Open AccessStatistical modeling of time‐dependent f MRI activation effects
Author(s) -
Kalus Stefanie,
Bothmann Ludwig,
Yassouridis Christina,
Czisch Michael,
Sämann Philipp G.,
Fahrmeir Ludwig
Publication year - 2015
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22660
Subject(s) - functional magnetic resonance imaging , time constant , voxel , constant (computer programming) , pointwise , hum , computer science , biological system , neuroscience , artificial intelligence , pattern recognition (psychology) , psychology , mathematics , mathematical analysis , biology , electrical engineering , programming language , engineering , art , performance art , art history
Functional magnetic resonance imaging (fMRI) activation detection within stimulus‐based experimental paradigms is conventionally based on the assumption that activation effects remain constant over time. This assumption neglects the fact that the strength of activation may vary, for example, due to habituation processes or changing attention. Neither the functional form of time variation can be retrieved nor short‐lasting effects can be detected by conventional methods. In this work, a new dynamic approach is proposed that allows to estimate time‐varying effect profiles and hemodynamic response functions in event‐related fMRI paradigms. To this end, we incorporate the time‐varying coefficient methodology into the fMRI general regression framework. Inference is based on a voxelwise penalized least squares procedure. We assess the strength of activation and corresponding time variation on the basis of pointwise confidence intervals on a voxel level. Additionally, spatial clusters of effect curves are presented. Results of the analysis of an active oddball experiment show that activation effects deviating from a constant trend coexist with time‐varying effects that exhibit different types of shapes, such as linear, (inversely) U‐shaped or fluctuating forms. In a comparison to conventional approaches, like classical SPM, we observe that time‐constant methods are rather insensitive to detect temporary effects, because these do not emerge when aggregated across the entire experiment. Hence, it is recommended to base activation detection analyses not merely on time‐constant procedures but to include flexible time‐varying effects that harbour valuable information on individual response patterns. Hum Brain Mapp 36:731–743, 2015 . © 2014 Wiley Periodicals, Inc.