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Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns.

Abstract：

:Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded feature spaces can be estimated from the data. We present here a number of different ways in which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in an open-source software toolbox.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Diedrichsen J,Yokoi A,Arbuckle SA

doi

10.1016/j.neuroimage.2017.08.051

subject

Has Abstract

pub_date

2018-10-15 00:00:00

pages

119-133

issue

Pt A

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(17)30698-5

journal_volume

180

pub_type

杂志文章,评审

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