This paper examined whether the belief that ASD is more common in males is an accurate representation of sex difference in ASD’s prevalence, or if it is representative of “measurement bias [that] hinder[s] early diagnosis in females.” Through modeling social communication (SC) and repetitive behaviors (RRB) for both males and females through directly assessing two groups of children from 6 to 60 months of age, the authors were able determine two latent classes in both models that had an equal sex ratio for the high-concern clusters. This paper’s findings indicate that it is important to avoid sex-related measurement bias in diagnoses by identifying sex-specific patterns of ASD emergence; and that the currently presumed discrepancy of ASD prevalence between men and women may be partially due to sex-related measurement bias in the past.
This Comment by J. M. Jebsen, K. Nicoll Baines, R. A. Oliver, and I. Jayasinghe discusses the disparity in STEM research funding, and how the funding process must be restructured in order to reduce biases against minoritized groups, such as women (particularly women of color). The authors explain the many reasons for this disparity, such as cumulative disadvantage and institutional gatekeeping, and discuss how the Universal Basic Research Grant may help–in which every eligible researcher will be given a certain amount of funding every year.
Organisers: John Cryan , David Baldwin , Andreas Reif , Anna Beyeler , Lucille Capuron , Christina Dalla , Damiaan Denys , Julia García-Fuster , Iiris Hovatta , Ewelina Knapska , Urs Meyer , Andreas Meyer-Lindenberg , Valeria Mondelli , Francesco Papaleo , Eduard Vieta
Total invited speakers (keynote + plenary) gender ratio: 3 Women: 4 Men (43%) Estimated base rate of women in the field: 43%* BWN rating: 3, at base rate or within 1 standard deviation above base rate
**Method of estimation: previously established base rate of women in the neuroscience field
This paper by Rebecca Schwarzlose discusses the biases often in place among neuroscience and psychology researchers, particularly in terms of assumptions concerning what type of brain is superior, and what counts as a deficit: “The standard approach to studying aging or stigmatized conditions is to compare neural or cognitive measures from the stigmatized group with those of a control group without the condition. While this approach is scientifically sound, our interpretations of the findings are often biased by the assumption that people without stigmatized conditions are neurally and mentally ideal.”
These assumptions can lead to poorer interpretations of results, as well as bias the data collection process itself. In addition to discussing this problem in research, this paper suggest several strategies to combat these biases–such as collecting other data potentially related to performance (i.e. sleep quality and hearing) and asking stigmatized groups about points of confusion within the protocol during the Pilot (and adjusting accordingly prior to capturing data).
This paper by Linzie Taylor and Karen S. Rommelfanger discusses how currently, neuroscience is not meeting its full potential–and how this can be improved. They argue that this is due to white Western individualist bias (WWIB), which leads to the ignorance of many factors in neuroscience research that would otherwise promote equality. One such example from the paper, among many given, is EEG scans, and how despite the fact that they have much more noise when used on scalps of people with coarse and curly hair (i.e. in populations of African descent), neuroscience has continued heavily depending on using EEG globally for about 100 years. After illustrating this problem, the authors develop a framework for a more “relational” approach to science–an approach that, if adopted by neuroscientists, will hopefully increase the inclusivity of research as a whole.
EDI-toolkit is an amazing resource for definitions of EDI, research behind its importance, and various plans for improving EDI in neuroscience. This set of tools was developed alongside and based off of resources from the Human Brain Project, and includes guidelines for EDI in project governance, and in various parts of research development.