that TMS and fMRI experiments were not simultaneously performed. It has indeed been argued that RSNs are non-stationary and may change within and between experimental sessions. In this view, the state of the subject might potentially alter the effects of TMS on EMG as well as on the connectivity of networks. For example, alertness, drowsiness, fatigue and so on could have considerable influence on the responsiveness as well as on the network activity and their spatial pattern.In conclusion, we demonstrate that resting state fMRI may represent an effective method to investigate specific neurophysiological circuits and to quantify the degree of neuronal functional connectivity within them. We showed that combining multimodal TMS and resting state fMRI can effectively improve the characterization of the anatomo-functional properties of some crucial brain connections. Multifocal TMS, which is able to provide unique information concerning the inhibitory or facilitatory nature of a certain pathway with a sub-millisecond temporal precision, can be complemented by the high spatial resolution afforded by fMRI, thus allowing to detect more precisely the cortical nodes underlying functional connectivity. This approach could also strengthen the clinical use of resting state fMRI in several physiological and pathological conditions Figure S1Correlation between TMS and fMRI data, adjusting for age and gender. In panel A and B, areas of correlation  between the dorsal attention network (DAN) and bifocal TMS are shown in green, while correlations between the DAN and trifocal TMS are shown in red. Overlapping regions are in yellow. Panel B also shows the sites of TMS stimulation (blue dots). The scatterplots reported in the upper row of panel C show the mean cluster Z-score  against the percentage change in MEP for the 3 highlighted regions. In the lower raw of panel C, the corresponding mean Z-score relative to the default mode network (DMN) are also plotted against the percentage change in MEP (bottom row), but correlations were not significant.(TIF)Click here for additional data file.