Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment

Reuma Gadassi-Polack, Gabriela Paganini, Julia Winschel, Hadas Benisty, Jutta Joormann, Hedy Kober, Gal Mishne

Research output: Contribution to journalReview articlepeer-review

Abstract

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

Original languageEnglish
Pages (from-to)151-167
Number of pages17
JournalSocial Neuroscience
Volume19
Issue number3
Early online date11 Aug 2024
DOIs
StatePublished - 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Magnetic resonance imaging
  • computational methods
  • daily-diaries
  • ecological momentary assessment
  • experience sampling methods

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