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Abstract
<p class="first" id="P1">Advances in neuroimaging have provided a tremendous amount
of in-vivo information
on the brain’s organisation. Its anatomy and cortical organisation can be investigated
from the point of view of several imaging modalities, many of which have been studied
for mapping functionally specialised cortical areas. There is strong evidence that
a single modality is not sufficient to fully identify the brain’s cortical organisation.
Combining multiple modalities in the same parcellation task has the potential to provide
more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation
methods are typically developed and tested on single modalities using a specific type
of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa),
an iterative framework designed to handle the large variety of available input modalities
to tackle the multi-modal parcellation task. At each iteration, we compute a set of
parcellations from different modalities and fuse them based on their local reliabilities.
The fused parcellation is used to initialise the next iteration, forcing the parcellations
to converge towards a set of mutually informed modality specific parcellations, where
correspondences are established. We explore two different multi-modal configurations
for group-wise parcellation using resting-state fMRI, diffusion MRI tractography,
myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome
Project database show that integrating multi-modal information yields a stronger agreement
with well established atlases and more robust connectivity networks that provide a
better representation of the population.
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