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Published in final edited form as: Neuroscience. 2025 Mar 4;572:68–72. doi: 10.1016/j.neuroscience.2025.03.005 Search in PMC Search in PubMed View in NLM Catalog Add to search Tips and Tricks for Quality Publishing; Lessons from the Neuroscience Editorial Team Francesca Cirulli Francesca Cirulli a Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy Find articles by Francesca Cirulli a, 1 , Rachael Dangarembezi Rachael Dangarembezi b Department of Human Biology, Neuroscience Institute, University of Cape Town, South Africa Find articles by Rachael Dangarembezi b, 1 , Victor de Lafuente Victor de Lafuente c Institute of Neurobiology, UNAM, Mexico Find articles by Victor de Lafuente c, 1 , Anthony J Hannan Anthony J Hannan d Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Vic., Australia Find articles by Anthony J Hannan d, 1 , Amanda C Kentner Amanda C Kentner e School of Arts & Sciences, Health Psychology Program, Massachusetts College of Pharmacy and Health Sciences, Boston, MA 02115, United States Find articles by Amanda C Kentner e, 1 , Tatsuya Mima Tatsuya Mima f The Graduate School of Core Ethics and Frontier Sciences, Ritsumeikan University, Tojiin Kitamachi 56-1, Kita-ku, Kyoto 603-8577, Japan Find articles by Tatsuya Mima f, 1 , Laurel Morris Laurel Morris g Department of Psychiatry, Icahn School of Medicine at Mount Sinai, United States Find articles by Laurel Morris g, 1 , Sarah J Spencer Sarah J Spencer h School of Health and Biomedical Sciences, RMIT University, Melbourne, Vic. 3083, Australia Find articles by Sarah J Spencer h, 1 , Long-Jun Wu Long-Jun Wu i Center for Neuroimmunology and Glial Biology, Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, United States Find articles by Long-Jun Wu i, 1 , Chen Zhang Chen Zhang j School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China k College of Basic Medicine, Inner Mongolia Medical University, Hohhot 010110, China Find articles by Chen Zhang j, k, 1 Author information Article notes Copyright and License information a Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy b Department of Human Biology, Neuroscience Institute, University of Cape Town, South Africa c Institute of Neurobiology, UNAM, Mexico d Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Vic., Australia e School of Arts & Sciences, Health Psychology Program, Massachusetts College of Pharmacy and Health Sciences, Boston, MA 02115, United States f The Graduate School of Core Ethics and Frontier Sciences, Ritsumeikan University, Tojiin Kitamachi 56-1, Kita-ku, Kyoto 603-8577, Japan g Department of Psychiatry, Icahn School of Medicine at Mount Sinai, United States h School of Health and Biomedical Sciences, RMIT University, Melbourne, Vic. 3083, Australia i Center for Neuroimmunology and Glial Biology, Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, United States j School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China k College of Basic Medicine, Inner Mongolia Medical University, Hohhot 010110, China 1 All are equal co-authors, alphabetical order. Issue date 2025 Apr 19. PMC Copyright notice PMCID: PMC12146853 NIHMSID: NIHMS2083423 PMID: 40049388 The publisher's version of this article is available at Neuroscience Abstract In pursuit of excellence in scholarly publishing, the Neuroscience editorial team shares valuable insights that are essential for authors, reviewers, and the broader scientific community. Firstly, we emphasize that impactful research is built on rigorous study design and execution. Beyond fundamental methodological safeguards such as randomization and blinded analysis, we highlight the importance of thoughtfully selecting study models, with deliberate attention to biological variables like sex and gender, as well as appropriate nomenclature. Secondly, as technological innovations reshape research landscapes, we advocate for combining methodological rigor with suitable analytical tools to ensure robust data collection and transparent reporting. Thirdly, for manuscripts reaching the revision stage, we frame the response to reviewers as a strategic process that requires objectivity, diplomacy, and evidence-based rebuttals where necessary. Finally, we call for intentional prioritization of inclusivity and diversity across all stages of scientific inquiry – from laboratory collaborations to editorial decisions – and urge stakeholders to actively counteract implicit biases in manuscript evaluation and citation practices. By embedding these principles into the scientific workflow, we argue that the research community can foster not only greater rigor but also a more equitable and innovative scholarly ecosystem. Introduction In the interests of improving article quality and providing guidance on how to write scientific articles, we, the Editorial team at Neuroscience have put together some tips and advice for quality publishing for you. Each contributing member of our editorial team has selected a topic that interests them, so this collection is by no means meant to be comprehensive. In some cases, we discuss specific examples, but these examples can be extrapolated to multiple areas of neuroscience. We also note that these are our opinions only – different scientific journals may have different approaches and there may be valid exceptions to every recommendation. Nonetheless, this is a collection of tips that are important to us, so this is worth a read as you plan your study and for when you are considering publishing in Neuroscience . Designing your study and introducing the topic Diversity and inclusion considerations Recently, Editors and publishers have taken a step forward to strengthen not only the importance of sex and gender as a biological variable in research but also to promote the relevance of inclusion and diversity across disciplines ( Heidari et al., 2024 ; Cirulli et al., 2025 ). If you are an author or a reviewer, you should consider your own implicit and explicit biases, even at the study design phase and when considering potential collaborators. Implicit bias can be defined as a negative attitude, of which one is not consciously aware, against a specific social group. Implicit bias is shaped by experience and based on learned associations between particular qualities and social categories, including ethnicity and/or gender. Individuals’ perceptions and behaviors can be influenced by the biases they hold, even if they are unaware they hold such biases. For authors selecting which collaborators to include, which subjects to examine, and which studies to reference, and for reviewers when judging a manuscript, it is important to be aware of any implicit or explicit bias you may hold and avoid letting these influence your decision-making. One important example of known bias in the scientific community involves article citations. As researchers, we most likely reference specific manuscripts or studies because of our personal preference or experience rather than searching more closely for what is relevant and appropriate. It is important to acknowledge that gender bias could underly the fact that women are cited less than men ( Bruck, 2023 ; Chatterjee and Werner, 2021 ). Journal strategies to offset this bias are appearing all the time ( Willis et al., 2023 ) and tools such as R can assist with testing our own biases in aspects like reference lists ( Ryan et al., 2023 ). However, the most important thing you can do as an author is to maintain awareness of your own biases. You may want to consider taking an implicit association test, such as the Harvard version, to find out. Sex and gender In preparing a manuscript that involves a species which has sexual biology (e.g. humans, rodents and other animal species), sex is a variable that should always be considered. If the study involves human subjects, the human-specific concept of gender (where more than two genders are possible) should also be considered ( Joel, 2021 ). The consideration of sex should, of course, occur early in the process, ideally in the design and execution of a study. The default consideration is that both sexes should be used and analysed separately. However, if it is a preclinical model of a disorder that is sex-specific (e.g. many X-linked disorders), the use of a single sex modelling clinical subjects is appropriate (but should be discussed). At Neuroscience we also acknowledge that research is expensive and time-consuming and funding limitations may preclude the use of both sexes or multiple genders all the time. In such cases, the decisions behind the use of only one sex, and the potential implications for the other sex, must be fully discussed. We also ask that if authors are obliged to run single-sex research, you consider using females as your first choice to broaden the available research. Notably, whatever the choice of sex or gender, the design of a study should extend to ensuring adequate statistical power to be able to detect any potential sex effects, based on expected effect sizes, and other statistical considerations. In cases of mixed sex design where appropriate power is not achieved, we encourage you to represent the sexes as different colours or shapes on your graphs anyway so that trends in the distribution can inform the design of future studies by other researchers (see results section below). Those who work in vitro (using cell lines, organoids or primary cell cultures) should consider and clearly report the genetic sex of the host from which the cells are derived, as sex chromosomes may also exert biological effects in vitro ( Villa et al., 2018 ). In preparing a manuscript, the sex of the animals used (or gender of the human subjects) should be clearly described in the methods, including the numbers of each sex within each experimental group and precise housing conditions (e.g. male mice and rats can establish strong dominance hierarchies, depending on numbers of animals per cage, environmental enrichment and other experimental parameters). Statistical analyses should ideally include analysis for sex (or gender) effects. In the past, many preclinical studies were performed only on males, often with the implied argument that the menstrual or oestrous cycle in female mammals represents an experimental confound. The reality is that it is not a major confound for the vast majority of studies ( Prendergast et al., 2014 ) and, where it is, it can be accounted for by measuring the menstrual/oestrous stage. To translate biomedical science, we must ensure that preclinical and clinical studies closely align, including considerations of sex and gender. Framing a paper on human neuroscience research Neuroscience with a clinical perspective on social, emotional or language functions, as well as on diseases, is inherently difficult to study adequately in animal models. This is particularly true of neuropsychiatric disorders, making human studies indispensable. However, conducting clinical human neuroscience research poses significant challenges in rigorously testing neurobiological hypotheses. This difficulty stems from the paramount importance of safeguarding human participants, as it is ethically impermissible to inflict harm on them. Additionally, practical constraints often arise in securing a sufficient number of subjects for a statistically robust design, particularly in the early, innovative stages of research projects. In the current landscape of clinical research, where evidence-based medicine (EBM) and future marketability are increasingly emphasized, large-scale, multi-center clinical studies with extensive subject recruitment are often required. However, such large-scale studies are typically beyond the reach of start-up or middle-sized laboratories, even when they propose highly innovative ideas. In such cases, Neuroscience can serve as a vital venue for publishing preliminary, exploratory research grounded in promising research hypotheses. The Editorial team, however, wishes to underscore that the mere emphasis on future clinical utility is insufficient. Overstating the prospective significance of a study risks compromising the objectivity of the work. Instead, it is crucial to articulate the neurobiological hypothesis with clarity, highlighting its novelty and originality, while explicitly positioning small-scale proof-of-concept studies as necessary precursors to full-scale clinical trials. The editorial team remains committed to fostering such high-risk, high-gain, hypothesis-driven studies. We believe that the publication of these ambitious investigations will enhance the diversity of the neuroscience field and contribute to its continued advancement. Microglia nomenclature How we think about and report various processes in biology is updating all the time as our understanding of a new phenomenon becomes more nuanced. It is essential to stay up to date on these developments. An important example of this is in our understanding of microglia, the resident immune cells in the central nervous system. In the advent of ‘omics era, exciting research has revealed many different dynamic states and subpopulations of microglia. The recent consensus in the microglia field is that we should avoid using the overly simplified terms such as “M1 vs. M2 microglia” ( Paolicelli et al., 2022 ). The single-cell ‘omics data clearly show that microglia in vivo do not polarize to the two states. Similarly, we should also refrain from using “resting vs. activated microglia” because microglial processes are extremely motile and always active even under physiological or homeostatic states. For microglia in brain diseases, they constantly react to changes in microenvironment to adopt different states and perform various functions. In addition, more research is needed to determine whether disease-associated microglia (DAM) can be generalized to different disease contexts. Therefore, when comes to microglia nomenclature, Neuroscience ’s editorial board recommends defining microglial states based on the specific contexts in the central nervous system in health and disease, such as species, sex, age, spatial locations, external stimuli, disease triggers and so forth without reference to binary categories. We advise you to keep up to date on advances in thinking in all fields of neuroscience so that your work reflects the community’s best understanding. How to write a great title The title is the first – and often the only – point of contact of an article with its intended audience. Most readers only skim the title, with a few then progressing to the abstract or full article. The title should therefore effectively capture the interest of the reader in a glance, and direct attention to the paper. It must therefore be clear, concise, and should (by itself) carry the main takeaway message of the study. Brief, informative titles are much better than lengthy, complex or vague titles. A good title accurately reflects the content of a paper and must be substantiated by evidence/data ( Anon., 2021 ). However, researchers should not overstate findings or use “click bait” to capture reader interest as this is unethical. In an effort to pique the reader’s curiosity, authors sometimes get tempted to create catchy, humorous titles but this approach may be counterproductive as humor is often context-dependent and may not translate well across different cultures or time periods. Similarly, highly technical, cryptic and/or complex titles often lose the wider audience. Good and effective titles use simple, accessible language, but still contain keywords – terms that attract readers interested in that field ( Tullu, 2019 ). These keywords are particularly important for indexing, the process by which search engines and readers locate your article online. In line with this, the Neuroscience editorial team discourages the use of nonstandard abbreviations and acronyms titles as these hinder clarity and accessibility for most readers who may be unfamiliar with them. We additionally recommend that the title be written in a descriptive or declarative manner rather than as a question. We also encourage reporting of the model species and gender assessed in the title to be as informative as possible to the reader. Thus, a good and effective title is one that is clear, concise, includes relevant keywords for discoverability, and conveys the paper’s main message in an engaging way. Considerations for your methods section Animal ethics – What to include Rigor and reproducibility are of paramount importance to the integrity of scientific investigations and key points to consider for all aspects of your study design and reporting in your methods section. This is particularly true in considerations of animal welfare. These ideas are not dissociable as best practices applied to animal welfare promote more rigorous and reproducible science ( Prescott and Lidster, 2017 ). When conducting animal research, the study protocols must be approved and carried out using appropriate guidelines and oversight procedures which should be reported in the methods section of all manuscripts. For example, journals, including Neuroscience , require authors to report (A) that their animal procedures were approved by their institutional review board, and (B) that they followed the National Institute of Health Guide for the Care and Use of Laboratory Animals ( Guide for the Care and Use of Laboratory Animals, 2011 ), the most up to date Guidance on the operation of the Animals (Scientific Procedures) Act and associated guidelines ( Guidance on the Operation of the Animals, 1986 ), or the European Union Directive of 2010 September 22 (Directive 2010/63/EU). Moreover, rigor and reproducibility are further strengthened by proper reporting of the animal research study design. The ARRIVE guidelines were established to improve documentation of important components of manuscripts including ethical statements, experimental animals and their husbandry, in addition to the study procedures and other considerations. Authors should consult the associated ARRIVE guidelines 2.0 checklist ( Percie du Sert et al., 2020 ) to ensure they are following best practices in reporting critical study details in their manuscripts. They should ensure they report any deviations from these guidelines as well so that their study may be best interpreted and replicated. How to report neuroimaging studies Neuroscience has made some incredible advances that have been possible because of the continual development of new tools. However, the information yielded from each of these tools is only as useful as the tool parameters and our ability to interpret them. Here we give the example of reporting the use of functional magnetic resonance imaging (fMRI), but this discussion on what information to include and considerations of reproducibility apply to almost everything we use in research. You can consider how they apply to flow cytometry, immunohistochemistry, ‘omics research and all of your favorite other techniques. Studies utilizing functional magnetic resonance imaging (fMRI) have been growing exponentially over the past two decades. With a range of cognitive, psychopathological and neural computational questions that can be answered with this tool, it is no wonder that experiments the world over utilize fMRI as an integral asset for their research questions. However, this tool comes with a vast array of limitations, and due to its myriad applications, experimental designs are rarely the same. To more clearly and comprehensively assess the overlaps and differences in experimental results, there must be consistency in reporting. Reporting on fMRI experiments must cover the full range of parameters that can be modulated during these experiments. First, acquisition specifications must be reported for both hardware (i.e., scanner type, coil), and scan specifications (i.e. repetition time (TR), time to echo (TE), slices, volumes, coverage, acceleration, resolution), which is often routinely already done. However, one reporting type that is often neglected is a justification for these parameters. Given recent demonstrations of acquisition speed, multi-slice acquisition and multi-echo acquisition effects on Blood Oxygenation Level Dependent (BOLD) estimations throughout the brain, the selection of these parameters must be justified. Justification could relate to the requirement for whole-brain coverage, or for improved signal-to-noise ratio in a specific region of interest, or specific applications in fast mental processes, but the countered limitations must also be addressed. Second, task-related timing and stimulus selection information must be reported. Additional considerations around the selection of the hemodynamic response function (HRF), TR length must also be considered in terms of task timing events, again with appropriate justifications. Information related to participant compliance must also be addressed, given striking changes in neural architectures during sleep versus wakefulness. Third, critical quality-control metrics must be reported. These include registration efficacy metrics (i.e. dice coefficients), estimated motion and noise parameters, and even temporal signal-to-noise ratio maps covering the whole brain, which can indicate spatial effects of signal power versus noise. Fourth, all preprocessing steps must be made full availability via online repositories of scripts used, including compute environment, software versions and ideally, justifications for each step, especially given controversies around some processing steps (i.e. global signal regression). This reporting is absolutely critical for reproducibility and replication efforts, as different processing software and steps can yield vastly different resultant BOLD estimates (a sad truth that few would like to admit). Fifth, the same level of transparency of reporting and justifications is required when it comes to statistical modeling and estimation of significance. Consideration must be applied to the selection of the statistical package used, again noting that estimation of significance can vary between software packages due to inherent differences in estimation of spatial autocorrelation or appropriate thresholding. Ideally, the same statistical test should be run through multiple software packages, and only clusters that are consistently demonstrated should be included as the most reliable results. Considerations of correction for multiple comparisons must be fully incorporated, as in the analysis of any type of data. This point was elegantly illustrated when Bennet and colleagues were able to demonstrate active voxel clusters in a deceased salmon that was presented with a social perspective task – a clear statistical anomaly that could, in less decisive circumstances, be strongly misinterpreted ( Bennett et al., 2009 ). Altogether, individual researchers must each give careful thought to every one of these sets of factors and we all must remain accountable for the precise decisions we make in each of these steps of this revolutionary but highly complex and variable data type. The main event (the results) Writing an informative results section Each paragraph in the results section of a scientific manuscript should be self-contained, clearly conveying a main idea along with the supporting experimental findings – much like an abstract. To improve readability, start each paragraph with a statement that defines a specific experimental question (e.g., “We first tested whether neural activity in the hippocampus might be related to memory retrieval.”). Even if the study objectives have already been explained in the Introduction, restating each question within the Results section enhances clarity. Next in the paragraph, briefly describe the methods and analyses used to address the experimental question (“To investigate this, we recorded hippocampal neuron firing rates while subjects attempted to recall previously learned word pairs.”). Follow this with a concise statement of the actual experimental results “Hippocampal neurons showed increased firing rates during successful recall compared to unsuccessful attempts.”). Reference the relevant figures and include F values, degrees of freedom and p values to indicate the statistical significance of findings ( p values help quantify the likelihood that observed effects arose by chance). Each Results paragraph should conclude with an interpretation of the results, highlighting their immediate implications (“These findings suggest that hippocampal activity plays a crucial role in the successful retrieval of stored memories.”). While the Discussion section will explore broader interpretations, the Results section should provide a succinct takeaway for each experimental outcome. Thus, a well-structured Results paragraph enables readers to quickly grasp (1) what was investigated, (2) how it was studied, (3) what was found, (4) and what it means. Representing individual samples on graphs Representing individual samples on graphs is essential for good reporting as it allows the reader to appreciate all the information. To illustrate this, in Fig. 1 , if we are given only the column graph ( Fig. 1A ), we would conclude that the treatment had no effect on the output measure (indeed, the p value here is 0.26). However, if we can see the individual data points, we can appreciate much more nuance in the data. In the case of Fig. 1B the control group contains an interesting sample that is potentially an outlier or is at least driving the mean higher. In Fig. 1C the data are distributed differently after treatment, with a bimodal distribution in controls that is not seen in the treated group. By representing different characteristics as different colours or symbols within the same bar, we can also appreciate a potential interaction between treatment and such characteristics, such as in Fig. 1D where females, in red, increase their measure of interest in response to the treatment, whereas males, in blue, decrease it. All such additional information informs the reader in interpretation of the results and in the design of their future studies. Fig. 1. Open in a new tab An example of the nuance that can be revealed by reporting individual samples on graphs. (A) illustrates no notable differences between the groups. (B) reveals a possible outlier. (C) indicates one of the groups may have a bimodal distribution. (D) indicates possible sex differences in the responses. Data were fabricated for illustrative purposes. At the revision stage Responding to reviewers’ comments Responding to reviewers’ comments is a delicate art that requires finesse. To begin with, authors must carefully consider and analyze the comments, addressing each point in a clear and logical manner. Although the revision process can be time-consuming, with numerous requests for changes to be addressed, it’s essential to view reviewers as collaborative advisors rather than adversaries. Maintaining a calm and objective attitude is crucial, and you should focus on the constructive aspects of the comments. In practice, it’s best to respond to all the comments, acknowledging the reviewer’s questioning even if you disagree. It is preferable to add all responses and changes to the manuscript, where feasible, rather than only in the letter to the reviewers. Provide clear explanations, evidence, or revisions as needed to support your arguments. For example, if a reviewer questions the research methodology, authors should clarify their approach by providing both positive and negative control experiments and citing relevant literature to justify their choices. If a reviewer’s comment is unclear, authors should politely seek clarification rather than making assumptions, ensuring that revisions align with the reviewer’s expectations. We also recommend that authors include their entire response within the response letter and provide very clear signposting as to where the changes can be found so that the reviewer can check the response easily. Additionally, you should verify that the revised manuscript meets the journal’s publication standards and submit revisions in a timely manner to avoid delays. Effective revision demands treating the process with both patience and expertise, which are crucial for getting manuscript accepted. As we mentioned at the outset, this collection is not comprehensive. There is clearly much discussion on statistics, figures, a discussion, control groups, sample sizes, the use of AI, etc.; this list could go on ad infinitum. However, our aim is to help you improve your scientific outputs by critically assessing all these aspects prior to submitting your manuscript (or even prior to collecting your first sample), with the ultimate goal of improving our understanding of all things neuroscience. Acknowledgements AJH is supported by funding from the EU-JPND (TREMENDOS; NHMRC co-funded); ERA-NET NEURON (MUSE; NHMRC co-funded) and NHMRC Ideas Program (2000661). AK is supported by NIMH under Award Numbers R15MH114035. VdL is supported by UNAM-PAPIIT (IN207325). SJS is supported by funding from the European Union (EU) Joint Program on Neurodegenerative Disease (JPND): (SOLID JPND2021–650-233); the National Health and Medical Research Council (NHMRC) Ideas Program (2019196) and the Australian Research Council Discovery Program (ARC; DP230101331). LJW is support by National Institutes Health (R35NS132326). References 1. Anon. Why the title of your paper matters Nature Hum. Behav. 2021; 5:665. 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