Work

Using larger dimensional signal subspaces to increase sensitivity in fMRI time series analyses

Year: 2002

Type: article

Abstract: It has been explained previously how using large dimensional signal-subspaces can reduce/eliminate bias in the estimated fMRI response (Burock and Dale [2000]: Hum Brain Mapp 11:249-260). It has also been explained how one can project this less biased estimate onto a one-dimensional subspace of interest (Burock and Dale [2000]: Hum Brain Mapp 11:249-260). In cases where there are multiple, correlated characterized response components per event type, separately projecting the full hemodynamic response onto one-dimensional subspaces of interest can lead to bias. We present an approach for both estimating the full hemodynamic response and obtaining from it unbiased estimates of effects of theoretical interest (in the context of ordinary least-squares estimation). The latter estimates are identical to those obtained by projecting the original data into the space defined by the (possibly multi-dimensional) effects of theoretical interest, but the ensuing statistical inference can be more sensitive. Hum.

Source: Human Brain Mapping

DOI: 10.1002/hbm.10036

Author: Eric Zarahn

Institutions: Columbia University Irving Medical Center, Columbia University

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: bronze

Funders: Wellcome Trust, National Institute of Mental Health

Awards: G1364414374, Power Analysis and Optimization for FMRI Experiments