Transport on Riemannian Manifold for Connectivity-based Brain Decoding

Bernard Ng (1, 2) , Gaël Varoquaux (1, 3) , Jean-Baptiste Poline (3, 4) , Michael D Greicius (2) , Bertrand Thirion (3, 1)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 Find Lab - Functional Imaging in Neuropsychiatric Disorders
3 NEUROSPIN - Service NEUROSPIN
4 Henry H. Wheeler Jr. Brain Imaging Center [Berkeley]

There is a recent interest in using functional magnetic resonance imaging (fMRI) for decoding more naturalistic, cognitive states, in which subjects perform various tasks in a continuous, self-directed manner. In this setting, the set of brain volumes over the entire task duration is usually taken as a single sample with connectivity estimates, such as Pearson's correlation, employed as features. Since covariance matrices live on the positive semidefinite cone, their elements are inherently interrelated. The assumption of uncorrelated features implicit in most classifier learning algorithms is thus violated. Coupled with the usual small sample sizes, the generalizability of the learned classifiers is limited, and the identification of significant brain connections from the classifier weights is nontrivial. In this paper, we present a Riemannian approach for connectivity-based brain decoding. The core idea is to project the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. For this, we propose a matrix whitening transport, and compare it against parallel transport implemented via the Schild's ladder algorithm. To validate our classification approach, we apply it to fMRI data acquired from twenty four subjects during four continuous, self-driven tasks. We show that our approach provides significantly higher classification accuracy than directly using Pearson's correlation and its regularized variants as features. To facilitate result interpretation, we further propose a non-parametric scheme that combines bootstrapping and permutation testing for identifying significantly discriminative brain connections from the classifier weights. Using this scheme, a number of neuro-anatomically meaningful connections are detected, whereas no significant connections are found with pure permutation testing.

Mots clés: brain decoding; connectivity; fMRI; Riemannian geometry; transport on manifolds

Domaines: Imagerie médicale

Bernard Ng, Gaël Varoquaux, Jean-Baptiste Poline, Michael D Greicius, Bertrand Thirion. Transport on Riemannian Manifold for Connectivity-based Brain Decoding. IEEE Transactions on Medical Imaging, 2015, PP (99), pp.9. ⟨10.1109/TMI.2015.2463723⟩. ⟨hal-01185200⟩