Robust regression for large-scale neuroimaging studies.

Virgile Fritsch, Benoit Da Mota, Eva Loth, Gaël Varoquaux, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Rüdiger Brühl, Brigitte Butzek, Patricia Conrod, Herta Flor, Hugh Garavan, Hervé Lemaitre, Karl Mann, Frauke Nees, Tomas Paus, Daniel J. Schad, Gunter Schümann, Vincent Frouin, Jean-Baptiste Poline, Bertrand Thirion
NeuroImage. 2015-05-01; 111: 431-441
DOI: 10.1016/j.neuroimage.2015.02.048

Read on PubMed

Multi-subject datasets used in neuroimaging group studies have a complex
structure, as they exhibit non-stationary statistical properties across regions
and display various artifacts. While studies with small sample sizes can rarely
be shown to deviate from standard hypotheses (such as the normality of the
residuals) due to the poor sensitivity of normality tests with low degrees of
freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations
from these hypotheses and call for more refined models for statistical inference.
Here, we demonstrate the benefits of robust regression as a tool for analyzing
large neuroimaging cohorts. First, we use an analytic test based on robust
parameter estimates; based on simulations, this procedure is shown to provide an
accurate statistical control without resorting to permutations. Second, we show
that robust regression yields more detections than standard algorithms using as
an example an imaging genetics study with 392 subjects. Third, we show that
robust regression can avoid false positives in a large-scale analysis of
brain-behavior relationships with over 1500 subjects. Finally we embed robust
regression in the Randomized Parcellation Based Inference (RPBI) method and
demonstrate that this combination further improves the sensitivity of tests
carried out across the whole brain. Altogether, our results show that robust
procedures provide important advantages in large-scale neuroimaging group


Know more about