Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases.Video abstractThe online version contains supplementary material available at 10.1186/s40168-021-01059-0. Recent advances in DNA sequencing technology have allowed community-wide investigation into the microbes that live on and within the human body. These communities and their cellular functions are known as the human microbiome, which has now been associated with multiple different influences on human health , 2. InveMicrobiome studies are particularly at fault for elevated levels of these biases due to the high sensitivity of DNA sequencing instruments and the relatively unknown underlying microbial compositions within a sample. Throughout a typical microbiome study, there are numerous areas where biases are introduced . The intSample collection and storage are a vital part of an experiment and if not carefully planned can often introduce unaccounted bias. The way in which the sample is collected, how long the sample is stored, and what the sample is stored in can all impact the underlying observed microbial composition. All these details should be noted during sample collection and accounted for during downstream analysis to ensure consistent results across studies.The method of sample collection is particularly important to consider as different types of systematic bias can be introduced depending on the sampling environment. This has been highlighted in studies of the various areas of the human microbiome. Work on the gut microbiome has compared colon biopsy samples to both stool and rectal swab samples. In both cases, it was found that collection of samples by biopsy introduced strong biases toward the identification of specific microbes such as those that adhere to the mucosal wall of the colon –12. ThisIn contrast, multiple studies have compared the observed microbial communities from rectal swabs and fecal samples and have found similar profiles. Bassis et al.  comparedAnother body site of interest that has been studied comprehensively is the human oral microbiome. Within this environment, it has been shown that while different sites of the oral cavity such as the tongue, dental plaques, and saliva accommodate unique microbial communities , 19, theSeveral other body sites contain microbial communities that have been examined using several different collection methods. Studies that have compared different sampling techniques used in these areas have shown variable results. For example, reports have shown that skin swabs and tape-stripping result in similar family level relative abundances (Rho 0.792-0.999)  and no dOverall, the most important thing to consider during sample collection is the use of consistent collection methods within any given analysis. The same aseptic techniques should be employed during the collection of all samples. Furthermore, due to the high sensitivity of sequencing instruments, the use of the same type and manufacturer of collection devices is heavily advised, as DNA from collection devices can be introduced into samples . When poWhile the proper collection of samples is critical for any microbiome study, samples need to be collected in a manner that can be used to answer the hypothesis in question. Common study designs for microbiome studies include longitudinal, cross-sectional, and cohort designs . While aAll studies should randomize samples during extraction and sequencing to ensure that technical variation is equally spread between samples. Previous work has shown that batch effects due to non-random sample preparation can lead to spurious results . FurtherFirmicutes to Bacteroidetes ratio. However, subsequent work by Fouhy et al. [Faecalibacterium and Leuconostoc may be biased due to sample freezing. Taken together, these results have helped reinforce the current standard of immediately freezing samples at −80°C upon collection [It often is impractical to immediately analyze fresh samples in microbiome research. As a result, many samples are preserved through storage at −80°C. It remains unclear whether freezing samples significantly affects the resulting community profiles. Work by Bahl et al.  comparedy et al.  comparinllection .Research on what happens to room temperature-stored stool, which can commonly occur when freezers are not readily available, has shown promising results. Work by both Shaw et al.  and BokuNot only does the length of time samples remain unpreserved need to be considered when looking at biases during microbiome studies, but so does the length of time samples are frozen. Work by Lauber et al.  comparedWhile the current gold standard of sample preservation is to immediately store samples at −80°C, this is not always feasible. This had led to the development of multiple different preservatives that allow samples to be stored at room temperature for extended periods of time. This is accomplished by inhibiting microbial growth within the sample while preserving microbial DNA. Currently, there are several different preservatives that can be used to store microbiome samples including RNAlater, OMNIgene preservatives, Tris-EDTA, FTA cards, and ethanol. While in many cases the use of preservatives has been shown to not impact DNA quality or yield , they ofA possible solution that could aid in dealing with batch effects during both sample collection and downstream processing is the increased usage of technical replicates. Currently, the use of technical replicates during a microbiome study is uncommon. While sequencing facilities may initially benchmark their sequencing process on multiple mock communities, biological samples or samples reflecting a composition like those of the sample of interest are rarely sequenced twice. This is often due to budgetary constraints and the thought that a larger number of unique biological samples is a better use of resources than the creation of technical replicates, especially when they are pseudoreplicates and not true biological replicates. While there may be some truth to this argument, the use of technical replicates has numerous benefits and serves as an important tool to help identify batch effects during sample processing. It allows the researchers to identify the amount of variance in their sample preparation procedures and, as such, allows one to determine the minimal effect size that can be reliably determined as being from biological signal vs. originating from technical variation.One possible avenue researchers could take to help with the budgetary constraints of the creation of technical replicates is to pick a small group (or one pool) of samples that will be processed throughout each batch of sample processing. By comparing the same biological sample throughout numerous sample batches, researchers can benchmark the variance in composition that they can expect between sample sequencing and preparation rounds. While in the past, mock communities have generally served this purpose, they often do not reflect the composition of the samples of interest, and, as such, a biological sample that is similar in composition to those under examination should be chosen.After sample collection and storage, the next step in most microbiome experiments is DNA extraction. During this step, there are three main areas that can result in significant biases within a study: the differing DNA extraction efficiencies of microorganisms, the introduction of contaminant DNA, and the introduction of DNA from non-viable microorganisms. Due to these issues, DNA extraction is one of the most biased steps in sequenced-based human microbiome studies , 45.Lactococcus lactis can exhibit different DNA extraction efficiencies. While cell wall architecture could account for many differences, it is not understood why closely related species would have different DNA extraction efficiencies. Without a better understanding of the factors underlying differential extraction efficiency, this step will remain highly biased. As such, it is recommended that future researchers identify the extraction efficiencies of health-related microbes starting from known cell counts for those organisms.It is well-known that many microbes exhibit different DNA extraction efficiencies that can result in lower or higher yields of DNA. For example, both bacterial endospores and gram-positive bacteria are known to produce less DNA upon extraction compared to gram-negative bacteria . These lFurther work in this area has also shown that the use of different DNA extraction kits can result in different DNA extraction efficiencies for taxa depending on the kit used . This caIn addition, the amount of systemic bias introduced due to differences in DNA extraction protocols has been recently linked to the level of biomass within a sample. A recent study by Davis et al.  has showIn fact, almost all systematic bias that is introduced during sample collection and processing is amplified when working with low-biomass samples such as breast milk , lung saContamination is a common issue that can be introduced during multiple steps of sequencing experiments including during sample collection, DNA extraction, and DNA sequencing. Sequencing experiments are particularly vulnerable to the introduction of contaminants due to the high sensitivity of DNA sequencing platforms. This is highlighted in their ability to sequence DNA in samples that appear DNA-free based on gel electrophoresis . In a reFortunately, in many cases, such as during the analysis of fecal samples or oral washes, contamination will only account for a very small proportion of total biomass and as a result only create a minor amount of bias . HoweverWhile DNA extraction is arguably the most susceptible step to contamination issues, there is evidence that sequencing and library preparation can also be affected . Index hDue to the significance of this issue, various bioinformatic techniques have been suggested as ways to identify potential contaminant sequences. In 2017, Edmonds and Williams  suggesteIn 2018, Davis et al.  presenteCurrently, to the best of our knowledge there are no bioinformatic methods that can deal with cross-contamination between samples in microbiome data. However, Minich et al.  have sugOverall, while the use of reagents to remove microbial DNA may be promising, they have yet to be consistently shown as a valid approach to reduce contamination. However, researchers might think about opting for the use of a similar strategy when working with low-biomass samples. Furthermore, the removal of contaminants post hoc using bioinformatic software such as “decontam” should be included as part of the standard analysis of low-biomass samples, but will only provide minimal improvements to high biomass samples such as stool . FinallyOne of the final bias that can be introduced during DNA extraction is the inclusion of extracellular DNA found in biofilms and dead cells. DNA included from these sources can often result in the obstruction of phylogenetic signals and the over-identification of specific microorganisms . This haVarious methods have been introduced to identify the living portion of microbial communities . One proA second promising approach takes advantage of cell membrane integrity and the ability to pass specific chemicals through the membranes of non-viable cells. While these techniques are commonly used in microscopy and flow cytometry, it has only recently been applied to DNA sequencing experiments . Recent Multiple factors can lead to biases at the PCR amplification step of microbiome studies. One of these factors is the inclusion of both inorganic ions and organic material that can interfere with PCR amplification . The incOne countermeasure to the reduced PCR amplification due to inhibitors is to increase the number of PCR cycles. Increasing PCR cycle numbers has been shown to significantly increase bacterial richness while not significantly impacting overall community structure due to the stronger detection of low abundant organisms . HoweverVarious strategies have been developed to reduce chimeric sequences in multiple template PCRs, with the use of high-fidelity polymerases with proof-reading capability being one of the most promising . In 2019Finally, one potential source of bias in PCR is the amount of template added to reactions. Although the overall goal would be to standardize the input DNA amounts across all community samples, achieving this goal is currently not possible for most microbiome samples. Due to the abovementioned extraction differences, copy-number variations between organisms differing between samples and, most importantly, the unquantified amounts of background “non-target” DNA that can be present in samples, it is quite difficult to assess the true “on-target” template amounts being added to PCRs. Given this, it is usually more productive to generate similarly strong amplification, regardless of template input differences, from as many samples as possible so that post-library normalization will generate as consistent sequencing between all samples as possible . In conclusion, researchers should keep a consistent protocol throughout their microbiome study and should report all PCR parameters within the methodology section of their report.The majority of DNA sequencing projects are now done on Illumina sequencing platforms, which generally show preferable DNA sequencing performance when compared to other short read technologies such as Roche 454 sequencing or Ion torrent sequencing . In 2016Recent advances in long-read sequencing technologies have now enabled the sequencing of large DNA fragments. These advancements have enabled full-length marker gene studies and more comprehensive evaluation of metagenomes , 99. HowIt should also be noted that advancements in DNA sequencing technology have introduced the use of linked reads and proximity ligation strategies to produce high-quality synthetic long reads . Using tActinobacteria and Propionibacterium, while over representing groups such as Streptococcus, Treponema, and Prevotella [Despite the recent advances in long-read technology, marker gene sequencing is still predominately performed with high-throughput short-read technologies. These technologies, such as the Illumina MiSeq and HiSeq, are limited to short fragments ranging from ~150 to 550 bp. This has resulted in the creation of multiple different primer sets to target different variable regions on marker genes, such as the bacterial 16S rRNA gene –107. Unfevotella , 107. ItFortunately, as mentioned previously, improvements in DNA sequencing technologies have now begun to enable higher throughput full-length marker gene sequencing. This has the promise to reduce biases between studies that use primer sets that target different regions on marker genes. However, the primers used in full length studies will still have different priming rates between taxonomic groups.Staphylococcus aureus, Brachyspira pilosicoli, and Streptobacillus moniliformis. Further investigation also revealed that minor biases toward high-GC content were also present in library preparations using mechanical fragmentation, likely due to non-random DNA shearing during sonication methods [Currently, the majority of metagenomic shotgun sequencing projects rely on high-throughput short-read Illumina DNA sequencing . Several methods ; howeverAfter sequencing has been accomplished, a significant amount of bias can be introduced during downstream bioinformatic analysis . A typicAs both sequencing and PCR can introduce base errors during sequencing experiments, it is not recommended to analyze sequences directly. Instead, users can choose to either cluster sequences together into OTUs or correct sequencing errors using denoising algorithms. The choice to use denoising algorithms or OTU-picking strategies has been shown to create systematic bias in marker gene sequencing studies , 115. WiCurrently there is mixed evidence on which strategy is best when attempting to define OTUs . While dTo help counteract these issues, various denoising algorithms have been created to resolve single-nucleotide accuracy while also generating sequences that can be compared between studies . CurrentThese algorithms have been compared to standard OTU-clustering methods and in many cases have shown preferable results. Nearing et al.  found thRegardless of the advantages and disadvantages of each algorithm, it is important to record which strategy was used to assign analytical units during analysis. Due to the intrinsic nature of each algorithm, they all generate their own systemic biases which become especially apparent during the examination of rare taxonomic groups .Taxonomic classification of ASVs or OTUs poses a second challenge in the bioinformatic analysis of microbiome sequencing data. There have been multiple different strategies used to assign taxonomy to ASVs or OTUs throughout the years . Common Currently, there are four commonly used marker gene databases for microbiome studies: Greengenes , SILVA , RibosomUnfortunately, not all marker genes that are studied are present as single copies within microorganisms. The commonly sequenced 16S rRNA gene is present in variable numbers among both bacteria and archaea . While iWork by both Kembel et al.  and AnglUnlike marker-gene sequencing, metagenomic shotgun sequencing involves the sequencing of all the DNA contained within a sample. This can significantly complicate bioinformatic workflows, and, as a result, two main approaches for data analysis are available: referenced-based metagenomics and de novo metagenomic assembly. Each of these methods have their own benefits and detriments and often analysis with one method can complement the other.Reference-based metagenomic shotgun sequencing strategies generally involve three different approaches by comparing sequenced DNA to databases containing reference genes, marker genes, or translated protein sequences . MultiplFinally, it was shown that all classifiers underperformed as the number of poorly described taxonomic groups increased within a sample . This isUnlike reference-based metagenomics, de novo assembly methods do not rely on the use of existing reference sequences. Instead, they take advantage of sequence overlaps between DNA reads to generate longer predicted sequences known as contigs and scaffolds . This prThis process has enabled the recent discovery of thousands of novel metagenome-assembled genomes in human microbiome samples –164. HowFinally, like reference-based metagenomics, the annotation of assembled bins and genomes is dependent on known reference databases. Several reference databases exist for this purpose including NCBI RefSeq  and the G. vaginalis using their modeling method correlated better with pH levels than the originally observed proportions.In 2015, Brooks et al.  presenteFurther work in later years by Krehenwinkel et al.  and BellFortunately, recent work by McLaren et al.  has showUnfortunately, all these methods for bias adjustment rely on the ability to use mock communities as calibration controls. While this may be feasible for a small number of bacteria as suggested by Brooks et al. , it is cUnlike the use of culturing, qPCR, or flow cytometry, DNA sequence-based microbiome studies currently lack the ability to determine absolute microbial abundances. This is due to the compositional nature of DNA sequencing data where one read does not correspond to one cellular unit . This caTkacz et al.  presenteA significant portion of the bias between microbiome studies is introduced through the use of different protocols , 176. ThDespite these issues, it is still recommended that whenever possible, protocols between closely-related studies should be replicated to reduce the amount of bias introduced between each study. As previously mentioned, when possible, manufactured kits or the use of well-validated protocols such as those outlined by the International Human Microbiome Standards project or Earth Microbiome project should be used. Furthermore, the use of classification tools such as random-forest modeling or neural networks should be validated on data from more than one study group to ensure the reliability of their model on real world data. Overall, when interpreting results, researchers should remember that the observed communities are only one representation of the true underlying abundances contained within the sample.While there are several promising approaches to help reduce the amount of systematic bias within microbiome studies, the best way to help between study comparisons is the creation of a comprehensive methodology section. Not only will this allow researchers to replicate your results but will also allow them to help identify why results may not be reproducible from one study to another. A number of studies including projects such as the Earth Microbiome project and more recently the STORMS microbiome project  have addAll sample collection procedures should be included within the methods section. This includes the collection method and sterile techniques used during sample collection, as well as the media used to store the samples. Furthermore, authors are also recommended to give the general length of time samples remained in storage before processing began. One of the most critical pieces of information included in the methods section should be the DNA extraction method used during sample preparation. As highlighted, above DNA extraction is one of the largest factors that introduces systematic bias within a study. If a DNA extraction kit is used, author must report the manufacturer and product name, along with any modification or optional steps that the authors used, such as the addition of proteinase K. Within this section, it is also recommended that the authors report all steps taken to reduce any possible sources of contamination, such as the use of biological safety hoods and inclusion of blanks  during DNA extraction.Depending on the type of DNA sequencing used to investigate the samples of interest, the exact reporting parameters will differ between marker gene or metagenomic shotgun sequencing. Researchers should report all parameters used during PCR amplification, including the number of cycles and the type of polymerase used. The authors should also report the primer names and sequences if custom primers are used, during marker gene sequencing. If a commercial metagenomic library preparation kit was used, the authors should report the manufacturer as well as the product name and any modifications or optional steps used during sample processing. The amounts of template and the method of library normalization should be reported within the methods for both marker gene and metagenomic shotgun sequencing studies. The DNA sequencing instruments, along with which consumable versions used during sequencing, should be reported.No matter what approach is used to investigate your samples, the bioinformatics method section of a manuscript should include the names and versions of all bioinformatic tools used within the workflow. This includes any databases that were used for classification throughout the study and all non-default parameters used. Any programs used for statistical analysis should be reported, along with any hypothesis tests that were used to investigate the samples of interest. Finally, all code along with its documentation and comments should be deposited in a public repository  and linked within the methodology section of its accompanying study.The study of the human microbiome has clearly created a better understanding of the relationship between humans and microbes. However, multiple different experimental steps have led to the inclusion of significant biases within the human microbiome literature. In many cases, this has led to the inability to compare results from one study to another. The use of consistent protocols on similar sample types could help address issues of between-study bias . This wiWhile reporting these key parameters will help with addressing between-study biases, it will not help address within-study biases that can lead to distorted observations of the true underlying microbial composition. These issues generally stem from multiple areas, including the inability to quantify extraction efficiencies between different microbes and different DNA amplification rates. Without solving these issues, the true view of the absolute abundances of microbes within the human microbiome will not be possible. This is critically important to understand when evaluating methods that rely on qPCR, flow cytometry, or DNA spike-in methods. Although recent work in the field of mathematical modeling shows the promising ability to adjust most biases within a microbiome experiment, they currently rely on the ability to create mock communities . UnfortuOverall, it is currently recommended that all methods in a study should be kept consistent and, when comparing to previous work, the same protocols should be followed. If this is not possible, protocols should be benchmarked between studies to identify whether consistent patterns of bias can be found between them. This will give researchers additional information that can be used when comparing results between studies. Finally, all biological conclusions generated from sequenced-based microbiome experiments should be validated through other molecular techniques to ensure that the underlying results are robust to biases introduced during microbiome sequencing experiments.


Incidental detection of species of concern  during biodiversity assessments based on high-throughput DNA sequencing holds significant risks in the absence of rigorous, fit-for-purpose data quality and reporting standards. Molecular biodiversity data are predominantly collected for ecological studies and thus are generated to common quality assurance standards. However, the detection of certain species of concern in these data would likely elicit interest from end users working in biosecurity or other surveillance contexts , for which more stringent quality control standards are essential to ensure that data are suitable for informing decision-making and can withstand legal or political challenges. We suggest here that data quality and reporting criteria are urgently needed to enable clear identification of those studies that may be appropriately applied to surveillance contexts. In the interim, more pointed disclaimers on uncertainties associated with the detection and identification of species of concern may be warranted in published studies. This is not only to ensure the utility of molecular biodiversity data for consumers, but also to protect data generators from uncritical and potentially ill-advised application of their science in decision-making. This approach may allow early detection of new unanticipated incursions or determination of the presence of extremely rare and ephemeral species. It could also enable efficient leveraging of resources, with HTS applications in a variety of biodiversity monitoring contexts being adopted secondarily as opportunities for SOC surveillance.High-throughput sequencing (HTS) is revolutionizing our ability to characterize biodiversity across ecosystems. Recent technological advances have enabled researchers to extract information about entire biological communities by generating DNA sequence data derived from bulk environmental samples, comparing those sequences to rapidly expanding reference databases, and ultimately inferring the presence of particular taxonomic groups, often with species-level resolution . These mUnfortunately, there are significant and possibly underappreciated risks to incidental HTS-based detections. Specifically, data quality and assessment criteria required in surveillance contexts—where the detection of SOC could have costly management, trade, or health implications and may even be exposed to legal challenge—are exceptionally rigorous and rarely met in broader biomonitoring settings . This scThese problems are exacerbated by the exceedingly broad taxonomic coverage available through HTS methods and their evergrowing application across biomes. Although incidental detections during general biomonitoring have always been possible with traditional methods, HTS technology has dramatically altered the monitoring landscape by offering access to diversity across the taxonomic spectrum. HTS biodiversity assessments may therefore capture components of biological communities previously not accessed by non-targeted efforts—specifically, parasitic and pathogenic taxa, many of which pose direct risks for agricultural resources or human populations . Indeed,Biosecurity Act 1993 oblige “every person” to report the presence of apparently new or notifiable organisms to the Ministry for Primary Industries (MPI). But despite its value to surveillance, public reporting is burdened by high rates of false alarms that consume limited resources  and the authors themselves. It is therefore critically important that researchers be sensitive to the possibility of reporting SOC in their HTS datasets, and be prepared for the heightened scrutiny imposed when their data are interpreted in the context of surveillance applications.post hoc quality control steps on already published data (The context for decision-making related to biosecurity response is inherently challenging both politically and emotionally; decisions must be made rapidly with limited data and may have highly unfavourable implications for various stakeholders. Appropriate vigilance around the quality of the data used to make decisions is vital. Unfortunately, there have been instances where identification of SOC in published HTS datasets has elevated management concerns and been later proved erroneous by additional hed data , 2015b. hed data . AlthougA case in point from New Zealand also highlights this concern. In 2018, a PhD project aimed at methodological advances for HTS monitoring of marine biodiversity recovered a single sequence from a pathogenic species notifiable to the World Organization of Animal Health and subject to management by MPI . FortunaThese examples suggest that researchers conducting broad biodiversity surveys based on HTS data often overlook the possibility that their results may be of considerable interest to end users concerned with SOC. There are multiple potential reasons for this apparent insensitivity, including a lack of accessible tools for recognizing SOC when they show up in diversity inventories and widely acknowledged pressures to rapidly publish impactful studies. Regardless, there is a clear need to develop and adopt standardized quality control and reporting criteria for HTS studies to enable appropriate evaluation of species detections by potential end users. With the implementation of, and adherence to, these criteria, assignations of genuine SOC could be flagged, allowing end users access to data known to be fit-for-purpose to aid management decision-making and enable appropriate responses. Though this arrangement would not eliminate nefarious attempts to mis-use scientific data or policy disputes rooted in genuine scientific uncertainty, it would limit errors made by those seeking to utilize the outputs of these studies in good faith. There is already broad international acknowledgement of the need to move towards standard best practices for analysing and reporting HTS-derived biodiversity data , with thThe development of HTS reporting criteria could lean on standards used for other diagnostic applications (e.g., international standards for detection of human and animal pathogens; As the research community moves towards such standards, interim solutions are needed (“initial changes” in It may be tempting for researchers to adopt a “buyer beware” approach to this issue, placing all onus on end users to consume HTS-derived biodiversity data only with caution and to resist the assumption that SOC identified in these data represent actionable observations. But so long as the research community continues to cite incidental detections of SOC as a fundamental benefit of HTS-based monitoring, it would be disingenuous to simultaneously deny responsibility for such detections when they arise. Given decision-makers’ appetite for information on SOC distributions and the potential risks associated with consumption of HTS data that are not fit-for-purpose, it should be the concern of all researchers—including even those not explicitly engaged in SOC-related studies—to rapidly move towards criteria that enable clear separation of methods appropriate for surveillance from those acceptable in general biodiversity monitoring contexts.


Recent advances have shed light on the importance of early therapeutic intervention for neurodegenerative diseases. Primary prevention trials present a potential disease-modifying strategy for pre-symptomatic patients of autosomal dominant neurodegenerative diseases (ADND), such as early onset familial Alzheimer’s disease (AD) and Huntington’s disease (HD). As trials target earlier disease stages, however, prospective participants face new ethical and logistical challenges, namely childbearing and reproductive health decisions. Since pregnancy is an exclusion criteria for such trials, participants of reproductive age must choose between participating in research and having a family. Such decisions carry significant burdens for ADND patients that if left unaddressed could impact patient well-being and the field as whole. We use our perspective as scientists, advocates, and ADND family members to highlight current shortcomings in the field regarding trial participation and family planning issues for ADND patients and call for the establishment of a normative standard to address these concerns. Extensive study of ADND has led to the discovery of the genes and specific mutations that give rise to these disorders , 2. DespThere are significant gaps in the field regarding the effect of family planning on ADND primary prevention trial participation for both men and women. These include: (1) the paucity of information and study on participant perspectives regarding family planning issues, (2) the lack of guidelines and implementation for family planning communication in trials, (3) the need for ethical consideration regarding informed consent on participant’s fertility-associated physical and emotional well-being, and (4) the need for an assessment of potential risks incurred by trials as well as the ADND and dementia field if these issues go unaddressed. For each of these issues related to childbearing in the ADND population, we highlight the gap within the ADND field, detail findings and strategies employed by the field of oncology to address these issues, and lastly suggest how the approach taken by the field of oncology could serve as a template to guide recommendations for ADND investigators . We acknIt remains unclear whether experimental treatments for ADND would reduce or eliminate fertility, however, committing to clinical trial participation during critical reproductive years and/or for the remainder of life may cause the loss of optimal childbearing years or push some patients out of their fertility window altogether, impacting patients’ emotional well-being. Due to the lack of data on the effects of potential fertility loss in the ADND community, we have turned to oncology—a field with experience in developing strategies for young patients concerned about their fertility future. Researchers in the cancer field have stressed that understanding the emotional aspects of infertility after cancer as well as the factors that influence a survivor’s decision about having children is of great importance . Cancer For the field of ADND, no data exists that captures the attitudes of potential trial participants on family planning issues. To gain a better understanding of patient perspectives as they relate to trial participation and family planning, we suggest that the ADND field undertake such studies. Particularly as the field moves toward treating younger pre-symptomatic and prodromal patients, investigating the experiences and perspectives of ADND patients of reproductive age will identify unmet family planning needs and provide necessary evidence for improved patient-centered care. Perspectives and questions to address Fig.  include:No guidelines are currently in place to facilitate discussions with ADND patients about fertility-related concerns regarding their disease or clinical trial participation. Guidelines established by the American Society of Clinical Oncology call for an individualized fertility consultation, including a discussion of fertility preservation options as early as possible and at every stage of treatment for oncology patients that are interested in or uncertain about their future fertility plans prior to cancer treatment . Prior tGiven the importance of this issue and the potential impact that loss of fertility can have on quality of life, there is a critical need to provide effective fertility counseling services to ADND patients of reproductive age, particularly before participation in primary prevention trials that require a fertility hold. When establishing future investigational trials, we implore investigators to facilitate conversations between patients, genetic counselors who often have experience with hereditary disorders and associated reproductive questions, and fertility specialists that can help guide family planning decisions . We postulate that taking these measures will improve psychological health, decision satisfaction, and quality of life for trial participants. We endorse the adoption of the recommendations set forth by the American Society of Clinical Oncology to facilitate clinician interactions with ADND patients both as part of trial enrollment and on an ongoing basis during trial participation as patient childbearing concerns evolve and disease-modifying treatments, hopefully, become the standard of care.There are four basic ethical principles in medicine: autonomy, beneficence, nonmaleficence, and justice. To protect human subjects during clinical research, informed consent is crucial to achieving these principles, which involves informing subjects of their rights, alternative treatment options, risks, and benefits prior to their trial participation. In the ADND field, we believe there is a current gap regarding ethical considerations and patient fertility, particularly informed consent and the measures taken to adequately address and acknowledge the potential risk to trial participants’ fertility during their participation in trials testing investigational treatments with unknown risk. In oncology, studies show that providing information to patients about treatment-associated reproductive risks is integral to helping them make informed decisions . FertiliIn ADND prevention trials, there is limited information on whether investigational drugs undergo testing for fertility-related effects, and whether this data is disclosed to patients. Although these experimental drugs are primarily brain-directed, some prevention trials are currently exploring systemic interventions given orally or intravenously. Thus, it is critically important, and necessary for adequate informed consent, for investigators to provide patients with data on the impact of these experimental drugs on egg supply, sperm health, teratogenicity, and fetal neurodevelopment, or the lack thereof. While an argument could be made that investigational trials should be held until the effects of these drugs on fertility are known, the desire for disease-modifying treatments within the ADND field cannot be overstated. Clinical trial participation forces patients to confront and weigh the importance of many major life situations and decisions: their desire to have biological children, their disease age of onset, their remaining natural fertility window, and the promise of an investigational drug to modify disease pathology Fig. . In addiInsight gained from the oncology field may shed light on potential risks that the ADND field may face for investigational treatments. While the effect of fertility concerns on trial participation for the ADND field is currently unknown, 29% of breast cancer patients report that infertility concerns affected their treatment decisions [Once a patient has decided to pursue fertility preservation, both intrinsic  and extrinsic  factors influence the patient’s ability to undergo fertility preservation. Additionally, sex can also be a factor as men and women face different barriers to fertility preservation due to ease/difficulty of sample retrieval and biological ineligibility criteria for women . However, financial burden remains the most significant deterrent against fertility preservation , cost foThe distress reported by oncology patients who lost fertility  and thei


Discrimination against older adults in the workplace is a pervasive issue that has important consequences for older adults, leading to lower well-being  and worse job outcomes . One area where discrimination manifests is in hiring practices, and thus research is needed to understand factors that impact willingness to hire older adults. One potential intervention target to reduce age discrimination in hiring is sense of purpose. Sense of purpose manipulations have previously been successful in increasing one’s comfort with diversity , and thus may prove successful in combatting age discrimination in the workplace. Therefore, the current studies sought to understand whether sense of purpose was related to ageist attitudes and hiring decisions, and how a purpose manipulation might serve as a tool to combat discrimination in hiring. Across two studies , participants were shown the resumé of either an older adult applicant (62-years-old) or younger adult applicant (32-years-old), and were asked how hireable they would rate the applicant. The research found that the purpose manipulation did effectively increase individuals’ sense of purpose. In addition, the research found that sense of purpose was negatively related to ageist attitudes. However, the purpose manipulation was unsuccessful in reducing ageist attitudes, and had no effect on ageist discrimination in hiring. These results suggest that while sense of purpose is negatively related to ageist attitudes, manipulating purpose may not be an effective tool to reduce ageist attitudes or discrimination.


Despite growing interest in improving cognitive abilities across the lifespan through training, the benefits of cognitive training are inconsistent. One powerful contributor may be that individuals arrive at interventions with different baseline levels of the cognitive skill being trained. Some evidence suggests poor performers benefit the most from cognitive training, showing compensation for their weak abilities, while other evidence suggests that high performers benefit most, experiencing a magnification of their abilities. Whether training leads to compensation or magnification effects may depend upon the specific cognitive domain being trained (such as executive function or episodic memory) and the training approach implemented (strategy or process). To clarify the association between individual differences in baseline cognitive ability and training gains as well as potential moderators, we conducted a systematic meta-analysis of the correlation between these two variables. We found evidence of a significant meta-correlation demonstrating a compensatory effect, a negative association between initial ability on a trained cognitive process and training gains. Too few papers met our search criteria across the levels of proposed moderators of cognitive domain and training approach to conduct a reliable investigation of their influence over the meta-analytic effect size. We discuss the implications of a compensatory meta-correlation, potential reasons for the paucity of qualifying papers, and important future directions for better understanding how cognitive trainings work and for whom. There has been steady and growing interest in developing effective methods to manipulate an individual's cognitive abilities. Work on this topic has exploded in recent decades, motivated by rehabilitative and therapeutic goals for impaired or aging populations , other studies yield little to no evidence of benefit from cognitive training. This variation among findings is well-documented and presents serious problems for determining the true effectiveness or utility of cognitive training in both clinical and research contexts , or tested for an interaction of training domain with training approach (process- vs. strategy-based). To our knowledge, only one study has explicitly investigated questions of training approach (strategy- or process-focused) in baseline individual differences effects on cognitive training, conducting a detailed investigation of change across an episodic memory training by sequentially administering strategy followed by process training manipulations. Analyses showed an initial reduction in individual differences post-strategy instruction (a compensation effect), but magnification of those differences after further process based practice . Far transfer tasks comprise those which measure performance on an ability other than that being trained . While some papers address the degree of transfer with regard to training outcomes and individual differences , assignment to low/high performance groups , or calculated gain scores . Each of these approaches has unique drawbacks. The use of only raw or scaled training performance to probe the influence of baseline individual differences may merely capture stable individual differences in general ability among participants, as opposed to differences in training outcome specifically. The use of low/high performance groups reduces the power of analyses . It therefore excludes studies that define this measure by some general cognitive ability such as fluid intelligence or studies that evaluate baseline individual differences in a cognitive domain different from that being trained. Second, this review constrains outcomes to measures derived from training or near transfer (provided the near transfer task is of the same cognitive domain as training), excluding far transfer outcomes. Far transfer measures could take on an array of forms and vary in how different they are from the trained ability. Currently there is no clear picture of what the relationship between gains in training, near transfer, and far transfer is  examine and synthesize evidence for the association of individual differences with baseline cognitive ability on cognitive training outcomes through correlation coefficients of this relationship and (b) investigate how this correlation is moderated. Specifically, these analyses are designed to answer the following research questions:What is the estimate of the average correlation coefficient of the association between baseline abilities and cognitive training outcomes?Is this correlation moderated by the targeted cognitive domain ?Is this correlation moderated by the cognitive training approach ?Is this correlation moderated by an interaction between domain and training approach?This review was conducted in accordance with recommendations outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) group guidelines  due to (a) the diversity of terms utilized in titles, abstracts, and keywords to describe research on cognitive training, and (b) the frequency with which individual differences analyses are discussed only in the body of an article, given that many of these analyses are exploratory or secondary to the primary question of the article. At the time this study was conducted, this restriction limited potential databases for systematic search to Google Scholar or PsychINFO. Google Scholar was selected because it is both an openly accessible and un-curated database. Unlike other “curated” academic databases such as PsychINFO, PubMed, Web of Science, and others—Google Scholar procures search results through an entirely automated algorithmic search of the internet-at-large, as opposed to searching a limited, pre-selected body of entries. While the downside of this approach is that the nature of this algorithm is unknown, this still arguably makes Google Scholar a more unbiased method of searching the literature. Google Scholar is neither beholden to specific database and publisher relationships nor are its searches influenced by curated database add-ons such as enhanced keywords. It also provides the substantial advantage of crawling a diversity of sources that include dissertations, conference proceedings, posters, unpublished work, and similar “gray” literature because of its broad un-curated search approach. Therefore, the use of Google Scholar helps to circumvent potential issues resulting from both a lack of common terminology in titles, abstracts, and keywords, as well as issues resulting from bias in search results attributed to the curated and financial nature of curated database services.As such, all searches were conducted in Google Scholar utilizing the following protocol:“cognitive training” AND “individual differences”While the use of such a broad protocol in a full-text search was expected to produce a large initial pool of studies, it was selected to ensure the capture of the majority of studies likely to contain relevant data in order to provide as unbiased a review of the available data as possible. The search protocol was constrained to the years 1980 to 2018 (inclusive) to best capture the modern literature on cognitive training. Searches were conducted within specific year intervals  to address Google Scholar's 1,000 results limit. This allowed for access to the complete set of articles determined by Google Scholar's search. For individual years with results exceeding 1,000 articles, the search protocol was separated into two parts by searching for articles based on the required inclusion or exclusion of a common word, “baseline”:“cognitive training” AND “individual differences”AND “baseline”“cognitive training” AND “individual differences”NOT “baseline”Returned search results were extracted from Google Scholar results pages using an automated script implemented in Python see . This scEligibility was determined by the first author of this paper (H.J. Traut) over the course of unblinded reviews of retrieved search results. Eligibility criteria were designed along the “PICOS” recommendations of the PRISMA guidelines . Participants of any age, neurological status, or other characteristics were eligible for inclusion. This decision was made to allow for the maximum number of results to be retrieved and due to the absence of hypotheses explicitly regarding differences in baseline individual difference effects between typical and atypical individuals.To be eligible, studies were required to include experiment(s) in which at least one group of participants underwent training with a task intended to manipulate the participants' executive function (EF) or episodic memory (EP) abilities according to the following definitions:Episodic Memory Training: Training intended to manipulate an individual's ability to retrieve explicit information such as facts, definitions, or experiences . This included, but was not limited to, trainings with manipulations such as cardio-vascular exercise, mindfulness, commercial/popular video-games , social skills training, and emotional skills training. It also included training interventions that involved administration of neurostimulation, neurofeedback, pharmacological interventions, therapeutic interventions , or other concurrent additional manipulations to cognitive training. These exclusions were included to ensure that analyzed effects could reasonably be assumed to result from cognitive training manipulations alone and not the influence of additional manipulations not of interest.Studies were required to include at least one eligible treatment group based on the criteria described above. They were not required to include a comparison group of any kind. If present, the type of comparison group was recorded for all eligible studies .Studies were required to have measured participants baseline ability in the trained cognitive skill(s) prior to training. For example, if a study tested the effects of a working memory training, a baseline measure of working memory ability prior to training was required for inclusion. This baseline measure could come from the same task that was later used for training or from a different task that assessed the same cognitive skill. Studies were required to have measured participants cognitive training outcomes using gain scores. Gain scores were defined as any calculation of the difference in performance between baseline performance and final performance. To be eligible these outcomes needed to measure the trained cognitive skill(s), and were thus derived from either final training performance or performance on a near transfer task.Studies were required to include a pre- and post- design such that there were measurements of participants performance before and after the cognitive training. Studies needed to be experimental or quasi-experimental in nature. Observational studies, case studies, case-series, qualitative studies, or studies not using a pre-/post- design were excluded.Studies were not excluded for administration of multiple types of cognitive training across multiple groups, provided that at least one cognitive training group met the above criteria. No exclusion criteria were imposed based on the country of origin, but only papers for which a full-text English version of the article was available were evaluated for eligibility.After search results were collected using the search protocol described above, results were saved in a systematized electronic format to be reviewed for eligibility. Prior to review, duplicate papers were flagged through the use of an automated script. To confirm automated results, the first author manually verified duplication of flagged entries.Post-duplication flagging, results were scanned for inclusion based on titles and abstracts. Results were flagged and removed at this stage if they were obviously ineligible  or failed to meet eligibility criteria . Full text documents were obtained for remaining articles. Each full-text document was screened for eligibility based on the established eligibility criteria discussed above. Results are reported below.All papers that passed the eligibility screening processes were coded for relevant data. These data included number of training groups, sample size (broken down by training group), cognitive training characteristics , and relevant statistical analyses. From the relevant statistical analyses, baseline measures and outcome measures were recorded. Data were also coded for participant characteristics , neurological status, population). Coders were not masked to the article's authors, institution, or place of publication.ad-hoc method of selecting a single effect size from each study for inclusion. Study is included as a level in our multi-level model as described below.Correlations between baseline ability and gain scores were selected for meta-analysis as this presented the most readily available effect size measure across retrieved studies. Studies were allowed to contribute multiple correlation coefficients if more than one was provided. For example, if a study reported multiple eligible correlation coefficients assessing the relationship between cognitive baseline and training outcomes—all of these correlations were recorded. This was done to allow the maximum amount of observations to be included in meta-analysis as well as to avoid introducing bias by determining an I2 measures. If a significant amount of heterogeneity was observed, initial analyses were followed with those described below.After extraction, correlation coefficients were scaled using a Fisher's Z transformation  and trained domain (episodic memory or EF) were included as predictors.Both training approach and trained domain predictors were dummy coded.The initial search protocol within Google Scholar (run between November–December 2018) produced 9,502 documents, of which 405 were identified as duplicates. During the screening of the remaining 9,097 unique documents abstracts and titles, 8,542 documents were excluded. The remaining 555 underwent full text screening for eligibility. A total of 42 papers met the eligibility criteria described above. Of these, 15 reported sufficient information for extraction or calculation of a correlation coefficient. Twenty-seven papers met eligibility criteria but did not report sufficient information for calculation of relevant effect sizes. Authors of these papers were contacted and requested to provide either the requisite correlation or the raw data necessary to calculate the requisite correlations. Ten papers were added via this contact procedure for a final sample of 25 papers ; Table 1All analyses were carried out using the R Opensource Framework, R version 3.6.0,  . Variability in the upper strata along the x axis suggests notable heterogeneity among observations, which is supported by heterogeneity analyses discussed below, and is not in-and-of itself indicative of asymmetry or bias  ((81) = 924.5982, p < 0.0001; I2 = 91.17, p < 0.0001] indicating a large amount of variability across the collected literature  estimated the average correlation of baseline ability with training gains. Results demonstrated a significant negative correlation between baseline cognitive performance and training gain (β 0.0001) . The dirAs analysis of heterogeneity in the base model indicated a significant amount of variability between observations, meta-regression analyses were conducted. However, the characteristics of the collected eligible correlations did not allow for the original model analyzing both the main effects and interaction between cognitive training domain and cognitive training approach to be carried out. As foreshadowed by prior work, executive function and episodic memory training studies in the present sample perfectly aligned with the use of process and strategy training approaches, respectively see .(80) = 891.1806, p < 0.0001]. There was no evidence for a significant relationship between training domain/approach and the correlation of baseline individual differences with cognitive training gain . The estimated average correlation remained negative and significant . Complete model results are reported in A large amount of residual heterogeneity remained after the addition of the domain/approach predictor [QEGiven the paucity of qualifying episodic memory/strategy cognitive training papers, a third exploratory analysis was conducted using only executive function/process cognitive training papers. This analysis evaluated the meta-analytic correlation coefficient for this subset of the retrieved effect sizes using the same equations described in Equation (1). A total of 22 papers with 76 effect sizes were included. Complete model results are reported in p = 0.0104). This restricted dataset again demonstrated a significant negative association between baseline ability and gains in that ability as a result of training  with a significant amount of heterogeneity [Q(75) = 676.4997, p < 0.0001)  .We highlight three main findings from this systematic meta-analysis of the cognitive training literature. First, baseline individual differences in the cognitive ability trained yield significant compensatory effects. Participants with initially weaker targeted abilities gain more from cognitive training than those who are initially more proficient. This meta-analytic finding is consistent with multiple prior accounts of the influence of individual differences on cognitive training gains, particularly for executive function and process based cognitive training approaches . Future work might include such multi-modal studies to increase the number of usable observations for analysis and more thoroughly represent the current characteristics of the cognitive training literature. While it was important to narrow our scope of inquiry to clearly identify sources of variation as was done here, it will also be valuable to evaluate the possible true effect size of baseline individual differences within the types of studies that are frequently conducted.Similarly, the eligibility criteria used in this systematic review revealed a paucity of studies meeting requirements for the specific baseline cognition and training gains association being analyzed. Eligibility for inclusion required studies to have investigated the association between individual differences in baseline cognitive ability, with that cognitive ability defined as being of the same type as the one trained, and with cognitive training gains derived from either the final training performance or near transfer performance. This isolated the specific relationship in question to initial ability in cognitive construct “X” and gains in “X” from training in “X.” Many papers were disqualified for deviating from either or both of the baseline cognitive measure and training outcome requirements, highlighting inconsistencies in what these common terms actually represent across the literature. For example, one of the most cited works on this topic analyzed the association of a baseline measure of general fluid intelligence with far transfer outcomes on another fluid intelligence task, after working memory training , likely because of the small number of atypical observations and the range of conditions within our sample. Future work might focus on the question of how typical vs. atypical groups' baseline abilities influence cognitive training outcomes within a specific condition  to probe the nuances of magnification or compensation effects based on specific patient characteristics.Responsiveness to training is likely also influenced by typical or atypical cognitive status—with specific aspects of an individual's condition potentially impacting the potency of baseline individual differences effects. Many meta-analyses of cognitive training effects limit eligibility to healthy populations for this reason  could influence who benefits from that training, with weaker interventions tending to benefit those who need them most and stronger interventions showing more equitable benefits. Heterogeneity in the present sample might be driven by moderating effects of any of these factors. While our investigation sought to clarify the pattern of compensation and magnification results seen across executive function and episodic memory training studies using process- and strategy-based approaches, an important direction for future work will be to systematically test the role of these other types of factors to more fully understand to what extent baseline abilities predict benefits from cognitive training.The availability of data limited both the number of eligible papers and the type of effect size coefficients that could be extracted for meta-analysis. To aid in future meta-analytic endeavors, our minimum recommendation is that papers include the following in their main or supplementary materials: complete correlation tables by group as well as complete descriptive statistics by group for all recorded measures. Complete correlation tables would aid in extraction of correlation coefficients  and complete descriptive statistics would aid in the calculation of Cohen's d for meta-analysis of group mean comparisons. Explicit separation of both types of information by group (if multiple groups are present) is essential to extracting information pertinent to cognitive training.post-hoc calculation of treatment effects controlling for baseline covariates. For example, baseline scores could be included as a covariate in an ANCOVA model, with group as an independent variable and outcome score as the dependent variables, to account for individual differences in cognitive training effects . In addition, in cases where studies include treatment and control groups, open datasets could allow for  Bonate, . Open da Bonate,  (for ext Bonate, .Using a focused, systematic approach to evaluate the literature, the present meta-analysis demonstrates evidence for a compensatory effect of initial baseline ability on cognitive training gains, suggesting that those best positioned to benefit from cognitive trainings are those who are relatively weaker in ability. Despite finding that few episodic memory or strategy-based studies met our stringent eligibility criteria, preventing us from evaluating the influence of cognitive domain and training type, we found that a substantial amount of heterogeneity still remained among our primarily executive function and process-based studies. This indicates that even in this refined sample, other pertinent moderators of the baseline individual differences effect are at play. The prolific complexity in this field was evident in this study. Future work should tackle defining the problem space of individual differences in initial ability and cognitive training gains. What initial abilities are we interested in—those directly related to the cognitive skill being trained, those nearly related, or those with a far relationship? The same question applies for change after cognitive training. What are the most productive definitions of cognitive training to use—the narrow ones implemented here or broader definitions including multi-modal trainings? What are the most appropriate means of capturing these questions statistically? The present meta-analysis indicates a productive direction forward in targeting cognitive training toward lower performing individuals, and reaffirms the urgency of continuing systematic work on this topic.The original contributions presented in the study are included in the article/HT conceived of the idea with feedback from YM. HT and RG carried out data collection. HT performed all analyses. All authors discussed the results and interpretation. HT took the lead in writing the manuscript. YM provided critical revisions. RG provided input. All authors gave final approval of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Perspective article, we synthesise the recent evidence from rodent, monkey and human neuroimaging literature to highlight how the habenula, a small evolutionarily conserved subcortical structure located in the midbrain, may orchestrate the behavioural expression of anhedonia across fronto-mesolimbic networks. We then review how this circuitry can be modulated by ketamine, an NMDA receptor antagonist with rapid antidepressant properties. We propose that experimental paradigms founded in reinforcement learning and value-based decision-making can usefully probe this network and thereby help elucidate the mechanisms underlying ketamine’s rapid antidepressant action.Anhedonia, a pronounced reduction in interest or pleasure in any of life’s daily activities, is a cardinal symptom of major depression. In this  The discovery that a sub-anaesthetic dose of ketamine can have rapid antidepressant effects has been hailed as the most important discovery in 50 years of depression research , 2. KetaPerspective article, we take a translational cognitive neuroscience approach and consider the potential mechanisms of ketamine across neural circuits important for reward and punishment. We present an overview of emerging evidence from rodent, monkey and human neuroimaging studies which cumulatively demonstrate how ketamine modulates the expression of anhedonic behaviours across habenula and fronto-mesolimbic networks.Key work on understanding ketamine’s antidepressant drug actions have focused more on cellular mechanisms (for a review see ). In thiAnhedonia is typically defined as diminished interest or pleasure in activities that people have previously enjoyed. Anhedonia can be operationalised into a set of subcomponents which recognise that anhedonic behaviour could be the result of dysfunction in several distinct processes, including the anticipation of, consumption of and learning about reward , 10. TheDistinct contributions of reward learning, anticipation, and consumption to the behavioural expression of anhedonia are also demonstrated in neuroimaging studies of reward processing. Auerbach et al. argued that different neuroimaging modalities may be optimised to study different subcomponents of anhedonia, recommending event-related functional magnetic resonance imaging (fMRI) of the basal ganglia for studying reward anticipation, and resting-state connectivity for reward consummation . AlthougOne important element of reward learning is the signalling of reward-prediction error (RPE), defined as the difference between an individual’s expectations about the outcome of an event and the actual outcome of said event  and the actual observed outcome of an event. The agent’s expectations are commonly estimated by reinforcement learning models such as the Rescorla-Wagner learning rule.• Expected uncertainty relates to the known unreliability of predictive relationships in a familiar environment. For example, unsurprising fluctuations in day-to-day air temperature.• Unexpected uncertainty relates to unsignaled shifts in context that result in highly unexpected outcomes, also known as environmental volatility. For example, a sudden peak in air temperature over and above seasonal average brought about by an hurricane.• Information prediction error is the discrepancy between how informative a cue predicting an event is expected to be, and the degree to which it leads to a discernible relationship between [probabilistic] cues and outcomes in an environment. For example, the relationship between grey skies and subsequent rainfall. Cues become less informative when their predictive value falls from 100% to 50%, also known as maximum entropy.information PEs  . In monk see Box , which dThe habenula is an evolutionarily conserved brain structure which receives a broad range of inhibitory and excitatory inputs while providing a relatively small range of outputs , 26. AreAn important excitatory input to the lHb is from the parvalbumin-positive neuronal populations  in the dAnimal studies have highlighted a possible function for the habenula as a relay station orchestrating signals from both dopaminergic and serotonergic regions that regulate affective response during reward-guided interactions with the physical environment. Two connections that are important for this function are the lHb inputs to the dorsal raphe nucleus (dRN) and the medial raphe nucleus (mRN) , 29. TheThus far, we have reviewed evidence that describes the habenula’s role in the processing of reward and punishment as well as its contribution to analogues of depressive behaviours, mostly based on findings from rodent studies , 42–44. A few studies investigating the habenula’s function in humans have provided complementary evidence to findings shown in rodent models. One of these human studies used a Pavlovian conditioning paradigm in which abstract fractals were probabilistically associated with different types of outcomes: electroshocks or monetary [win/loss] outcomes . In healIn a follow-up study, the same group of authors reported that this habenula response is sensitive to severity of self-reported depressive symptoms in nonclinical volunteers, such that a subset of participants who reported higher depressive symptoms did not show a positive habenula response to shocks. Furthermore, a group of patients with MDD showed a deactivation in the same region, leading to significant differences between patients and non-depressed healthy volunteers . InteresConsistent with the findings from monkey neurophysiology studies , 49, funIn this section, we will highlight the role of brain regions that play a key role in ketamine’s anti-anhedonic effects in preclinical models. As we highlighted in the introduction, the lHb has recently been identified as an important site for ketamine’s rapid antidepressant action . OptogenThe other key region involved in ketamine’s antidepressant action in rodent models of depression is the medial prefrontal cortex (mPFC), which notably projects to the habenula , and invAnother rodent study demonstrating a role for the mPFC used an affective bias model of depression and compared ketamine with a traditional antidepressant (venlafaxine) . NegativThe evidence reviewed in this section highlights that ketamine acts on both cortical and subcortical structures, specifically the mPFC and lHb. Action at both of these sites rapidly mitigates analogues of depressive symptoms in rodent models.So far, we have synthesised findings from key animal experimental and neuroimaging studies of the habenula which have focused on its functional relationship with striatal dopaminergic regions during RL, and with other key neurotransmitter hubs within the mesolimbic network  involvedThe use of multivariate approaches in human fMRI has enabled measurement of the heterogeneity in responses within the mPFC, the ventral sections of the mPFC in particular are consistently implicated in the relative coding of value in humans , 59 and The sgACC, broadly defined based on the Brodmann areas highlighted above, has been consistently implicated in the aetiology of depression –69, predHuman neuroimaging studies also demonstrated ketamine related changes in functional connectivity across the fronto-striatal circuitry. For example, a recent study showed increased functional connectivity between the caudate and PFC regions (dlPFC and vlPFC), and between the putamen and PFC regions  in treatment-resistant depression (TRD) patients . HoweverTaken together, the studies that we reviewed in this section suggest that reward circuitry distributed across fronto-mesolimbic and fronto-striatal networks are important in understanding ketamine’s antidepressant effect. A comprehensive understanding of ketamine’s action would require studying its direct effects on reward processing  as well as its indirect effects on reward processing through an action on anhedonia . However, there are not any studies to our knowledge, which have investigated the direct effects of ketamine on reward processing in humans or monkeys. On the other hand, only a limited number of rodent studies have investigated how ketamine modulates reward processing within a temporal discounting framework. This is a relevant domain as previous literature suggests that temporal discounting decisions are also impaired in patients with depression , 77 who Perspective article, we have reviewed evidence showing that habenula and midbrain dopaminergic neurons work closely in signalling reward and punishment PEs during tasks performed over a single testing session , and thearning” , 82 or wAlthough both Pavlovian and instrumental conditioning frameworks have been adopted by previous studies, RL tasks involving reversals in which the better option in the environment changes at time points unknown to the participant, might be the best experimental approach to probe these systems. Continuous reversals in the task environment mean that the participant will continue experiencing PEs over the course of the experiment, resulting in continuous engagement of the subcortical structures implicated in RL. In RL terminology, unexpected uncertainty arising from dynamically changing outcome contingencies is known as volatility  Box 1)..1). It iIn human physiology studies, the activity of the pupil-linked arousal systems is shown to encode the volatility of punishment contingencies  more strWe believe that converging evidence presented in this manuscript from existing rodent and monkey models warrants an interdisciplinary approach for the next decade of human research, designed to translate these insights into tractable models of rapid antidepressant action in depressed patients and thereby produce a radical improvement in the management of clinical mood disorders. Moreover, given anhedonia is a transdiagnostic feature of many psychiatric and neurological conditions, the translational approach we outlined here may have utility for clinical improvement in all neuropsychiatric conditions in which anhedonia manifests.Box 2 Summary and future directions• The habenula, a subcortical brain structure that is evolutionarily preserved across species, orchestrates behavioural expression of anhedonia across fronto-mesolimbic and fronto-striatal networks.• Translational evidence suggests that ketamine, a noncompetitive NMDA receptor antagonist with a rapid antidepressant/anti-anhedonic profile that is consistently observed in patients with depression, modulate neural underpinnings of anhedonic behaviours in these circuits, acting on the habenula, dACC, mPFC and sgACC.• Reversal learning paradigms which capture learning and reward/punishment processing domains of anhedonia can effectively probe the dACC, vmPFC, vSTR and habenula simultaneously, and elucidate mechanisms of ketamine action.• Overall human neuroimaging studies contributing to our understanding of these circuits are lagging behind animal studies in terms of quantity. Future studies employing neuroimaging methods with higher temporal and spatial resolution are needed to develop a better understanding of ketamine’s anti-anhedonic action.


To describe differences between patients with chronic, non-cancer pain (CNCP) who were successfully able to cease full mu agonist chronic opioid analgesic therapy (COAT), and those who exhibited refractory COAT reliance, among those who participated in a multidisciplinary program designed for COAT cessation.A retrospective review of electronic medical records (EMR) data was organized for preliminary analysis.A multicenter private practice specializing in CNCP, which received patient referrals from the surrounding geographical area of primary and specialty care offices in Northern California.Data from 109 patients with CNCP who participated in a multidisciplinary program to cease COAT between the dates of October 2017 to December 2019 were examined.EMR data, pre-COAT cessation, of oral morphine milligram equivalence (MME) and validated questionnaire responses assessing anxiety and fear-based beliefs and behavior, as well as opioid misuse, were extracted and compared between those who successfully ceased COAT and those who did not.Patients who were unsuccessful at COAT cessation reported significantly higher Fear Avoidance Beliefs Questionnaire (FAB) scores. No significant differences were found based on incoming MME amounts, Current Opioid Misuse Measure (COMM) or Tampa Scale of Kinesiophobia (TSK) scores. Pain Catastrophizing Scale (PCS) scores showed a split pattern with unclear significance.Results suggest that fear avoidance beliefs and behavior, as measured by the FAB, play a significant role in refractory COAT reliance for patients with CNCP. The practice of using full mu agonist chronic opioid analgesic therapy (COAT) in the setting of CNCP has had detrimental impacts on an individual as well as a societal level in The United States . Even whIn terms of a comprehensive treatment approach, the practice of using COAT for CNCP has been correlated to worsened outcomes. COAT has been shown to impede vocational and social return to function and increase length of disability in injured workers . It is aCalled to action by the 2016 guidelines from The Centers for Disease Control and Prevention . DespiteAnxiety and fear-based beliefs and behavior have been strongly implicated in certain aspects of the negative chronicity experienced by those who suffer from CNCP. Such beliefs and behavior have been associated with increased disability , 43–48 ,Clinically similar schematics between COAT reliance and the negative chronicity of CNCP further support the hypothesis that both phenomena have a foundation in anxiety and fear-based beliefs and behavior. First, fear avoidance of pain, from the stance of learning theory, is a self-perpetuating dynamic in which anticipated consequences require little reinforcement to create long-term habitual behavior .ExpectatThe Fear Avoidance Beliefs Questionnaire (FAB), Pain Catastrophizing Scale (PCS), and Tampa Scale for Kinesiophobia (TSK) are frequently employed to assess anxiety and fear-based belief and behavior. While these tools assess similar phenomena, their interchangeability has been negated in investigative comparisons , 61–64, There are so many similarities between the classic fear avoidance model of CNCP , 6768–6967The above similarities prompted an exploration of anxiety and fear-based beliefs and behavior in relation to COAT use in the clinical setting. The inquiry undertaken by the researchers was: if it is so that disability and negative chronicity in the setting of CNCP is strongly motivated by anxiety and fear-based beliefs and behavior , 47, couData were collected via a retrospective review of the electronic medical records (EMR)—decoupled from individually identifying features––comparing the incoming measures of 109 patients with CNCP who participated in a multidisciplinary program  designedThe multidisciplinary program commenced as a circumscribed, higher acuity treatment entity under the auspices of a multicenter private practice specializing in CNCP, which received patient referrals from the surrounding geographical area of primary and specialty care offices in Northern California. These referral sources were diverse and included Federally Qualified Health Care Centers, unaffiliated private practices, hospital-affiliated clinics, occupational medicine providers and workers compensation entities. Two centers and two different clinical teams participated in administering the program. Funding for the program occurred via routine medical fee-for-service billing for treated patients. The researchers analyzed the data presented here through the course of program-related quality assurance measures. No other funding was present for this study. This study was reviewed by a private IRB and was determined exempt from full review.By default, the subjects of the present study met the inclusion criteria of the clinical program—adults who consented (as opposed to were mandated) to a group program for the purpose of COAT cessation and carried a diagnosis of CNCP from any etiology; had used daily COAT at the time of admission or had struggled to maintain recent opioid cessation; had tried and failed or plateaued on an opioid wean previously; and were failing to meet realistic functional and analgesic goals, despite participation in traditional outpatient COAT for CNCP treatment, as determined by the individual patients and/or their primary care teams. COAT used by participants at program initiation were any form of commercially available oral or transdermal long and short-acting preparations obtained while under the care of a physician. Program participation was determined via a semi-structured, motivational interviewing style evaluation between each participant and one of the qualified program clinicians.Exclusion criteria from the program were candidates who:Did not carry a diagnosis of CNCPWere actively engaging in opioid diversionHad an active substance use disorder, or comorbidity, of significant acuity to be appropriate for higher than Level 1 (outpatient) services as defined by The American Society of Addiction Medicine (ASAM)  , includHad a neurocognitive or neurodegenerative disorder that precluded the ability to actively participate in care-planning decisions and/or reliably follow written instructions.The group multidisciplinary program was built around a standardized curriculum designed to transition patients with CNCP off COAT. The treatment occurred once a week in a 6-hour session for 10 weeks. The curriculum entailed group cognitive behavioral therapy (CBT) emphasizing pain coping skills and mood regulation, complementary care modalities delivered in a group setting , and individualized medication management. Every activity was led by a licensed or credentialed expert in that field—such as a physician, nurse practitioner, psychologist, licensed acupuncturist, and physical therapy assistant. Extended panel urine drug screen was mandated at every meeting to corroborate participant compliance. Participants were determined to have successfully graduated from the program if COAT cessation was achieved in one of four ways: a complete transition to buprenorphine, an abstention without transitional medication assistance, a maintenance of recent COAT abstinence in the context of reported struggle with craving or coping, or a reduction of incoming buprenorphine dose by over 50% in incoming patients not using full mu agonist opioids for analgesia. For the purposes of this paper, graduation and COAT cessation, as defined by the pathways above, are synonymous.The PCS determines patients' levels of pain catastrophizing . CatastrThe FAB consists of two subscales: The Work subscale (FAB-W) and The Physical Activity subscale (FAB-PA). Several studies have supported the validity and reliability of the FAB for the assessment of fear avoidance among patients with CNCP , 75, 79.The TSK is a measure of fear of movement, injury or reinjury. It is reliable with a Cronbach alpha of 0.77 . A scoreThe COMM is a self-report questionnaire that was developed to identify patients prescribed COAT for CNCP who are exhibiting aberrant and/or opioid misuse behavior . The COM2 statistics. Originally, incoming MME was a continuously-scaled variable; however, the distribution was skewed. To rectify the situation, incoming MME was converted into an ordinal variable using the value of 90 as the cutoff; 90 MME was chosen because it is the generally recognized cutoff for high dose opiates, as the 2016 CDC guidelines suggest clinicians should avoid increasing dosage beyond it , wh, wh89], The decision tree approach, also known as the partition tree or the classification tree , 91 was Descriptive statistics were derived from the retrospective data found in the demographic and prescription sections of the EMR. A 90% success rate of participant COAT cessation by the time of graduation was revealed across all participants. Participants ranged in age from 27 to 88 years old; 69% were identified as female. There were only two unsuccessful male participants, making the association between gender and COAT cessation indeterminant, due to the small sample size.Inferences can be made about the socioeconomics of the studied population based on the program design and the EMR report of patients’ insurance payers: approximately 30% of participants were insured by Medicare, 25% by industrial insurers, 10% by Medicaid, <1% participated without insurance, and the remainder had commercial insurance (approximately 44%). Program participation occurred during customary business hours, making it only available to participants who had flexible work schedules, were not working due to retirement or disability, or were employed part-time.P value: .7069.The result of penalized regression, as shown in The relationship between the outcome and the predictors were further investigated by linking and brushing. The top panel of The decision tree approach, also known as the partition tree or the classification tree , 91 was The present findings give strength the theory that elevated anxiety and fear-based beliefs and behavior support COAT reliance, much as they support disability and the negative chronicity of CNCP , as measIt is notable that the PCS and TSK did not uniformly trend with the FAB-PA in relation to refractory COAT reliance. The consideration must be entertained that the FAB-PA results could be a red herring and should be confirmed with repeated or larger studies. However, while the TSK, FAB, and PCS all measure anxiety and fear-based beliefs and behavior around pain, previous investigations into their instrumental interchangeability have frequently failed to show cross-over reliability without reducing contributory revelations , 61–64. Also remarkable in this study is the data pertaining to COMM scores, in that there was no correlation between higher scores and refractory COAT cessation. It has been warned in previous COMM validations that this test is vulnerable to respondent manipulation and false report , which cFinally, the noncontribution of incoming COAT MME is a noteworthy finding. Anecdotally, clinicians frequently assume that patients using COAT with a higher MME are more likely to be refractory to cessation. As a result of this, so many patients have been ostracized from medical clinics based on their high MME values that popular media has coined the term “Opioid Refugee”  to descrIt is the goal of the researchers that the data presented here may inform the effort within the medical field toward continuing to establish efficient and effective best-practice approaches for the treatment of patients with CNCP who rely upon COAT. It has been documented that targeted educational campaigns matching specific treatment to certain patient characteristics can have a positive effect on beliefs and clinical outcomes , 95, 96.The data above are presented as preliminary due to the fact that it was collected in such a way as to make it vulnerable to factors that dilute generalizability. The data was gathered retrospectively from a modestly sized and specific group of nonrandomized, nonblinded patients who were treated in a private practice setting. The subjects were selected to knowingly participate in patient care-planning toward an agreed upon goal of COAT cessation, which is a design weakness, despite the fact that the inclusion criteria required prior attempts and failures at opioid weaning and cessation, which suggests that this population was actually at higher risk for refractory reliance. Further, though varied insurance payer sources suggest a variety of socioeconomic backgrounds among participants, demographic data are limited. However, studies showing focused potential targets for interventions to decrease reliance upon COAT are valuable and scarce. Thus, we argue that sharing information gathered from such a notable clinical observation—as was seen here—is contributory, despite the limitations inherent in the study design.The present results suggest that fear avoidance beliefs and behavior, as measured by the FAB-PA, play a significant role in refractory COAT reliance for patients with CNCP and that incoming MME and COMM scores are noncontributory. While reproducibility of these preliminary results in larger and more varied settings will be key to better understanding the relationship between COAT cessation and anxiety and fear-based beliefs and behavior, this study builds upon an on-going discussion within the medical community aiming to identify and address factors relating to refractory COAT reliance. The investigators are hopeful that insights gained from this report will help researchers and clinicians narrow the scope of inquiry around useful assessments and interventions for best-practice approaches to COAT cessation in the population with CNCP.


How proteins respond to pulling forces, or protein nanomechanics, is a key contributor to the form and function of biological systems. Indeed, the conventional view that proteins are able to diffuse in solution does not apply to the many polypeptides that are anchored to rigid supramolecular structures. These tethered proteins typically have important mechanical roles that enable cells to generate, sense, and transduce mechanical forces. To fully comprehend the interplay between mechanical forces and biology, we must understand how protein nanomechanics emerge in living matter. This endeavor is definitely challenging and only recently has it started to appear tractable. Here, I introduce the main in vitro single-molecule biophysics methods that have been instrumental to investigate protein nanomechanics over the last 2 decades. Then, I present the contemporary view on how mechanical force shapes the free energy of tethered proteins, as well as the effect of biological factors such as post-translational modifications and mutations. To illustrate the contribution of protein nanomechanics to biological function, I review current knowledge on the mechanobiology of selected muscle and cell adhesion proteins including titin, talin, and bacterial pilins. Finally, I discuss emerging methods to modulate protein nanomechanics in living matter, for instance by inducing specific mechanical loss-of-function (mLOF). By interrogating biological systems in a causative manner, these new tools can contribute to further place protein nanomechanics in a biological context. Biological systems generate and respond to mechanical forces, determining cell and tissue behavior in health and disease Guck . ClassicThe evolving view is that the landscape of force sensing and force generation mechanisms by cells is broad, highlighting the fact that cells have to ensure correct processing and integration of different mechanical signals for optimal fitness, proliferation, differentiation, and migration  simulations  to the pulling force (F) in an elastic manner. Indeed, the elastic behavior of random coil polypeptides follows predictions by polymer physics models such as the worm-like chain   Figure :ro is the transition rate in the absence of force and ∆x is the distance to the transition state. A modification of the Bell-Evans model considers that the position of the transition state changes with force, which can explain deviations from simple exponential behavior at extreme forces  has captured the attention of muscle researchers since its discovery  mutations in titin are the most common cause of human dilated cardiomyopathy , located in the C-zone of sarcomeres, interacts with both thick and thin filaments in such a manner that myofilament sliding is predicted to generate load on the protein  or substrates  Figure . For insStaphylococcal adhesins reach covalent-like mechanical stability via coordination of calcium ions  to illustrate how protein nanomechanics contribute to these seemingly different mechanical functions, although many other mechanical proteins fall in any of these categories  tools make it possible to evaluate biological response following acute protein nanomechanical modulation Figure . A numbeThe field of protein nanomechanics has produced seminal contributions regarding the behavior of proteins under a pulling force, yielding highly accurate models of how mechanical force shapes the free energy of proteins. This remarkable success has been enabled by key experimental and theoretical developments over the last 25 years. Nowadays, nanomechanical probing of single proteins has become routine in many laboratories. Current biophysical experiments often investigate how protein nanomechanics is modulated by factors present in living tissues or by mutations, including those that cause human disease. However, instead of replicating in vivo conditions in single-molecule force spectroscopy experiments, there is a growing interest to do just the opposite, i.e., developing strategies to modulate protein nanomechanics in living systems to examine biological response. Among them, I anticipate that protein-cleavage mLOF tools will open new windows of opportunity to investigate the connection between protein nanomechanics and other biological functions. Efforts in this direction include the generation of cell and animal models in which protein nanomechanics can be specifically modulated at will.


We read with interest the recent study by Hu et al., in which they described an approach to elucidate the genomic landscape of synchronous colorectal cancer (SCRC). They used a cohort of paired tumors located in different sites of the colon (right and left colon) and analyzed single nucleotide variation, somatic mutation, and copy number alteration by whole-exome sequencing. The authors finally suggested the heterogeneity between lesions and the polyclonal origin of the paired tumors in the same individual . They adt-test alone, while our results showing clonality were based on a likelihood ratio statistic calculated by the R software package Clonality , such as metachronous colorectal cancers, recent studies have hypothesized that a small proportion could also be from the same origin, as they showed similar molecular signatures . An anal