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Encoding and decoding in fMRI.

Abstract：

:Over the past decade fMRI researchers have developed increasingly sensitive techniques for analyzing the information represented in BOLD activity. The most popular of these techniques is linear classification, a simple technique for decoding information about experimental stimuli or tasks from patterns of activity across an array of voxels. A more recent development is the voxel-based encoding model, which describes the information about the stimulus or task that is represented in the activity of single voxels. Encoding and decoding are complementary operations: encoding uses stimuli to predict activity while decoding uses activity to predict information about the stimuli. However, in practice these two operations are often confused, and their respective strengths and weaknesses have not been made clear. Here we use the concept of a linearizing feature space to clarify the relationship between encoding and decoding. We show that encoding and decoding operations can both be used to investigate some of the most common questions about how information is represented in the brain. However, focusing on encoding models offers two important advantages over decoding. First, an encoding model can in principle provide a complete functional description of a region of interest, while a decoding model can provide only a partial description. Second, while it is straightforward to derive an optimal decoding model from an encoding model it is much more difficult to derive an encoding model from a decoding model. We propose a systematic modeling approach that begins by estimating an encoding model for every voxel in a scan and ends by using the estimated encoding models to perform decoding.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Naselaris T,Kay KN,Nishimoto S,Gallant JL

doi

10.1016/j.neuroimage.2010.07.073

subject

Has Abstract

pub_date

2011-05-15 00:00:00

pages

400-10

issue

2

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(10)01065-7

journal_volume

56

pub_type

杂志文章,评审

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