  Population-wise matching of the cortical fold is necessary to identify
biomarkers of neurological or psychiatric disorders. The difficulty comes from
the massive interindividual variations in the morphology and spatial
organization of the folds. This task is challenging at both methodological and
conceptual levels. In the widely used registration-based techniques, these
variations are considered as noise and the matching of folds is only implicit.
Alternative approaches are based on the extraction and explicit identification
of the cortical folds. In particular, representing cortical folding patterns as
graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a
graph-matching problem. In this paper, we propose to address the problem of
sulcal graph matching directly at the population level using multi-graph
matching techniques. First, we motivate the relevance of multi-graph matching
framework in this context. We then introduce a procedure to generate
populations of artificial sulcal graphs, which allows us benchmarking several
state of the art multi-graph matching methods. Our results on both artificial
and real data demonstrate the effectiveness of multi-graph matching techniques
to obtain a population-wise consistent labeling of cortical folds at the sulcal
basins level.
