JOURNAL ARTICLE

Misophonia matters: A case study of the role of brain imaging in debates over new diagnoses.

  • Published In: Sociology of Health & Illness, 2024, v. 46. P. 92 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Perez, Victor W.; Friedman, Asia 3 of 3

Abstract

Misophonia has gained attention in scientific circles that utilise brain imaging to validate diagnoses. The condition is promoted as not merely a symptom of other psychiatric diagnoses but as a discrete clinical entity. We illustrate the social construction of the diagnostic category of misophonia through examining prominent claims in research studies that use brain imaging to substantiate the diagnosis. We show that brain images are insufficient to establish the 'brain basis for misophonia' due to both technical and logical limitations of imaging data. Often misunderstood as providing direct access to the matter of the body, brain images are mediated and manipulated numerical data (Joyce, 2005, Social Studies of Science 35(3), p. 437). Interpretations of brain scans are further shaped by social expectations and attributes considered salient to the data. Causal inferences drawn from these studies are problematic because 'misophonics' are clinically pre‐diagnosed before participating. We argue that imaging cannot replace the social process of diagnosis in the case of misophonia, nor validate diagnostic measures or otherwise substantiate the condition. More broadly, we highlight both the cultural authority and inherent limitations of brain imaging in the social construction of contested diagnoses while also illustrating its role in the disaggregation of symptoms into new diagnoses. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Sociology of Health & Illness. 2024/03, Vol. 46, p92
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:0141-9889
  • DOI:10.1111/1467-9566.13679
  • Accession Number:176104824
  • Copyright Statement:Copyright of Sociology of Health & Illness is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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