Subjects compulsory at KS3
Core subjects | Foundation subjects | |
---|---|---|
English | Art and design | Modern languages |
Mathematics | Design and technology | Music |
Science | Geography | Personal, social, health and economic education (PSHE) |
History | Physical education (PE) | |
Information and communication technology (ICT) | Religious studies |
Role of interviewee | Nature of establishment | No. |
---|---|---|
Librarian | School | 5 |
FE | 3 | |
University | 3 | |
Admin and learning support | School | 3 |
FE | 3 | |
Educator | School | 5 |
FE | 20 | |
University | 8 | |
Student | School (KS3) | 33 |
School (KS4) | 8 | |
School (sixth Form) | 2 | |
FE | 31 | |
University (UG1) | 14 | |
University (UG2) | 5 | |
University (UG3) | 9 |
Numbers of UK-educated students responding to the questionnaire
Key stage 3 | Key stage 4 | A Level/BTech | Undergraduate (years 1-3) | Undergraduate (years 4+) | Postgraduate | |
---|---|---|---|---|---|---|
Total | 203 | 121 | 255 | 559 | 53 | 165 |
M | 104 | 55 | 110 | 199 | 21 | 49 |
F | 99 | 66 | 145 | 360 | 32 | 116 |
Composite index based on student preferences for maths and ICT
ICT | Maths | |||
---|---|---|---|---|
(% likes) | ||||
1 | Student likes both maths and ICT | 105 | 33.0 | 20.0 |
2 | Student likes one | 440 | 44.0 | 57.3 |
3 | Student neither likes nor dislikes either | 145 | (% dislikes) | |
4 | Student dislikes one | 344 | 34.4 | 51.1 |
5 | Student dislikes both | 130 | 34.4 | 31.4 |
Number of students who liked just one of the two subjects (maths or ICT)
Student likes maths but dislikes ICT | 119 | 8.9% |
Student likes ICT but dislikes maths | 73 | 5.4% |
Spearman correlations between maths/ICT preference index and choice of hard or soft course at university
Correlation | Significance level | |
---|---|---|
Hard/soft (hard=1, soft=2) | 0.274 | <0.001 |
Correlation | Significance level | |
---|---|---|
I like subjects that give me the opportunity to express my own opinions | −0.327 | <0.001 |
I like subjects where there are clear right and wrong answers | 0.454 | <0.001 |
Correlation | Significance level | |
---|---|---|
(a) When I am given a piece of work to do I have to find information for myself in order to do the work | −0.328 | <0.001 |
[…] | ||
(b) I feel confident that I will be able to find the information I need | −0.131 | <0.05 |
(c) I get satisfaction from knowing my information has come from a trustworthy source | −0.212 | <0.01 |
(d) I put a lot of effort into making sure I get my information from sources that are trustworthy | −0.185 | <0.01 |
(e) I get better marks when I focus on the most relevant information | −0.208 | <0.01 |
(f) I can get good marks without really understanding the information I am using | 0.166 | <0.025 |
(g) I get satisfaction from knowing I have understood the information I am using | −0.281 | <0.001 |
(h) I put a lot of effort into understanding how all the information fits together before I use it | −0.151 | <0.025 |
Correlation | Significance level | |
---|---|---|
(a) Wikipedia is a reliable source | 0.292 | <0.001 |
(b) My teachers tell me NOT to use Wikipedia for my school work | −0.159 | <0.025 |
(c) I use information from Wikipedia in my school/university work | 0.323 | <0.001 |
(d) I can tell whether the information on Wikipedia is true or not | 0.134 | <0.05 |
Notes: n =231. 1=Strongly agree, 5=Strongly disagree
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This research and its publication was supported by a grant from the Arts and Humanities Research Council. The authors would like to express their gratitude to the many staff at schools and colleges in and around Sheffield who helped to make this research possible, and to the reviewers for their constructive and helpful criticism. Ethical guidelines: the research was carried out in accordance with the University of Sheffield’s ethics policy available at www.sheffield.ac.uk/rs/ethicsandintegrity/ethicspolicy/general-principles/homepage . All interviewees were provided with an information sheet Sheffield. This was presented to volunteers prior to the interview. An edited form of the sheet also formed part of each questionnaire. Conflicts of interest: no conflicts of interest were identified in the course of this research.
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Catriona mcmillan.
School of Law, University of Edinburgh, Edinburgh, UK
Graeme laurie, emily postan, nayha sethi, annie sorbie.
In this article, we argue that the relationship between ‘subject’ and ‘object’ is poorly understood in health research regulation (HRR), and that it is a fallacy to suppose that they can operate in separate, fixed silos. By seeking to perpetuate this fallacy, HRR risks, among other things, objectifying persons by paying insufficient attention to human subjectivity, and the experiences and interests related to being involved in research. We deploy the anthropological concept of liminality – concerned with processes of transformation and change over time – to emphasise the enduring connectedness between subject and object in these contexts. By these means, we posit that regulatory frameworks based on processual regulation can better recognise and encompass the fluidity and significance of these relationships, and so ground more securely the moral legitimacy and social licence for human health research.
It is a near-universal legal truism that almost all regulated entities are held to fall into one of two categories: subject or object (classically: ‘person’ or ‘thing’). Within these two broad legal realms, but particularly with respect to ‘objects’, further ontological and moral demarcation usually occurs, conferring differing degrees of legal protection in accordance with the relative value that society places on the object in question. For example, we place different social, monetary and moral value on different ‘objects’, from pet animals, to historical artefacts, to ideas. It is no different within the field of health research regulation (HRR). The objects of HRR are myriad; they include ‘personal data’, DNA and RNA, cell lines, ‘human tissue’, ‘gametes’, and a range of legally prescribed embryos’, and all are bounded in law by their own definitions, frameworks, and rules of production, storage and use in health research. 1 As a technocratic domain that is fundamentally concerned with managing risks to humans, HRR regimes are understandably focussed on the definition and categorisation of suitable objects of regulatory capture – such as what counts as ‘personal data’ 2 or which kinds of embryo can be created for research purposes 3 – but there is often a deep irony that also arises, viz., the focus is then lost from the person(s) to whom these objects relate or from whom they have been derived, or who have contributed materially to the creation of these objects. 4
From the annals of medico-legal lore, we have the infamous Moore case in which it was held that the cell line derived from Mr Moore’s spleen was ‘factually and legally distinct’ and on this basis, his claim to some legal interest in the cell line was refused by the Supreme Court of California; 5 equally, in the Source Informatics case in the UK, it was held to be no breach of confidence when identifiers were stripped from patient data to allow those data to be passed on to third parties for statistical analysis purposes, despite the lack of consent to this from the subjects concerned. 6 Indeed, the role of such anonymisation techniques in ‘breaking’ the connection between subject and object also finds considerable support in many statutory regimes of HRR, such as using personal data or human tissue for research purposes. 7 In stark contrast, extensive social science literature shows that citizens continue to experience connection with the objects that they donate to health research or which are produced from their data or tissue to promote new scientific research, even if they are anonymised. 8 A crucial question therefore arises: in legally fixing these ‘objects’ in pre-determined categories, does current HRR sufficiently capture the subjective, experiential dimensions of health research processes and the persons necessarily involved?
To better understand the nature of the relationship between human research participants (conceived of as subjects in more than one sense) and objects within the research process (a process that we suggest is fluid rather than fixed), we employ the concept of liminality: this is the anthropological concept coined in the early twentieth century by Arnold van Gennep: ‘ … [d]eveloped to make sense of ritual, structure, and agency, the notion of liminality refers to a threshold phase characterised by uncertainty, possibility, marginality, and transformation.’ 9 Given that health research is precisely concerned with uncertainty and transforming materials to produce new human understandings, this conceptual lens helps to reveal – as we will argue – that the relationship between the legal categories of ‘subject’ and ‘object’ is poorly understood, and that existing categorisations within HRR are inflexible and insufficient by obscuring the important transformations that take place between subjects and objects.
As we have shown elsewhere, 10 liminality focuses attention on process and transition as experienced by human beings at times of change in their lives. In particular, the important change that occurs is to the status of persons, 11 and the classic transitions are those from childhood to adulthood, from singledom to state/religious-sanctioned union, from wellness to illness, and from illness to death. The processes in question – when designed correctly – ought to support those persons and lead them through and out of the liminal phase. It is important to do so because liminality can represent the breakdown of order and pre-existing norms and social structures; liminality, therefore, is inherently uncertain, and can be chaotic and disruptive if not recognised and managed well. For present purposes, we posit that the processes of conducting human health research can usefully be subjected to the lens of liminality. This is so for two reasons: first, the nature of research itself is inherently transformative and uncertain, as suggested above. 12 Second, the act of taking part in research is neither morally nor socially neutral. While this is self-evident in the case of clinical trials participation, we suggest that even the act of donating tissue or giving permission to use personal data in research involves a change of social status for the persons involved: they are transformed from everyday citizens into research participants. 13 As we propose in Part 3 of this article, it may also transform them in broader and more fundamental ways. A liminal framing further suggests that we must follow all of this process through because the research endeavour is, in fact, comprised of a series of thresholds and phases, each with different implications and actors (such as recruitment of participants, and/or legitimate obtaining and use of data and tissues; involvement of research nurses and clinicians, and compliance with a host of regulatory approvals). Manifestly, ethical and socially responsible research does not begin and end with a successful ethical review. The social value of the research must also be realised (or at least there must be a reasonable prospect of this). Seen in this light, the entire research endeavour is inherently a liminal experience from the perspective of the individual who becomes a research participant . We are transformed by this process of being involved in research. This change of status in itself need not be great, nor necessarily significant to the lives of particular individuals. Nonetheless, we are confronted by the fact that it is a reality and a product of these biomedical practices. This, then, leads us to ask: what can and should be done to lead people through and out of the liminality of human health research?
It is our position that by binding objects of health research in their various regulatory silos, the law does not adequately capture the relationships between human research participants and the regulatory objects deriving from them , nor does it pay adequate attention to the experience of participants who may at times be both subject and object. In other words, by binding ‘objects’ within these silos, HRR does not capture persons’ (or subjects’) potential on-going interest(s) in objects relating to them and used in health research; moreover, these are interests that can persist even once any physical connection or de facto control has ceased.
This article proceeds as follows. In Part 2, we argue that while the law requires categorisation to provide a degree of certainty, doing so in HRR can deflect us from attending appropriately to the important subjective and experiential aspects of research. Part 3 explores three case studies that illustrate the most common subject-object transitions experienced in health research: (1) from object to subject (viz. research participation); (2) from subject to object (viz. data use); and (3) ‘subject/objects’, where something does not fit easily within this legal binary (viz. embryos in vitro ). Finally, Part 4 draws from these case studies to offer a new framework – grounded in the idea of processual regulation 14 – that provides a more nuanced way of capturing these three transitions. We argue that by employing processual regulation, it is possible to better reveal the subjective, experiential and fluid nature of the relation between subjects and objects in HRR and so ground more robustly the legitimacy of research.
A core function of law and regulation is to prescribe and proscribe certain behaviours among human actors to achieve certain desired outcomes. A common thread that runs through this core function, no matter the law or regulation in question, is the steering of human behaviour through the mechanism of object categorisation . That is to say, law and regulation affect human behaviour by connecting desired conduct to an underlying object of regulatory attention, and this object (or set of objects) will often fall within one or more larger overarching legal categories. To categorise and ‘object-ify’ is the modus operandi of law. How things are sorted and grouped carries significant weight for the ways in which the force of law may be felt. As Sarat and colleagues put it:
In its basic operation, law attempts to create, police, and occasionally transgress social, spatial and temporal boundaries. […] Within law’s spatio-temporal grid, complex classifications are established, creating boundaries that define individuals, communities, acts and norms … . 15
This is exemplified in much of the civil law legal system, where civil codes promulgated by legislatures lay out a corpus of general principles organised in a systematic way, such as the Napoleonic Code’s tripartite organisation into the law of persons, property law and commercial law. But it is equally reflected in the Common Law tradition, where statutes, regulations and judge-made law alike shape actions and identities of individuals and groups by creating boundaries around objects (broadly defined), be they personal data, embryos, organs, medical devices, investigational medicinal products, and so on. For example, the function of data protection law in Europe 16 is to protect the fundamental rights of ‘data subjects’ – those whose personal data is processed by another person or entity – and to promote the smooth flow of personal data across systems and countries for economic and social benefit. It accomplishes this two-pronged function by making ‘personal data’ the object of the law. Behaviours of key actors – specifically ‘data controllers’ and ‘data processors’ – are steered through rules governing how they may use the personal data of data subjects. Once the actions of the persons in question generate, process, or in any way interact with the category of ‘personal data’, they are immediately brought within the purview of data protection law and the said persons must discharge a range of associated responsibilities to ensure that their actions are lawful. Conversely, if those actors are not dealing with ‘personal data’, then their actions are not subject to this legal regime. Thus, much of the discussion in the field of data protection is concerned not only with what constitutes ‘personal data’ but also when does personal data exist. The obvious example is the technique of adequately anonymised data which renders it non-personal for the purposes of this law.
To take another example, the function of clinical trials law in Europe 17 is to protect the rights, safety, dignity and well-being of research participants (research subjects) in ways that – at the same time – generate reliable and robust data and avoid unnecessary administrative delays for starting a clinical trial, thus making Europe a relatively attractive place to conduct research. Here, desirable human behaviour of key actors – specifically clinical trial sponsors and investigators – is steered through rules governing how they may set up and run a clinical trial. The object of the law is the ‘clinical trial’, specifically to test medicinal products for human use. Both data protection law and clinical trials law fall within the larger category of the law governing scientific research involving humans, though of course each of these laws may fall also within other larger categories (e.g. the law governing commercial entities, the law governing the marketing of pharmaceuticals).
While we might accept prima facie that law and regulation need to ‘object-ify’ and categorise to fulfil their core functions, in the context of health research, we must also recognise that this can yield problematic outcomes in certain areas of law. Through its object categorisation, the law can inadvertently spur an overly technocratic view of the world that pays insufficient attention to human subjectivity and experience. Arguably, this is most pronounced in the health research sphere precisely because the human serves here as both subject and object. Because the human is at the centre of health research – as an investigator, participant, and ultimate beneficiary of the research – it is not enough for law and regulation merely to steer the behaviour of those who interact directly with those human ‘subjects’ to do so in ethically and socially desirable ways (whatever that might entail). That is, the focus of law ought not to be simply at the locus of the researcher-participant interaction, e.g. when recruiting someone to a clinical trial; nor should it only be at the interface of clinician/researcher and her patient/research participant; nor only on the biobanker and the responsible management of her cohort of participants. This is not to deny that these are crucially important foci of legal attention: the inter-subjective relationships involved are vital to the entire research enterprise, and they are founded on the human value of trust. But, it is precisely the entire research enterprise that is at stake and that should be under scrutiny. Trust must be maintained throughout the totality of the research process, i.e. from design and approval of the research protocol to the realisation and delivery of social value from the research itself. Yet, from a legal standpoint, as the research participant becomes less physically proximate to the research – that is, as the human subject becomes reduced to research ‘objects’ such as ‘personal data’ or ‘human tissue’ – the focus of law shifts, and some important human interests are overlooked as a result.
Human ‘subjects’ all too easily become the diced and deconstructed ‘objects’ of the law by having their component parts stripped down and gridded within artificial boundaries and categories that do not necessarily reflect the expectations or experiences of participants who take part in research. Personal data, embryos, cells, DNA, blood, and organs are both physically and legally excised from the body and slotted within silos or sections of rules. This observation in no way denies that protections do occur across this process, but they appear in isolation and at disparate junctures under diverse legal regimes. For example, there are no legal mechanisms to trace how far and how well research participants’ interests are protected across the entire research trajectory. The ‘promise’ of research made to the research ethics committee (which, we note, is before the research even begins) is not always adequately monitored over time; 18 trust remains fragile throughout these processes, and can be broken for want of a clear social licence; 19 downstream uses of research data and materials remain a legitimate concern of citizens, notably commercial access and use, even when core privacy and physical interests have been well-protected. 20 All of this suggests that the siloed approach of law provides an incomplete picture, at least from the perspective of the human experience of being involved in research.
How then are we to make sense of this? We do not challenge the need for law and regulation to categorise, but we do suggest that in the process of object categorisation in health research, law and regulation could do better to (re)incorporate the subject (the participant) into the processes of human HRR. In other words, by better understanding how law and regulation create objects and categories of objects and, in so doing, separate the human subject from object, we can consider the ways in which we may devise legal and regulatory frameworks that better account for any enduring connections between subjects and objects. This analysis, in turn, can help to minimise disruptions or controversies that can arise in health research. To do so, we now turn to liminality, an anthropological concept concerned with processes of transformation and change.
In case study one, From Object to Subject , we argue that important normative work is done by recognising the identity interests that citizens have and retain throughout the research endeavour, that is, by recognising that research practices and findings can have important impacts on participants’ own identities to the extent that the experience of participation contributes to or detracts from their own narratives about who they are as persons.
In case study two, From Subject to Object , we suggest that governance frameworks that over-emphasise mechanisms such as the anonymisation of research participants’ data, or the role of informed consent, fail adequately to account for the interests that individuals might retain in their data, even when this may be no longer identifiable or ‘connected’ in the eyes of the law.
Finally, in case study three, Between Subject and Object , we offer insights into how an inherently liminal being – the human embryo – is currently categorised. We argue that the failure to recognise what it means to lead embryos out of liminality towards diverse ends of either reproduction or research use, make current regulations increasingly morally questionable. Moreover, we suggest that the liminal analysis offered here coupled with the normative account of what it means to be a research participant, viz. the experiences of women and men as progenitors and donors, can provide good reasons why gamete and embryo donors ought to have more of a say in the research that is done with and on their embryos.
In ordinary language, there is ample room for ambiguity about whether participants in health research may most appropriately be thought of as the objects or the subjects of the endeavour. In one sense, they may be seen as the objects of study – passive parties to whom research activities are done. 21 This is perhaps most marked where research is conducted using data such as patient records, or stored tissue samples, without any direct contact with, or active involvement of, the participant. However, regulatory instruments and ethics guidelines governing health research commonly refer to ‘research subjects’ or ‘trial subjects’. 22 This is fitting inasmuch as participants’ bodies, behaviours, tissues or data are the focus and material – the subject matter – of inquiry. Nevertheless, it is not always or obviously the case that the participant – qua person – is the true subject of research, when most studies aim not to investigate individual-level traits or health, but to develop generalisable knowledge derived from analysis of a large amount of aggregate information on multiple participants.
This section will propose one important reason why we should not lose sight of the research participant as subject in one important sense, even if we do not always use this term to refer to them. This is because the language of subjecthood serves to highlight an ethically significant aspect of the process and experience of participation. 23 That is, taking part in health research can serve to construct the participant as the subject of her own life and experiences, by contributing to the development of her identity in a more thoroughgoing and normatively significant way than by merely acquiring the label of ‘research participant’.
To make sense of these suggestions, it is necessary to say a little more about what it might mean for someone to have and to develop an identity. Although the account outlined here is only one possible way of conceptualising what makes each of us the particular individuals we are, it is one that resonates with many of our everyday ideas about what identity looks like and how it works; it is also receiving increasing (though not universal) support in philosophical and bioethics debates. 24
A narrative conception of identity holds that each of our identities is constituted by the story we would each give of who we are. This story includes, amongst other features, our accounts of what we have experienced, what we have done and plan to do, our characteristics, desires, beliefs and values, and our relationships with other people, our bodies and the world around us. It is thus a constellation of many kinds of experiences, self-descriptions and modes of self-understanding. And it is a ‘story’, not only in the sense that extends and develops over time, but also because the features of which it comprises are not discrete elements, but part of a whole from which these various features derive their role, meaning and significance. A key feature of narrative theories of identity is the claim that our self-narratives do not merely describe us, but actually constitute who we are. An individual is the subject of her own self-narrative, in the sense that she is the protagonist, albeit one whose story is shaped by others and her environment, including that of her own body. 25 And the contents and nature of her self-narrative provide the interpretive and evaluative frame through which she experiences herself and the world, it shapes her particular subjectivity.
We will first examine the suggestion that the sheer act of taking part in research may itself contribute to the constitution of the participant as a subject. The proposal here is that taking part could provide experiences or plotlines that feed into an individual’s identity narrative and present opportunities for particular characteristics or self-descriptors to come to the fore or recede within this narrative.
According to a narrative conception of identity, our self-narratives are interpretive and ‘curated’ stories, in which some aspects of our lives feature (some prominently, others less so), while others do not. 26 At the most straightforward level, then, taking part in health research is a plausible candidate for acquiring a place in an individual’s account of who they are simply because, for most of us, it is not a quotidian activity. It will bring novel interactions and experiences, and often involve unusual exposure to risk or scrutiny and a degree of burden. It may well then stand out as noteworthy amongst life’s other activities and thus ‘make it into’ someone’s account of who she is.
Similarly, as one of us has argued elsewhere, 27 to the extent that taking part in research entails encounters with information about one’s own health, body or relationships in the form of research findings (either aggregate results or individually-relevant observations), 28 these too may impact upon participants’ accounts of who they are. They may do so, for example, by adding, subtracting, reinforcing or reconfiguring threads of participants’ self-characterisations, including their relationships to others. Such findings might include, for example, results indicating increased susceptibility to mental illness, or those that cast doubt on assumed genetic heritage. 29 To be clear, the suggestion here is not that research findings reveal who a participant ‘really is’, but rather that they may affect the ways in which participants construct the stories that constitute their identities.
However, even allowing that research findings and other experiences associated with participation may feed into someone’s self-narrative, it might still be queried whether the addition of even novel and noteworthy life experiences or insights supplied by research findings actually makes any substantial difference to who someone is, to their own identity and subjectivity. One response to this is that these new narrative threads do not occupy an isolated role in our identity narratives. Rather they serve to contextualise, colour and reconfigure other aspects of these stories and add to the lens through which we view and interpret our other experiences. A further response is that the experience of participation, and perhaps particularly the choice to do so in the first place, may be seen as playing normatively, not merely qualitatively, significant roles in constituting the participant as someone with particular commitments and values. This suggestion needs to be unpacked a little further.
Empirical studies that have investigated people’s motivations for taking part in various kinds of health research suggest that there are a number of possible ways that participation could contribute to the construction of the participant as a ‘moral subject’. Beyond being motivated by potential clinical benefits (for example access to check-ups or therapeutic interventions, which some may hope to gain from taking part), 30 it is not uncommon for individuals to characterise their participation as an expression of their principles, values and concern for others. For example, they may report being motivated by a desire to help others – either future patients with a particular condition or, more generally, by contributing to the generation of scientific knowledge as a public good. 31 Conversely, they may be less willing to participate when they doubt the social value of a study. Particularly in the field of genetic research – due to the inherently shared nature of genetic data and family histories – taking part may be a manifestation of particularly relational aspects of participants’ identities and associated bonds of care and concern for others. For example, participants undergoing testing to improve understanding of genetic diseases that run in their families may express their motives in terms of concern and love for relatives who could be at risk, or be a means of honouring those lost to the disease. 32 Meanwhile, a quite distinct way in which participation may function as a kind of enactment of self-image, may be witnessed amongst participants who report that donating their genomic data to research via commercial direct-to-consumer services is a way of manifesting their identities as techno-pioneers or ‘early adopters’. 33
The proposal here is that participation may do more than simply express some of an individual’s identifying characteristics. If we accept that to participate in health research is to enter into a liminal transformative process, then the introduction of the analytical frame of narrative identity offers a stronger interpretation – that taking part can actually serve to constitute who someone is. This is grounded in the claim that a self-narrative is not simply a set of inert self-descriptors, adopted or rejected at will. Rather, the relationship between one’s self-narrative and conduct is practical, normative and reflexive, meaning that the self-descriptors we adopt only plausibly define who we are to the extent that we actually act (or choose or judge) in accordance with them when it is possible and appropriate to do so. 34 According to this analysis, then, participation is constitutive of identity to the extent that it provides opportunities for important threads of self-characterisation to be enacted and thus reinforced as part of the kind of person someone is. This offers a way of interpreting the inferences that Hallowell and colleagues draw from their own observations – that participants in health research ‘construct themselves as responsible and caring individuals’ and as ‘moral being[s]’ – in such a way that ‘construct’ can be understood literally, not as merely in terms of self-presentation. 35 On this view, taking part in a genetic study (for example) may not merely demonstrate someone’s affection and concern for her (potentially at risk) siblings and children, but actually comprise part of what it means to her to be a loving parent and sister. 36
According to the same reasoning, if it is plausible that acting in accordance with the characteristics with which we define ourselves reinforces their roles in our identities, then the converse will also hold. That is, taking part in research that is at odds with our values or other characteristics with which we identify – for example, as might be the case if it serves commercial interests rather than the interests of patients, or uses unsafe or unethical methods 37 – may undermine, and be experienced as undermining, the corresponding aspects of our self-narratives and thus threaten our existing, and valued, accounts of who we are. Where the aims or conduct of studies are antithetical to the self-conception of participants, or the experience of participation is in some way distressing or contrary to their expectations, this could instead disrupt, or re-configure their identities in ways that are unwelcome or that that they struggle to inhabit or make sense of.
The suggestions made here, then, are that taking part in health research can be (re)constitutive of the participant as subject: the subject of her own identity narrative and her perspective on the world. As a result of participation, the particular self-descriptors, roles, relationships and experiences that make up an individual’s self-conception and provides the qualities of her subjectivity, may be gained, lost or rearranged in ways that are more, or less, welcome, desirable or ‘inhabitable’. Of course, the participant exists as a subject prior to participation but, through consenting and committing to participate and permitting herself to be the object of scrutiny and inquiry, she continues on the perpetual journey of evolving and becoming the particular subject she is.
Participation in health research may therefore be seen as a process through which, in serving as the object of research, the participant undergoes and experiences development as a subject, for better or perhaps worse. This transition from being an object of study to a particular subject is even more apparent where the means of participation is via literal objects – that is where it does not require the involvement of, or interventions upon, the whole person, but rather research conducted upon their data or tissue samples in their absence. As described above, the health research landscape is changing. Increasingly it is characterised by secondary data uses and studies that apply AI and bioinformatics to vast datasets. 38 ‘Remote’ participation of the kind entailed by these types of studies might not entail the kind of notable or risky life experiences described earlier in this section, and thus not feature as narrative plotlines on these grounds. However, it may still result in the generation of personally significant research findings. And, as noted above, many of the empirical studies investigating motivations for participation focus precisely on the role of data-focused research in the construction of the ‘moral subject’. These studies provide some indication that this aspect of identity constitution is present even in data-led non-contact research. In this, we may witness the very epitome of the transition from research object to research subject: the constitution of the participant as subject from the objects of study, for example, her health records or tissue samples.
It might be queried why the potential impacts of health participation on our identities would, or should, make any difference to how health research is regulated. After all, if each of our identities is perpetually in development, why worry about one possible route of development? However, this is to overlook the implicit normativity in narrative conceptions of identity, normativity that is indicated in the examples given above. That is, we care about what kinds of individuals we are. We have interests in being able to develop and maintain aspects of our identities, and in being able to make sense of and comfortably inhabit our own accounts of who we are. 39 If this is indeed the case, it carries ethical implications for practices that impinge on these capacities, amongst which – we suggest here – health research practices may be counted. This, in turn, has implications for the responsibilities of those conducting health research and the policies governing its ethical conduct. Seen through our liminal lens, the process of becoming and being a research participant makes it incumbent on those responsible for research processes to lead participants through and out of the entire research trajectory.
What, then, are some of the ways in which current health research regulatory norms and practices might recognise and respond to the ways that participation may serve to constitute the participant as subject? Here, we make only brief suggestions that will be picked up further in the discussion of a processual approach to regulation in Part 5. First, this analysis emphasises that the value of trust in the research relationship – and efforts to foster and protect this – are not only important for reasons of recruitment and social licence. 40 In particular, it underscores the need to characterise fully and accurately the nature and aims of a study in recruiting participants and obtaining their consent to participate so that they can properly appraise how it fits with their ideas of who they are and their life projects. Also implicated are policies regarding the return of individually relevant research findings to participants. 41 The preceding analysis suggests a need to recognise the potential significance of research findings to participants beyond their clinical actionability. 42
Lastly, a key implication of this first example of the relationship between object and subject in health research is that the need to attend to possible impacts on participants’ identities are not severed by their physical remoteness from the research that is conducted, but may be mediated by their donation of research objects such as data or tissues. In the next example, we take a closer look at this relationship in the context of data governance, specifically the consent-or-anonymise binary, which is paradigmatic of the way in which the subject-object distinction draws focus away from the transformative experiences of the research participant.
Big data provides us with an illustration of the problems associated with attempting to categorise and distinguish subjects and objects as separate, fixed constructs, as well as the consequences of prioritising the object over the subject. Here, we use the term ‘subject’ to refer to individuals to whom data pertains. We use the term ‘object’ to refer to the data and information which relate to the data subject. In this section, we consider the problems associated with current approaches to conceptualising subject/object relationships in the data context. In particular, we suggest that the dominant ‘consent-or-anonymise’ model represents a caricature of subject/object separations created, to some extent, by current regulatory frameworks and the ways in which big data strives to prioritise the object at the expense of important considerations around the subject, including narrative interests as outlined above. In this realm, we find that the law seeks to impose bright lines/thresholds across the data reuse research endeavour, and incorrectly assumes that these are reflective of the realities, including human experiences around data use.
However, we also acknowledge that research practice has a part to play here. A liminal approach, which emphasises the human experience and the process of transformation involved in health research, requires us to revisit the current constructs provided for within the law and to account for the subjects’ interests in their data and connections to them. Taken together, we suggest that governance frameworks that over-emphasise mechanisms, such as the anonymisation of research participants’ data, or the role of informed consent, fail to adequately account for the interests that individuals might retain in their data, even when this may be no longer identifiable or ‘connected’ in the eyes of the law. Data protection law makes the privacy-related interest of ‘identifiability’ the core concern; however, in addition to narrative identity interests, research subjects might also have reputation-related interests at stake – for example, not being associated with, or facilitating, research with which they fundamentally disapprove on moral grounds.
Alongside the clear benefits of data use and reuse, not least the rich insights these may provide across a wide variety of health, health-related, and even non-health concerns, come significant regulatory challenges. 43 These include important questions around consent, privacy and the role and value of public interest, which we have discussed at length elsewhere. 44 What has not received attention to date, but which is central to how we approach the regulation of data or research, are the ways in which the big data revolution, powered in part by secondary uses of patient and research data and the analytical capacities of artificial intelligence, is shifting the focus away from data subjects and towards big data objects. We see this reflected perhaps most starkly in the ‘consent-or-anonymise’ paradigm that, in our view, presents a false choice to data subjects as to the (ongoing) connections they may have with their data; it also fails to recognise the full range of subjects’ interests that are at stake.
As mentioned above, law’s creation of regulatory silos that attempt to draw bright lines around distinct categories of data (identifiable, anonymous, sensitive) is based on an assumption that such bright lines necessarily capture and sufficiently protect the core interests at stake. Indeed, in the case of anonymisation, the working assumption is that these technical processes adequately sever the relationships between data subjects and data objects: by rendering the data no longer ‘personal’, it also can no longer attach to a data subject. Under data protection law, anonymous data means ‘information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable’. 45 We suggest that such an assumption of relationship-severing is problematic in that it overlooks the important on-going (and at times implicit or symbolic) relationships between subjects and objects in terms of their interests in how data pertaining to them may be used including: who data are shared with (e.g. public/private organisations?), the purposes for data sharing (what kinds of research?) and the outcomes (does data use lead to commercial profit? If so, how are these profits used?).
The law’s relatively myopic approach results in prioritising legal focus on the object, i.e. the data relating to the individual. This is particularly so when data objects are used in large-scale datasets where the emphasis is placed on maximising the potential research value of the data objects. As datasets increase in volume and are continually linked to additional datasets, not only are the risks to privacy increased, but the opportunities to (re)create the legal category of ‘personal data’ are multiplied (because the likelihood of re-identification of individuals can increase through data linkage). This means, in practice, multiple third parties within a big data environment might become data controllers for the purposes of data protection law, so coming within this legal domain. Moreover, from the data subject’s perspective, as her data move further and further away from her immediate control and knowledge of what is being done, her influence is weakened or removed altogether while her interests in her data in terms of how it may be used, with whom it may be shared, and so on, remain the same – and become arguably stronger in some cases, e.g. if non-approved uses are made that impact negatively on reputational or identity interests.
Taken together, these examples show why the ‘consent-or-anonymise’ paradigm is increasingly problematic, in that it does not adequately reflect the interests that may be retained in how data is used, even if an individual is not identifiable in a legal sense. This is not to say that such interests should always be determinative – such an approach would be disproportionately detrimental to health research – but, as we will go on to argue below, neither should they be wholly discounted or ignored.
Consider, for example, the use of personally identifiable information versus non-identifiable data, and the distinct regulatory frameworks which each triggers. Personally identifiable information is regarded as information which relates to an identified or identifiable individual. Subject to certain caveats, to use personally identifiable information, there are both legal and ethical requirements to obtain the consent of the data subject prior to use. This can be problematic. For example, obtaining consent is timely and cost-intensive, particularly considering that big data studies rely upon data relating to thousands of individuals. Often, research projects have time-limited funding which severely impedes the ability to contact every single individual to obtain their consent. Likewise, questions arise as to whether or not obtaining individual consent is always desirable when data subjects may not necessarily wish to be contacted to obtain permission for every single use of their data in research.
In recognition of the impracticalities (and impossibilities) of obtaining consent, regulatory responses have been developed to facilitate the use of data without consent requirements via (amongst other approaches) the use of anonymisation. Anonymisation involves techniques that make the identification of a data subject highly unlikely, and thus the use of such data no longer falls within legal requirements to obtain the data subject’s consent or another lawful basis before use. We add a caveat to this point by acknowledging that data protection law (at least in Europe) generally offers a variety of lawful bases to process personal data, of which consent is but one. In principle, then, the choice to researchers (as data controllers) is more akin to ‘anonymise the data or choose a lawful basis’; in health research, there are projects that tend not to operate on consent for the lawful basis to process personal data, such as retrospective chart reviews and epidemiological research. 46 This said, it is through long-standing practice in health research (foremost through the normative weight placed on consent) that the lawful basis chosen by many researchers to process personal data in their projects is, indeed, consent. The result has been the perpetuation of the ‘consent-or-anonymise’ paradigm, 47 where researchers wishing to use data either obtain consent from data subjects or anonymise the data prior to use.
This model can be viewed as a paradigmatic example of regulatory attempts to separate the subject (the person) from the object (their data); it suggests that merely through ‘stripping’ identifiable information from data pertaining to a subject, the connection between the data subject and object is severed. The practical, ethical and social realities are, however, far more complicated. Each time datasets are linked together, more and more information pertaining to an individual is connected and the potential for re-identification increases. Technological developments in data use have also provided increased means of generating data that is re-identifying of persons; this, in turn, renders ‘true anonymisation’ highly unlikely. 48 Moreover, despite the benefits for a research agenda of obviating consent requirements, the use of anonymous data (where re-identification is no longer possible) is not always ideal in the research setting when both (i) identifiable information and (ii) the ability to link together multiple datasets (which cannot be done via anonymisation) provide richer datasets with greater research potential. Consent-or-anonymise threatens to thwart many big data objectives while at the same time creating illusions of control and protection that – in many instances – simply do not respect the moral connection between a subject and her data.
One technocratic solution which has been developed to mitigate the limitations of the consent or anonymise paradigm is pseudonymisation. This is an alternative methodology that provides a means of retaining the ability to link together data about individuals across multiple datasets, without re-identification. Identifiers within datasets are attributed codes/pseudonyms which are held separately from original datasets. Thus, the potential to re-identify individuals and link together multiple datasets is maintained, whilst also paying due regard to privacy concerns. Indeed, pseudonymisation demonstrates the fluid nature of data, from identifiable to non-identifiable and back to identifiable, which occupies the research space and which manifestly contrasts with the concept of separation between subject and object. Thus, the prominence of the data subject waxes and wanes over time, depending upon the research context. It could be argued that data objects in the case of pseudonymisation may simultaneously be considered to fall within both categories of identifiable and non-identifiable or, in fact, within neither category. This feature of in-betweenness could be seen as an example of the liminality of things – data themselves are in processes of transforming and becoming something else (notably, or possible future relevance to a data subject). 49 As such, our liminal framing suggests, once again that there are continued obligations to lead the data objects – and the data subjects – through and out of this digital liminal phase, and potentially also to close feedback loops between research participants and research outcomes, as we have argued in detail elsewhere. 50
We suggest that anonymisation does not absolve us of thinking about broader ethical issues and from considering the full range of interests that are engaged – be they collective (e.g. the benefits of research) or individual (e.g. narrative or reputational). This illustrates that our analysis is not merely descriptive, but also can also direct us towards governance frameworks that are able to speak to this nuanced relationship. Indeed, when we engage with the limitations of such an approach as a default – both in terms of the researchers’ needs for a rich dataset, as well as individual narrative and reputational interests in the use of the data, identifiable or not – this forces us to consider alternative approaches. We address what some of these might look like in Part 5.
By acknowledging the ebb and flow from subject to object, this also directs our attention to the potential move between object and subject. As we have seen, the legal and moral status of the entity as object or subject is critically ambiguous and depends on the vagaries of what is done to it and the regulatory paradigm under which this falls, as explored further in the following section where we turn to a case study that exemplifies this, embryos in vitro , which under UK law are attributed a ‘special status,’ not quite person (subject) or thing (object).
The human embryo in vitro is paradigmatic of an entity that does not fit neatly into either of the legal categories of ‘subject’ or ‘object.’ Embryonic development is the most rapidly unfolding biological process in any stage of human life. This complexity is mirrored in legal frameworks that are, at the same time, detailed and ambiguous. This section explores that complexity and exemplifies the need not to dismiss categories completely; in some cases, it might be appropriate to make the implicit ‘object’ status of the embryo explicit. This analysis suggests that even where we recognise the need for categorisation in some cases, our normative claim regarding the importance of subjects’ experiences remains. In this case, there is room for the law to better account for the process and experience of becoming a donor, and their experience of their embryos becoming decidedly a research ‘object.’
All in vitro embryos that are created, stored, used, implanted, and disposed of in the UK are governed by the Human Fertilisation and Embryology Act 1990 (as amended) (‘the 1990 Act’). The intellectual basis for this Act lies in the 1985 Warnock Report, which recommended that embryos created in vitro be afforded a ‘special status’. While the exact nature of this status was and remains unclear, we know that it involves affording embryos ‘respect’, 51 and not treating them with ‘frivolity’. Every embryo created in vitro enjoys the Warnock Report’s ‘special status’; this is reflected in provisions for how embryos are to be used either in reproduction or in research; it is found in the rule against implanting human-animal hybrids; 52 and it is the reason behind the 14-day time limit on research with human embryos. 53 Like other frameworks for HRR (e.g. the Human Tissue Act 2004), the 1990 Act itself focuses primarily on embryos in vitro (for the purposes of this section, the ‘object’), and what we can and cannot do to them, while little recognition is given to the context from which the embryos are created or obtained.
But what category is assigned to the in vitro embryo by law: subject or object? It is arguable that embryos in vitro are treated neither as a legal subject nor as a legal object by the 1990 Act, but rather as something that falls in between this binary. The unarticulated construction of embryos in law as subject-objects has been a result of attempting to regulate an uncertain space and to accommodate potentially fundamentally conflicting values: that is, showing respect for the embryo while promoting reproductive medicine and embryonic research in the public interest, the last of which destroys the embryo in the process. And, as noted above, while all embryos created in vitro are governed by this ‘special status’, there are at least two distinct paths which they may go down once created (or unfrozen): (1) towards reproductive ends and (2) towards research ends. While it is clear, legally speaking, that embryos are neither subjects nor objects in the traditional sense – they do not have legal personhood, but neither are they a mere ‘thing’ and so cannot be property – their subject-ness or object-ness in research practice is arguably affected by the ultimate end for which they are used. As some of us have argued elsewhere:
The liminal states and the subject/object dyad are important for the future of artificial reproduction and embryo research because the notion of ‘the moral status of the embryo’ underpins the entire legal architecture of human reproductive regulation. A liminal perspective suggests, however, that at best, the law may be perpetuating a moral myth, and at worst, the compressed regulatory regime is fundamentally flawed. 54
To expand on this further, if embryos are placed on a ‘reproductive path’, their treatment as a subject, rather than as an object, could be said to intensify. This is clearly not to say that they are immediately treated as subjects in law (e.g. with personhood), but more as a subjects-to-be . For example, clinics responsible for reproductive IVF must consider a host of factors when deciding whether to accept clients for treatment and many of these are about the well-being of the future person. 55 Equally, once these embryos are implanted, the Abortion Act 1967 makes it increasingly difficult, as the foetus develops, for the pregnancy to be terminated. Conversely, placing embryos on a research path intensifies their treatment as ‘object’. Once determinedly a research embryo (whether created for research purposes, or donated), these entities may only be researched on and then disposed of; they can never cross to the reproductive path. Admittedly, there are strict limits on what one can and cannot do with them, and before 14 days (or before the primitive streak, whichever happens sooner), they must be disposed of. Thus, although they are ‘not nothing’ 56 in moral and legal terms, research embryos are, for all practical intents and purposes, treated as ‘artefacts’. 57 In other words, they become legal objects. This does not mean that they cannot be ‘special’ in some continuing sense; many legal objects and ‘artefacts’ engender considerable amounts of legal protection that reflects the ways in which we ‘value’ them – for example, celebrated works of art. Thus, object-hood does not necessarily render the embryo as ‘nothing’, but at the end of the day, research embryos are still disposed of much like any other object that outlives its usefulness. Still, embryos can matter in other ways. For example, empirical research has shown that ‘Some participants understood existing trajectories (including embryo research) as feeding back into the field of reproduction, hence, maintaining narrative consistency of hope where fertility treatment provides a technical solution for childless people’, but it has also been shown that voices of donors have been ‘marginalised’ in the research process and debates about research. 58 Therefore, not only is the process of becoming a donor ridden with anxiety, 59 but so is the possibility of ‘their’ donated embryos contributing to the advancement of the very endeavour they are trying to achieve as fertility patients.
Our analysis thus far encourages us to embrace fluid, experiential aspects of being involved in health research, and this case study also provides the opportunity to call out certain processes for what they are. The reality is that there is no such thing as the unitary ‘ in vitro embryo’ as a legal category. In vitro embryos are liminal entities when they are created, but the decision to place them on the reproductive or the research path has moral implications that must be recognised. In other words, there is an imperative, here, to make the implicit explicit: the embryo that becomes a research artefact is being treated as more object-like. The point in time when this occurs is a moral and ethical crossroads that should be acknowledged. Moreover, in doing so, we open the potential for a more honest debate about what we can and should do with the embryo as an artefact (object) – which is distinct to the embryo as a future person (subject). At the same time, the relationship that donors have (as subjects) with their embryos (as objects) is imbued with moral meaning that is not yet fully reflected in the legal and governance arrangements that are currently in place.
The processes of transformation and change in the regulation of embryos in vitro are radically different depending on the outcome that is envisioned for them, and the ultimate end points are diametrically in opposition: one results in life, the other in destruction. Van Gennep’s original formulation of liminality describes processes of change in time and space, but, as we have argued elsewhere, another way of thinking about liminality is to focus on the spatial and temporal aspects of the very processes under consideration. A liminal lens reveals that being in-between bounded legal categories is only ever a temporary state; indeed, liminality itself is often only a fleeting matter because the tendency is to proceed through a liminal state towards a transformative end point. 60 For the in vitro embryo, these end points are practically and morally irreconcilable. Yet, it is arguable that the all-encompassing ‘special status’ is blind to this; conceptually, it severs in vitro embryos from the multiplicity of futures that the law has regulated for (i.e. reproductive use/implantation or research/disposal), and leaves out the possibility for constructive debates (inclusive of embryo/gamete donors) surrounding how any next steps in embryo research regulation should take place. 61 It perpetuates a myth of considerably dubious moral character.
In sum, we suggest that not only are the categories of subject and object helpfully problematised by our liminal lens but also that the failure to create appropriate categories for those whose subject-ness or object-ness is temporally dependent undermines the ethical legitimacy of law in human health research. Recognising the research embryo as a legal object has several normative implications. For one, it can open up new avenues of research; it might also give reason to provide a framework that allows us to tell donors more accurately and clearly what will happen to their donated embryos, and to give them more input to those future research processes.
Ultimately, the analysis of these three case studies allows us to consider more deeply who has a say in research processes, depending on what these processes are. Indeed, if an option for further donor involvement in research were deemed desirable, it might enable us to address more fully the critique that donors can feel as if they are on the side-lines when it comes to research. 62 As we have demonstrated in this section, the regulatory landscape in health research severs a moral, and personal, a connection that many research participants may have with their research contributions, be it tissue, data, or embryos. We, therefore, suggest the need to rethink regulation in processual terms, as an endeavour that occurs over a much longer time period than HRR currently recognises; health research is a liminal process, yet we fail to treat it as such. 63 We consider how we might do so using our framework for processual regulation in the next section.
4.1. challenging the subject-object paradigm.
Each of the three case studies above presents challenges to the suitability of a siloed approach to subjects and objects in HRR. Together, these challenges may be summarised as follows:
Liminality, as a lens, has revealed several insights about the ways in which research occurs in practice, and the fluidity of research participants’ experiences (and relation to) their contributions. Liminality also focuses our attention on the subject as the experiencer, often going through a transformation of identity as the research processes take place, from being a participant to a person affected by research results. We have argued that a rigid separation of the categories of subject and object in HRR fails to reflect the reality of research practices, where, for example, tissue becomes data. Each of these bounded spaces relates to each other, but the nature of the frameworks surrounding them mean that the experiences of research participants as being either research subject and/or object, or their relation to their contributions (be it tissue, embryos, or otherwise), are insufficiently captured by current regulatory regimes. Our approach redirects regulatory attention from one chiefly focused upon legal ‘objects’ e.g. tissue, to one that highlights the experiences of human ‘subject,’ i.e. the research participant.
But how can law and regulation better reflect this changing materiality, not only in a physical sense but also in terms of their importance (i.e. how much they matter ) to their subjects, i.e. to research participants? Moreover, how can law and regulation reflect the fluidity of connection between research participants and research objects, such as data, tissue and embryos? Each of the above case studies captures a process in health research: the process of becoming a research participant or ‘research subject’ and their experiences as their contributions (be these data, tissue, or embryos) become research objects. These processes, oftentimes fluid and unfixed, 64 are a key feature of research practices that, as our case studies show, is not currently reflected by HRR. To better reflect the experiences of research participants in the regulation of health research, be it their connection to their contributions/objects (e.g. data), however great or small, 65 we need a regulatory framework that accounts for the greyness of the spaces between research participants and research objects. This may mean we need to consider who has a say in research processes, when they get to have a say, and how they might do so. We argue that this may be done through a framework for ‘processual regulation’.
As mentioned above, we have collectively and individually explored previously notions of processual regulation as it relates to HRR. Processual regulation is more than a ‘mere focus on process in regulation. […] ‘[S]uch an approach requires a temporal–spatial examination of regulatory spaces and practices as these are experienced by all actors, including the relationship of actors with the objects of regulation.’ 66 In a previous piece, we suggested that processual-oriented regulation has the following features:
One of us has built upon this first iteration in the context of the regulation of embryos in vitro to suggest a specific, ‘context-based approach’ which recognises (a) the multiplicity of intersecting pathways that the law leads embryos through (i.e. research, reproduction, PGD, freezing); (b) each of these pathways is relational i.e. dependent on the decisions and experiences of those who lead them into, on and out of those pathways; and (c) that at the end of those pathways there are only two possible outcomes: implantation in vivo, or destruction. 68 This approach, borne from a liminal lens , brings together the experiential and time-sensitive aspects of the processes that in vitro embryos are taken through by donors, researchers, and other stakeholders. Another of us, in the context of public interest and the sharing of health research data, used a processual lens to suggest that ‘a fuller account of the public interest is provided through the application of a processual approach that pays attention to (1) a holistic view of the operation of law, beyond the statute book; (2) the dynamic nature both of law (broadly conceived) and publics’ views over time; and (3) the actors, activities and subjectivities that are in play’. 69
In this article, we have used a liminal lens to establish that the experience and interests of research participants are a key normative basis for better recognising the continuing connection between subjects and objects in HRR. In this section, we put forward a new framework for processual regulation, building on our previous iterations (5.2, above) and our findings from our analysis of the above case studies. We argue that this framework helps us to consider broader governance tools, within and beyond HRR. By binding objects of health research, law overlooks the experiences of the research participants qua subject of research and the donors of data, tissue and embryos. We suggest an alternative regulatory perspective, processual regulation , which has the following key features:
Our analysis therefore suggests a need for greater transparency of research processes and more commitment to opportunities for co-production of regulation. For example, this means that research participants should have the opportunity to have a greater knowledge of, if not a real say in, what happens to their research contributions. In practical terms, processual regulation may therefore mean giving subjects the following opportunities:
Our framework exposes the opportunity to introduce mechanisms that give participants the options to be fed back information about the uses to which their contributions are put and – where appropriate – to participate meaningfully in processes of deciding on the direction of research. Equally, we do not claim that research participants always have a marked interest in research objects deriving from them. Instead, it is the nuances of the fluidity that can occur between subject and objects that is inadequately reflected in HRR. More generally, it should be noted that we do not suggest that processual regulation is necessarily tied to liminality; this is simply offered as a lens that helps us more closely to interrogate the spaces between conventional legal spaces in which much of health research practice takes place. Moreover, we also do not suggest that processual regulation should be limited to the realm of health research. In today’s world, where lines between common legal categories are increasingly blurred (especially ‘person’ and ‘thing’), and a plethora of technological developments are taking place (much faster than law and regulation can keep up with), processual regulation as an adaptive framework can help us to tackle these relatively new challenges in law and society.
We began this article by highlighting law’s current approaches to categorisation; law almost always focuses on the ‘thing’ and not the person from which the thing derives. We explained that this is particularly acute in HRR, which takes its cue from law, where the subject/object divide is more pronounced and unstable as most ‘things’ in this field of regulation come from the human body. HRR has thus tended to adopt a siloed approach, severing research objects from research subjects. Our normative position, however, is that the connection between subject and object within and across regulatory environments in human health research is a matter of profound ethical and social importance, and the law must recognise and respond to this by capturing HRR holistically as a process over a much longer period of time than it currently acknowledges.
Liminality, an anthropological concept concerned with processes of transformation and change, enabled us to emphasise the enduring connectedness between subjects and objects in these contexts, specifically in three case studies: the identity of the research participant, the anonymisation of data, and the embryo in vitro. Through our analysis of these case studies, we showed that the relationship between subjects (oftentimes research participants) and their objects is not so easily severed.
Overall, our core contribution has been to suggest that the notion that material of enduring human value – such as personal data and tissue – can be stripped of its moral significance by spatio-temporal distance, or by techno-bureaucratic measures such as anonymisation, is simply inadequate as a grounding for ethically robust research regimes – that is, regimes which recognise and respect the interests engaged through experience of participating in health research. We therefore offered a framework for ‘processual regulation’ to better capture HRR as an experiential process of transformation and change, particularly as these impact on the interests of subjects/participants involved in the research. This framework has significant practical implications for research participants (subjects), whom we argue should be recognised as having an on-going relationship with their research objects throughout the research lifecycle.
This article is based on research conducted with support from a Wellcome Senior Investigator Award entitled ‘Confronting the Liminal Spaces of Health Research Regulation’ (Award No: WT103360MA): http://www.liminalspaces.ed.ac.uk/ .
Catriona McMillan is a Senior Research Fellow in Medical Law and Ethics, School of Law, University of Edinburgh.
Edward Dove is a Lecturer in Health Law and Regulation, School of Law, University of Edinburgh.
Graeme Laurie is a Professorial Fellow, School of Law, University of Edinburgh.
Emily Postan is a Senior Research and Teaching Fellow in Bioethics, School of Law, University of Edinburgh.
Nayha Sethi is a Chancellor’s Fellow in Data Driven Innovation, School of Medicine, University of Edinburgh.
Annie Sorbie is a Lecturer in Medical Law and Ethics, School of Law, University of Edinburgh.
1 Graeme Laurie, ‘Liminality and the Limits of Law in Health Research Regulation: What Are We Missing in the Spaces In-Between?’ (2016) 25 Medical Law Review 47, 49.
2 Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (herein ‘GDPR’).
3 Human Fertilisation and Embryology Act 1990 (as amended) s4A.
4 Samuel Taylor-Alexander and others, ‘Beyond Regulatory Compression: Confronting the Liminal Spaces of Health Research Regulation’ (2016) 8 Law, Innovation and Technology 149, 158.
5 Moore v. Regents of the University of California , 793 P.2d 479 (Cal. 1990).
6 R v Department of Health, Ex Parte Source Informatics Ltd [C.A. 2000] 1 All ER 786.
7 See GDPR (n 2) Recital 26, Data Protection Act 2018, Human Tissue Act 2004.
8 See, for example: Pam Carter, Graeme Laurie, and Mary Dixon-Woods, ‘The Social Licence for Research: Why Care. Data Ran into Trouble’ (2015) 41 Journal of Medical Ethics 404; Alexis Clarke, Annie Mitchell and Charles Abraham ‘Understanding Donation Experiences of Unspecified (Altruistic) Kidney Donors’ (2014) 19 British Journal of Health Psychology 393; Sarah Parry, ‘(Re) Constructing Embryos in Stem Cell Research: Exploring the Meaning of Embryos for People Involved in Fertility Treatments’ (2006) 62 Social Science and Medicine 2349.
9 Taylor-Alexander and others (n 4) 150.
10 See Taylor-Alexander and others (n 4); Laurie (n 1).
11 See Barney Glaser and Anselm Strauss, Status Passage (Routledge and Kegan Paul, 1971).
12 Ezekiel Emanuel, David Wendler and Christine Grady, ‘An Ethical Framework for Biomedical Ethics’ Ezekiel Emanuel and others (eds), The Oxford Textbook of Clinical Research Ethics (Oxford University Press, 2018) 127.
13 Agomoni Ganguli-Mitra, Edward Dove, Graeme Laurie, and Samuel Taylor-Alexander, ‘Reconfiguring Social Value in Health Research Through the Lens of Liminality’ (2017) 31 Bioethics 87, 89.
14 Taylor-Alexander and others (n 4); Catriona McMillan, The Human Embryo In vitro: Breaking the Legal Stalemate (CUP, forthcoming); Annie Sorbie, ‘Sharing Confidential Health Data for Research Purposes in the UK: Where are “Publics” in the Public Interest?’ (2019) 16 Evidence and Policy 249.
15 Austin Sarat and others, ‘The Concept of Boundaries in the Practices and Products of Sociolegal Scholarship: An Introduction’ in Austin Sarat and others (eds), Crossing Boundaries: Traditions and Transformations in Law and Society Research (Northwestern University Press, 1998) 3–4.
16 Specifically, as seen in the GDPR (n 2).
17 Specifically, as seen in the EU Clinical Trials Regulation 536/2014.
18 Edward Dove, Regulatory of Stewardship of Health Research: Navigating Participant Protection and Research Promotion (Edward Elgar, 2020).
19 Carter and others (n 8).
20 Sara Davidson and others, ‘Public Acceptability of Data Sharing Between the Public, Private and Third Sectors for Research Purposes’ (2013) Scottish Government; Ipsos Mori ‘The One-Way Mirror: Public Attitudes to Commercial Access to Health Data’ (2016) Report prepared for the Wellcome Trust.
21 Oonagh Corrigan, and Richard Tutton, ‘What’s in a Name? Subjects, Volunteers, Participants and Activists in Clinical Research’ (2006) 1 Clinical Ethics 101.
22 For example, the Declaration of Helsinki and The Medicines for Human Use (Clinical Trials) Regulations 2004. Though the language of ‘participant’, with its more positive connotations of inclusivity and respect, has become more prevalent over recent decades (see Corrigan and Tutton, n 21).
23 This is not necessarily to the exclusion of the terminology of ‘participant’. However, contrary to the idea that the term ‘subject’ is in some sense less respectful of the agency and individuality of the individual than the term ‘participant’, a possible implication of the proposal made here is that this terminology may actually serve to emphasise the impact of participation on these attributes.
24 See, for example, Kim Atkins and Catriona Mackenzie, Practical Identity and Narrative Agency (Routledge 2013), David DeGrazia, Human Identity and Bioethics (Cambridge University Press, 2005), Emily Postan, ‘Defining Ourselves: Personal Bioinformation as a Tool of Narrative Self-Conception’ (2016) 13 Journal of Bioethical Inquiry 1331; Marya Schechtman, The Constitution of Selves (Cornell University Press, 1996).
25 Postan (n 24).
26 Schechtman (n 24).
27 Postan (n 24).
28 That they will receive such findings is by no means a given, particularly when these are classed as ‘incidental’ to the core purpose of the research. See, for example, Susan Wolf and others, ‘Managing Incidental Findings in Human Subjects Research: Analysis and Recommendations’ (2008) 36 Journal of Law, Medicine & Ethics 219.
29 Postan (n 24); Kristof Van Assche, Serge Gutwirth, and Sigrid Sterckx, ‘Protecting Dignitary Interests of Biobank Research Participants: Lessons from Havasupai Tribe v Arizona Board of Regents ’ (2013) 5 Law, Innovation and Technology 54.
30 It should not be assumed, however, that hopes for personal therapeutic benefit are necessarily distinct from identity development. It is possible that for some, participation offers a means of being informed and proactive in the face of risk or ill-health, and thus, embracing a responsible, engaged and biologised mode of self-identification, see Carlos Novas and Nikolas Rose, ‘Genetic Risk and the Birth of the Somatic Individual’ (2000) 29 Economy and Society 485.
31 Mary Dixon-Woods and Carolyn Tarrant, ‘Why Do People Cooperate with Medical Research? Findings from Three Studies’ (2009) 68 Social Science & Medicine 2215; Nina Hallowell and others, ‘An Investigation of Patients’ Motivations for Their Participation in Genetics-Related Research’ (2010) 36 Journal of Medical Ethics 37.
32 Hallowell and others (n 31); Ann Hurley and others, ‘Genetic Susceptibility for Alzheimer’s Disease: Why Did Adult Offspring Seek Testing?’ (2005) 20 American Journal of Alzheimer’s Disease & Other Dementias 374.
33 Richard Tutton and Barbara Prainsack, ‘Enterprising or Altruistic Selves? Making Up Research Subjects in Genetics Research’ (2011) 37 Sociology of Health and Illness 1081.
34 Christine Korsgaard, Self-constitution: Agency, Identity, and Integrity (Oxford University Press, 2009).
35 Hallowell and others (n 31) 43–44.
36 Lori d'Agincourt-Canning, ‘Genetic Testing for Hereditary Breast and Ovarian Cancer: Responsibility and Choice’ (2006) 16 Qualitative Health Research 97.
37 Dixon-Woods and Tarrant (n 31).
38 Graeme Laurie and Nayha Sethi, ‘Towards Principles–Based Approaches to Governance of Health–Related Research Using Personal Data’ (2013) 4 European Journal of Risk Regulation 43.
39 Postan (n 24).
40 Carter, Laurie and Dixon-Woods (n 8) 404–409.
41 Wolf and others (n 28).
42 Emily Postan, ‘Disclosure of Research Findings: Changing Roles and Relationships’ in Graeme Laurie and others (eds), Cambridge Handbook of Health Research Regulation (CUP, forthcoming).
43 Graeme Laurie and Nayha Sethi, ‘Information Governance of Use of Health-Related Data in Medical Research in Scotland: Current Practices and Future Scenarios’ (2011) University of Edinburgh School of Law Working Paper No. 2011/26.
44 Nayha Sethi and Graeme Laurie, ‘Delivering Proportionate Governance in the Era of eHealth: Making Linkage and Privacy Work Together’ (2013) 13 Medical Law International 168; Laurie and Sethi, ‘Towards Principles-based Approaches’ (n 38) 43–57; Sorbie (n 14).
45 GDPR (n 2), Recital 26.
46 Edward Dove and Jiahong Chen, ‘Should Consent for Data Processing Be Privileged in Health Research? A Comparative Legal Analysis’ (2020) 10 International Data Privacy Law 117.
47 Academy of Medical Sciences, Personal Data for Public Good: Using Health Information in Medical Research (AMC, 2006).
48 Paul Ohm, ‘Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization’ (2010) 57 UCLA Law Review 1701.
49 Subjects may also retain interest in their data post-mortem. See Sethi and Laurie (n 51); Laurie and Sethi (n 45) 43–57; Sorbie (n 10).
50 Taylor-Alexander and others (n 4).
51 Report of the Committee of Inquiry into Human Fertilisation and Embryology (Cmnd 9314, 1984) (‘The Warnock Report’), 11.15.
52 Human Fertilisation and Embryology Act 1990 (as amended) s3(2), s3ZA.
53 Ibid s3(4).
54 Taylor-Alexander and others (n 4) 168.
55 E.g. the ‘welfare of the child principle’, contained in s13(5) of the Human Fertilisation and Embryology Act 1990 (as amended); and the age limit for IVF treatment, see National Institute for Health and Care Excellence, Fertility Problems: Assessment and Treatment (NICE, 2013).
56 Mary Ford, ‘Nothing and Not Nothing: Law’s Ambivalent Response to Transformation and Transgression at the Beginning of Life’ in Stephen Smith and Ronan Deazley (eds), The Legal, Medical and Cultural Regulation of the Body: Transformation and Transgression (Routledge, 2009).
57 John K Mason, ‘Discord and Disposal of Embryos’ (2004) 8 Edinburgh Law Review 84.
58 Parry (n 8) 2358.
60 We recognise that states of permanent liminality can exist. Indeed, the embryo that is frozen in perpetuity could be an example of this. Space does not permit us to discuss the implications of this particular scenario
61 Any amendment also needs robust, open enquiry into public attitudes, see Giulia Cavaliere, ‘A 14-Day Limit for Bioethics: The Debate Over Human Embryo Research’ (2017) 18 BMC Medical Ethics 38.
62 Parry (n 8).
63 Laurie (n 1) 47.
64 Although, as we have shown above in the third case study, in vitro embryos, sometimes these processes are indeed fixed.
65 See Parry (n 8).
66 Taylor-Alexander and others (n 4).
68 See McMillan (n 14).
69 Sorbie (n 14) 261.
70 It is recognised here that in the context of health research, which is increasingly exploratory, translational and reliant on algorithmic analysis of big data, a rigid distinction between intended and incidental research finding is increasingly unsupportable.
No potential conflict of interest was reported by the author(s).
Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.
An experimental research design helps researchers execute their research objectives with more clarity and transparency.
In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.
Table of Contents
Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .
Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.
A researcher can conduct experimental research in the following situations —
To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.
By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.
Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:
A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.
Pre-experimental research is of three types —
A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —
This type of experimental research is commonly observed in the physical sciences.
The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.
The classification of the research subjects, conditions, or groups determines the type of research design to be used.
Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:
There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.
Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.
Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.
Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.
This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.
Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.
The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.
In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)
By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.
Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.
Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!
Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.
Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.
There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.
The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.
Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.
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Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.
It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.
Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.
There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.
Theoretical research.
Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.
Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.
Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.
This type of research is subdivided into two types:
Exploratory research.
Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.
Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.
The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.
In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.
Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.
The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.
Qualitative research.
Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.
In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).
Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.
Experimental research.
It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.
Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.
It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.
Deductive investigation.
In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.
In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.
It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.
Longitudinal study (also referred to as diachronic research).
It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .
Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.
Primary research.
This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.
Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.
Documentary (cabinet).
Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.
Field research study involves the direct collection of information at the location where the observed phenomenon occurs.
Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.
Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.
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Glossary of research terms.
This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.
Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.
Educational resources and simple solutions for your research journey
Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.
Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.
After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.
Table of Contents
If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.
Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:
Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.
Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.
Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.
Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.
Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.
Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.
Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:
Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.
There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.
Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.
Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.
Now, let’s consider some descriptive research examples.
These examples showcase the versatility of descriptive research across diverse fields.
There are several advantages to this approach, which every researcher must be aware of. These are as follows:
On the other hand, this design has some drawbacks as well:
To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.
When should researchers conduct descriptive research.
Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.
Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.
Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.
No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.
The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.
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Methodology
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
Second, decide how you will analyze the data .
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
Qualitative | to broader populations. . | |
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Quantitative | . |
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | . | methods. |
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Secondary |
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
Descriptive | . . | |
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Experimental |
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Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
The research methods you use depend on the type of data you need to answer your research question .
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
Other students also liked, writing strong research questions | criteria & examples.
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Alzheimer's Research & Therapy volume 15 , Article number: 193 ( 2023 ) Cite this article
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The pathological process of Alzheimer’s disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.
The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.
Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.
Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.
Alzheimer’s disease (AD) as a progressive neurodegenerative disorder is the main cause of dementia, which is characterized by a decline in cognitive functions such as thinking, remembering, and reasoning. AD can be divided into two major categories: sporadic AD and familial AD. The familial AD that accounts for less than 5% of all AD cases [ 9 ] is usually caused by a genetic mutation, whereas sporadic AD accounting for the majority of AD cases is multifactorial [ 18 ]. Pathologically, AD is characterized by extracellular deposits of Aβ peptides as senile plaques, intraneuronal neurofibrillary tangles, reduced brain glucose metabolism and large-scale neuronal loss in the most affected regions of the brain, such as the medial temporal lobe and neocortical structures [ 13 , 23 , 31 , 67 , 68 ].
Non-invasive brain imaging techniques such as magnetic resonance imaging (MRI) play a vital role in detecting early changes in the brain associated with AD. Gross cerebral atrophy [ 37 ], white matter (WM) degeneration [ 3 , 19 , 20 , 29 , 51 ] and altered functional connectivity (FC) [ 2 , 12 , 15 , 34 , 39 ] were found to be relevant biomarkers. Recently, resting-state FC has been proposed to identify individuals at risk for Alzheimer’s disease in the early stages [ 45 , 110 , 112 ]. The characterization of the temporal progression of microstructural and FC changes promises to provide an understanding of disease mechanisms, an effective disease staging, and a window for therapeutic intervention.
As the pathological cascade of AD takes up to years or even decades from the dementia onset to full-blown manifestations, it remains challenging to acquire comprehensive longitudinal data on prospective AD subjects. As an alternative, animal models can be valuable tools to obtain data across the lifespan and study each of the contributors to the AD cascade individually, thus untangling direct effects of contributors and their interactions. Although numerous animal models have been developed to replicate the AD phenotype, most of them are transgenic models which are less representative of sporadic AD and are primarily based on the Aβ hypothesis [ 16 , 59 ], which is increasingly challenged [ 56 ]. However, with glucose hypometabolism being increasingly recognized as a potential cause of AD [ 21 , 46 , 62 ], animal models of brain insulin resistance have been developed by an intracerebroventricular (icv) injection of streptozotocin (STZ) [ 60 , 61 , 91 ]. The icv-STZ animals have been reported to manifest typical pathological features of AD such as extracellular accumulation of Aβ, tau hyperphosphorylation, neuronal loss, axonal damage, and demyelination in the hippocampus and fimbria [ 30 , 60 , 91 , 97 ], reduced glucose uptake [ 43 , 97 ] and oxidative stress [ 66 , 91 ], without developing systemic diabetes. From a behavioral perspective, STZ rats demonstrate lower post-shock latency time in the passive avoidance test [ 60 ], higher escape latency in the elevated plus maze, shorter exploration time of the novel arm in the Y-maze, poorer object recognition and tone fear memory [ 75 , 95 ], all pointing to impaired short-term memory.
In a previous work, we performed a comprehensive longitudinal study [ 97 ] in an icv-STZ rat model to quantitatively characterize alterations in FC and in WM microstructure using resting-state functional MRI (fMRI) and advanced diffusion MRI techniques, respectively, as well as in brain glucose uptake captured by 18 FDG-PET. By comparing the STZ group to the control group, non-invasive MRI-derived measures of functional breakdown and WM degeneration were identified and evaluated in the context of brain glucose hypometabolism. Alterations in resting-state FC in STZ rats were found in brain regions closely associated with AD [ 2 , 15 ] with broadly increased then decreased connectivity at early and late timepoints, respectively. WM microstructure metrics derived from DKI (an extension of diffusion tensor imaging (DTI) that provides complementary information about tissue heterogeneity [ 53 ]) and the WMTI-Watson biophysical model [ 32 , 54 ] revealed specifically intra-axonal damage and axonal loss in the corpus callosum, fimbria and cingulum of STZ rats. The temporal dynamics of both WM integrity and FC were consistent with previously reported nonmonotonic trajectories of brain alterations along AD progression in humans [ 26 , 29 , 84 , 88 ]. These findings not only reinforced the suitability of the icv-STZ animal model for sporadic AD but also proposed MRI-derived features to identify alterations in the prodromal stage and monitor disease progression.
In this study, we go beyond descriptive statistics and evaluate the microstructural and functional measures for their potential to discriminate between control and STZ groups at a given timepoint and across time. The data used is the current analysis is more extensive than the ones underlying the group difference analysis in [ 97 ] through the addition of a fourth longitudinal timepoint and the inclusion of more animals, in particular for FDG-PET, to better evaluate regional brain glucose metabolism in the icv-STZ rats. We utilize quantitative MRI measures as features using machine learning (ML) to train classification models such as logistic regression (LR) to differentiate individual subjects. Moreover, we employ explainable artificial intelligence methods to interpret ML model outcomes. For example, the importance of each feature in terms of the absolute value of LR coefficients is used to identify features best discriminating STZ rats from controls. SHAP values (SHapley Additive exPlanations) [ 69 ], a model-agnostic approach, are used to interpret the model outcomes and to improve model transparency. Finally, the dynamic relationships between the functional and microstructural measures in STZ rats are highlighted at the early and late timepoints of disease progression. In a nutshell, our study highlights the MRI-derived measures that best discriminate STZ vs control rats at various stages of the disease, with potential translation to humans.
Male Wistar rats (236 ± 11 g) underwent a bilateral icv-injection of either streptozotocin (3 mg/kg, STZ group) or buffer (CTL group) as previously described [ 97 ]. When delivered exclusively to the brain, streptozotocin induces impaired brain glucose metabolism and is used as a model of sporadic AD [ 40 , 66 ]. Resting-state fMRI, diffusion MRI, and FDG-PET data were acquired longitudinally at four timepoints (2, 6, 13, and 21 weeks since icv injection) (Fig. 1 ). Timepoints were chosen to be consistent with previous rat STZ studies while also accommodating for constraints of repeated MRI scanning related to anesthesia, cannulations, and scanner availability.
Experimental timeline. MRI: fMRI and diffusion MRI data were collected at 2, 6, 13, and 21 weeks after icv-STZ injection. N = 24 rats were included in total (12 STZ / 12 CTL) as reflected in the diffusion MRI datasets at 13 weeks. A lower number of datasets at other timepoints are due to poor data quality (2, 6 weeks) or missing datasets at 21 weeks due to an MRI system upgrade. For fMRI, two runs per rat were acquired for each MRI session which increased the number of datasets. The 4 timepoints were further grouped into early and late time groups and finally the pooled dataset. Sample sizes (STZ/CTL) in the 3 datasets for fMRI and diffusion MRI are as follows. Early: 47/47 (fMRI), 21/24 (diffusion); late: 34/34 (fMRI), 19/19 (diffusion); pooled: 81/81 (fMRI), 40/43 (diffusion). PET: FDG-PET data were also collected at the four timepoints in a subset of rats ( N = 20 total) and were used to assess group differences in regional brain glucose uptake. Dataset numbers at each timepoint vary due to PET scanner unavailability (especially fewer datasets at 2 weeks) or missing MRI at 21 weeks which also prompted dropping the PET scan acquisition
Animals were initially anesthetized using isoflurane (4% for induction and 1–2% for maintenance in an oxygen/air mixture of 30%/70%) and positioned in a homemade MRI cradle equipped with a fixation system (bite bar and ear bars). A catheter was inserted subcutaneously on the back of the animal for later medetomidine delivery. One hour before starting the resting-state fMRI acquisition, anesthesia was switched from isoflurane to medetomidine (Dorbene, Graeub, Switzerland), which preserves neural activity and vascular response better than isoflurane [ 83 , 104 ], with an initial bolus of 0.1 mg/kg followed by a continuous perfusion of 0.1 mg/kg/h [ 85 ]. The commercial solution at 1 mg/mL was diluted to 0.033 mg/mL. Throughout the experiment, the breathing rate was monitored using a respiration pillow and a rectal thermometer, respectively. Body temperature was maintained around (37 ± 0.5) °C. The breathing rate under medetomidine was around 85 bpm. At the end of the scanning session, animals were woken up with an intramuscular injection of atipamezole (Alzane, Graeub, Switzerland) at 0.5 mg/kg.
MRI experiments were conducted on a 14.1 T small animal scanner. As a result of the system upgrade, data were acquired with two different consoles for the magnet: Varian system (Varian Inc.) equipped with 400 mT/m gradients (cohort 1, N = 17 rats) and Bruker system (Ettlingen, Germany) equipped with 1 T/m gradients (cohort 2, N = 7 rats), both using the same in-house built quadrature surface transceiver. The acquisition parameters were the same for the two cohorts. Each cohort comprised animals from both groups: cohort 1 (CTL/STZ, N = 8/9 rats) and cohort 2 (CTL/STZ, N = 4/3 rats).
Structural \({T}_{2}\) -weighted images were collected using a fast spin-echo sequence with the following parameters: TE/TR = 10.17/3000 ms, echo train length: 4, matrix size = 128 × 128, FOV = 19.2 × 19.2 mm 2 , voxel size = 0.15 × 0.15 mm 2 , 30 coronal 0.5-mm slices, scan time = 10 min.
Diffusion-weighted data were acquired using a pulsed-gradient spin-echo segmented echo-planar-imaging (EPI) sequence, with the following protocol: 4 b = 0 images and 3 shells at b = 0.8/1.3/2.0 ms/µm 2 , with 12, 16 and 30 directions, respectively; δ/Δ = 4/27 ms; TE/TR = 48/2500 ms; 4 shots; matrix size = 128 × 64, field-of-view = 23 × 17 mm 2 , voxel size = 0.18 × 0.27 mm 2 , 9 coronal 1-mm slices, 4 repetitions, scan time = 1 h.
Resting-state fMRI data were acquired using a two-shot gradient-echo EPI sequence as follows: TE/TR = 10/800 ms, TR volume = 1.6 s, matrix size = 64 × 64, field-of-view = 23 × 23 mm 2 , voxel size = 0.36 × 0.36 mm 2 and 8 coronal 1.12-mm slices, 370 repetitions, scan time = 10 min. Two fMRI runs were acquired in each MRI session.
It should be noted that the phrase “resting-state fMRI” refers to the fact that the fMRI acquisition was performed during an idle state of the rat, as opposed to “task fMRI” which would present the animals with a sensory stimulation paradigm for example. Nonetheless, all animals were in fact anesthetized using medetomidine, which does alter brain activity as compared to awake animals. While awake rodent fMRI is a promising lead in the field, anesthetized fMRI is still the norm in rodent experiments [ 38 ].
All procedures are identical to those described in [ 97 ], where more details can be found. Briefly, rats housed with free access to food and water were anesthetized using isoflurane (2% for induction) for tail vein cannulation for tracer delivery, and subsequently transferred on a temperature-regulated PET scanner bed. Within the first minute of PET acquisition, a bolus of roughly 50 MBq 18 F-FDG (Advanced Accelerator Applications, Geneva, Switzerland) in 50–300 µL was manually injected through the tail vein and followed by a saline chase.
All PET experiments were performed on an avalanche photodiode-based LabPET-4 small-animal scanner (Gamma Medica-Ideas Inc.) as described in [ 63 ]. Briefly, data were collected in list-mode and images of the labeling steady-state were reconstructed from coincidences between 30 and 50 min after tracer injection using the built-in maximum likelihood expectation maximization (MLEM) iterative reconstruction algorithm (30 iterations) with a circular field of view (FOV) of 80 mm. The reconstructed voxel size was 0.25 × 0.25 × 1.18 mm. Steady-state radioactivity density images were then normalized for the effective injected FDG dose and the animal weight to generate standardized uptake value (SUV) maps.
FMRI data processing followed the PIRACY pipeline [ 25 ] which included denoising [ 100 ], susceptibility distortion correction [ 94 ], slice-timing correction [ 42 ], spatial smoothing, and removal of physiological noise following independent component (IC) analysis decomposition. FC matrices between 28 regions of interest (ROIs) based on the Waxholm Space Atlas were computed, co-varying for the global signal [ 25 ]. Statistical comparisons of FC between the STZ and CTL groups at each timepoint were performed using NBS [ 109 ] to identify network connections that showed significant between-group differences. Specifically, NBS uses one-tailed two-sample t -test to detect differences in group averaged FC between the two groups. Thereby, two contrasts (STZ > CTL and STZ < CTL) were tested separately. A t -statistic threshold of 2.2 was chosen on the basis of medium-to-large sizes of the subnetwork comprised connections with their t -statistic above the threshold [ 98 ] as well as the underlying p -values. Significance ( p ≤ 0.05) was tested after family-wise error rate correction using non-parametric permutation ( N = 5000).
Diffusion data processing included MP-PCA denoising [ 100 ], Gibbs-ringing correction [ 57 ], and correction for susceptibility distortions and eddy currents using FSL’s eddy [ 5 ]. The diffusion and kurtosis tensors were estimated using a weighted linear least squares algorithm [ 101 ], and typical DTI and DKI-derived metrics were computed: fractional anisotropy (FA), mean/axial/radial diffusivity and mean/axial/radial kurtosis. The DTI diffusivities correspond to the average overall diffusivity in the voxel, along the main orientation of the WM bundle (AD), perpendicular to that (RD), and averaged over all directions (MD). Kurtosis is a clinically feasible extension of DTI that also estimates the non-Gaussian nature of diffusion in the tissue, and is thus a measure of heterogeneity or variance in the diffusion properties of the water molecules at the voxel level (AK, RK, MK). The biophysical WMTI-Watson model [ 54 ] (Fig. 2 ) was estimated voxel-wise in WM regions using nonlinear least squares fitting to extract its microstructure parameters: axonal water fraction \(f\) , a proxy for axonal density; intra-axonal diffusivity \({D}_{a}\) , a proxy for the crowding of the intra-axonal space and thus for axon integrity; extra-axonal parallel and perpendicular diffusivities \({D}_{e, \parallel }, {D}_{e,\perp }\) , sensitive to myelination, packing, and cell crowding in the extra-axonal space; and axon’s orientation coherence within the WM bundle \({c}_{2}\) , where 1 corresponds to axons perfectly parallel to each other and 1/3 to isotropically distributed axons (without a preferential orientation of the bundle). In parallel, fractional anisotropy (FA) maps were registered to an FA template in the Waxholm Space using linear and non-linear registration in FSL [ 52 ] and the corpus callosum (CC), cingulum (CG) and fimbria (Fi) of the hippocampus were automatically segmented. For each ROI, average tensor and biophysical model metrics were calculated. Group differences were tested using t -test.
A Schematic of the WMTI-Watson biophysical model. The diffusion signal is described in terms of two non-exchanging compartments, the intra and extra-axonal spaces. Here, the axons are modeled as sticks with a radius equal to zero. The intra-axonal space is described by a relative volume fraction of water f and by the parallel intra-axonal diffusivity \({D}_{a}\) . The perpendicular intra-axonal diffusivity is negligible at the relevant diffusion times and weightings. The bundle of axons is embedded in the extra-axonal space, characterized by its parallel \({D}_{e, \parallel }\) and perpendicular extra-axonal diffusivities \({D}_{e,\perp }\) . The axons’ orientations are modeled by a Watson distribution, which is characterized by \(\langle {(\mathit{cos}\psi )}^{2}\rangle \equiv {c}_{2}\) . B The white matter ROIs
FDG-PET steady-state SUV maps were registered to their corresponding T 2 -weighted anatomical MR images with cross-correlation using ANTs [ 7 , 8 ], which was in turn registered to the Waxholm Space Atlas of the rat brain ( https://www.nitrc.org/projects/whs-sd-atlas ) using linear and non-linear registration [ 8 ] and 26 ROIs were automatically segmented. SUV images were normalized by the mean SUV over the brain to obtain SUVr maps corrected for inter-rat experimental variability [ 44 ]. Regional differences in SUVr between STZ and CTL groups were evaluated at each timepoint using a one-tailed Mann–Whitney U -test (STZ < CTL), at a significance level of α = 0.05.
For FC-based classification, correlation coefficients between ROIs in the FC matrix were taken as classification features by vectorizing the upper triangle of the FC matrix since FC is symmetric. To study the connection between statistical differences and classification performance in discriminating the two groups, significant edges from the NBS analysis were selected as a reduced list of features for classification. Datasets were grouped as early (2 and 6 weeks, N = 94) and late timepoints (13 and 21 weeks, N = 68), as well as all timepoints (pooled, N = 162). At each timepoint, the number of available samples was relatively small, which can pose challenges in building robust machine learning models. By merging data from two or four time points, we aimed to enhance the dataset size, thereby improving the model's ability to generalize and make reliable predictions. Furthermore, the datasets combined from distinct timepoints are not purely duplicates, and they exhibit inherent variabilities due to disease progression and MRI inter-run variability. STZ/CTL classification using a LR model was trained and tested on each subset (pooled, early, and late), which was normalized to [-1, 1] and randomly split into training (70%) and test datasets (30%). Since the data size was relatively small, the procedure of data splitting, training, and testing was repeated 1000 times, and results were aggregated in order to reduce bias.
For microstructure-based classification, there were two types of features for each of the three WM ROIs: I) DKI tensor metrics including FA, axial, mean, and radial diffusivities (AxD, MD, RD), axial, mean, and radial kurtosis (AK, MK, RK); II) WMTI-Watson model parameters including f , \({D}_{a}\) , \({D}_{e, \parallel }\) , \({D}_{e,\perp }\) , and \({c}_{2}\) (Fig. 2 ). These two kinds of features were used in two ways: as independent feature sets (i.e., DKI only and WMTI only) and combined as a single feature set. As for FC, diffusion datasets were grouped as early (2 and 6 weeks, N = 45), late (13 and 21 weeks, N = 38), and all timepoints (pooled, N = 83). LR models of STZ/CTL classification were trained and tested on the three datasets independently with 70% data for training and 30% for testing. The procedure was also repeated 1000 times.
Considering the small data size, feature dimensionality reduction was also tried for each classification by employing the principal component analysis (PCA) with various numbers of components.
Moreover, we tested classifying STZ and CTL rats by combining the FC and WM microstructure metrics in two distinct ways. One was to create a single classifier based on the concatenation of features of FC and microstructure metrics. The second way was using ensemble learning [ 81 , 87 ] where three independent classifiers were built each based on one of the three types of features (FC, DKI, and WMTI). Their predictions for each class were aggregated and the class with the majority vote was retained. Datasets for which both FC and dMRI were not available jointly (e.g., as a result of partial or artefacted data) were removed, resulting in a slightly reduced sample size (STZ/CTL = 38/41, instead of 40/43 possible datasets across both groups and timepoints).
Finally, cross prediction was performed, which means a classifier was trained on the dataset of one timepoint (e.g., early) and tested on the other timepoint (e.g., late) and vice versa. Cross prediction was tested on classifiers built on both separate and joint features.
Classification accuracy was used to assess the performance of a LR model in classifying STZ and CTL rats. However, to better interpret and explain the model outcome, we further calculated the importance of each feature in driving a model to predict the STZ class in terms of the absolute values of LR coefficients [ 93 ]. The mean feature importance was computed by averaging the absolute LR coefficient of each feature over the 1000 repetitions of training/test data splits, along with mean classification accuracy and standard deviation.
SHAP values that have been widely used for interpreting ML models were also calculated. In this study, SHAP values were computed for different types of features (i.e., significant FC connections, DKI metrics, or WMTI parameters) at each of the three timepoints (early, late, and pooled) to measure the individual impact of each feature on the model outcome. With the combination of classification accuracy and SHAP values, we were able to validate each measure’s ability to discriminate STZ rats from controls. As SHAP values are instance-based, they cannot be averaged over repeated training scenarios like the LR coefficient. Instead, we selected representative SHAP value sets from the 1000 candidates by choosing the ones that had relatively high classification accuracies (> 0.9) in both training and test datasets such that the model would have good performance as well as high generalizability.
The SUVr in STZ rats was reduced in multiple brain regions as compared to CTL, confirming the locally impaired glucose metabolism (Fig. 3 ). Glucose hypometabolism concerned mainly DMN and LCN regions. Differences were present across time, with the most widespread changes occurring at 6 weeks after icv injection.
Group differences in SUVr at each timepoint. Green: ROIs with significantly lower SUVr in STZ ( p < 0.05 using one-tailed Mann–Whitney U test, STZ < CTL). Dark yellow: trend of lower SUVr ( p < 0.1). Correction for multiple comparisons was not applied given the small number of animals per group. ACC, anterior cingulate cortex; RSC, retrosplenial cortex; PPC, posterior parietal cortex; MTL, medial temporal lobe; Hip, hippocampus; Sub, subiculum; Au, auditory; V, visual; S1/S2, primary/secondary somatosensory; M, motor cortices; Str, striatum; Tha, thalamus; HTh, hypothalamus; L/R, left/right
In Fig. 4 , graph networks highlight the group differences in nodal connections in the pooled, early, and late datasets. Up to 6 weeks after icv injection (early), the STZ group displayed increased connectivity within the default mode network (DMN) (including the anterior cingulate cortex (ACC), retrosplenial cortex (RSC), hippocampus and subiculum) as well as striatum, and decreased connectivity between the DMN (RSC, posterior parietal cortex (PPC) and hippocampus) and the lateral cortical network including primary and secondary somatosentory cortex (S1, S2) and the motor cortex, as compared to CTL rats. From 13 weeks on (late), reduced connectivity became more widespread within the DMN and lateral cortical network in STZ rats.
Graph networks of significant group difference using NBS with p < 0.05 (family-wise error rate corrected) for the 3 datasets (Pooled, Early and Late). Blue/red edges represent edges where STZ rats have weaker/stronger FC than CTL
When using all connections as features ( N = 378), prediction accuracy on the pooled, early, and late datasets was 0.75, 0.69, and 0.83, respectively. The most relevant edges involved the ACC, hypothalamus, RSC, hippocampus, and subiculum as nodes (Fig. 5 A), in agreement with edges found as significantly different between groups in the NBS analysis (Fig. 4 ). When only significant edges from the NBS analysis were selected as a reduced list of features for classification ( N = 49, 38, and 71 features in the pooled, early, and late datasets, respectively), the classification accuracy improved to 0.79 for pooled, 0.72 for early, and 0.90 for late datasets (Fig. 5 B). Improved accuracy was not strictly related to feature reduction: reducing features using PCA deteriorated classification accuracy (data not shown). Notably, the highest classification accuracy was found on the late dataset which is consistent with the advanced stage of disease and more marked differences between STZ and CTL.
A Top ten features (out of 378) and their importance in terms of absolute LR coefficient in rat classification on the FC dataset (mean ± standard deviation, averaged over 1000 repetitions). Each feature is an edge. The most relevant edges that discriminate between CTL and STZ rats involve ACC, hypothalamus (HTh), RSC, hippocampus (Hip), and subiculum (Sub). B Only connections surviving the NBS significance test were selected as features for classification (top 10 displayed). Classification accuracy was improved from 0.75 to 0.79 for pooled, 0.69 to 0.72 for early, and 0.83 to 0.90 for Late dataset by this feature pre-selection. Higher classification accuracy in late dataset is consistent with the advanced stage of disease and more marked differences between STZ and CTL
However, the top 10 edges with the highest feature importance in the first classification (all features, Fig. 5 A) did not overlap strongly with that from the second classification (reduced features, Fig. 5 B) perhaps due to the small sample size. The nodes involved in the top 10 edges did however overlap strongly between the two classifications.
Figure 6 displays SHAP plots for each instance of the most important features used to classify STZ and CTL subjects in each of the three datasets. Top features were generally consistent with those from LR in Fig. 5 A. Distribution of values for each feature (edge) in the STZ and CTL groups also agreed with the group difference test in the form of graph networks (Fig. 4 ). For example, in the early timepoints, both methods revealed the STZ group had stronger connectivity between the right hippocampus and motor cortex, left ACC and S1, left hypothalamus and right S1, and reduced connectivity between right PPC and left visual cortex, as well as right PPC and left hippocampus. In the late timepoints, the STZ group had increased connectivity between left S2 and striatum, left ACC and right striatum, left S1 and striatum, and weaker connectivity between left subiculum and hypothalamus, left RSC, and visual cortex. Overall, the distribution of SHAP values demonstrated that the STZ group had hyperconnectivity in the early timepoint but hypoconnectivity in the late timepoint, which confirmed the findings in the previous study [ 97 ].
Exemplary SHAP summary plots for the three datasets (pooled, early, and late) based on the model using FC significant connections as features. The summary plot combines feature importance with feature effects. Each point on the summary plot is a SHAP value for a feature and an instance. The position on the y -axis is determined by the feature and on the x -axis by the SHAP value. The color represents the value of the feature from low (blue) to high (red). The features are ordered according to their importance (top 9 displayed). Positive SHAP values lead the model to predict 1 (STZ) while negative ones lead the model to predict 0 (CTL)
For classification based on WM microstructure features, the mean test accuracy and top features with the highest importance are displayed in Fig. 7 . When using the combined diffusion metrics (DKI + WMTI) as features, the FA in fimbria and corpus callosum stood out as the best discriminating features in the early timepoints while the axonal density ( f ) of the WMTI-Watson model in the fimbria was the most important feature in the late timepoints as well as in the pooled data. Overall, the fimbria microstructure was the best discriminator between groups. FA was sensitive to early changes in STZ rats, which drove the DKI-based model to achieve better classification accuracy than the WMTI-based model at the Early timepoint (Table 1 ). While the accuracy of the DKI-based classifier decreased significantly at the Late timepoint, the accuracy of the WMTI-based classifier remained stable across time. The classifier built on combined DKI + WMTI metrics obtained the highest accuracy in the early stage and similar accuracy in the late.
Feature importance and test classification accuracy using different microstructure metrics (mean ± std over 1000 repetitions). Displayed are the top 5 most import features on the three datasets using DKI metrics (blue) and WMTI parameters (green) altogether. fi, fimbria; cc, corpus callosum; cg, cingulum; FA, fractional anisotropy; AD/RD, axial/radial diffusivity; AK, axial kurtosis; f, axonal density; D a , intra-axonal diffusivity; D e,|| , extra-axonal parallel diffusivity; c 2 : orientation dispersion
Figures 8 and 9 report the SHAP value for each feature and each prediction of the LR classifiers based on DKI and WMTI parameters. As for FC, a high degree of consistency was found between metrics with high SHAP values and those displaying group differences between STZ and CTL rats. Specifically, lower FA and higher RD in corpus callosum, and lower FA and RK in fimbria were major drivers of STZ difference to CTL in the early timepoints. In the late timepoints, reduced AxD and AK in the corpus callosum; decreased MK, AK, and RK in the cingulum; and decreased FA, MK, and RK, as well as increased RD in fimbria, were found to be the most prominent features distinguishing the STZ group from the CTL. WMTI-Watson parameters provided us with more specificity to differences between STZ and CTL groups. In both early and late timepoints, the white mater of STZ rats was characterized by lower intra-axonal diffusivity ( D a ) in CC, indicating intra-axonal damage, and lower axonal water fraction ( f ) in CC, cingulum, and fimbria, indicating demyelination and axonal loss.
A DKI estimates in three white matter ROIs (top row: corpus callosum (CC), middle row: cingulum (CG), and bottom row: fimbria of the hippocampus (Fi)). FA, fractional anisotropy; AxD/RD, axial/radial diffusivity; MK/AK/RK, mean/axial/radial kurtosis. Two-tailed t -test for inter-group comparison (red bars) and one-way ANOVA with Tukey-Cramer correction for within-group comparison across time (black and blue bars). ∗ : p < 0.05, ∗ ∗ : p < 0.01, ∗ ∗ ∗ : p < 0.001. + : outlier values (but not excluded from the analysis). B SHAP summary plots combining feature importance with feature effects based on DKI estimates. The position on the y -axis is determined by the feature and on the x -axis by the SHAP value. The color represents the value of the feature from low (blue) to high (red). The features are ordered according to their importance (top 10 displayed). Positive SHAP values lead the model to predict 1 (STZ) while negative ones lead the model to predict 0 (CTL)
A WMTI-Watson model estimates in three white matter ROIs (top row: corpus callosum (CC), middle row: cingulum (CG), and bottom row: fimbria of the hippocampus (Fi)). Two-tailed t -test for inter-group comparison (red bars) and one-way ANOVA with Tukey-Cramer correction for within-group comparison across time (black and blue bars). ∗ : p < 0.05, ∗ ∗ : p < 0.01, ∗ ∗ ∗ : p < 0.001. + : outlier values (but not excluded from the analysis). B SHAP summary plots combining feature importance with feature effects based on WMTI-Watson model estimates. The position on the y -axis is determined by the feature and on the x -axis by the SHAP value. The color represents the value of the feature from low (blue) to high (red). The features are ordered according to their importance (top 10 displayed). Positive SHAP values lead the model to predict 1 (STZ) while negative ones lead the model to predict 0 (CTL)
After combining the FC and microstructure features, the number of total rat subjects in the pooled dataset was reduced from 83 to 79 (Table 1 ) due to the absence of either fMRI or diffusion MRI data for four datasets. The two combination methods — concatenation vs ensemble — had similar mean classification accuracy on the Late dataset, but the ensemble method achieved much higher accuracy (10% improvement) on the Early dataset, and slightly better accuracy on the Pooled dataset. Overall, neither combined classifier outperformed single classifiers at a given timepoint: best early classification accuracy was achieved by DKI + WMTI and best late classification accuracy by FC.
Looking at the cross-prediction performance for all classifiers, the WMTI classifier trained on the early dataset obtained an outstanding accuracy on the late dataset, which was even higher than that of the classifier trained on the late data (0.87 vs 0.82). In addition, both cross-prediction classifiers (late-to-early and early-to-late) based on WMTI features had better accuracy than those based on DKI features or combined DKI + WMTI features. The FC classifier however had poor cross-prediction performance. This may indicate inter-group differences in FC evolved significantly from the early stage towards the late stage, which was consistent with early hyperconnectivity and late hypoconnectivity in STZ (Fig. 3 ). The combination methods had moderate performance both in early-to-late and late-to-early predictions. With the exception of DKI, all classifiers had higher accuracy in early-to-late prediction than late-to-early.
Based on Table 1 , a summary plot of classification accuracy based on either FC or microstructure metrics as well as the ensemble method on the three datasets (pooled, early, and late) is shown in Fig. 10 . On the Pooled data, the ensemble method achieved the highest overall accuracy among classifiers, which revealed that the best strategy was to combine all three types of features (FC, DKI, WMTI) in an ensemble-learning way. However, at the early timepoint, classification based on WM microstructure, especially DKI, provided substantially higher accuracy than FC-based classification while at the late timepoint, the FC-based classification significantly outperformed the microstructure-based classification. One possible explanation is that WM microstructure damage happens earlier than alterations in functional connectivity in the STZ group. However, this assumption needs to be further validated in human Alzheimer’s studies.
A summary plot of classification accuracy on the three datasets (pooled, early, and late) for each individual classifier and the ensemble classifier
The classification of individuals with AD or mild cognitive impairment from healthy controls using MRI-based features and ML has been increasingly proposed. Several studies have reported promising results of employing resting-state FC as major features to this end [ 45 , 47 , 58 , 76 , 103 , 105 , 112 ]. A few studies have also proposed using WM DTI-based features such as FA and MD for the classification of AD subjects [ 11 , 28 , 55 , 70 ].
Indeed, longitudinal studies of MCI and AD populations compared to healthy controls have revealed distinct WM degeneration patterns, as characterized using DTI, between patient and healthy populations. Decreased FA and increased MD over the course of one year were reported in the hippocampal cingulum of the AD group [ 72 ], both in the cingulum and fornix in an MCI and AD cohort [ 79 ], and genu of the corpus callosum in an MCI cohort [ 96 ]. Rates of WM structural decline were also faster in subjects initially enrolled in the preclinical phase of MCI and AD and who eventually developed dementia, mainly evidenced by a decrease in FA in the right inferior fronto-occipital fasciculus and splenium of corpus callosum [ 90 ]. Another study reported higher rates of change in FA and RD in the splenium of the corpus callosum, posterior cingulum, and left superior temporal region over the course of one year in AD [ 1 ]. Cross-sectional studies where FDG-PET and dMRI were available jointly suggested strong correlations between hypometabolism and altered DTI metrics in the hippocampus or posterior cingulate in early AD and amnestic MCI patients, with higher DTI sensitivity to early disease [ 107 , 115 ].
Similarly, longitudinal fMRI studies showed early stages of the disease are characterized by hyperconnectivity of certain brain networks, while follow-up in time inevitably leads to decreased connectivity throughout the brain. For example, subjects with an initial Clinical Dementia Rating (CDR) of 0.5 displayed reduced activity in the right hippocampus after 2 years, while those CDR of 0 did not, while the rate of decline correlated positively with high hippocampal activity at baseline, further supporting the non-monotonic pattern of initial hippocampus hyperconnectivity followed by hypoconnectivity as dementia progresses [ 80 ]. Similarly, initial hyperconnectivity of the anterior and ventral DMN transitioned to hypoconnectivity at follow-up in AD patients [ 22 ]. In cross-sectional studies of simultaneous FDG-PET/fMRI, the spatial brain patterns of hypoconnectivity and hypometabolism overlapped only partially, while each maintaining the good predictive value of cognitive decline [ 71 , 111 ].
However, multi-model longitudinal studies in humans over a significant time span are extremely challenging to achieve. To our knowledge, no study reported longitudinal metabolic, microstructural, and functional connectivity changes jointly, allthemore using advanced diffusion metrics beyond DTI. Furthermore, the value of WM microstructure and FC features for subject classification and cross timepoint prediction has not been evaluated. A recent cross-sectional study has evaluated the value of amyloid, tau, glucose hypometabolism, and structural atrophy in classifying MCI and AD patients, with amyloid and tau being better predictors of MCI and early AD, while glucose hypometabolism and atrophy were better predictors of later AD [ 41 ].
Animal models are very well suited to perform comprehensive longitudinal studies over a time period that covers a broad range of pathology evolution. The icv-STZ rat model induces impaired brain glucose metabolism, which is an excellent biomarker for disentangling AD from other forms of dementia. Rats further exhibit several features of Alzheimer’s at multiple levels: pathological (tau, amyloid, neuronal loss, atrophy), behavioral (short-term memory impairment), and neuroimaging (same trends in diffusion and rs-fMRI metrics as in humans).
To our knowledge, this study is the first one attempting to evaluate FC and WM microstructure features separately as well as their combination in a ML-based classification context. Furthermore, apart from the conventional DTI metrics, features based on more advanced DKI metrics and especially on biophysical models were also assessed in this study.
In support of the empirical relevance of the icv-STZ model for sporadic AD, the most important discriminating features in FC and WM integrity aligned with brain regions and WM tracts affected in human sporadic AD itself. Discriminating FC connections involved regions of the default mode network such as the hippocampus, cingulate, and posterior parietal cortex [ 2 , 15 , 97 ], as well as the hypothalamus which is responsible for recruiting alternative sources of energy to glucose, such as ketone bodies, in response to impaired brain glucose metabolism by the STZ [ 17 , 33 , 36 , 64 , 106 ]. Many FC connections with top feature importance in the Early stage also involved the visual and motor cortices, areas that are related to non-cognitive manifestations such as vision and motor decline and have been reported to precede the cognitive deficits in humans [ 14 , 27 , 44 , 48 , 73 , 74 , 102 ]. For classification based on WM integrity, microstructural features in the fimbria of the hippocampus played the most important role in distinguishing STZ rats, which was consistent with the fact that hippocampus is especially vulnerable to AD [ 77 , 89 ] and to the icv-STZ rat model of AD [ 4 , 92 ].
Our results show DKI brings valuable complementary information to DTI for classification purposes, and the WM model narrows down the identification of microstructure changes to intra-axonal damage, demyelination, and axonal loss. This is in line with the expectation from biophysical models to increase specificity to microstructure features over signal representation metrics such as DTI or DKI [ 49 , 50 , 78 ]. Going forward, the acquisition of multi-shell diffusion MRI data (at least two non-zero b -values, e.g., b = 1000 and 2500 s/mm 2 ) in clinical studies of dementia or other brain diseases is highly recommended to enable the estimation of DKI metrics brain-wide, and of WM microstructure features using the WMTI-Watson model, for which analysis code is readily available [ 24 ]. WMTI metrics were arguably the most stable features in discriminating STZ and CTL subjects compared to the DKI- and FC-based features, as evidenced in the cross-timepoint prediction accuracy (> 0.80). This might indicate the possibility of early screening and prognosis of AD in clinical applications using WM microstructure features derived from the WMTI-Watson model of diffusion. In other words, subjects with early WM alterations at high risk of developing further neurodegeneration might be identified and receive intervention when they are still in the early stage [ 99 ].
When using FC to classify STZ/CTL rats, only choosing connections significantly different between groups (using NBS) as features naturally improved mean classification accuracy. When translating our classification approach to discriminate AD patients from healthy controls, FC edges identified as driving group differences between diagnosed AD patients and controls could be used as features for classification in future diagnostic-blind studies, or to discriminate prospective AD patients from controls.
From the perspective of pathological progression and biomarker timeline within the course of the disease, microstructure-based features achieved better performance than FC in the early timepoint as well as for cross-timepoint predictions. Performance in the early timepoint suggests that WM degeneration in the STZ group could happen earlier than FC breakdown. Similar findings have been reported by human studies in subjective cognitive impairment as well as AD [ 6 , 70 , 82 ]. Performance in the cross-timepoint prediction suggests that microstructure degeneration is relatively consistent across time. In contrast, the pattern in FC metrics was non-monotonic and shifted from early hyperconnectivity to late hypoconnectivity in the STZ rats, as also previously reported in human studies [ 26 ]. However, more data are required to fully validate these hypotheses, especially in humans.
Nevertheless, the best overall strategy for STZ vs CTL classification was aggregating the three individual classifiers using ensemble learning. Not only was the ensemble classification more accurate on the pooled dataset (0.85) than any of the individual classifiers, but it also maintained a high level of accuracy at each of the separate timepoints. This demonstrated that microstructural and functional information can be complementary and have their unique value in identifying STZ rats, and possibly mild cognitive impairment and early AD.
As to limitations, first, this study is based on a relatively small dataset with 24 rats followed across four timepoints. Second, we only used male rats, which was based on practical reasons. As female rats are more resistant than males to STZ-induced alterations [ 10 , 35 , 86 ] and hormonal modulation plays an important role in females, future studies should consider rats of both sexes. Third, in FC-based classification, each connection (ROI pair) was treated as an individual feature leading to the loss of the topological information among them. For future studies, graph neural networks can be used to replace LR for the FC-based classification [ 65 , 114 ] since they consider the functional network as a whole thus better preserving spatial information. However, this will also require more advanced explainability methods to interpret the classification results [ 108 , 113 ]. Finally, no amyloid or tau information was available for these rats in vivo. However, histological stainings performed after sacrifice at 21 weeks revealed amyloid plaques and neurofibrillary tangles in icv-STZ brains, as reported in our previously published study [ 97 ] as well as other studies of this animal model [ 30 , 60 , 91 ].
Our work examined potential discriminators of Alzheimer’s disease in the icv-STZ rat model using functional connectivity and WM microstructure features. For the first time, we evaluated those two types of MRI-based features separately as well as in combination, in a context of ML-based classification. WM microstructure features achieved higher classification accuracy in the early timepoints of neurodegeneration, and FC in the later timepoints, suggesting structural damage precedes functional damage. Combining all the FC and microstructure metrics in an ensemble way was the best strategy to discriminate between STZ and CTL rats, with a consistent accuracy over time above 0.85. However, for cross-time prediction, WMTI model features yielded the highest accuracy from early-to-late timepoints and vice versa, possibly thanks to the more specific metrics they capture from the microstructure, that project well across timepoints. Foreseeably in human datasets, the best microstructure (or ensemble microstructure + FC) classification features would be extracted from late timepoints with known subject diagnosis (e.g., healthy vs AD), the ML model trained on late timepoint datasets of those reduced features, and then applied to early timepoint populations to aid early diagnosis and prediction of disease evolution.
The datasets generated and/or analyzed during the current study are available in the OpenNeuro repository, https://openneuro.org/datasets/ds003520/versions/1.0.2 (resting-state fMRI, cohort 1, N = 17 rats) and https://openneuro.org/datasets/ds004441 (diffusion MRI, cohorts 1 + 2, N = 24 rats).
Anterior cingulate cortex
Alzheimer’s disease
Axial diffusivity
Axial kurtosis
Corpus callosum
Diffusion kurtosis imaging
Default mode network
Diffusion tensor imaging
Fractional anisotropy
Functional MRI
Intracerebroventricular
Mean diffusivity
Mean kurtosis
Magnetic resonance imaging
Principal component analysis
Posterior parietal cortex
Radial diffusivity
Radial kurtosis
Region of interest
Retrosplenial cortex
Primary somatosensory cortex
Secondary somatosensory cortex
Streptozotocin
White Matter Tract Integrity Watson model
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The authors thank Catarina Tristão Pereira and Ting Yin for contributing code for white matter segmentation and resting-state fMRI analysis, respectively.
Open access funding provided by University of Lausanne This work was funded by the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE), and Geneva University Hospitals (HUG) (collection, analysis and interpretation of data, manuscript writing, to Y.D., B.L. and I.O.J.), and by the Swiss National Science Foundation Eccellenza Fellowship PCEFP2_194260 (interpretation of data and manuscript writing, to I.O.J.).
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Yujian Diao, Bernard Lanz & Ileana Ozana Jelescu
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YD collected, analyzed and interpreted the data and wrote the manuscript. IOJ designed the study, interpreted the data and edited the manuscript. BL designed and performed the FDG-PET experiments. All authors read and approved the final manuscript.
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Additional file 1: figure s1..
Example of rs-fMRI images of 8 coronal slices (A), matching anatomical MR images (B) and atlas-based anatomical labels registered to the fMRI images (C). (Image taken from https://www.frontiersin.org/articles/10.3389/fnins.2021.602170/full). Table S1. A list of 28 atlas-defined regions of interest (ROIs, 14 per hemisphere) for the fMRI data. The ROIs were regrouped based on the original labels of the Waxholm Space (WHS) Atlas of the rat brain (https://www.nitrc.org/projects/whs-sd-atlas).
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Diao, Y., Lanz, B. & Jelescu, I.O. Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning. Alz Res Therapy 15 , 193 (2023). https://doi.org/10.1186/s13195-023-01328-0
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As schools have become ethnically, culturally, and linguistically diversified, multicultural mathematics education is emerging as the paradigm of school mathematics reform. When considering that an enacted curriculum is a set of beliefs put into action by a teacher, it is important to understand teachers’ beliefs for the successful implementation of multicultural mathematics education. From this perspective, this research analyzed mathematics teachers’ narratives to describe the multicultural transformation of their beliefs about mathematics as a school subject in the context of a multicultural mathematics teacher education course. The analysis shows that through the course participation, the teachers came to see mathematics as a cultural construct and challenged the Eurocentric perspective of mathematics. This change facilitated the teachers to seek ways to make school mathematics inclusive and equitable. The analysis also revealed the teachers’ contradicting beliefs, which led them to engage in dialogues for collective reflection to nurture their narratives of multicultural mathematics education. The results of this research imply that a multicultural teacher education program should be extended into a community to support teachers’ lifelong learning within a collaborative network of sharing and nurturing their narratives by integrating theory and practice about multicultural mathematics education.
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First of all, we express our deepest gratitude to the teachers of our MMTE course. This research was impossible without their enthusiastic participation and support. We are ineffably indebted to Professor Carl Grant, University of Wisconsin-Madison for his guidance to formulate our analysis. We sincerely thank Professor Lee Moon Woo, Hanyang University, for her thoughtful comments on our draft.
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Song, RJ., Ju, MK. The trajectory of teachers’ multicultural transformation: an analysis of teachers’ beliefs about mathematics as a school subject. Asia Pacific Educ. Rev. (2024). https://doi.org/10.1007/s12564-024-09986-x
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Finding scientific papers and journals relevant to a particular area of research is a concern for many people including students, professors, and researchers. A subject classification of papers facilitates the search process. That is, having a list of subjects in a research field, we try to find out to which subject(s) a given paper is more ...
The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on "study designs," we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.
A defining aspect of classification systems, particularly those used in research evaluation, is the level of aggregation of the classification, such as at the journal or the article level, or at the conference or the presentation level. The classification of scientific work at the journal and article levels has been extensively studied [9-12 ...
Subject classification of research papers based on interrelationships analysis. August 2011. DOI: 10.1145/2023568.2023579. Authors: Mohsen Taheriyan. University of Southern California. To read the ...
Gough, Thomas, and Oliver (2012) provided another classification system, focusing on variations in the aims and approaches, structures and components, and breadth and depth of research reviews. They identified several dimensions (or continua) on which reviews vary, including philosophy, relation to theory, approaches to synthesis, use of iterative or a priori methods, search strategies ...
The research population was drawn from eight schools, two further education colleges and three universities. ... This should be considered when developing teaching programmes.,The findings offer a new perspective on subject classification and its association with IB, and a new model of the association between IB and tool acquisition or ...
While article-based subject classification schemes are believed to be superior, the study found that Web of Science, which uses a journal-based classification system, had the most accurate subject ...
Part 3 explores three case studies that illustrate the most common subject-object transitions experienced in health research: (1) from object to subject (viz. research participation); (2) from subject to object (viz. data use); and (3) 'subject/objects', where something does not fit easily within this legal binary (viz. embryos in vitro).
Types of Research Designs Compared | Guide & Examples. Published on June 20, 2019 by Shona McCombes.Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do.. There are many ways to categorize different types of research.
The case study as a key research method has often been criticized for generating results that are less generalizable than those of large-sample, quantitative methods. This paper clearly defines generalization and distinguishes it from other related concepts.
Present your findings in an appropriate form for your audience. Types of Quantatitive Research. 1. ... Choose main methods, sites, and subjects for research. Determine methods of documentation of data and access to subjects. 3. Decide what you will collect data on: questions, behaviors to observe, issues to look for in documents (interview ...
The classification scheme differentiates between two major attributes of methods—consolidation technique and type of content to be consolidated—that may reflect the positivist or constructivist research paradigm. ... Voils C., Sandelowski M., Barroso J., Hasselblad V.: Making sense of qualitative and quantitative findings in mixed research ...
The discussion section is often considered the most important part of your research paper because it: Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
The classification of the research subjects, conditions, or groups determines the type of research design to be used. ... The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes. ... The results are specific. Post results analysis, research findings from the same dataset can be ...
Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time. According to The Sources of Information Primary Research. This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.
It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
Journal classification into subject categories is an important aspect of the journal indexing systems. From a theoretical perspective, this classification is an external expression of the internal structure of science and thus, it can foster research on the inherent relationships between scientific fields, institutes and researchers as well as many other scientometric phenomena.
As the framework of scientific research, subject-classification plays an important role in the development of science. In order to combine the development of science with the current expert subject-classification system and further give a more appropriate description of scientific output analysis from subject level, We study the relationship between the natural science related sub-categories ...
Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account ...
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Background The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established ...
Hello!In this video I shared the step by step on how to look for significant research findings for the Activity 1 in Research in Daily Life 1, STEM Grade 11....
RESEARCH FINDINGS : Designed to keep you and your partner effortless comfortable, the sleep number 360 smart beds uses responsive air technology to sense your movement the automatically adjusts firmness, comfort and support to keep you both sleeping blissful, all night SUBJECT CLASSIFICATION : TECHNOLOGY / HEALTH IMPLICATION TO IMPROVEMENT TO THE QUALITY OF LIFE : It is good for us to use to ...
Multicultural perspectives on mathematics and its educational practice. In the twenty-first century when cultural diversity is outwardly recognized and respected, mathematics is still represented as a universal and transcendental subject discovered by a few geniuses with great reasoning power, such as Descartes, Newton, and Pascal, who were all male professionals from European academia (Ju, et ...