Files and links (1)
Accepted (AM)Open Access (License Unspecified), Open
Journal article
Change point estimation in multi-subject fMRI studies
NeuroImage (Orlando, Fla.), v 49(2), pp 1581-1592
15 Jan 2010
PMID: 19733671
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being Studied are known. However, in many areas of psychological inquiry, it is hard to specify this information a priori. Examples include studies of drug uptake, emotional states or experiments with a sustained stimulus. In this paper we assume that the timing of a subject's activation onset and duration are random variables drawn from unknown population distributions. We propose a technique for estimating these distributions assuming no functional form, and allowing for the possibility that some subjects may show no response. We illustrate how these distributions can be used to approximate the probability that a voxel/region is activated as a function of time. Further a procedure is discussed that uses a hidden Markov random field model to cluster voxels based on characteristics of their onset, duration, and anatomical location. These methods are applied to an fMRI study (n = 24) of state anxiety, and are well suited for investigating individual differences in state-related changes in fMRI activity and other measures.
Metrics
Details
- Title
- Change point estimation in multi-subject fMRI studies
- Creators
- Lucy F. Robinson - Columbia UniversityTor D. Wager - Columbia UniversityMartin A. Lindquist - Columbia University
- Publication Details
- NeuroImage (Orlando, Fla.), v 49(2), pp 1581-1592
- Publisher
- Elsevier
- Number of pages
- 12
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000272808400041
- Scopus ID
- 2-s2.0-70749158247
- Other Identifier
- 991021011844604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging