The pursuitworthiness of team science collaborations in translational neuroscience

Abstract

Neuroscientists investigating the neural bases of cognition in health and disease increasingly face choices about how to structure their programs of research. On the one hand, they can pursue independent investigator-led disciplinary specific research projects and seek funding through traditional granting mechanisms. On the other, they can experiment with interdisciplinary and transdisciplinary collaborations of varying scale and scope, applying for funding for multi-year milestone driven projects. Although team-based initiatives have become a prominent feature of the neuroscientific research landscape in recent years, relatively little is known about how scientists evaluate whether participation in them is worth pursuing. In this paper, we present findings from semi-structured interviews with 10 neuroscientists participating in the TRanslational Initiative to De-Risk NeuroTherapeutics (TRIDENT) that bear on this question. Drawing on Laudan’s distinction between the contexts of acceptance and pursuit and conceptual tools from decision theory to frame the empirical findings, we argue that scientists’ assessments of the pursuitworthiness of interdisciplinary collaboration are shaped not only by expectations about the epistemic benefits that such collaborations may potentially afford, but also by forms of experiential knowledge acquired through prior collaborative experiences. Three key themes emerge from the interviews. First, scientists explore team-based approaches to achieve emergent epistemic benefits that they do not regard as attainable through independent investigator-led disciplinary research. Second, positive and negative experiences occurring in the contexts of these explorations generate forms of experiential knowledge about the costs of interdisciplinary collaboration and the interpersonal dynamics that can facilitate and hinder its success. Third, scientists subsequently leverage this experiential knowledge to determine which future collaborations are worthy of pursuit. We conclude by situating our findings within the relevant philosophical literature on interdisciplinary collaboration, acknowledging the limitations of our study, and identifying some future directions for research.

Introduction

The neuroscientific study of neurodegenerative and neuropsychiatric disease emerged in the final decades of the 20th century within a global system for funding biomedical research built on the assumption that investments in basic science occurring in universities, colleges, and research institutes would produce scientific knowledge that researchers working in industrial settings could later convert into practical applications (e.g., Butler, 2008; cf. Bush & Holt, 2021). Within this system, individual investigators competed for grants to pursue relatively autonomous programs of research organized around a set of specific aims, and disseminated their findings primarily in academic journals (e.g., Finkelstein et al., 2002). The task of converting those findings into improvements in human brain health was left to scientists working outside of academia.1

By the turn of the 21 st century, on the heels of the “Decade of the Brain” (Goldstein, 1994), basic research in the neurosciences of cognition had begun to provide some insights into the neural mechanisms of cognition. While these findings were considered to be relevant for understanding and treating psychiatric and neurological conditions known to be accompanied by cognitive impairments, they were not being translated into treatments (e.g., Cohen & Insel, 2008). Although the reasons for this “translational bottleneck” (e.g., Hyman & Fenton, 2003) were then, as they are now, complex and varied (See for e.g., Rust, 2025; Sullivan, 2014, 2017, 2021; Sullivan et al., 2021; Williams, 2009)2, neuroscientists working at the US National Institutes of Health (NIH) argued that a key contributing factor was how neuroscientific research was funded (e.g., Fenton et al., 2003; Hyman & Fenton, 2003; Cohen & Insel, 2008). Academic neuroscientists, whose career progression and ability to obtain research funding was not contingent on improvements in human health, had little incentive to transform basic findings into clinical applications. Clinicians and scientists working in government and industry, however, lacked the time, resources, and expertise required to understand and integrate basic neuroscience findings into therapeutic development (e.g., Cohen & Insel, 2008). Thus, no one was incentivized to translate knowledge about the neural mechanisms of cognition in health and disease into treatments for patients.

This growing chasm between academic research benches and clinical bedsides became widely known as the “Valley of Death” (e.g., Butler, 2008; Seyhan, 2019). Given the assumption that bridging the chasm required team-based approaches rather than individual research efforts, in 2003, Dr. Elias Zerhouni, then Director of NIH, began rolling out novel funding mechanisms for team science as part of NIH’s Roadmap for Biomedical Research (Zerhouni, 2003). These new multi-year, milestone-driven funding opportunities were designed to incentivize basic and clinical scientists working in academia, government, and industry to organize themselves into interdisciplinary and transdisciplinary Research Teams of the Future whose task it was to collaboratively translate basic science discoveries into clinical applications (e.g., Zerhouni, 2003, 2005, 2006). Funding agencies across North America and Europe soon followed suit, rolling out similar funding opportunities for team science initiatives (e.g., Frégnac, 2023; Stensbøl & Kapur, 2015). The key message that accompanied these developments was that solving complex biomedical research problems required a paradigm-shift in how basic science research was organized, oriented, and funded (e.g., Maienschein et al., 2008).

The emergence and continued expansion of funding opportunities for team science over the past two decades has led some cognitive neuroscientists to recognize that they now face a strategic choice with respect to how to structure their programs of research.3 On the one hand, they may continue to compete for grants to pursue relatively autonomous programs of research within their areas of expertise, hopeful that their findings will one day have a positive impact on human brain and cognitive health. This strategy has the benefit of fitting squarely within the incentive structures by which academic institutions and peers evaluate scientific success, at the potential risk of not having real epistemic benefit or practical impact. On the other hand, they may seek out collaborators to participate in interdisciplinary and/or transdisciplinary teams of varying scale and scope to collaboratively try to translate their research findings into clinical outcomes. While this strategy has potential epistemic benefits and the promise of real-world impact in its favor (e.g., Ivinson, 2014), it may be accompanied by a number of known and unknown risks that may actually serve to thwart realization of these benefits (e.g., Andersen, 2016; Klein, 1990; MacLeod, 2018; Reijula et al., 2023; Salmela et al., 2021).

The choice of competing strategies that neuroscientists face for structuring their programs of research resemble the kinds of choices between alternative scientific traditions that scientists have routinely faced throughout the history of science (e.g., Kuhn [1962], 1996). An interest in understanding as rational the decisions that some scientists make when faced with such choices prompted Larry Laudan in Progress and Its Problems (1978) to distinguish between the context of acceptance and the context of pursuit. While Laudan’s framework sheds interesting light on the decision-making processes that may accompany researchers’ choices to continue to work in an accepted research tradition while allocating some resources to a rival one, relatively little attention has been paid to how contemporary scientists make such judgments in practice. In particular, we know surprisingly little about how neuroscientists evaluate the pursuitworthiness of large-scale interdisciplinary and transdisciplinary collaborations that compete with more traditional forms of independent investigator-driven research.

In this paper, we take a step towards addressing this gap in the literature by describing empirical findings that shed light on how contemporary neuroscientists navigate this decision landscape. To this end, we report findings from qualitative interviews with neuroscientists participating in the Translational Initiative to De-Risk NeuroTherapeutics ((TRIDENT) https://www.tridentpreclinicaltrials.org/), a six-year CAD$24 million interdisciplinary research initiative funded through the New Frontiers in Research Fund (NFRF) Transformation Program (https://sshrc-crsh.canada.ca/funding-financement/nfrf-fnfr/transformation/transformation-eng.aspx). TRIDENT brings together researchers from multiple institutions and disciplinary backgrounds including cellular and molecular neurobiology, pathobiology of neurodegenerative disease, neurochemistry, functional and resting state neuroimaging, computational neuroimaging, rodent behavioral neuroscience, non-human primate neurophysiology, and sex and hormone influences on brain health and behavior. TRIDENT’s aim is to develop an open-science preclinical platform for evaluating potential therapeutics for neurodegenerative diseases. More specifically, by coordinating experimental work across multiple model systems (e.g., neuro-organoids, mice, and marmosets), methodological approaches (e.g., light sheet microscopy, neuroimaging, behavioral assessment techniques), and disciplines (e.g., biochemistry, neurophysiology, computational neuroscience), the initiative seeks to improve the reliability of preclinical evidence used to determine whether candidate drugs for neurodegenerative diseases (e.g., Parkinson’s disease) should advance to human clinical trials. As such, TRIDENT provides a unique opportunity to examine how scientists evaluate whether participation in large-scale interdisciplinary translational neuroscience initiatives is worth pursuing.

The rest of the paper proceeds as follows. In Sect. 2, we revisit Larry Laudan’s distinction between the contexts of acceptance and pursuit and integrate his insights about pursuitworthiness with conceptual tools from decision theory (e.g., Robbins, 1952) and reinforcement learning theory (e.g., Sutton & Barto, 1998). In Sect. 3, we describe the qualitative methods used in the research study. In Sect. 4, we present findings from interviews with TRIDENT researchers concerning the perceived epistemic benefits of interdisciplinary collaboration, the positive and negative experiences they describe encountering in such collaborations, and the forms of experiential knowledge that these experiences generate that they leverage when making decisions about which future collaborations to pursue. We end by situating our findings within the relevant philosophical literature on interdisciplinary collaboration, acknowledging the limitations of our study, and identifying some future directions for research.

Conceptual framework

One of Laudan’s aims in Progress and Its Problems (1978) is to demonstrate the insights we gain when we conceive of science primarily as a problem-solving enterprise and interpret the actions of scientists through this lens. He is particularly interested in the evaluative criteria that inform scientists’ appraisals of scientific research traditions. Laudan claims that these appraisals never happen in a “competitive vacuum” (Laudan, 1978, p. 71). Rather, scientists compare scientific research traditions in terms of their respective problem-solving capacities. When they commit themselves to one scientific research tradition rather than one of its competitors, they do so because of its superior problem-solving capacity. Such acceptance is rational and coherent on Laudan’s account insofar as the aim of science is to increase the number of problems it can solve.

However, according to Laudan, if we look at choices that scientists have made throughout history, we have to make sense of the fact that sometimes they pursue research traditions that not only do they not accept, but that are “patently less acceptable, and less worthy of belief” than the research traditions that they do (Laudan, 1978,110). Although some philosophers of science characterize such split allegiance as inconsistent and even irrational, Laudan argues that this conclusion follows only if we equate rationality in science with working exclusively within that scientific tradition one accepts. However, if we instead take seriously the idea that “the central aim of science” is to solve “a maximum number of empirical problems” while simultaneously keeping “conceptual problems and anomalies” to a minimum (Laudan, 1978, p. 111), then we “cannot [accuse]” scientists “of inconsistency or irrationality if [they] pursue (without accepting) some highly progressive research tradition, regardless of its momentary inadequacy” (Laudan, 1978, p. 112). Laudan goes one step further in claiming that while accepting a “budding research tradition” that exhibits a high rate of progress would be irrational, failing to pursue it “if it has exhibited a capacity to solve some problems (empirical or conceptual), which its older, and generally more acceptable, rivals have failed to solve” would also be a mistake (Laudan, 1978, p. 111). Thus, the comparative evaluative criterion that scientists appeal to in the context of pursuit is not “problem-solving adequacy”, but rather, how progressive two scientific research traditions are with respect to the rate at which they can solve problems.

Inspired by Laudan’s account of the pursuitworthiness of scientific traditions, philosophers of science have recently sought to understand how scientists determine whether experiments are pursuitworthy, and drawn on economic models to illuminate Laudan’s notion of pursuitworthiness (Duerr & Fischer, 2025; Fischer, 2024, 2026; Nyrup, 2015).4 This work has shed light on how contemporary scientists evaluate the benefits and costs of pursuits under conditions of uncertainty and make decisions based on cost-benefit analyses. Here, we adopt a complementary decision-theoretic framework originating in statistics (Robbins, 1952) and later developed in cognitive science and reinforcement learning theory (Sutton & Barto, 1998). 5 A key benefit of this framework is that it can be used to accommodate the role that scientists’ experiential knowledge from past pursuits plays in cost-benefit analyses of related future pursuits. It also may be used to shed light on how such experiential knowledge may be relied upon to reduce uncertainty associated with future pursuits, to increase the likelihood of epistemic payoffs, and to reduce the potential risks that may accompany such pursuits.

The multi-armed bandit problem is a classic problem in decision theory. In its standard formulation, a rational agent aiming to maximize overall rewards faces a set of slot machines (i.e., “one-armed bandits”), uncertain about which one will yield the greatest payout. The agent has limited resources, but their goal is to maximize cumulative rewards over time. Two options for handling the problem immediately present themselves. On the one hand, the agent could sample different machines to gather information relevant to the overarching goal, thereby adopting an exploration strategy. On the other, they could choose to allocate their resources to a single machine that has produced the highest average payoff so far (assuming they have tried one), adopting an exploitation strategy. The problem they confront is that neither strategy on its own will enable them to maximize overall rewards over time. If they pick a single machine and stick with it, they may lose out on an opportunity to find a machine that will provide them with a better overall payout. If they choose to explore indefinitely, trying one machine after another, they will not be able to implement what they have learned to maximize overall rewards. They face what is referred to as “the exploration-exploitation dilemma” (Sutton & Barto, 1998, Chapter I), namely, how to strike a balance between these two strategies to maximize overall reward while minimizing regret.

Interpreting Laudan’s account through this decision-theoretic lens sheds novel light on the strategic considerations that may shape scientists’ judgments of pursuitworthiness. In the historical case studies that Laudan considers, scientists working within an established research tradition that had generated problem-solving returns in the past were confronted with a rival tradition that appeared highly progressive and exhibited significant problem-solving potential. These scientists faced a structurally similar dilemma to the multi-armed bandit problem: continue to exploit the tradition that had served them well or explore the problem-solving potential of the rival tradition. Using this framework to interpret Laudan’s claim: on the one hand, given that the scientists to which he is referring had some inkling about the potential problem-solving payoffs of exploring the budding yet rival research tradition, it would have been (1) irrational for them to exclusively continue to exploit the scientific tradition they accepted (i.e., not to explore) but also (2) irrational for them to commit to exploring the rival tradition at the cost of losing the problem-solving benefits of their accepted research tradition. Laudan argues that scientists who hedged their bets, continuing to devote most of their efforts to the accepted tradition that had performed well for them in the past, while allocating some resources to exploring its progressive rival, were the ones who acted rationally. From a decision-theoretic perspective, they adopted a mixed strategy aimed at balancing exploitation of an established research tradition having historical problem-solving success with exploration of a potentially more progressive alternative tradition, to maximize overall problem-solving returns.

The scientists in the contemporary translational neuroscience case study that we consider here can be understood as confronting a structurally analogous decision problem, although their choice is between different ways of structuring their programs of research. On the one hand, they may continue to exploit the strategy of conducting independent investigator-led research within the disciplinary tradition in which they have expertise, which historically has provided them with relatively predictable problem-solving returns. On the other hand, they may explore a rival approach widely regarded as more progressive in delivering epistemic benefits, participating in interdisciplinary collaborations of varying scale and scope. They also have a third option to adopt a mixed strategy, exploiting their own disciplinary traditions while allocating some resources to explore interdisciplinary collaborations. How do neuroscientists navigate the exploration-exploitation trade-off? What kinds of factors do they consider when evaluating whether interdisciplinary collaborations are worth pursuing?

Methods

To conduct this qualitative study, the lead author recruited a philosophy graduate student with prior experience using qualitative research methods including semi-structured interviews and qualitative data analysis. Over the course of the study, we interviewed 10 neuroscientists who are primary investigators in TRIDENT, representing different career stages and diverse areas of expertise. The sample also included members of different sexes and ethnic backgrounds. Because the sample size is small and the neuroscientists we interviewed occupy distinctive roles within the collaboration, to preserve participants’ anonymity, specific demographic information is not presented. In what follows, we describe the development of the interview questions, participant recruitment and the interview protocol, and data analysis.

Development of interview questions

We developed an interview guide for conducting semi-structured interviews consisting of both closed-ended and open-ended questions that were posed to each participant.6 This format ensured comparability across interviews, while enabling us to identify patterns across participants and probe more deeply into emerging themes. These interviews were designed to elicit participants’ experiences with and perspectives on collaboration and open science, including their motivations for participating in interdisciplinary team science collaborations, their expectations about potential benefits, and the challenges they perceived in collaborative research. Representative open-ended questions posed to participants about collaboration included:

• In your view, what does it mean to collaborate?

• Have you had any previous experiences with collaboration? What was the context and what was the outcome?

• What is your role in the current collaboration?

• What motivated your participation in the current collaboration?

• What are the benefits and what are the challenges of participating in a collaboration of this type?

Recruitment & interview protocol

Invitations to participate in the study were sent by email to all 13 neuroscientists identified as TRIDENT primary investigators at the start of the initiative. Invitations included a letter of information describing the details of the study and a consent form. Twelve participants responded to the invitation agreeing to participate in the study. Ten participants were interviewed, with two unable to schedule an interview within the study period. Interviews were conducted between October 2023 and February 2024, six months after the start of TRIDENT (April 2023). Approval to conduct the research study was granted by Western University’s Research Ethics Board (Project ID: #123269).7

We began each interview by reviewing the study information and obtaining participants’ verbal consent to proceed with the interview. Interviews were conducted either in person or on Zoom and digitally recorded. Interviews lasted approximately 35–60 min in length, with variation depending on how much participants wanted to share. We transcribed the interviews verbatim using NVivo transcription software, and the corrected interview transcripts were imported into NVivo qualitative data analysis software.

Data analysis

Interview transcripts were analyzed using inductive thematic analysis (e.g., Braun & Clarke, 2022). We first familiarized ourselves with the data and collaboratively developed an initial set of codes based on a close reading of the interview transcripts. We then worked separately, applying the codes to the data set. The initial set of codes was iteratively refined as we worked through the transcripts. Once we agreed on a final set of codes, we applied them systematically across the dataset. The lead author then used the coded data to conduct a more detailed thematic analysis aimed at identifying excerpts from the transcripts that shed light on participants’ views about the pursuitworthiness of interdisciplinary team science collaborations. To strengthen the reliability and precision of the analysis, we subsequently undertook a systematic reverification of our coding across all themes. This involved returning to the original transcripts and recalculating participant counts for every theme and category based strictly on excerpts that met the criteria for inclusion. Where excerpts were found to reflect a different phenomenon than originally coded, they were reassigned or excluded accordingly. This process resulted in revised participant counts in several places and led us to reorganize aspects of the findings around a more precise set of analytic categories. The themes and analytic categories reported in the findings section emerged through this iterative process of coding, thematic analysis, reverification, and refinement.

Findings

Three interrelated themes emerged from the interviews that shed light on the decision-making processes researchers engage in when determining whether to participate in interdisciplinary collaborations. First, participants reported pursuing such collaborations primarily for the epistemic benefits they potentially afford, which they described as exceeding what they could achieve independently. Second, participants described positive and negative experiences they had in previous collaborations. Third, they described drawing on knowledge gained from these experiences when assessing whether prospective collaborations were worthy of pursuit. We discuss each theme in turn before examining their interconnections. As we explain, our findings suggest that researchers’ decisions to pursue future collaborations are shaped not only by the epistemic benefits they anticipate, but also by information they have gained from their previous collaborative experiences about the conditions under which those benefits are likely to be realized.

Why pursue? the emergent epistemic benefits of successful interdisciplinary collaborations

1. Expansion of Epistemic Range. The first type of emergent epistemic benefit identified by participants (n=4) were forms of understanding, insight, and problem-solving capacity that arise when researchers recognize the limits of their own disciplinary perspectives, tools, and techniques and come to rely on the expertise of others with different but complementary expertise, thereby expanding their knowledge and broadening the range and complexity of the problems they can solve. One participant articulated this idea particularly clearly:

Well, benefits. I mean, tremendous benefits in my opinion. Like I was saying, the best collaborations are synergistic, so that the whole really is greater than the sum of the parts and you can tackle questions and challenges that you would never be able to tackle on your own. So, bringing in these different expertises in terms of technologies that you can bring to bear on the problem or different models--all are just different ways of thinking and that helps, you know, push you out of your particular worldview. And I think that’s where--sort of where the boundaries of these disciplines are -- I do believe this is where some of the really productive stuff happens. So that’s a big benefit.

This participant indicated that they valued interdisciplinary collaboration precisely because it enabled an appreciation of the limitations of their own disciplinary lens while simultaneously providing an opportunity to productively move beyond it. This “expansion of epistemic range”, to borrow a phrase from Catherine Elgin (2024), which this participant described as “synergistic”, is one form of emergent epistemic benefit that participants attributed to interdisciplinary collaboration.

Another participant pointed specifically to the TRIDENT collaboration as illustrative of the epistemic benefits that emerge when an interdisciplinary team collaboratively develops resources—in this case a platform for de-risking neurotherapeutics—that a single individual would be unable to produce:

But the advantages are many. So, I think the sum of the parts is much more than the individual parts. Obviously, that’s the major advantage. If the sum of the parts was equal, what would the individual parts be worth? It wouldn’t be worth it. But now we are much more efficient; we are able to accomplish more. We are also able to create an infrastructure that can be used by many other people. Not just us, right? And support other colleagues that are not directly involved with TRIDENT.

This participant located the emergent epistemic value of the collaboration in the overarching products the interdisciplinary team could generate. By combining their tools, technologies, and expertise, they could build a platform that exceeded each of these individual sets of components taken in isolation.

2. Convergent evidence. A second emergent epistemic benefit identified by participants but articulated less frequently and in more technical terms than epistemic range expansion, was the strength that hypotheses acquire when independently generated data from different laboratories, methods, models, or disciplinary approaches point in the same direction.8 Three participants emphasized that insofar as interdisciplinary collaborations bring multiple complementary approaches to bear on the same question, they have the potential to produce better evidential support for scientific hypotheses. For example, one participant discussed what is gained when researchers collaborate to use different experimental approaches to test the same hypothesis and get similar results:

It can be so satisfying when another lab has expertise and some sort of complementary approach and they do something, some sort of orthogonal experiment, and you get the same sort of results that are in line with your hypothesis. And it’s just that sort of independent validation that gives you confidence that you’re studying something real and something important.

This participant implied that data produced by a single method do not, on their own, warrant belief in the truth of a hypothesis or that one is studying a bona fide phenomenon. A key function of interdisciplinary collaboration, then, is to validate one’s own methods, and collaboratively generate a more diverse body of evidence in support of a hypothesis than one could generate on one’s own.

Participants also described additional benefits of interdisciplinary collaborations that they valued, but which were not their primary motivation for pursuing them. These included the expansion of their professional networks (n = 8), the development of friendships and collegial relationships within research teams (n = 6), and opportunities for intellectual enrichment from exposure to other fields (n = 5).

What participants experience when they explore interdisciplinary collaborations

1. Experiences associated with Interdisciplinary Collaboration vs. Independent Research. When comparing their experiences of interdisciplinary collaboration to independent disciplinary-specific research, most participants (n = 8) pointed to the additional time interdisciplinary collaboration demands. Many participants (n = 8) expressed dissatisfaction with the amount of time collaborators must regularly invest in administrating and participating in large-scale interdisciplinary collaborations. One participant, referring specifically to the TRIDENT collaboration, compared the time required to support an initiative of its scale with temporal demands typically associated with smaller independent investigator-led grants:

But the flipside is that there’s an enormous amount of administrative stuff which you would not see when you address things on the small-scale individual kind of CIHR9 grant. And that actually takes more time than the science.

This participant explained that the non-scientific work involved in collaborating across disciplines and sites far exceeded that required for independent research while simultaneously reducing the time available to do the collaborative scientific work itself.

Another participant described the sustained investment researchers must make across the life of an interdisciplinary collaboration for the duration of its funding compared to more localized time investments in independent research projects:

I think it is kind of a long-term commitment in a way. So, you have to really commit your time. You have to really kind of then be involved for whatever number of years. Often, maybe, you have to follow other people’s ideals or milestones, whereas maybe if you were just doing it yourself, you would follow your own trajectory.

This participant explained that collaborating may involve sacrificing autonomy over the direction of one’s research program for the sake of achieving the team’s goals and that this loss of autonomy can persist for the duration of the funding. In other words, participation may mean giving up one’s own research interests and career goals.

Other participants (n = 3) pointed to related but distinct problems associated with investing time in collaborative compared to independent research given how funding agencies award grants to collaborative teams and how institutions recognize time invested in collaborative work. One participant explained that even though their institution encouraged interdisciplinary collaboration and participation in collaborative grant applications, their contributions often went largely unnoticed:

The challenges are around the sort of incentive structures in academia. People say they value team science. But then when you are evaluated annually, if you’re not the senior author on a paper, then you don’t get very much credit for it. The same with grants. You might put a huge amount of work into a grant proposal, but if you’re not the nominated principal investigator, then, you don’t get very much credit for it.

As this participant noted, large-scale grants are typically awarded and visibly attributed to a single lead investigator, even when multiple team members contribute substantially to developing the proposal and carrying out the funded research. Similarly, institutional merit evaluations often rely on authorship position and one’s role in awarded grants as proxies for individual contributions. Yet, because these measures do not adequately capture the time and labour invested by all collaborators, substantial contributions to collaborative research may remain institutionally invisible and yield less professional recognition than comparable investments in independently led research.10

2. Experiential Contrasts Between Positive and Negative Past Collaborations. When participants described previous collaborative experiences, they tended to focus on the conduct and dispositions of past collaborators more than any other aspect. Many participants (n = 8) associated positive experiences with collaborators who were personable, trustworthy, reliable, able to recognize the limits of their own expertise, and willing to defer to others when appropriate. Negative experiences, in contrast, were associated with collaborators who were difficult, untrustworthy, unreliable, unable to recognize the limits of their own expertise, and unwilling to defer to the expertise of another when appropriate (n = 7). Against this background, we identified three contrastive themes in participants’ descriptions of positive and negative experiences with past collaborators.

2a. Interpersonal harmony v.s Interpersonal dysfunction. Three participants described experiences of interpersonal dysfunction in past collaborative relationships. One participant simply indicated that “There are personal issues sometimes. Personality clashes. Abusive behavior.” Another participant elaborated on the interpersonal dynamics that can give rise to problems in collaborations:

Problems always just come around to personalities more so. Often, sometimes the better projects or collaborations work when you kind of work in an equal way and sometimes the collaborations that don’t work are when people have their ideas of how it should go. They often like to micromanage. That’s something that’s quite common in academia and people almost think they’re an expert in everything.

This participant claimed that a hallmark of successful collaborations was that they preserve the autonomy of individual researchers by fostering shared decision-making rather than allowing a single researcher to dictate the direction of the collaboration. They suggested that collaborations are likely to fail when individuals with dominant personalities attempt to impose their vision on other members of the collaboration rather than treating everyone as equals.

In contrast, other participants (n = 4) described friendship, mutual enjoyment, and compatibility in working style as key features of their most successful collaborations. One participant described why they preferred working collaboratively to working independently:

There’s a social component to science, and it’s much more fun to do this in a collaborative team than it is to be hidden in a laboratory all by yourself, where no one knows what the hell you’re doing.

This participant claimed that the social aspects of collaboration contributed substantially to their enjoyment of such research. Positive interpersonal relationships made collaborative work more rewarding than conducting research independently.

Another participant described a long-standing collaboration with another scientist that combined complementary expertise with personal rapport, as “probably the best collaborative relationship that I’ve ever had” and noted that they were “dear friends”. A third participant, referring to a long-standing interdisciplinary collaboration with a team of scientists offered a similar reflection, indicating that “It has always been a lot of fun, and people are friendly and open and willing to collaborate.”

2b. Trustworthiness and Reliability vs. Untrustworthiness and Unreliability. Three participants mentioned having had negative experiences with collaborators who failed to fulfill agreed-upon responsibilities, did not contribute equally to shared tasks, or violated trust. One participant discussed previous experiences in which collaborators had failed to deliver their part of the collaboration:

There have been issues of collaborators getting their work done. Some people would say they’re going to do something, and they don’t do it, but they still expect to get all the benefits of the collaboration without putting in the effort.

This participant’s concern was not simply that some collaborators failed to complete assigned tasks, but that they also continued to expect recognition and support for participating in the collaboration. Yet, such failures undermined the reciprocity on which the collaborative arrangement was supposed to be based.

Another participant described experiences in which collaborative partners published work behind their back and without acknowledging their contribution:

So, I’ve had all sorts of collaborations where I have done work, we’ve had an equal exchange of ideas, and then they publish without me, in a good journal.

This participant described investing substantial time and effort in a collaboration, only to be excluded from the publication through which those contributions received professional recognition. As a result, the time and labour invested became a sunk cost: the collaborators benefited from the jointly produced work, while the participant received neither authorship nor appropriate credit for their contribution.

These three participants who described experiences with untrustworthy and unreliable collaborators also described themselves as having had positive collaborative experiences with reliable and trustworthy collaborators. For example, one participant indicated that an interdisciplinary collaboration in which they were participating “was working perfectly because everybody trusts each other.” A second participant likewise described one of their most rewarding collaborations as one built on friendship and mutual trust, without elaborating on this point at length in the interview.11

2c. Epistemic humility vs. expertise overstepping and expertise retreating. Four participants identified the degree to which collaborators respected the boundaries of their own expertise as a significant condition for the success or failure of interdisciplinary collaboration. Although they did not explicitly frame what they experienced as the presence or absence of “epistemic humility” (e.g. Klein 1999; Zagzebski, 1996), in their descriptions of the behaviors of past collaborators they implied that “good” collaborators were ones who recognized the limits of their knowledge and were open to learning from others, whereas “bad” collaborators exhibited the opposite traits. Yet, participants also described behaviors of past collaborators that exemplified more than merely epistemic arrogance. For example, one participant described the kinds of challenges that can arise between collaborators with different expertise.

So sometimes you wind up with a collaborator who has expertise in a different area, but they want to micromanage your part of the collaboration, and that just slows everything down. And so, I think it’s really important in a collaboration for people to feel comfortable with what their expertise is and with what their expertise is not. And you have to have a lot of trust in your collaborator that they’re going to be able to handle the part that you’re not an expert in. And that’s how it becomes synergistic, right? Otherwise, there’s not much point.

This participant explained that some collaborators not only refuse to defer to the expertise of others when appropriate but also behave as if they have the expertise required to undertake that very work. Their epistemic arrogance, in other words, disposes them to engage in behavior that qualifies as what we are here dubbing “expertise overstepping”. Harry Collins’ & Robert Evans’ original distinction between contributory and interactional expertise may be used to clarify the underlying dynamics at work in such cases (Collins & Evans, 2007; See also Plaisance & Kennedy, 2014; See also Plaisance & Doody, 2026). Collaborators who overstep their expertise not only assume they have the credentials to make bona fide epistemic contributions in an area of science in which they lack the requisite expertise, but they also refuse to respect the authority of individuals who have the required credentials to make such contributions.12

Another participant, elaborating on the types of challenges they have faced in previous collaborations, described one type of context in which expertise-overstepping-like behaviors can manifest in interdisciplinary collaborations:

Often it’s more [that] people go in with a preconceived notion about what to expect. They’re like, “oh, this is my idea, this is going to be the outcome” and then when you actually test it, it’s no, I’m sorry, the data is actually going in the opposite direction. Your idea doesn’t work. So, either we rethink the whole project, or maybe we just have a mutual departure.

This participant explained what can happen when researchers enter a collaboration expecting their collaborator’s data to converge with their own, only to find that the results diverge. In such cases researchers must decide whether to accept that their collaborator has produced reliable data and acknowledge that their own results may be mistaken, or to reject the collaborator’s findings and decline to defer to their expertise. One possible response is to insist that additional experiments be conducted, a demand that the collaborator may interpret as expertise overstepping. Another is to withdraw from the collaboration altogether, a response that may be understood as “expertise retreating”. Although these responses differ, they both reflect difficulties in negotiating the epistemic disagreement and determining when deference to another collaborator’s expertise is warranted.

Two participants identified other contexts in which epistemic overstepping-like behaviours had emerged. One participant described an experience in which collaborators unfamiliar with challenges associated with setting up and running their experiments had unrealistic expectations for how quickly their research could proceed and expected adherence to unreasonable timelines given the nature of that research. Another participant recounted being invited to participate in an interdisciplinary collaboration precisely because of their expertise, only to have their perspective and expertise dismissed in a collaborative team meeting.

In contrast to interpersonal harmony and trust, participants did not explicitly recount positive experiences in which collaborators effectively navigated the boundaries of their expertise. This may suggest that experiences of epistemic arrogance and expertise overstepping are especially salient because they are aversive, whereas epistemic humility and expertise relegated to its proper boundaries are more likely to be experienced as aspects of the routine functioning of a successful collaboration.

Leveraging experiential knowledge from prior collaborations

1. Learning how to select collaborators. Six participants discussed how earlier collaborative experiences helped them to identify “good” collaborators for future collaborations. One participant explicitly made this connection.

Oh, there’s always lots of problems, you know, depending on the collaboration. So, with some of the smaller-scale collaborations, there were sometimes differences in sort of collaboration style that would crop up. And so, you know, [I] learned how to find good collaborators or, you know, who [I] thought would be good collaborators based on some of those experiences.

Although this participant did not specify the precise criteria they used to identify “good” collaborators, their comments suggest that prior experiences prompted them to attend to behaviors that indicated a comparable approach to collaboration. In this way, experiences with incompatible collaborators afforded opportunities for this participant to identify what they regarded as hallmarks of compatibility in prospective collaborators.Some participants (n = 5) were more explicit about the criteria they used to select future collaborators. For example, one participant distinguished lack of reliability as the hallmark of a bad collaborator:

I think mostly identifying early on people who will not deliver. And that was the most common sort of issue I have had, you know, collaborating with people that will not deliver their part of the operation.

This participant explained that frequent experiences working with unreliable collaborators prompted them to prioritize identifying early warning signs that a prospective collaborator might fail to be reliable. Given that the success of a collaboration is contingent on each collaborator fulfilling their responsibilities, lack of reliability on the part of a single participant can threaten the progress of the entire collaboration.

Another participant described prioritizing the trustworthiness of potential collaborators and relying on pleasantness and likeability to gauge trustworthiness:

What people tell you and is absolutely true: it’s better to collaborate with people that you like. Somebody told me always make sure they’re FN, which I was like, I was very confused over that. But anyway, FN stood for, and I’m sorry, I’m going to swear: [*$%&] nice. So, you want to make sure that when you’re collaborating– because it’s a pretty – you’re opening things up right? – that there’s a certain amount of trust. And if that person doesn’t do good things with that trust, it’s not really great.

This participant indirectly pointed to the vulnerability that accompanies collaboration. Because collaboration requires individuals to open their work and ideas to others, they regarded trustworthiness as an essential quality in a prospective collaborator. However, given that how trustworthy a prospective collaborator may be is not always easy to assess in advance, they relied on interpersonal cues, including how pleasant, nice, and likeable the person seemed.

2. Learning how to navigate interpersonal relationships in collaborations. Participants (n = 4) described the lessons they had learned in the context of previous collaborations that informed and shaped how they approached future collaborations and interactions with their teammates. Past experiences collaborating primed them to anticipate the kinds of interpersonal and related logistical challenges that may arise and to be psychologically prepared and have strategies in place to handle them.

One participant explained learning that collaborations in general are not so dissimilar from other kinds of human relationships, and that strategies that have been successful for navigating other relationships may be effectively applied there as well:

So, a collaboration you kind of envision as this idealized situation. But in reality, sometimes it’s just like any kind of relationship. There are ups and downs and sometimes things are unexpected. And if you have, I think—a key quality of a good collaboration is understanding that and not being too demanding and being understanding when there are delays, unexpected delays.

This participant suggested that collaborators may enter a collaboration believing that it will function perfectly in practice, only to learn that it is subject to many of the same fluctuations, disappointments, and unexpected difficulties that accompany any human relationship. Learning that collaborations require flexibility and understanding enabled this collaborator to anticipate and be appropriately responsive to interpersonal and logistical issues that might potentially arise in future collaborations.

Another participant indicated that they had learned that an important factor in ensuring the progress of collaborative projects related to setting and enforcing deadlines on oneself and one’s teammates:

I will say it’s a common thing with collaboration in general—the question of how to ensure things progress. Sometimes you are waiting for the other person, and you wait and you wait. So, you have to push back a little. It is often a research related thing about timelines. If you don’t put those specific deadlines in place, sometimes people say, okay, I will do it later. But then we know we all have crazy schedules.

In contrast to the participant who pointed to the need for flexibility in collaborations, this participant emphasized the need for strict adherence to agreed-upon deadlines specifically because progress on one collaborator’s work often depends on another collaborator completing their contribution first. This participant explained that they had learned that when a collaborator falls behind, they needed to be proactive to ensure they would receive the deliverables they required to complete their part of the collaborative work within the specified timeframe.

Referring to the TRIDENT collaboration, one participant explicitly described learning, through experience, that participating and sustaining large-scale interdisciplinary and institutional collaborations requires ongoing effort to maintain good interpersonal relationships among team members:

The difficulty is that you have to be very careful with your teammates. Everybody needs to be respectful and careful with our relationships. But, you know, we have to always keep that relationship alive and nurture it to make sure that it thrives. And that takes time. You have to make an effort-make sure things are smooth with everybody. That’s part of this project, because it’s a large project. Communication is also important. It is difficult because we have people with different backgrounds, in different institutions, and we need to make sure we are all communicating and understand each other.

This participant explained that healthy and productive collaborations require collaborators to actively cultivate their relationships with one another through regular and open communication aimed at sustaining mutual understanding and respect. Their previous experiences had prepared them to recognize the time and effort this work requires. Rather than treating communication and relationship maintenance as peripheral to the scientific work, they understood them as integral to the success of the collaboration.

Discussion

Summary of findings

The findings from this study shed novel light on the kinds of considerations that may inform academic scientists’ decisions to pursue interdisciplinary collaborations. Three main findings emerged from the interviews. First, participants described pursuing interdisciplinary collaborations because of the promise of emergent epistemic benefits that they could not attain by means of independent discipline-specific research. Second, they had positive and negative experiences in previous collaborations. In describing their experiences, they contrasted the costs associated with independent compared to interdisciplinary research. They also contrasted positive and negative experiences they had in previous collaborations. Third, participants described drawing on knowledge gained through these experiences to select prospective collaborators and to prepare themselves for the interpersonal and logistical demands of participating in future collaborative teams.

Our aims in the remainder of this discussion section are two-fold. First in Sect. 5.2, we use the conceptual tools introduced in Sect. 2 as a framework for interpreting our findings and illuminating the decision-making processes through which scientists evaluate the pursuitworthiness of interdisciplinary team science collaborations. In Sect. 5.3, we consider some of the broader implications of this interpretation and situate the findings in relationship to relevant philosophical literature on interdisciplinary collaboration. We end by acknowledging the limitations of our study and identify some future directions for research.

Learning which collaborations are worthy of pursuit

As we understand the decision landscape, neuroscientists engaged in translational research may have several options for structuring their programs of research: (1) conduct discipline specific research independently, (2) participate in interdisciplinary or transdisciplinary collaborations of varying scale and scope, or (3) allocate resources to both. How do they decide among these alternatives?

Laudan provides one set of tools for addressing this question. Scientists may opt to pursue interdisciplinary collaborations because they recognize that the questions they want to answer and the problems they wish to solve exceed the scope of their individual disciplinary expertise and accepted research traditions. To use Laudan’s terminology, participation in interdisciplinary and transdisciplinary collaborations of varying scale and scope is more progressive insofar as it affords opportunities to expand what scientists can know and potentially enables them to solve more problems, and more significant problems, than they can independently. Given the promise of such emergent epistemic benefits, some neuroscientists may therefore allocate a portion of their resources to interdisciplinary collaboration while continuing to conduct their own independent disciplinary-specific research. Allocating only a modest portion of their resources to collaboration may be rational insofar as they have a basic understanding of the kinds of epistemic benefits they can continue to achieve on their own, and cultivating and maintaining their disciplinary expertise is important not only for continuing to secure those benefits but also for ensuring the success of any collaboration to which they contribute their expertise (cf. Griffiths, 2022).

The exploration-exploitation framework affords a complementary understanding of scientists’ decisions to pursue interdisciplinary collaborations. Seeking to maximize their overall epistemic gains and minimize regrets associated with decisions that compromise that goal, scientists may strike a balance by continuing to exploit established programs of independent research and allocating some of their resources to explore collaborations of varying scale and scope. Combining Laudan’s account of pursuitworthiness with the conceptual resources of the multi-armed bandit problem and the exploration-exploitation framework enables us to make sense not only of why researchers pursue interdisciplinary collaborations in the first place, but also of how their assessments of pursuitworthiness may change over time as they acquire experiential knowledge through successive collaborative experiences (See e.g., Thagard, 1997, 2006). Researchers may subsequently exploit what they have learned through earlier collaborative explorations when deciding which future opportunities to pursue and how to collaborate once they have agreed to participate in them.

In the remainder of this section, we apply these conceptual tools to an abstract decision-making scenario that draws directly on our findings. The scenario brings together the themes and categories we identified and is intended to illustrate how a scientist might evaluate whether an interdisciplinary collaboration is worth pursuing and how their evaluations may change over time in response to their experiences participating in collaborative research.

Consider a neuroscientist who wants to advance the discovery of treatments for Parkinson’s disease but has no prior experience collaborating across disciplines. Presented with an opportunity to join a group of researchers with complementary expertise who share this goal, they decide to allocate some of their resources to participate given the possibility that the team can accomplish more collectively than they each can on their own. They have no experiential basis for determining whether this allocation is likely to yield greater epistemic benefits than their independent research. However, representative case studies and institutional discourse emphasizing the epistemic benefits of interdisciplinary collaboration may be sufficient to convince them that the opportunity is worth exploring.

So, the neuroscientist begins working with the team. After they have invested some time and effort, the team receives funding to support its collaborative activities for several years. What began as a relatively limited exploration turns into a multi-year commitment. During this period, the neuroscientist may undergo a range of experiences corresponding to those described by the participants in our study. They may come to appreciate, for example, that the collaboration consumes considerable time because of mandatory team meetings and the coordination required to make progress towards shared deliverables. The temporal demands may exceed what they had anticipated, leaving them with progressively less time for their independent research. The first institutional merit evaluation they receive after the project has begun may yield disappointing returns.

Yet, in the context of the collaboration, they may have positive experiences and even productive relationships with collaborators who are personable, dependable, hard-working, attentive to deadlines, aware of their own epistemic limitations, and willing to learn from others. They also may have negative experiences with collaborators who are unpleasant, unreliable, untrustworthy, dictatorial, or disposed to overstep the boundaries of their expertise. Although these scenarios do not exhaust the possible experiences the neuroscientist may have over the course of the collaboration, they correspond to the major types of experiences described by our participants. A single collaborator also may exhibit a combination of positive and negative characteristics, and the neuroscientist will likely gain some experience navigating these interpersonal dynamics.

Suppose that our hypothetical neuroscientist emerges from this multi-year collaborative exploration having learned a host of things about interdisciplinary collaboration. Perhaps the team achieved its goals, and our neuroscientist may have learned something about the conditions that contributed to its success. Alternatively, the team may have failed to achieve its aims or may have achieved only some of them. In this case, the neuroscientist may have learned something about the factors that contributed to those failures. Although they entered the collaboration with no experience working across disciplines and no blueprints for doing so, they may emerge with first-hand experiential knowledge of how interdisciplinary collaborations operate in practice.

As future collaborative opportunities arise, the neuroscientist may exploit this knowledge in an effort to make better decisions about which collaborative opportunities to pursue and how to participate in them. Participants in our study described relying on previous collaborative experiences to select more compatible collaborators for future interdisciplinary endeavors and to navigate interpersonal dynamics in ways intended to facilitate progress and maintain collaborative relationships. Exploiting experiential knowledge neither eliminates the risks associated with interdisciplinary collaboration, nor guarantees the success of future collaborations. It may, however, improve scientists’ capacity to assess collaborative opportunities and prepare them to respond to difficulties once a collaboration is underway.

The exploration-exploitation framework also draws attention to differences in scientists’ past histories of collaboration. These histories may not only influence their decisions about which future collaborative opportunities to pursue, but also shape how they behave and respond to others in new collaborative contexts. Early career researchers (ECRs), who typically have less experience collaborating, may therefore be at a disadvantage both when determining which interdisciplinary collaborations are worthy of pursuit and when navigating challenging interpersonal dynamics that arise within them. Moreover, courses intended to teach ECRs how to collaborate across disciplines often lack important features of real-world collaborations, including the pressure of deliverables. Such courses are no substitute for the tacit knowledge gained through collaboration, and may thus be insufficient to prepare ECRs for how interdisciplinary collaboration unfolds in practice. Yet, the production of knowledge is widely thought to require the contributions of individuals from diverse backgrounds and career stages (e.g., Longino, 1990), and funding agencies emphasize the inclusion of ECRs in collaborative research. More should be done, then, to better prepare ECRs for participation in interdisciplinary collaborations of varying scale and scope, and to safeguard their well-being in the context of such collaborations.

Interpersonal conditions and the realization of emergent epistemic benefits

A key finding of our study was the connection participants drew between interpersonal dynamics and the realization of emergent epistemic benefits in interdisciplinary collaborations. Specifically, they identified relationships among team members as important in determining whether those benefits would be realized. The significance they attached to friendliness and affability, trust, reliability, recognition of the boundaries of one’s own expertise, and respect for the expertise of others, makes sense, given that interdisciplinary collaboration requires researchers to depend on one another’s contributions, judgments, and expertise. When these things are absent, collaborators may be less willing or able to rely on one another in ways required to produce emergent epistemic benefits.

Many scholars have discussed the important role that trust plays in interdisciplinary collaborations and scientific collaboration more generally (e.g., Hardwig, 1985, 1991; Andersen & Wagenknecht, 2013; Wagenknecht, 2015; Elgin, 2025). In the context of large-scale interdisciplinary collaborations like TRIDENT, some members may enter the collaboration as complete strangers. Depending upon the aims of a given collaboration, it may bring together researchers who are drawn from distinct and previously separate professional networks. Under such circumstances, one researcher may trust another, not on the basis of personal experience, but because a collaborator whom they already trust vouched for that person.

Once a collaboration is underway, it provides a context in which researchers can get to know each other and form their own assessments of one another’s trustworthiness. In the absence of an established history of collaborating with someone, they may rely on observable behaviors as indicators of whether others are friendly, trustworthy, dependable, and respectful. This may partially explain why some participants placed importance on the likeability of prospective collaborators, as it can function as an early interpersonal cue that may signal the presence or absence of these other important characteristics.

Some scholars have emphasized the necessity of a cognitive division of labor for the success of interdisciplinary collaborations (Andersen, 2016; MacLeod, 2018; Reijula et al., 2023). As Reijula and colleagues (2023) explain, interdisciplinary collaborations often involve “an iterative form of problem solving” and “back and forth dependency” relationships among team members to solve complex problems. Our findings suggest that positive interpersonal relationships among members of collaborative teams are instrumental to problem-solving success. Trust and reliability, for example, enable collaborators to depend on work that they lack the requisite expertise to evaluate (e.g., Hardwig, 1985). Recognition for the limits of one’s own expertise and respect for the expertise of others make it possible for collaborators to defer when appropriate yet still contribute their disciplinary knowledge. Friendliness may facilitate communication, make disagreements easier to navigate, and help sustain relationships. Unreliability, distrust, interpersonal hostility, and expertise overstepping, in contrast, may mean that these dependency relationships never materialize, nor the emergent epistemic benefits to which they are supposed to give rise.

Limitations and future directions for research

Although our findings correspond with available philosophical accounts of interdisciplinary collaboration and its challenges, our research study has several limitations. First, our findings are based on interviews with a relatively small number of neuroscientists participating in a single translational research initiative. Thus, it remains an open question whether the considerations that participants described as informing their judgments about the pursuitworthiness of interdisciplinary team science collaborations are representative of those that guide scientists’ decisions about collaboration in other contexts. Second, the study relies on participants’ retrospective accounts of their decision-making processes, which may not fully capture the factors that informed their decisions at the time they were made, nor the ways that those factors interacted in the decision-making context.

Despite these limitations, our findings point to the need for further research examining other translational research initiatives and interdisciplinary team science collaborations, both within neuroscience and across other scientific domains to determine whether the findings we describe here generalize beyond TRIDENT. Prospective and longitudinal studies would be valuable insofar as they could examine how scientists’ expectations, interpersonal relationships, and judgements of pursuitworthiness change while collaborations are underway rather than relying exclusively on retrospective accounts.

Our findings also suggest the important function that meta-level reflection on their previous collaborative experiences may play in enabling scientists to better evaluate future collaborative opportunities, select collaborators, and better prepare for the potential interpersonal and logistical difficulties that may arise in interdisciplinary collaborations. Sharing such experiential knowledge may also benefit researchers with less experience participating in collaborative research, especially early career researchers, who have had fewer opportunities to acquire it.

Conclusion

Our aim in this paper was to shed light on how neuroscientists involved in a large-scale translational research initiative evaluate the pursuitworthiness of such initiatives. The qualitative findings presented here suggest that scientists pursue such collaborations because they expect them to generate emergent epistemic benefits that exceed those they can achieve on their own. At the same time, their experiences in such collaborations provide them with forms of experiential knowledge that they later draw upon when deciding whether participation in future collaborations is worth pursuing. By analyzing these findings through the lens of Laudan’s concept of pursuitworthiness and the explore–exploit trade-off, we have argued that judgments about whether interdisciplinary collaborations are worth pursuing are shaped not only by scientists’ expectations about their potential emergent epistemic benefits, but also by experiential knowledge acquired through prior collaborative exploration. Our findings further suggest that the realization of emergent epistemic benefits depends partly on the interpersonal conditions under which epistemic interdependence within interdisciplinary collaborations is established and maintained.

Author contributions

Jacqueline Sullivan: conceptualization, methodology, ethics approval/project administration, investigation, formal analysis/coding, interpretation of findings, writing—original draft, writing—review and editing. Saher Panjwani: investigation, formal analysis/coding; interpretation of findings; review and editing. Both authors approved the final version of the manuscript.

Funding

This research is financially supported by TRIDENT (TRanslational Initiative to DE-risk NeuroTherapeutics), which is funded by the New Frontiers in Research Fund, application NFRFT-2022-00051.

Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Declarations

Ethical approval

The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements of Canada and received approval from the Research Ethics Board of the University of Western Ontario.

Informed consent

The authors confirm that all research participants provided informed consent and that this research was approved by the university research ethics review board.

Conflict of interest

 The authors declare no conflict of interest.