Giving Bodies back to Data: MRI Scanning, Data Visualisation and Art-Science Collaboration in the Age of Operational Images

Guest Speaker Dr. Silvia Casini, Senior Lecturer in Film and Visual Culture, University of Aberdeen, and author of Giving bodies back to data: image makers, bricolage, and reinvention in Magnetic Resonance Technology Cambridge, Mass,: MIT Press (2021)

Commentator/ Respondent Professor Alastair Macdonald, Senior Research Fellow, School of Design

Dr. Casini will speak about her research and its implications for a critical evaluation of the claims of neuroscience, popular myths about biomedical imaging, and the role of artists and creative practitioners who wish to engage with these fields.

ABSTRACT

Today, almost forty years after development of the world’s first clinical full body Magnetic Resonance Image scanner in John R. Mallard’s Aberdeen medical physics laboratory, biomedical images help interpret the clinical significance of biodata for patients all around the world. But what is it that the image-makers in Mallard’s lab first saw before a protocol solidified for generating images out of data, data from signal/noise, and interpretation of the data from the data-generated image? How do scientists visualise when an emerging image-generating technique is under development, when the protocol for image generation/data interpretation has not yet crystallised into a visual output? What do they see? And how is what they see reinvented? What visual practices and theories do artists and creative practitioners adopt when they use biomedical imaging techniques to engage with and reconfigure the neurosciences and other biomedical imaging fields? The talk will engage with such questions through a reflection on my own experience as researcher and curator of a small-scale art-science collaborative project which combined laboratory ethnography and archival research.

Mallard’s pioneering work around data visualisation problems and aesthetic decisions in the development of MRI show how scientists can be attentive to the construction of visual practices and their meaning. In Giving Bodies back to Data I open up the black-box of MRI by following laboratory microhistories, tracing historical and aesthetic resonances, to identify a possible resistance to reductionism from within biomedicine and the neurosciences themselves. Similarly, I argue that artists and creative practitioners who desire to engage with the neurosciences and biomedical practice, need to become familiar with the techno-scientific features of the image-making process. Not understanding the visual practices (and cultural assumptions) that have become embedded in the image generating technology, might prevent artists that want to work with the culture of biomedical images from ever grasping what is at stake in the materiality of the image-making process. Artists have approached the materiality of the image-making process in different ways. Some have taken MRI and its visual output (for example, a brain scan) as a point of departure to explore personhood and re-invent portraiture; Some have used MRI as a creative medium to explore alternative ways of making art. More recently, to move beyond the arc of what they felt could be accomplished with the biomedical “scan-portrait”, bioartists have adopted a materialist perspective to work directly with living entities such as neurons to investigate processes such as memory, consciousness and decision-making. The pattern of neuroscience-based art moves on from the critique of the myth of the transparent body in order to create configurations between bodies, data and technologies that can exercise a critical function from within biomedicine and the neurosciences themselves.

The second part of the talk will move beyond the specific laboratory and technology to problematise the question of what space humans can claim back in the era of so-called ‘operational images’, that is, images created by technical apparatuses (Farocki 2001). What are the implications of the regime of imaging based on software, a grid-based spatiality, electrical impulses, and statistics? What are the bodily and sociotechnical imaginaries thus created? Opening the black box of technologies used to visualize the body not only means being able to understand the data-visualization pipeline and its hidden assumptions; it also means being able to explore how a given technology and the data it generates fit within the wider system of operations (sociocultural, economic, political) and global infrastructures into which technologies such as MRI work. What is the labour required to imagine sociotechnical alternatives within the regime of operational images? How can our bodies be emancipated from existing trajectories and patterns of thinking, movement, and looking that had been set up by the infrastructures that permeate our lives (whether the architecture of a building with the exchanges it facilitates/prevents or the image-mediated doctor–patient relationship)? Machine learning and artificial intelligence (AI) remind us how easily the world seems never to be completely human centred. However, the works of art I critically examine in this second part of the talk ask us not to give up our responsibility as humans in the process of analysing, interpreting, and displaying information from operational images, giving bodies back to data.

Biographical note:

Silvia Casini lectures in Film and Visual Culture at the University of Aberdeen and her courses are attended also by students of medicine. Her work is situated at the crossroad of visual culture, science studies, and the medical humanities. She is the author of several articles on the aesthetic, epistemological and societal implications of scientific visualisation. Her work features in journals such as Configurations, Leonardo, Contemporary Aesthetics, Nuncius Journal of the Material and Visual History of Science, The Senses and Society. Among her most recent publications there is “What counts as data and for whom? The role of the modest witness in art-science collaboration” (Routledge Handbook of Art, Science, and Technology Studies, 2021).

Image: Dr. Silvia Casini