Epistemological challenges for neural correlates of consciousness: A defense of medical research on consciousness

Recent work in the field of consciousness science has predominantly focused on the search for neural correlates of consciousness (NCC). However, despite significant technological advances in recent decades, defining NCC remains an ambitious goal in consciousness research. The main difficulty stems from an epistemological challenge known as the "Problem of coordination", which hinders or at least slows down the experimental process inherent to the study of consciousness. Fundamental research has mainly focused on a content-based conception of consciousness, often referred to as a "local" conception of consciousness. This approach suffers from the Problem of coordination and its consequences. However, an alternative, more reliable approach could be considered, namely, the global or "state-based" approach, which is grounded in clinical research on consciousness disorders.

[Multidimensional and computational theory of mood]

What is mood? Despite its crucial place in psychiatric nosography and cognitive science, it is still difficult to delimit its conceptual ground. The distinction between emotion and mood is ambiguous: mood is often presented as an affective state that is more prolonged and less intense than emotion, or as an affective polarity distinguishing high and low mood swinging around a baseline. However, these definitions do not match the clinical reality of mood disorders such as unipolar depression and bipolar disorder, and do not allow us to understand the effect of mood on behaviour, perception and cognition. In this paper, we propose a multidimensional and computational theory of mood inspired by contemporary hypotheses in theoretical neuroscience and philosophy of emotion. After suggesting an operational distinction between emotion and mood, we show how a succession of emotions can cumulatively generate congruent mood over time, making mood an emerging state from emotion. We then present how mood determines mental and behavioral states when interacting with the environment, constituting a dispositional state of emotion, perception, belief, and action. Using this theoretical framework, we propose a computational representation of the emerging and dispositional dimensions of mood by formalizing mood as a layer of third-order Bayesian beliefs encoding the precision of emotion, and regulated by prediction errors associated with interoceptive predictions. Finally, we show how this theoretical framework sheds light on the processes involved in mood disorders, the emergence of mood congruent beliefs, or the mechanisms of antidepressant treatments in clinical psychiatry.

A Proposal to Make Biomedical Research into Alzheimer's Disease More Democratic Following an International Survey with Researchers

Background

Therapeutic research into Alzheimer's disease (AD) has been dominated by the amyloid cascade hypothesis (ACH) since the 1990s. However, targeting amyloid in AD patients has not yet resulted in highly significant disease-modifying effects. Furthermore, other promising theories of AD etiology exist.

Objective

We sought to directly investigate whether the ACH still dominates the opinions of researchers working on AD and explore the implications of this question for future directions of research.

Methods

During 2019, we undertook an international survey promoted with the help of the Alzheimer's Association with questions on theories and treatments of AD. Further efforts to promote a similar study in 2021 did not recruit a significant number of participants.

Results

173 researchers took part in the 2019 survey, 22% of which held "pro-ACH" opinions, tended to have more publications, were more likely to be male, and over 60. Thus, pro-ACH may now be a minority opinion in the field but is nevertheless the hypothesis on which the most clinical trials are based, suggestive of a representation bias. Popular vote of all 173 participants suggested that lifestyle treatments and anti-tau drugs were a source of more therapeutic optimism than anti-amyloid treatments.

Conclusion

We propose a more democratic research structure which increases the likelihood that promising theories are published and funded fairly, promotes a broader scientific view of AD, and reduces the larger community's dependence on a fragile economic model.

The Many Faces of EpigeneticsOxford, December 2017

The conference ‘The Many Faces of Epigenetics: Multidisciplinary Perspectives “over” Genetics’ was held in Oxford (6–8 December 2017) and offered a valuable window into the domain of Epigenetics and its promises. The workshop revealed that, among a wealth of discourses about Epigenetics, it is not so easy to decipher which discourses are to be trusted. Because Epigenetics is a rather old notion that has generated many debates and promises, defining precisely what has changed and where we are currently is a challenge in itself. Interestingly, the conference allowed debates beyond statements such as ‘If you don’t know the cause, you say it’s epigenetic’ (Deichmann 2016), pointing out that the lack of a precise definition of Epigenetics was no hindrance to the discussions. Finally, it highlighted the grounds of (dis)agreement among communities of natural and social scientists; but eventually the discussions showed that epigenetic tools open the path to new topics and challenges that are awaiting us.

Marine Biodiversity Databanks

This chapter presents the contribution of databanks to the development of biodiversity knowledge through the example of marine biodiversity databanks. Focusing on the marine field allows us to insist on the imbalance of the unknown vs. the better known part. The chapter emphasizes the role of taxonomic and genetic databanks as well as the ongoing transformations that databanks are submitted to in order to answer pressing demands due to the biodiversity crisis. It aims to analyse the requirements biodiversity databanks have to satisfy in order to help both researchers and conservationists in their respective endeavors. It begins by pointing out the main characteristics and limits of biodiversity knowledge and defend the view that databanks are well-suited to overcome these limits as soon as they are widely accessible and interoperable. These constraints are analysed as both technical and scientific. Their dynamic dimension is emphasized as databanks must comply with the rapid evolution of scientific knowledge. We also propose a view on the relationships between biodiversity knowledge, assessment, and conservation.

Perspective: plasticity, the enemy of the good

Plasticity is an important feature of modern cancer research. However, the level at which we should consider it remains an open question. Such debate is not new in the field of cancer and can be exemplified by the different models explaining carcinogenesis. Those models mostly explain cell transformation through the deregulation of the internal circuitry. In the last years, those models dramatically increased our knowledge and led to a series of short-term successes in terms of therapeutics. However, cancer drug resistance inevitably arises. Recently, studies on the so-called tumor microenvironment enriched the cell-centered perspective but it also enlarged the complexity of cancer etiology in particular for advanced diseases. Here, we suggest that the plastic and multi-sites specific nature of cancer combined with our incapacity to promise cure should push towards a new perspective where early clinical actions, instead of late ones, should be heralded as the priority of cancer research and care.

Empirical agreement in model validation

Empirical agreement is often used as an important criterion when assessing the validity of scientific models. However, it is by no means a sufficient criterion as a model can be so adjusted as to fit available data even though it is based on hypotheses whose plausibility is known to be questionable. Our aim in this paper is to investigate into the uses of empirical agreement within the process of model validation.