Genotyping has become more cost-effective and less invasive with the use of buccal cell sampling. However, low or fragmented DNA yields from buccal cells collected using FTA cards often requires additional whole genome amplification to produce sufficient DNA for genotyping. In our case–control study of childhood leukaemia, discordance was found between genotypes derived from blood and whole genome amplified FTA buccal DNA samples. We aimed to develop a user-friendly method to correct for this genotype misclassification, as existing methods were not suitable for use in our study.Discordance between the results of blood and buccal-derived DNA was assessed in childhood leukaemia cases who had both blood and FTA buccal samples. A method based on applying misclassification probabilities to measured data and combining results using multiple imputations, was devised to correct for error in the genotypes of control subjects, for whom only buccal samples were available, to minimize bias in the odds ratios in the case–control analysis.Application of the correction method to synthetic datasets showed it was effective in producing correct odds ratios from data with known misclassification. Moreover, when applied to each of six bi-allelic loci, correction altered the odds ratios in the logically anticipated manner given the degree and direction of the misclassification revealed by the investigations in cases. The precision of the effect estimates decreased with decreasing size of the misclassification data set.Bias arising from differential genotype misclassification can be reduced by correcting results using this method whenever data on concordance of genotyping results with those from a different and probably better DNA source are available. The Australian Study of causes of Acute Lymphoblastic Leukaemia in Children (Aus-ALL) was a population-based case–control study, conducted between 2003 and 2007, designed to investigate environmental and genetic risk factors for childhood ALL as well as their interactions. The study has been described in detail elsewhere . BrieflyIn this paper, we briefly present the concordance results for a set of six polymorphisms and describe the performance of a method we developed to correct genetic association analyses for the unexpected misclassification we observed. Because of the differential nature of the misclassification  and the large number of covariates required and analyses planned, particularly all those requiring more than one gene in a model or examination of gene-environment or gene-gene interactions, we needed a comparatively simple and easy-to-use method for addressing this issue. While there is a considerable literature on methods for correction of measurement error or misclassification  in accordance with the manufacturer’s instructions. DNA concentrations were quantified using a ND-1000 spectrophotometer . FTA cards were stored at room temperature with desiccant until processing. Four discs of 1.2 mm diameter were trephine punched from the sampling area and placed collectively into a single tube and amplified using the GenomiPhi DNA Amplification Kit . Briefly, 9μL of sample buffer, 9μL of reaction buffer and 1μL of enzyme mix were added to the punches with thermo cycling conditions according to the manufacturer’s instructions. DNA concentrations were quantified using a ND-3300 fluorospectrometer. The quantified DNA aliquots were then frozen at minus 30 degrees and thawed when required for genotyping.Genotyping for single nucleotide polymorphisms (SNPs) involved in folate-metabolism, xenobiotic biotransformation and DNA repair pathways was performed using either restriction fragment length polymorphism analysis or TaqMan® SNP Genotyping Assays  with allele calling was performed by two independent researchers. For quality assurance purposes, 10% of samples were selected at random for repeat analysis, performed by laboratory staff blinded to sample identity. The specific SNPs genotyped in Aus-ALL are not identified in this methodological paper, as they are used for illustration purposes only. Full analyses of their associations with childhood ALL will be published elsewhere.Across all 26 genotyped SNPs, the overall concordance within runs  was 99.7% for blood samples  and 99.6% for WGA’d buccal DNA from the FTA cards (hereafter referred to as wgaDNA). The overall genotype failure rates for these SNPs were 0.16% and 1.68% for the gDNA and wgaDNA genotyping respectively. There were no departures from Hardy-Weinberg predictions observed in either the gDNA or wgaDNA genotype results. Among pairs of gDNA and wgaDNA samples collected from 249 case children, the average genotype discordance across all 26 diallelic SNPs (excluding discordance due to genotyping failure of one of the samples) was 3.48%, with a range of 0.40% to 12.96%. This discordance was the reason we developed the adjustment method described in this paper.It appeared that the most likely source of error was preferential amplification from one chromosome of a pair, leading to loss of heterozygosis or “allelic drop-out” during WGA of frozen-thawed buccal DNA samples extracted from the FTA cards. Only WGA’d buccal DNA was available for control subjects; thus allelic drop-out in control subjects’ genotypes could bias associations between genotypes (and genotype-exposure interactions) and risk of disease. A modified unconditional logistic regression modeling technique was therefore developed to correct for this bias.We assumed the genotype from the gDNA sample was the ‘true’ genotype (gold standard). 92.2% of the genotyping error in the wgaDNA samples involved a change from heterozygous in the gDNA to homozygous wild type (Aa→AA) or homozygous mutant in the wgaDNA (Aa→aa) Table . Only 7.A subset of 6 SNPs from the Aus-ALL study was selected for use in the testing of the devised algorithm. These SNPs were selected as they covered the full spectrum of gDNA-wgaDNA genotype discordance seen. The mean level of discordance (excluding discordance due to genotyping failure of one of the samples) for these SNPs was 5.3% (range 0.40% to 12.96%).In brief, a discordance table is created by comparing the genotyping method that has error with a gold standard method. If discordance is present, then a proportion  of the records with genotypes containing error are randomly selected, their genotypes are corrected and a logistic regression model is computed. This process is repeated multiple times, ensuring a different set of records is randomly selected each time, and controlling for the fact that the proportion is an estimate. Results from the multiple iterations of the model are then compiled and imputed effect estimates are calculated. SNP A from Table The proportions of discordant results for each SNP in the paired gDNA-wgaDNA samples are shown in Table Table p. Therefore, the proportion of records to be corrected can be sampled from a binomial distribution with probability of success parameter p, which is automatically calculated from the discordance table. To make this correction within control subjects, the genotyping results were treated as follows:The discordance tables prepared in Stage 1 are only an estimate, from one sample, of the true misclassification proportion 1. The SNP genotype variable was re-assigned to a temporary new genotype variable.p (e.g. 1/96 for AA to Aa).2. A single value (cT) was determined by multiplying this proportion  of subjects which required genotype reclassification were randomly sampled .4. From the control subjects with genotype (AA), the calculated number .6. Steps 2 through 5 were repeated for each instance of misclassification Where two changes were required within one genotype for a SNP , the following 6 steps replaced steps 2 through 6 in the above process.p (e.g. (2 + 1)/90 [2/90 for Aa to AA + 1/90 for Aa to aa]).2. A single value (cT) was determined by multiplying this proportion  of subjects requiring genotype reclassification were randomly sampled .4. From the control subjects with genotype (Aa), the number (p1 and p2 were temporarily assigned as the proportions of cT that were required for each change. Here p1 = 2/(2 + 1) for the reclassification of Aa to AA and p2 = 1/(2 + 1) for the reclassification of Aa to aa.5. Then c1 and c2 were then calculated (c1 = p1*cT and c2 = p2*cT)6. Temporary scalars c1 and c2 of the randomly selected subjects’ genotypes from step 4 were reclassified (c1 from Aa to AA and c2 Aa to aa).7. In the new genotype variable, 8. Steps 2 through 7 were repeated for each instance of this type of misclassification Correction of the misclassified genotypes, as outlined in Stage 2, was then done in each run of an unconditional logistic regression model that was repeated 50 times. Relevant covariates were included in the model. For each variable in the model, the mean ß coefficient from the 50 iterations was used as the best estimate of the true coefficient. These estimates were used to calculate Wald Test p-values  and to gThe algorithm outlined in this paper was implemented in the statistical package R. For the R code to implement the outlined method, see our institutes website  or AdditTo test the efficacy of the correction method, we created a series of hypothetical datasets that reflected our complete study dataset. These datasets were based on the genotype frequencies of two artificial SNPs: ‘SNP 1’ with an approximate dominant risk model  and ‘SNP 2’ with an approximate log additive risk model . The proportional amounts of misclassification shown in Aus-ALL SNPs A and B (the latter representing the SNP with the most discordance) among gDNA-wgaDNA pairs  and corrected analyses were identical Table  when no As seen in Table To verify the optimal number of iterations provided, we used the highest level of discordance (SNP-B type error) on the artificial SNP 1, and completed 1000 repeats of 25, 50 and 100 iterations to examine the distribution of the resulting imputed OR for the aa genotype. 95% confidence intervals for the spread of this imputed OR were (1.96 – 2.10), (2.01 – 2.11) and (2.02 – 2.09) respectively. We felt the precision gained in 50 iterations compared to 25 was of value, and that the extra precision gained in 100 iterations compared to 50 was insufficient to warrant the observed modest increase in computation time. It should be noted that increasing the number of iterations would further increase the precision of the estimate, with the only expense being in added computation time.The uncorrected and corrected odds ratios and 95% confidence intervals for risk of ALL associated with each genotype are shown in Table Almost 40% of the discordance in the gDNA-wgaDNA sample pairs was in the Aa→AA direction Table . The effWe aimed to correct for bias introduced by differential error in the genotyping results for control subjects compared with case subjects in our case–control study of childhood ALL. Over 90% of discordance observed in the case gDNA-wgaDNA sample pairs involved a loss of the Aa genotype in the wgaDNA samples Table , consistOur method for correcting logistic regression results for genotyping error can be applied to any study where misclassification is known to be present in genotypes , and where validation data are available, or where informed estimates of probable misclassification can be made. The method can also be adapted to allow associations between gene-environment interactions and risk of disease to be investigated.Our correction technique involved using the empirically observed patterns of discordance in matched gDNA and wgaDNA samples from cases to adjust the estimated ORs for the associations between each of these SNPs and the risk of ALL. As expected, the primary effect of the correction on real data was to reduce the ORs associated with the Aa genotype. It also tended, less predictably, to increase the ORs for aa genotypes. There are two likely explanations for this less predictable effect. First, the aa genotype is generally the rarest so that even small adjustments in frequency can produce relatively large proportional changes in the OR. Second, for most SNPs, the correction method involved simultaneous adjustment to the frequency of the AA genotype relative to the Aa genotype, so the reference category for the aa OR was also adjusted.Applying the discordance seen in our quality control sample pairs to two artificial SNPs showed that the method was successful: when the correction was applied to hypothetical data with a known degree of misclassification, estimates were more similar to those of the error-free hypothetical data. This was the case whether the degree of misclassification was relatively small (SNP A-type) or relatively large (SNP B-type). Although the effect of the correction method on the ORs for the aa genotype tended to be less predictable than for the Aa genotype when the ‘true’ result was unknown, our simulations showed that the corrections made to the aa genotypes were as appropriate and effective as for the Aa genotypes. These results give us confidence that the correction method we devised can adjust appropriately for the genotyping error and, therefore, should reduce bias in our estimated ORs. However, since the adjustment is probabilistic, there is likely to be a reduction in precision. Simulating an increase or decrease in the size of the misclassification data set narrowed or widened (respectively) the confidence intervals Table .The statistical method we have described in this paper provides a novel and user-friendly method of correcting for differential genotyping error. Unlike previously described approaches, it can be used with modest sample sizes, allows correction of multiple misclassified variables, takes account of the size of the validation sample, and does not require the validation sample to include controls. Other genetic association studies based on cases and controls with samples derived from different sources or treatments of DNA should seek to investigate concordance between genotypes measured in DNA from both sources and, if found to be materially discordant, consider applying this adjustment procedure.ALL: Acute lymphoblastic leukaemia; DNA: Deoxyribonucleic acid; OR: Odds ratio; SNP: Single nucleotide polymorphism; WGA: Whole genome amplification.The authors declare that they have no competing interests.MC devised and coded the statistical method, and drafted the paper, NDK advised on statistical methods, and drafted the paper, EM was involved in study design and management and drafting the paper, KG ran the analysis of real and mock genotyping data and assisted in drafting the paper, SJ advised on genotyping analysis and methods, DA advised on statistical methods, FB coordinated sample processing, genotyping methods, study design and assisted in drafting the paper, BA was involved in study design and drafting the paper. All authors approved the final version of the paper.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/12/141/prepubR functions for misclassification correction method. A text file containing the R functions required to run the logistic regression corrected for known misclassification, as outlined within this manuscript.Click here for file


Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids.Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects.We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes.phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR.With this approach we found several genes that already are considered markers of GC induced IR, such as With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks. Glucocorticoids (GCs) are often prescribed for the treatment of inflammatory diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis-3. DespiGCs bind to the glucocorticoid receptor (GR), which then dimerizes and translocates to the nucleus where it influences gene transcription. Positive regulation of genes (transactivation) is mainly mediated by direct binding of the GR-GC complex to glucocorticoid response elements located in the regulatory region of a target gene. The GR-GC complex may also bind to negative glucocorticoid response elements, which leads to a negative regulation of genes (transrepression). It is believed that transrepression, in which proinflammatory genes are downregulated, is mainly responsible for the efficacy of GCs as anti-inflammatory drugs,7, whileAn important side effect is the development of insulin resistance (IR), because it is the onset of many metabolic diseases and conditions such as obesity, diabetes mellitus and hypertension. IR is a physiological condition in which a given concentration of insulin produces a less-than-expected biological effect. These biological effects are different depending on the tissue in which they occur. For instance, under IR conditions, fat and muscle cells fail to adequately respond to circulating insulin, which results in reduced glucose uptake, and subsequently higher glucose levels in blood,11. In lGLUT4 glucose transporter, which results in reduced insulin-stimulated glucose transport in skeletal muscle[One way by which GCs induce IR is by inhibition of the recruitment of l muscle. HoweverIt has been widely recognized that a system approach in which networks of genes in their functional context are studied, contributes to a better understanding of the mechanisms and pathways related to the disease and the drug effects-17. To sLiterature databases such as Medline contain many studies about IR and the molecular effects of synthetic glucocorticoids and thus are a good resource that can be used to create and study disease related gene networks.The retrieval of relevant gene-disease associations out of the millions of abstracts in Medline is very labor intensive and thus a text mining system is needed to this in an automated fashion.In previous work we reported about CoPub-20, a puWe used CoPubGene to create a network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes.phosphoenolpyruvate carboxykinase (PCK) and glucose6phosphatase, catalytic subunit (G6PC)[By using this method, we identified several genes that already are considered markers of GC induced IR, such as t (G6PC),28. Evenhttp://www.copub.org.We constructed CoPubGene as a SOAP based web service Table. This CoTo create disease related gene networks, we used CoPubGene to retrieve gene-disease and gene-gene associations from Medline abstracts. Disease terms which had significant gene associations based on the R-scaled score (rs > 35) and literature count (lc > 5) in Medline abstracts, were extracted from the CoPub thesaurus.http://www.r-project.org) using the pvclust R package with “complete” setting for hierarchical clustering, based on correlation distance of R-scaled scores between genes and diseases, with 100 bootstrap replications. The hierarchical cluster was visualized using Denroscope[Disease clustering was done in R . Subsequently the IR-gene network was created by connecting genes that had significant co-occurrences with each other.Keyword enrichment analysis on the list of IR related genes was done against disease and drug terms from the CoPub database. Threshold values were chosen using default values.gene g with term d is calculated in the following way:The IR gene network was analyzed by mapping specific occurrences of the IR related genes with ‘inflammation’ and ‘dexamethasone’ in Medline abstracts onto the network. For the evaluation of the involvement of a gene, calculation of the literature score for a given gene and a given disease term, also the effects of dexamethasone and inflammation on the connecting genes are included. The literature score for g1 is the R-scaled score of gene g with term d, and Ns is the literature score of its neighboring genes with term d. This latter score Ns is calculated using the R-scaled score of each neighboring gene of gene g with term d  relative to its relation  with gene g .In which We developed CoPubGene by creating a number of web service operations that can be used to construct networks of genes based on their co-occurrences in Medline abstracts. These web service operations can be combined to answer a variety of biological questions Table. For exaOur aim was to get insight into the pathways and genes that are involved in insulin resistance, and the effect of glucocorticoids on this network. As a first step we created a list of genes associated with insulin resistance using CoPubGene. This yielded a list of 384 genes each of them connected to IR with an R scaled score , insulin receptor substrate 1 (IRS1), vakt murine thymoma viral oncogene homolog 1 (AKT1), insulin receptor (INSR), solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) and insulin (INS) which were connected to more than 100 other genes. The resulting network is a scale free network, as indicated by the distribution of connectivity that follows a power law distribution which is indicative for a scale free network , glycolysis , glucose uptake, lipid metabolism  and carbohydrate metabolism (GPD1). Other ones are directly involved in insulin signaling .The majority of the genes in FigureCYP17A1, CYP19A1 and CYP21A2, which are key regulator enzymes in the steroid synthesis . A decline in DHEA and also its sulfated ester (DHEA-S) has been suggested to be causally linked to insulin resistance and obesity[Cyp17a1[ obesity-56. The [Cyp17a1,58 suggeCYP21A2 is a cytochrome P450 enzyme coding for the 21-hydroxylase that is involved in the biosynthesis of the steroid hormones aldosterone and cortisol. A defect in this gene leads to Congenital adrenal hyperplasia (CAH) in which there is a disbalance in cortisol and aldosterone secretion. CAH patients are characterized by insulin resistance, lower insulin sensitivity and hyperinsulinemia[ulinemia,59-61. Sulinemia-64. WhetTNFRSF11A, TNFRSF11B, TNFSF11, SP7 ,CTSK), in bone mineralization  or are part of the wnt signaling pathway that is involved in the regulation of bone formation [TNFRSF11B) and the ligand RANK-L (encoded by TNFS11)[BGLAP, COL1A1 and SP7 are affected by GCs[Another side effect of GC treatment is the development of glucocorticoid induced osteoporosis (GIOP). GIOP is5, LRP6). Among t TNFS11). Here wed by GCs-72, haveIn the work presented here we used Medline abstracts to study mechanisms and genes involved in glucocorticoid induced insulin resistance. We created CoPubGene, a number of web service operations that can be used to retrieve relevant gene-disease, gene-drug and gene-gene associations out of Medline abstracts, using the CoPub technology.The clustering of disease terms based on their associations with genes in Medline abstracts showed that CoPubGene is able to generate a list of specific IR genes that can be used for further analysis, and that this method also can be used to generate a variety of other gene disease associations. We used this clustering to evaluate the quality of disease related gene lists, generated using a text mining approach, because to our knowledge there is no real gold standard data set that covers a sufficient range of gene-disease associations that can be used. Databases such as OMIM and the KEGG disease database only covNext, we studied the IR genes in their functional context, by including genes with which they co-occur in Medline abstracts. In this gene network we focused on genes that are strongly linked to dexamethasone and less strongly to inflammation. These genes are thought to be more exclusively related to GC induced IR and therefore might be interesting markers for this effect.However, all of them are to a certain extent related to inflammation, either directly or indirectly by their neighbors, which suggests that these genes cannot be used as an exclusive marker for GC induced IR. This might have consequences for the search of dissociating compounds, i.e. compounds which only have the immune suppressive properties and not the unwanted side effects. Instead the search should focus on compounds that show a reduced effect on the expression of these IR genes.The majority of the IR genes that have a low literature neighbor score for inflammation (< 25) and a high score for dexamethasone (literature neighbor score > 25) code for enzymes and hormones directly involved in important metabolic processes, such as glycolysis, gluconeogenesis, glucose uptake and lipid metabolism. All these processes are tightly regulated by insulin. This suggests that at a first instance, the search for mechanisms of GC induced IR should be focused on these processes.CYP17A1, CYP21A2 and CYP19A1 keep the balance between several steroids, and an impairment of this balance could possibly result in metabolic disturbances such as IR. Additional topological analyses could further prioritize this sub-network for follow-up studies to determine the influence of GCs on sex steroid synthesis and the relation to IR. In such a study one could look at the influence of GCs on the balance between the steroids in combination with their influence on insulin stimulated glucose uptake in glucose sensitive tissues such as adipose and muscle tissue.Additionally, we also identified a sub network of genes involved in sex steroid synthesis that to our knowledge, not have been recognized yet as mediators of GC induced side effects. Key enzymes involved in steroid synthesis, i.e. Using CoPubGene we are able to construct an informative literature network of IR related genes by only using information from Medline abstracts. Our approach revealed genes, that on a first glance were not considered to be involved in GC induced IR and thus gave new insights that might lead to a better understanding of the mechanisms behind GC induced IR.Authors have no conflict of interest.WF performed all data analysis, research design and wrote the paper. ET supervised with biological interpretation and helped with writing the paper. SV, TH developed the web service operations. TR, RVS, RF and JDV helped with research design. WA supervised the work and helped with research design, analyzing the data and writing the manuscript. All authors read and approved the final manuscript.Author WF was supported by the Biorange project (BR4.2) “A Systems Bioinformatics Approach For Evaluating And Translating Drug-Target Effects In Disease Related Pathways” of NBIC.Part of the disease matrix, which has been used for the clustering.Click here for fileTable S3. Gene disease profiles. Table S4A. Enriched drug terms. Table S4B. Enriched disease terms. Table S4A.2. Genes linked with enriched drug terms. Table S4B.2. Genes linked with enriched disease terms. Table S5. 131 genes that linked with osteoporosis.384 genes that are linked with insulin resistance in Medline abstracts. Click here for fileHierarchical cluster of disease terms from the CoPub database with bootstrapping values. Red numbers at the nodes represent Approximately Unbiased (AU) bootstrap values (%). Green numbers at the nodes represent Bootstrap Probability (BP) value (%).Click here for fileEnriched disease terms found per sub-cluster when searching with the DAVID annotation server.Click here for fileDistribution of connectivity of IR related gene network. The node connectivity follows a significant power law distribution .Click here for fileNetwork of top scoring genes with osteoporosis. Genes in blue have a co-occurrence with dexamethasone in Medline abstracts . The strength of the link with dexamethasone is given by the color shading, ranging from no link (white) to a strong link (dark blue). The strength of the link with inflammation  is given by the size of the node of the gene, ranging from no link  to a strong link with inflammation (large size of the node).Click here for file


We found that neither learning behaviour, nor hippocampal spine density was affected by long term rTMS. Our negative results highlight the lack of deleterious side effects in normal subjects and are consistent with previous studies suggesting that rTMS has a bigger effect on abnormal or injured brain substrates than on normal/control structures.Repetitive transcranial magnetic stimulation (rTMS) is thought to facilitate brain plasticity. However, few studies address anatomical changes following rTMS in relation to behaviour. We delivered 5 weeks of daily pulsed rTMS stimulation to adult ephrin-A2 Importantly, rTMS induces long term potentiation (LTP) in rodent hippocampusin vitro2 and several sessions of high-frequency rTMS increases the capacity to induce LTP compared to untreated controls, suggesting it may also regulate metaplasticity4. Because rTMS acts on the same plasticity mechanisms as learning and memory, it has been hypothesised that rTMS may serve as a "priming" mechanism to facilitate long-term synaptic and structural modifications6. The implication is that repeated rTMS stimulation sets up a "plastic" brain state that is conducive to long term functional and structural changes5. For this reason, rTMS is being explored in combination with behavioural training tasks to see whether it can be used to prime or improve learning and cognitive performance in humans8. However, the potential mechanisms whereby rTMS might accelerate learning remain unknown.Repetitive transcranial stimulation (rTMS) generates electrical currents in the brain by electromagnetic induction and has been shown to induce synaptic plasticity in human and animal models10. High dendritic spine density in hippocampal neurons is associated with learning ability and higher performance on cognitive tasks14. Furthermore, LTP and LTD induce structural changes in dendritic spines, with LTP induced electrically or via learning increasing dendritic length and spine density16. Because higher spine density is associated with higher spine mobility and turnover rates16, this measure is thought to reflect a greater capacity for synaptic reorganisation.Declarative and spatial learning tasks are strongly associated with the hippocampus. More specifically, hippocampal dendritic spines have been identified as the likely loci of activity-dependent synaptic plasticity and possible structural correlates of memory and learning2. Therefore, very little has been done to investigate the impact of long term rTMS on spine density in the hippocampus, or how it might interact with the learning process. Given the significant structural changes induced in the mouse visual system following repeated stimulation sessions, and evidence for structural changes in the human brain17, we hypothesised that a similar long-term rTMS regime in combination with a hippocampus-dependent learning task, would rescue impaired learning strategies previously found in ephrin-A2-/- mice18 and alter spine density in the hippocampus.To date, the only study to examine changes in dendritic spines after rTMS did so following a single stimulation and showed no change in spine density, although the size of the smallest spines was increased-/- and wildtype mice undergoing a visual learning task and analysed learning performance, as well as spine density in the dentate gyrus molecular and CA1 pyramidal cell layers in Golgi-stained material.We delivered 5 weeks of daily pulsed rTMS stimulation to ephrin-A2-/- mice because they have previously been shown to have a specific learning deficit18. In addition, although ephrin-A2 is expressed in the mouse hippocampus throughout life and has been implicated in its topographic organisation20, there is no evidence that it is involved in synaptic plasticity or spine dynamics21. Thus we aimed to examine a learning-mediated effect of rTMS on dendritic spines. Although mice of both genotypes learned the task, their performance remained suboptimal due to lack of motivation to obtain food rewards through insufficient food restriction22 and neither learning behaviour, nor hippocampal spine density were affected by long term rTMS. Our negative results are consistent with previous data showing that rTMS has a selective effect on abnormal or injured brain circuitry23, and the lack of deleterious side effects observed in normal human subjects24.We used ephrin-A2-/- knockout mice, with equal number of males and females. Wildtype mice were purchased from Animal Research Centre . Ephrin-A2-/- mice were a generous gift from David Feldheim25 and carry a homozygous null mutation of the ephrin-A2 gene. Ephrin-A2-/- mice were bred from heterozygous parents at the Biomedical Research Facility  and backcrossed for >10 generations on a C57Bl/6J background. Randomised littermates were not used because the breeding colony was structured to produce ephrin-A2/A5 double knockout mice for other studies and no WT littermates were obtained. Mice were genotyped at weaning, as described previously21. Mice were age matched, aged 8–10 weeks old  when commencing the experiment. For the duration of the study, mice were kept in standard caging in a controlled environment  based on sex and genotype (2–4 per cage)). Food restriction began two days prior to commencing training. This aimed to reduce mice to 90% of their free-feeding body weight. Mice were weighed daily and food intake adjusted using a daily-based controlled diet to reach and maintain target body weights and ensure animals remained healthy. Water was availablead libitum throughout the experiment. All procedures in this study were conducted in accordance with US NIH guidelines and approved by The University of Western Australia Animal Ethics Committee.This experiment used 10 wildtype (C57Bl/6J) and 10 ephrin-A2Mice completed a visual discrimination task in two phases. Mice were initially rewarded for one stimulus (‘learning phase’). After mice learned the task (defined as 75% correct responses for three consecutive days), the rewarded stimulus was switched to the opposite, previously incorrect stimulus (‘reverse phase’). rTMS was applied (as described below) for 10 minutes daily immediately following the task during the reverse phase. We chose to stimulate after the task because we hypothesized that rTMS would enhance LTP-like processes, stabilizing new spines, and the associated synaptic connections, that had formed during learning. Because most mice in the study failed to learn the reverse task, we decided to focus on the relationship between rTMS and dendritic spine density, therefore, mice were terminally anesthetised with pentobarbitone  24 hours after 35 days of rTMS so that all mice received the same amount of stimulation.2 box, with visual stimuli at each end of the Y maze arms . Stimuli consisted of two 5 cm2 laminated black and white striped cards at 0.37 cycles per degree oriented horizontally or vertically. Both genotypes are capable of distinguishing this spatial frequency26. The position of the horizontal and vertical stimuli (left vs. right maze arm) was randomly altered across 30 trials with the constraint of equal number of trials in right and left arms. The 30 trial schedule changed each day, repeating every seven days. Random allocation determined which stimulus was rewarded in the learning phase, with the constraint that half the mice in each genotype received rewards for the horizontal and half for vertical. The rewarded stimulus was also counter-balanced across cage groups (i.e. mice housed together were rewarded for opposite stimuli) and sex. Inferential statistics confirmed no significant performance differences between sexes or stripe orientation first rewarded . The coil was designed to ensure a similar coil-to brain ratio as is used for induction of focal electric fields in humans27 and was driven by an electromagnetic pulse generator . Under these conditions, the coil delivered a magnetic field of 10 mT. This relatively low intensity was imposed by the constraints of the coil’s size but had the benefit of allowing us to evaluate the effects of stimulation without the confounding factors of stimulation-induced movement or the use of anaesthetic or restraint, with their associated changes to neuronal excitability and circulating stress hormones28. Furthermore, low intensity magnetic fields are clinically relevant for two reasons. Firstly, in humans, fields in the millitesla range delivered to the brain induce analgesia31, and alleviate depression32. Secondly, even though traditional rTMS is considered to be focal, magnetic fields of lower intensity are delivered outside of the focal area33, raising the possibility that low intensity stimulation may be contributing to therapeutic effects by acting on interconnected brain regions.To deliver rTMS, we built a small coil created for mice  300 windings, 16 Ω, outer diameter 8 mm;23 59.9-ms trains of 20 pulses at 3 different frequencies as follows: 1 min warm-up at 6.71 Hz, 8 min treatment at 10.1 Hz, and 1 min cool down at 6.26 Hz) and has been shown to induce structural changes in mice23. The pulse was monophasic with a 300µs rise time and 100µs fall time. A Hall device probe  inserted into different brain regions of a euthanized mouse estimated that the dorsal hippocampus received roughly 6 mT when the coil was held 1 mm above the mouse’s head, as described below. The surface temperature of the coil was measured after 10 min of stimulation and did not exceed 35°C.We chose a complex pattern of stimulation that is based on biomimetic principles (described in detail35). Unlike in cat studies, the coil was not in direct contact with the mouse head but was held as close as possible to the scalp (~1mm). The gap between the coil and the head does not attenuate the field because magnetic fields decrease with distance from the source but are not modified by air or biological tissue . Unlike in the cat study, stereotaxic delivery was not attempted because the dimensions of the coil ensured that the field reached the entire dorsal hippocampus, which in the mouse, is relatively large in proportion to total brain size. Consistent with the low intensity of the magnetic field, mice did not display any head-eye or gross motor movements, nor altered behaviour in response to the stimulation. Sham stimulation involved the same procedure but with the stimulator switched off. This control was chosen as our coil did not produce any audible sound23.As mice had completed the initial learning phase of the visual discrimination task before commencing rTMS they were accustomed to handling and remained relatively still without restraint. This allowed the stimulation coil to be held by the experimenter above the mouse’s head. We thus delivered reproducible rTMS in the awake animal  subbed with 0.5% gelatin . Sections were dried in the dark for 2–7 days, washed in distilled water and developed in solution D+E for 10 minutes. Sections were dehydrated in increasing concentrations of ethanol and mounted in Entilin .Terminally anesthetised mice were transcardially perfused with 4% paraformaldehyde ; Right hemispheres of brains underwent a silver impregnation staining protocol, and a Golgi stain , which allows the visualisation of morphology on a subset of neurons38. Dendrites were deemed suitable for analysis if they had a relatively flat orientation and were uniformly and strongly stained. Photographs of the dendritic arbour were taken throughout multiple planes ensuring the entire arbour was photographed in focus for later image analysis. Between 2 and 12 cells were analysed per animal and values averaged within animals for statistical analysis. Due to variability in Golgi staining, the number of dendrites counted varied between regions .Slides were analysed by a researcher blinded to stimulation condition and genotype. Sections were photographed by an Olympus DP70 digital camera at a 4× objective zoom, which encompassed the entire section. We analysed dendrites that could definitively be attributed to cells in the CA1 pyramidal and the molecular dentate layer of the hippocampus because dendritic spines on these cells have previously shown changes in dendritic spine density in response to various interventionsThe images for each dendritic arbour were combined into a single image, using the Image J plug-in "Stack Focuser" and dendrite length and number of dendritic spines counted using the Image J ("Cell counter" plug-in). The number of spines and the dendrite length data were then used to calculate a dendritic spine density value, defined as the number of spines per unit length (10 µm).-/- and wildtype mice. Inferential statistics confirmed no pre-existing differences between groups in the learning phase, before commencing rTMS (data not shown). The first day of reverse phase training was excluded from analyses as rTMS commenced after this training session. Two mice reached the learning criterion in the reverse phase and were terminally anesthetised before 35 days ) thus there was non-random reduction in sample size over time, precluding use of daily performance measures in statistical analyses. To overcome this problem, data were divided into three blocks for each subject, with one third of total days training included in each block, reflecting early, middle, and late stages of the reverse learning phase. Mean percentage correct was analysed by a two-way mixed ANOVA to assess differences between stimulation conditions (rTMS vs. sham) and between genotypes (ephrin-A2-/- vs. wildtype) and changes over time . As circuitry abnormalities and connections between measured hippocampal regions have not been characterised in ephrin-A2-/- mice, it is unknown whether these measures should be considered independent for statistical analyses. Accordingly, a MANOVA was conducted to assess effects of stimulation condition (rTMS vs. sham) and genotype (wildtype vs. ephrin-A2-/-) on dendritic spine densities in both regions. Pillai’s Trace (V) was used as the multivariate test statistic. Follow-up ANOVAs were conducted separately for each region, testing the same factors as used in the MANOVA. The F-test statistic (F) and probability (p) values are reported for each ANOVA. When the assumption of sphericity was violated degrees of freedom were adjusted with Greenhouse-Geisser correction. Data were analysed using SPSS statistics software .We examined the effect of long-term rTMS on reverse learning performance in ephrin-A2Column 1: Mouse ID Column 2: Genotype  Column 3: Stimulation condition  Column 4: Gender ; Column 5: Stripe orientation  Column 6: Learning phase early (% accuracy) Column 7: Learning phase mid (% accuracy) Column 8: Learning phase late (% accuracy) Column 9: Reverse learning phase early (% accuracy) Column 10: Reverse learning phase mid (% accuracy) Column 11: Reverse learning phase late (% accuracy) Column 12: Dentate gyrus molecular layer, mean spine density per 10 micrometer Column 13: CA1 pyramidal layer, mean spine density per 10 micrometerClick here for additional data file.F  = 0.28,p = 0.60, nor between genotypes,F  = 0.86,p = 0.37, and no significant interactions across blocks . Within groups, the percentage correct increased significantly across blocks, indicating that mice adjusted to the rule reversal and successfully learned the task although this was not to the desired criterion  = 71.80,p < 0.001 (Greenhouse-Geisser corrected)).As shown in-/- and wildtype mice, contrasting rTMS to sham. There was a slight (non-significant) trend towards rTMS increasing spine density in ephrin-A2-/- mice in both regions. In wildtypes rTMS appeared to have no effect on spine densities in CA1 pyramidal cells, as means were almost identical. However, wildtypes showed a similar (non-significant) trend to ephrin-A2-/- mice in the dentate molecular layer.Spine density (spine number/10 µm) measures were obtained for each dendrite and averaged within mice.V = 0.13,F  = 0.67,p = 0.54, nor genotype,V = 0.08,F  = 0.39,p = 0.69, on spine densities. Follow up two-way ANOVAs were also performed separately for each region, with no significant differences between stimulation conditions or between genotypes in either region, allp values were >0.05  = 0.03,p = 0.86; genotype,F  = 0.86,p = 0.38. Dentate molecular layer: stimulation condition,F  = 1.03,p = 0.34; genotype,F  = 0.42,p = 0.53).MANOVA, using Pillai’s trace showed there was no significant effect of stimulation condition,-/- mice and wildtype controls. We show that rTMS had no significant effect on learning and no significant effect on hippocampal dendritic spine densities. Although ephrin-A2-/- mice have abnormal brain circuitry and associated abnormal behaviours, in the present study, the previously reported learning deficit18 was not observed due to low levels of food deprivation22. Although it is difficult to draw conclusions from the null results presented here, the absence of observed behavioural and structural change is consistent with previously reported rTMS specificity for abnormal systems23. The lack of adverse effects in our long-term study suggests that up to 5 weeks of daily sessions of low intensity pulsed magnetic field stimulation at the parameters used in this study appears safe to use in healthy participants.We investigated the effects of long-term daily rTMS on learning and hippocampal dendritic spine density using ephrin-A2-/- mice18 with minimal or no effect on wildtype mice. However, both genotypes failed to demonstrate the strategy deficit, due to insufficient food restriction22. Our results nonetheless indicate that rTMS does not adversely affect performance, but nor does it improve motivation or accelerate learning when deficits are absent. This is consistent with previous reports that long-term rTMS effects are specific to abnormal brain circuitry: two weeks of rTMS improved visual tracking, visual electrophysiological function and topographical accuracy in a different strain of mice (ephrin-A2A5-/- double knockouts) with abnormal circuitry but produced no lasting effects in wildtype mice23. Human studies also support specificity of rTMS for abnormal brain circuits, as a meta-analysis of rTMS effects on cognitive performance found patients tend to improve more than healthy participants8. Although some human studies using healthy participants show a single-session of rTMS enhances cognitive task performance, such as analogous reasoning39 and reaction time7, results are mixed, with other studies showing no effect of rTMS on knowledge acquisition40 or accuracy in a go/no-go task41. Furthermore, there is a lack of studies assessing cognitive effects of long-term rTMS in patients together with healthy controls, which presents a large gap in knowledge8. Because of the lack of understanding of fundamental interactions between rTMS and behaviour, it would be of great interest to perform an exhaustive battery of behavioural tests in healthy wildtype mice  in conjunction with various rTMS protocols. Subsequent anatomical and physiological analyses could then be carried out to elucidate the neural mechanisms of rTMS and gain insight into the treatment of human disease.We originally hypothesized that rTMS would rescue the learning strategy deficit in ephrin-A2-/- mice, suggesting that spine density is not solely dependent on ephrin-A2, in agreement with the literature21. As such, the null effect of rTMS on dendritic spine density may be attributed to the absence of both a specific spine and learning deficit in both wildtype and ephrin-A2-/- mice. It will be important to examine other brain regions to determine whether the selectivity of rTMS for normal and abnormal brain circuits is also observed. Our result that spine density was not significantly altered is in agreement with a previous study, showing no change in spine density in CA1 pyramidal neurons following a single rTMS stimulation2. However, it is surprising that dendritic spine density remains unaffected after long-term stimulation, given our previous results using the same stimulation parameters, demonstrating structural reorganisation in abnormal axon terminals following multiple stimulations, but not a single rTMS session23. As neither long-term nor short-term rTMS results in dendritic spine density changes, these negative results may suggest different susceptibility of axons and dendrites to rTMS. The functional characteristics of these neuronal compartments require different expression of ion channels and growth factor receptors42, which could provide a molecular basis for differential rTMS effects on excitability and spatially localised structural and functional change.Each mouse received a controlled amount of daily rTMS, allowing us to investigate how long-term rTMS combined with daily training influences spine density. Importantly, we found similar spine densities in sham wildtype and ephrin-A22, this may “prime” the brain for learning. With such pre-treatment, an effect of rTMS might even have been detected in improved performances on a day to day basis.Alternatively, the timing of rTMS delivery relative to the behavioural task may have influenced the outcome of our experiments. Here we stimulated after the task, however rTMS might have been more effective if delivered before. Because a single session of rTMS increases the size of dendritic spines and may activate silent synapsesin vivo in various areas of the cortex of adult mice43. As a rule, established spines are pruned during the initial experience of the new stimulus, while new ones are established, which may result in some studies of fixed tissue showing no net change in spine density45. Consistent with the change in spine dynamics initiated by enrichment, a recent imaging study in the hippocampus identified two phases in spine dynamics following repeated induction of LTP. Initially both generation and retraction of spines increased, followed by a cessation of spine retraction46. This is consistent with post-mortem studies showing an initial period of apparent spine stability, followed by a detectable increase in density. The possibility that rTMS changes spine dynamics, as opposed to density, is further supported by an increase in the size of small spines following a single stimulation, which the authors suggested may indicate the activation of silent synapses by membrane recruitment of AMPA receptors, precluding the need for de novo synapse generation2. Future live imaging studies of spine dynamics in animals that have received single or multiple rTMS stimulation, potentially in combination with learning tasks will provide much needed insight into the mechanisms underpinning the plastic changes elicited by rTMS in humans.An alternative interpretation of our null finding is that our rTMS treatment changes spine dynamics without affecting their final density, a result that would not be possible to detect in our fixed post-mortem tissue. Hence, these results highlight the limitations in Golgi staining of fixed tissue, a technique still commonly used in examining dendritic spine density. Sensory manipulation  strongly alters spine dynamics3: although these deliver the same fields used in humans, the focal nature of the stimulation is lost. Additional effort in designing appropriate small animal rTMS coils is urgently needed to improve the construct validity of animal rTMS research.Importantly, we are conscious of the limitations of our rodent scaled rTMS delivery device which may have contributed to the lack of effect observed here. Although our coil had a relevant coil to brain ratio for mice, because of its small size, the intensity of the magnetic field did not reach the magnitude commonly used in humans (6mT compared to 1-2T), raising concern that our stimulation paradigm is not comparable to human rTMS. However, this raises a more general issue because similar criticism applies to studies that employ larger coils I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a well written paper describing the effects (or lack thereof) of rTMS over 5 weeks in wild-type and ephrin-a2 knockout (KO) mice. The data are clearly presented in a usable format and are discussed appropriately. The initial referee Antoni Valero-Cabre, who is an expert in TMS and neuro-stimulation, has already extensively covered the key points, both positive and negative. I am therefore only going to make a few additional comments.The title and abstract should ideally reflect the findings of the study more precisely, for example:‘Long-term delivery of pulsed magnetic fields does not alter visual discrimination learning or dendritic spine density in mouse CA1 pyramidal or dentate gyrus neurons’.The nature of such negative findings means that the authors cannot rule out potential significant effects of this rTMS protocol on other learning tasks or other classes of neurons/dendrites in the mouse hippocampus. Furthermore, the abstract should mention that they were adult mice and the background strain used was C57Bl/6J, as it is possible that different aged mice and/or a different genetic strain of mice might respond differently to the same rTMS protocol.One concern regarding the methods and design is that the authors appear to have used a separate wild-type mouse colony and compared them to ephrin-a2 KO mice inbred via a backcrossed colony. I strongly feel that all such experiments should always involve wild-type littermates randomised from age-matched litters to control for both genetic (e.g. sub-strain and genetic drift) and epigenetic differences between different colonies. This is particularly important for behavioural experiments where even subtle genetic and epigenetic differences can often have significant impacts. The authors report no significant gene effects, and it is therefore not a major confound in this case, however if randomised littermates were not used (as the methods imply) this should be noted.The discussion and conclusions are balanced. However, it might be worth noting that an extensive battery of behavioural tests  on wild-type mice would be worth pursuing to assess whether this rTMS protocol  has any effects on brain function analogous to human studies. If any positive results were found, then cellular, physiological and molecular follow-up studies could be targeted towards understand specific cognitive/behavioural effects of rTMS (thus nicely complementing human studies).If such comprehensive TMS animal studies are done but still show inconsistencies with matching human TMS studies then one possibility is that the animal TMS needs to better match the exact spatial, temporal and biophysical aspects of human TMS. For example, the human brain is much larger, the skull much thicker and the spatial extent and physical effects of a given TMS coil are no doubt difficult to match. The need to further improve such ‘construct validity’ for animal TMS may also be worthy of further discussion.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is an outstandingly planned and well-executed study that shows that the rodent brain, after a daily 10 minute exposure to low field pulsed magnetic stimulation for 5 weeks (i.e. 35 sessions) exhibits null effects on learning behavior and hippocampus dendritic spine density in both Wild Type and Ephrine A2-/- mice. The effort of the authors deserves a lot of credit, as studies like this one are extremely hard, long and laborious to perform and the possibility of significant results always uncertain. In spite of the current null results, this kind of work is greatly needed in the field of non-invasive brain stimulation in order to understand the cellular underpinnings of these techniques and assess the safeness and real therapeutic potential of stimulation technologies in clinical neurorehabilitation. Overall, the neurostimulation community should strongly thank the authors for investing their know-how, time and effort to carry over this type of animal research and we all should encourage them to pursue their efforts in the future.GENERAL POINTS The only limitation of the current manuscript is related to the difficulties in interpreting null outcomes; in this case a triple negative result. Not only did low field pulsed magnetic stimulation prove unable to modify visual learning behavior or hippocampus spine density in these two population of mice but neither of the two showed (as should have been at least expected) significant differences in any of these two measures. The authors do an excellent job going over the different possibilities that could explain this null pattern of outcomes, even if they do not have much basis to rule out any of the hypotheses in particular. Among other important issues, the study emphasizes once more the importance of task titration. Task titration is strongly related to animal motivation (and thus intake restriction) and is a key variable and therefore needs to be manipulated carefully to make sure that the potentially effective impact of magnetic pulses can be demonstrated behaviorally. With regards to the question however of whether or not increasing food restriction could have rendered, as the authors suggest, behavioral learning tests more sensitive, the answer remains unknown. It could also be that highly motivated animals would rapidly show ceiling learning effects and render the task less sensitive to modulation. To this reviewer, the performance of both groups of animals in the inversion phase of the task seems to show enough room for behavioral improvement, even after 5 weeks of stimulation; thus it is tempting to also speculate that such a complex task for a rodent  may not have been well suited to the functions of the brain regions aimed at in this study and/or that their impact could have rapidly compensated for by other systems or regions.Adding to the authors’ arguments with regards to the lack of spine density effects, one could also argue that a direct impact of the stimulation (and not an indirect effect of the stimulation on learning behavior driving subsequent effects on memory related regions) on such measures could and should have occurred primarily in the cortical regions overlying the hippocampus  rather than in the more deeply located hippocampus CA1 and DG neurons. Of course, if the targeted region theoretically related to the behavior that is being measured is not at least directly or indirectly impacted and modified in activity by the pulses, then no behavioral differences between sham and real rTMS patterns should be expected in either population of mice. All these explanations are directly or indirectly mentioned in the discussion and when not explicitly mentioned in the text, they emerge logically from the results. However any effort to nuance and expand such justifications even further would be welcome. To this reviewer, the only possibility  that is not explicitly mentioned in the discussion is that the delivered low field pulsed magnetic patterns  were not effective at all. Several reasons that could account for that eventuality are; that the field was too weak to penetrate deep enough with a minimal intensity, imprecise widely distributed targeting or a field strongly attenuated by the space left, as indicated in the manuscript, between the stimulating coil and the top of the head. This reviewer cannot demonstrate that this possibility is more likely than any of the others already mentioned in the manuscript. Nonetheless, in the absence of any positive sign of a stimulatory impact, this possibility cannot be ruled out and should probably be briefly discussed. Additionally, it should be clear throughout the manuscript, that the ability of the current experiment to highlight the innocuity of 5 weeks of low field pulsed magnetic brain stimulation needs to be interpreted carefully and associated with the field strengths delivered by the modified e-cell device by Global Energy Medicine, which is far from the normal field strength for TMS/rTMS devices operated in humans for either research or therapeutic purposes. MINOR COMMENTS AND SUGGESTIONSIntroduction, Page 1, Col 1, Par 1, Line 6. Could the authors re-evaluate the adequacy of the term “metaplasticity” with regards to TMS-induced LTP effects, as to this reviewer, the main effect of high frequency stimulation are LTP or LTP-like effects. Metaplasticity is a consequence of forcing homeostatic plasticity beyond a particular boundary, which may result in paradoxical modulatory effects.Introduction, Page 1,  Col 1, Par 2, Lines 7-9. With regards to this issue, in this particular study were the authors hypothesizing a direct impact of low field pulsed magnetic stimulation on specific cortical locations translated through connectivity into a declarative and spatial memory region such as the hippocampus, or a cortical effect of stimulation leading to subsequent changes in behavior that could leave a distinctive memory trace in the hippocampus?Introduction, Page 1, Col 1, Par 3. The paper byMay et al.2007, Cereb. Cortex, in which the effect of 5 straight days of rTMS on humans assessed with MRI methods and regional size increases  could be relevant for this introduction. Methods, Page 1, Col 2, Par 1, Lines 7-8. Could the authors provide an idea of how far in their development (if possible in age compared to humans) are 8-10 week old mice? It seems that in a prior study by the same authors that showed significant results after 14 days of low field rTMS on the visual cortex by Rodger et al. 2012, FASEB J, the mice were slightly younger (6-8 weeks). Is this period crucial in terms of postnatal development for mice? Could a less plastically sensitive brain of 8-10 week old mice vs. 6-8 week old mice explain the current null results? Please comment briefly in the manuscript.Methods, Page 2, Col 2, Par 2, Lines 6-7. Why were mice tested before the low field pulsed magnetic stimulation session and not also immediately thereafter, when the impact of the stimulation should have been stronger? Although I understand the authors sought a long-term effect, such a measure could have proven useful to reveal at least an immediate day-to-day impact of stimulation and become a proof of their efficacy?  Given the order of events, one could be tempted to speculate the possibility of non-synergistic interaction between the lasting effects of task practice and immediately subsequent low field rTMS stimulation, cancelling the modulatory effects.Methods, Page 3, Col 1, Par 2, Lines 7-8. As this is not a standard rTMS stimulation device, some additional information on the stimulation source should be given to be able to compare the efficiency of low field pulsed magnetic stimulation with current human rTMS equipment. More specifically, what is the shape of the stimulating pulse  and what is the rise time of the field? The pattern of stimulation used seems essentially an “excitatory” 10 Hz rTMS pattern, preceded and followed by short instances of ~6 Hz stimulation. Was this high frequency employed as a way to induce LTP phenomena in the hippocampus or as a tool to enhance cortical excitability and facilitate learning behavior leading then to functional and anatomical modulations of the hippocampus? Please comment briefly.Methods, Page 3, Col 1, Par 2, Lines 7-8. Navigation of the TMS coil of course could have been improved given the high-focality of the device used by the authors and the fact that any small head movements in a small rodent brain could easily lead to a completely different area of stimulation. It is true that prior studies in awake felines by our own group  have used a similar manual procedure, but it is also the case that the coils and brains in those studies were larger and precise location on a specific region of the posterior parietal cortex was guided day to day by stereotaxic based measures and references. In many instances, we additionally benefited from parietal bone transparency during surgery, and identified in each animal the sulci/gyral pattern and labelled the exact area of interest . Furthermore, in groups that were to be followed longitudinally, we placed a dental acrylic plug, that could be palpated through the skin of the scalp  which was then used as consistent localization marker. The correct positioning of those plugs with regards to the stimulated areas was often verified pre-treatment by anatomical MRI methods and also in post-mortem brain dissections at the end of the follow up. In the current study, given the elongated shape of the hippocampus in rodents, which part of the rodent cortex or which stereotaxic level was aimed at during the stimulation? Also, in contrast with the quoted feline study, the authors indicate here that the coil was not in direct contact with the scalp. As air is a strong isolator, could that have attenuated the strength of such a low intensity field even further? Were Hall probe field measures in the hippocampus performed with the stimulating cell also separated from the scalp?Discussion, Page 5, Col 1, Par 1, Lines 4-6. The statement “The absence of observed behavioral and structural change is consistent with previously reported rTMS specificity for abnormal systems” needs to be expanded upon as the Ephrin-A2-/- mice have indeed abnormal systems. Maybe a short comment on how to reconcile the current findings with those reported in  Rodger et al. 2012, FASEB J, would be important as in that study by the same authors similar stimulation patterns were able to correct cortico-collicular connectivity only in Ephrin-A2-/- and not in Wild type mice, supporting the above-mentioned statement.Discussion, Page 5, Col 1, Par 1, Lines 6-8. I would strongly advise the authors to alter the contents of the sentence “Furthermore, the lack of adverse effects in our long term study contributes evidence that rTMS is safe to use in healthy control participants”. Could they rephrase to something like “Furthermore, the lack of adverse effects in our long term study suggests that up to 5 weeks of daily sessions (35 session) of low field pulsed magnetic stimulation at the parameters used in this study appears safe to use in healthy participants”. This is important as the intensity levels employed are very different of those employed in human rTMS, which are much higher, and this information could have some public health implications for future use of rTMS in humans.Discussion, Page 5, Col 2, Par 1, Lines 19-21. Please check the accuracy of the sentence “To our knowledge, there have been no studies assessing cognitive effects of long-term rTMS in patients and healthy controls, which presents a large gap in knowledge”Discussion, Page 5, Col 2, Par 1, Lines 21-22.  As indicated above (comment 10), would the authors agree to rewrite the sentence “Although more research is needed, our results support that rTMS is safe to use in healthy control participants…” and revise the second part of it  “… invaluable in assessment of rTMS effects clinically” which to this reviewer is confusing? Discussion, Page 5, Col 2, Par 2, Lines 5-8. This sentence can be a bit surprising to readers, why did the authors chose a knock out for Ephrine A2 in a study that aimed to analyze spine density in the hippocampus if as they affirm in this paragraph, this molecule is not involved in spine dynamics? Maybe the rational for that choice needs to be explained more carefully.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.