Simultaneous modeling of reaction times and brain dynamics in a spatial cueing task. Issue 6 (24th December 2021)

• Record Type: Journal Article
• Authors: Steinkamp, Simon R.; Fink, Gereon R.; Vossel, Simone; Weidner, Ralph
• Is Part Of: Human brain mapping. Volume 43:Issue 6(2022)
• Page Start: 1850
• Page End: 1867
• Publication Date: 2021-12-24
• Subjects: behavioral -- dynamic causal modeling -- effective connectivity -- fMRI -- simultaneous modeling -- spatial attention; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25758
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Understanding how brain activity translates into behavior is a grand challenge in neuroscientific research. Simultaneous computational modeling of both measures offers to address this question. The extension of the dynamic causal modeling (DCM) framework for blood oxygenation level‐dependent (BOLD) responses to behavior (bDCM) constitutes such a modeling approach. However, only very few studies have employed and evaluated bDCM, and its application has been restricted to binary behavioral responses, limiting more general statements about its validity. This study used bDCM to model reaction times in a spatial attention task, which involved two separate runs with either horizontal or vertical stimulus configurations. We recorded fMRI data and reaction times ( n = 26) and compared bDCM with classical DCM and a behavioral Rescorla–Wagner model using Bayesian model selection and goodness of fit statistics. Results indicate that bDCM performed equally well as classical DCM when modeling BOLD responses and as good as the Rescorla–Wagner model when modeling reaction times. Although our data revealed practical limitations of the current bDCM approach that warrant further investigation, we conclude that bDCM constitutes a promising method for investigating the link between brain activity and behavior. Abstract : To understand how brain and behavioral dynamics are linked, it is essential to model both simultaneously. An approach that allows this to be done is behavioral dynamic causal modeling (bDCM), which we further extend to continuous behavioral readouts. In the current study, we explore its applications and limitations and demonstrate that bDCM performs similarly or better than single modality models.