Improved Functionnectome by dissociating the contributions of white matter fiber classes to functional activation

Nozais V(1)(2), Theaud G(3)(4), Descoteaux M(3), Thiebaut de Schotten M(1)(2), Petit L(5).

Author information:
(1)Groupe d’Imagerie Neurofonctionnelle – Institut des Maladies
Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA,
Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case
28, 33076, Bordeaux Cedex, France.
(2)Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
(3)Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada.
(4)Imeka Solutions Inc, Sherbrooke, QC, Canada.
(5)Groupe d’Imagerie Neurofonctionnelle – Institut des Maladies
Neurodégénératives (GIN-IMN), UMR 5293, Université de Bordeaux, CNRS, CEA,
Centre Broca Nouvelle-Aquitaine-3éme étage, 146 Rue Léo Saignat-CS 61292-Case
28, 33076, Bordeaux Cedex, France. .

Integrating the underlying brain circuit’s structural and functional
architecture is required to explore the functional organization of cognitive
networks. In that regard, we recently introduced the Functionnectome. This
structural-functional method combines an fMRI acquisition with
tractography-derived white matter connectivity data to map cognitive processes
onto the white matter. However, this multimodal integration faces three
significant challenges: (1) the necessarily limited overlap between tractography
streamlines and the grey matter, which may reduce the amount of functional
signal associated with the related structural connectivity; (2) the scrambling
effect of crossing fibers on functional signal, as a single voxel in such
regions can be structurally connected to several cognitive networks with
heterogeneous functional signals; and (3) the difficulty of interpretation of
the resulting cognitive maps, as crossing and overlapping white matter tracts
can obscure the organization of the studied network. In the present study, we
tackled these problems by developing a streamline-extension procedure and
dividing the white matter anatomical priors between association, commissural,
and projection fibers. This approach significantly improved the characterization
of the white matter involvement in the studied cognitive processes. The new
Functionnectome priors produced are now readily available, and the analysis
workflow highlighted here should also be generalizable to other
structural-functional approaches. We improved the Functionnectome approach to
better study the involvement of white matter in brain function by separating the
analysis of the three classes of white matter fibers (association, commissural,
and projection fibers). This step successfully clarified the activation maps and
increased their statistical significance.

© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

 

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