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March 2020 Estimating causal effects in studies of human brain function: New models, methods and estimands
Michael E. Sobel, Martin A. Lindquist
Ann. Appl. Stat. 14(1): 452-472 (March 2020). DOI: 10.1214/19-AOAS1316

Abstract

Neuroscientists often use functional magnetic resonance imaging (fMRI) to infer effects of treatments on neural activity in brain regions. In a typical fMRI experiment, each subject is observed at several hundred time points. At each point, the blood oxygenation level dependent (BOLD) response is measured at 100,000 or more locations (voxels). Typically, these responses are modeled treating each voxel separately, and no rationale for interpreting associations as effects is given. Building on Sobel and Lindquist (J. Amer. Statist. Assoc. 109 (2014) 967–976), who used potential outcomes to define unit and average effects at each voxel and time point, we define and estimate both “point” and “cumulated” effects for brain regions. Second, we construct a multisubject, multivoxel, multirun whole brain causal model with explicit parameters for regions. We justify estimation using BOLD responses averaged over voxels within regions, making feasible estimation for all regions simultaneously, thereby also facilitating inferences about association between effects in different regions. We apply the model to a study of pain, finding effects in standard pain regions. We also observe more cerebellar activity than observed in previous studies using prevailing methods.

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Michael E. Sobel. Martin A. Lindquist. "Estimating causal effects in studies of human brain function: New models, methods and estimands." Ann. Appl. Stat. 14 (1) 452 - 472, March 2020. https://doi.org/10.1214/19-AOAS1316

Information

Received: 1 July 2019; Revised: 1 November 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200179
MathSciNet: MR4085101
Digital Object Identifier: 10.1214/19-AOAS1316

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 1 • March 2020
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