Statistical control for spatio-temporal MEG/EEG source imaging with
desparsified multi-task Lasso
- URL: http://arxiv.org/abs/2009.14310v2
- Date: Wed, 25 Nov 2020 10:49:36 GMT
- Title: Statistical control for spatio-temporal MEG/EEG source imaging with
desparsified multi-task Lasso
- Authors: J\'er\^ome-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon,
Bertrand Thirion
- Abstract summary: Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques.
The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge.
We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
- Score: 102.84915019938413
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Detecting where and when brain regions activate in a cognitive task or in a
given clinical condition is the promise of non-invasive techniques like
magnetoencephalography (MEG) or electroencephalography (EEG). This problem,
referred to as source localization, or source imaging, poses however a
high-dimensional statistical inference challenge. While sparsity promoting
regularizations have been proposed to address the regression problem, it
remains unclear how to ensure statistical control of false detections.
Moreover, M/EEG source imaging requires to work with spatio-temporal data and
autocorrelated noise. To deal with this, we adapt the desparsified Lasso
estimator -- an estimator tailored for high dimensional linear model that
asymptotically follows a Gaussian distribution under sparsity and moderate
feature correlation assumptions -- to temporal data corrupted with
autocorrelated noise. We call it the desparsified multi-task Lasso (d-MTLasso).
We combine d-MTLasso with spatially constrained clustering to reduce data
dimension and with ensembling to mitigate the arbitrary choice of clustering;
the resulting estimator is called ensemble of clustered desparsified multi-task
Lasso (ecd-MTLasso). With respect to the current procedures, the two advantages
of ecd-MTLasso are that i)it offers statistical guarantees and ii)it allows to
trade spatial specificity for sensitivity, leading to a powerful adaptive
method. Extensive simulations on realistic head geometries, as well as
empirical results on various MEG datasets, demonstrate the high recovery
performance of ecd-MTLasso and its primary practical benefit: offer a
statistically principled way to threshold MEG/EEG source maps.
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