Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits.
Studying the topography of the cortex has proved valuable in order to characterize populations of subjects. In particular, the recent interest towards the deepest parts of the cortical sulci - the so-called sulcal pits - has opened new avenues in that regard. In this paper, we introduce the first fully automatic brain morphometry method based on the study of the spatial organization of sulcal pits - Structural Graph-Based Morphometry (SGBM). Our framework uses attributed graphs to model local patterns of sulcal pits, and further relies on three original contributions. First, a graph kernel is defined to provide a new similarity measure between pit-graphs, with few parameters that can be efficiently estimated from the data. Secondly, we present the first searchlight scheme dedicated to brain morphometry, yielding dense information maps covering the full cortical surface. Finally, a multi-scale inference strategy is designed to jointly analyze the searchlight information maps obtained at different spatial scales. We demonstrate the effectiveness of our framework by studying gender differences and cortical asymmetries: we show that SGBM can both localize informative regions and estimate their spatial scales, while providing results which are consistent with the literature. Thanks to the modular design of our kernel and the vast array of available kernel methods, SGBM can easily be extended to include a more detailed description of the sulcal patterns and solve different statistical problems. Therefore, we suggest that our SGBM framework should be useful for both reaching a better understanding of the normal brain and defining imaging biomarkers in clinical settings.
Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Jean Moulin, 13005 Marseille, France; Aix-Marseille Université, CNRS, Laboratoire d'Informatique Fondamentale UMR 7279 Faculté des Sciences, 163 avenue de Luminy, Case 901, 13009 Marseille, France. Electronic address: Sylvain.Takerkart@univ-amu.fr.
Sylvain
S
Takerkart
Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Jean Moulin, 13005 Marseille, France; Aix-Marseille Université, CNRS, LSIS laboratory, UMR 7296 Bâtiment Polytech Saint Jérôme, Avenue Escadrille Normandie-Niemen, 13013 Marseille, France.
Guillaume
G
Auzias
Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Jean Moulin, 13005 Marseille, France; Aix-Marseille Université, CNRS, LSIS laboratory, UMR 7296 Bâtiment Polytech Saint Jérôme, Avenue Escadrille Normandie-Niemen, 13013 Marseille, France.
Lucile
L
Brun
Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Jean Moulin, 13005 Marseille, France; Aix-Marseille Université, CNRS, LSIS laboratory, UMR 7296 Bâtiment Polytech Saint Jérôme, Avenue Escadrille Normandie-Niemen, 13013 Marseille, France.
Olivier
O
Coulon
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
Brain; Graph kernel; Morphometry; Multi-scale methods; Searchlight; Sulcal pits