Predicting brain activity using a Bayesian spatial model.

Abstract:

:Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.

journal_name

Stat Methods Med Res

authors

Derado G,Bowman FD,Zhang L,Alzheimer's Disease Neuroimaging Initiative Investigators.

doi

10.1177/0962280212448972

subject

Has Abstract

pub_date

2013-08-01 00:00:00

pages

382-97

issue

4

eissn

0962-2802

issn

1477-0334

pii

0962280212448972

journal_volume

22

pub_type

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