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  Statistical Modeling of Time-Dependent fMRI Activation Effects

Kalus, S., Bothmann, L., Yassouridis, C., Czisch, M., Sämann, P. G., & Fahrmeir, L. (2015). Statistical Modeling of Time-Dependent fMRI Activation Effects. HUMAN BRAIN MAPPING, 36(2), 731-743. doi:10.1002/hbm.22660.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-01B5-C Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-01B6-A
Genre: Journal Article

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 Creators:
Kalus, Stefanie1, Author
Bothmann, Ludwig1, Author
Yassouridis, Christina1, Author
Czisch, Michael2, Author              
Sämann, Philipp G.2, Author              
Fahrmeir, Ludwig1, Author
Affiliations:
1external, ou_persistent22              
2Max Planck Institute of Psychiatry, Max Planck Society, ou_1607137              

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Free keywords: functional magnetic resonance imaging, time varying activation and hemodynamic response function, varying coefficient model, panalized least squares estimation, event-related functional magnetic resonance imaging, auditory oddball
 Abstract: 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. (c) 2014 Wiley Periodicals, Inc.

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Language(s): eng - English
 Dates: 2015-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: 000348378800025
DOI: 10.1002/hbm.22660
 Degree: -

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Title: HUMAN BRAIN MAPPING
Source Genre: Journal
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Publ. Info: Hoboken, NJ 07030-5774, USA : Wiley Periodicals Inc.
Pages: - Volume / Issue: 36 (2) Sequence Number: - Start / End Page: 731 - 743 Identifier: ISSN: 1065-9471