Population Variability and Bayesian Inference

Terran Lane

in The Dynamic Brain

Published in print January 2011 | ISBN: 9780195393798
Published online September 2011 | e-ISBN: 9780199897049 | DOI:
Population Variability and Bayesian Inference

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Neuroscience data, from single-neuron recordings to whole-brain functional neuroimaging, is swamped with variability. The system under examination changes from subject to subject, trial to trial, moment to moment. Such variation can be regarded in two fundamentally different ways: as noise (typically additive Gaussian) or as an effect of some underlying latent state variable. The best effort were made to control for such hidden conditions, but it is virtually impossible to control all possible variability. Having done our best to control the system, we typically treat the remaining variation as noise and “average it out” across subjects or trials. But doing so neglects the fact that variability due to latent variables carries real information that can tell us a great deal about the system. Bayesian statistical reasoning gives us a powerful tool for exploiting this additional information. Using these inferential mechanisms, variables can be estimated directly from observable data. This chapter describes the use of Bayesian inference of latent variables to solve key data analysis problems including identification of brain activity networks, group-level variability analysis, and identification of comorbid conditions.

Keywords: Bayesian inference; noise; latent variables; parameter estimation; Dynamic Causal Model

Chapter.  9811 words.  Illustrated.

Subjects: Neuroscience

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