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Record W4362589316 · doi:10.1162/leon_r_02389

Giving Bodies Back to Data: Image Makers, Bricolage, and Reinvention in Magnetic Resonance Technology

2023· article· en· W4362589316 on OpenAlexaff
Roberta Buiani

Bibliographic record

VenueLeonardo · 2023
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsFields Institute for Research in Mathematical Sciences
Fundersnot available
KeywordsIconCitationBricolageComputer scienceDownloadSearch engine optimizationWorld Wide WebInformation retrievalLibrary scienceArtSearch engineVisual arts

Abstract

fetched live from OpenAlex

Giving Bodies Back to Data takes us on a fascinating journey into the history and complexities of MRI technologies. Specifically, the book emphasizes the role that aesthetics, affectivity, and craft practice play in the processes leading to the production of and the artistic responses to these technologies.The introduction presents readers with three compelling scenes that immediately grab their attention and set the tone for the entire book. In the first, Santiago Ramon Cajàl, equipped with pen and ink, sketches neurons by hand while sitting at his desk in Madrid in 1882. For the second scene the author fast-forwards to 1974, when physicist James Hutchinson, biologist Margaret Forster, and the biomedical physics laboratory team in Aberdeen produced the iconic first MRI image of a mouse with a broken neck. The data resulting from a small magnetic system were translated into an easily readable color-coded image with the help of color crayons. The third scene transports the reader to an exhibition by Mark Didou, where the apparently abstract form of a sculpture features data recast from an MRI scan. The sculpture in turn is reflected onto an anamorphic mirror, where it rematerializes as a skull. The three scenes introduce us to different ways of approaching, producing, and processing images as complex and unequivocally relational: Cajàl’s scientific image is not just a representation of a phenomenon but a way to “interrogate the object made visible” (p. xiii). Hutchinson’s image is the product of an interdisciplinary effort. It manifests a “striking copresence of numbers and pictorial elements” showing how “scientists combine physics, mathematics, and aesthetic choices” (p. xv); it is an effort to make visually legible a phenomenon that the technology is expressing in the form of data. Didou’s Skull I takes data extracted from the MRI data-visualization scanning process and turns them into an image again. The installation inverts the scanning process, starting by showing the abstracted data-turned-sculpture, then displaying in the mirror the body from which these data originated. In these scenes, data are never just data but are always the product of technological interfacing and mediation and human processes. They come to life when they are processed and transformed into something else; they must be interpreted, translated, and reworked in order for them to make sense to different scientists and practitioners, as well as to a primarily vision-oriented culture.The three scenes introduce three important themes, extending across the book’s seven chapters: Casini weaves together a well-documented cultural-historical account of MRI technologies; an analysis of the transformation of vision and cognition as it emerges from the techno-mediated image; and a multifaceted critical discussion on how art, aesthetics, and science intersect and collide both in the work of artists collaborating with scientists and working in labs and in the work of scientists themselves.The book is composed of two distinct parts linked through an Intermezzo. To a certain extent, the two parts could be read as separate books, depending on whether readers are interested in technoscience, technological history, and scientific ethnography, or are rather attracted by interdisciplinary collaborations or art-and-science explorations. Whereas the first part maps the technoscientific history of MRI technologies and MRI data visualization as they were conceived and developed during long-term experiments and fabrication conducted at the University of Aberdeen, the second part focuses on the interpretation of MRI technologies and data visualization by artists and curatorial teams working at the intersection of art and science and on the difficult yet surprisingly productive dialogues that artists and scientists establish when engaging in collaboration. The central intermezzo, exploring the significance of the grid in transforming vision and the perception of one’s body (be it the body of the patient or the body of the artist), cleverly connects the two parts of the book, pulling readers back in and compelling them to see the grid transversally, that is, as a crucial cross-disciplinary structure used in the arts as much as in the sciences.Casini’s historical and ethnographic analysis covers two important periods in the history of MRI technologies: the data visualization challenges emerging at the Aberdeen’s biomedical physics lab between the 60s and 80s leading to the development of the first whole-body MRI scanner; and the latest (in-progress) research on the FFC-MRI, which Casini discusses as a product of the recent discourse, imagery, and practice of personalized/predictive medicine. This is a rich and multi-faceted history: The author carefully excavated archival material including official documentation, detailed description of equipment, and its technical developments.Importantly, archival material revealed the “intimate process” behind technological development: Lab notes and personal notes by scientists revealed their epistemological approaches and their preoccupations not only with more obvious scientific aspects but also with the well-being of the potential patient. Notes also contained important reflections on the relationship between data and imaging. Casini uses the important concept of bricolage to describe the ecology of instruments, tools, wires, and cables, but also DIY creativity and problem-solving, as essential components of any scientific innovation (p. 63). This is an interdisciplinary history, demonstrating how scientific innovation always involves people with different disciplinary trainings and agenda, different ways of seeing and understanding, and different ideas on the role of imaging.Among the thematic threads traversing the book, the reader finds an important examination of the role of images, not only as they pertain to the realm of MRI technology but also in the context of data visualization used in the medical field and in the arts. For Casini, operational images based on machine learning algorithms have come to challenge our vision and cognition, becoming “spaces of possibilities of human intervention” (p. 207). She demonstrates this by reflecting on the transformative arc from the first image appearing to demonstrate the effectiveness of MRI (the image of the dead mouse with a broken neck), where static data were associated to pictorial clues to facilitate effective comprehension, all the way to the latest images produced with the assistance of algorithms, where dynamic data are connected with probability and uncertainty.A very important aspect of Giving Bodies Back to Data is its sustained reflection on the often uneven and nonetheless very rich relationship between art and science. Casini’s engagement with the many roles played by artistic expression is particularly refreshing. She is well aware of the power-relations involved in art and science collaborations, as the arts have traditionally been considered subordinate or accessory to science: In the book, she narrates such relation with minutiae through a series of examples that flesh out the different stages of art-making, starting from the encounter with the scientists (and the first impression of the artist and the ethnographer) to the actual production of the artwork. Art-making is a long process, encompassing dialogues with scientists, negotiations, misunderstanding, and research. It certainly is not just the object displayed in the gallery exhibition. Here, Casini uses the same ethnographic method she employed while unraveling the history of MRI technology, made of notes, drawings, and dialogues. She speaks about this relationship personally, as an observer coming from a humanities background being parachuted into a lab during her ethnographic research; she also speaks about it as a curator involved in the delicate process of mediation between the artist and the scientist during an arts residency leading to an exhibition at Suttie Art Space.In both cases, she reclaims the important role of the arts in the fields of art and science. Art is not just a way to reveal and explain, literally opening the black box of MRI technologies; it is not just a way for the scientific team to communicate science, or to elevate its exposure outside of the lab. The artists featured in this book are certainly not scientific communicators or tools at the service of science. They demonstrate a deep understanding of what working with data means. The artistic practices by artists such as Ilona Sagar, Mark Didou, Liz Orton, and Beverley Hood (all artists working with data) emerge from intense research into the technoscientific, historical, and social aspects of MRI technologies. Their works play the important role of illuminating the profoundly relational features of data by showing how the scientific process leading to visualization is connected to the body, the emotional journey of patients, and their relationship with the practitioner. In addition, they explicate the changing role of data (from static to dynamic, from exact to probabilistic, etc.) in today’s machine learning era.Giving Bodies Back to Data is a must-read book for a range of readers: Whether interested in understanding the journey leading to the development of MRI technology, or the processes of art-making in an art and science context, they might find themselves becoming inextricably entangled with and benefiting from both approaches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.312
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2023
Admission routes1
Has abstractyes

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