TY - JOUR
T1 - Estimating causal effects in studies of human brain function
T2 - New models, methods and estimands
AU - Sobel, Michael E.
AU - Lindquist, Martin A.
N1 - Funding Information:
Acknowledgments. We thank Tor Wager for supplying the data. For helpful comments, we thank the anonymous reviewers and the Editor in charge of the manuscript. The code and data is available on the authors GitHub page (https://github.com/mal2053/CausalCode/). It consists of MATLAB code implementing the methods from the paper. This research was supported by NIH Grant R01EB016061.
Publisher Copyright:
© Institute of Mathematical Statistics, 2020.
PY - 2020/3
Y1 - 2020/3
N2 - 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 aver-aged 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.
AB - 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 aver-aged 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.
KW - Causal inference
KW - FMRI
KW - Functional connectivity
KW - Pain
KW - Region of in-terest
KW - Systematic error
UR - http://www.scopus.com/inward/record.url?scp=85083737645&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083737645&partnerID=8YFLogxK
U2 - 10.1214/19-AOAS1316
DO - 10.1214/19-AOAS1316
M3 - Article
AN - SCOPUS:85083737645
SN - 1932-6157
VL - 14
SP - 452
EP - 472
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
IS - 1
ER -