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Functional Neuroimaging Group Studies

Bertrand Thirion 1 
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
Abstract : Multi-subject statistical analysis is an essential step of neuroimaging studies, as it makes it possible to draw conclusions that hold with a prescribed confidence level for the population under study. The use of the linear assumption to model activation signals in brain images and their modulation by various factors has opened the possibility to rely on relatively simple estimation and statistical testing procedures. Specifically, the analysis of functional neuroimaging signals is typically carried out on a per-voxel basis, in the so-called mass univariate framework. However, the lack of power in neuroimaging studies has incited neuroscientists to develop new procedures to improve this framework: various solutions have been set up to take into account the spatial context in statistical inference or to deal with violations of distributional assumptions of the data. In this chapter, we review the general framework for group inference, the ensuing mixed-effects model design and its simplifications, together with the various solutions that have been considered to improve the standard mass-univariate testing framework.
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Submitted on : Monday, December 19, 2016 - 3:19:09 PM
Last modification on : Saturday, June 25, 2022 - 8:27:55 PM
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  • HAL Id : hal-01419347, version 1


Bertrand Thirion. Functional Neuroimaging Group Studies. Hernando Ombao; Martin Lindquist; Wesley Thompson; John Aston. Handbook of Neuroimaging Data Analysis, Chapman & Hall / CRC, 2016, Handbook of Modern Statistical methods, 9781482220971. ⟨hal-01419347⟩



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