Extracting the Core Structural Connectivity Network: Guaranteeing Network Connectedness Through a Graph-Theoretical Approach

Demian Wassermann (1) , Dorian Mazauric (2) , Guillermo Gallardo-Diez (1) , Rachid Deriche (1)

1 ATHENA - Computational Imaging of the Central Nervous System
2 ABS - Algorithms, Biology, Structure

We present a graph-theoretical algorithm to extract the connected core structural connectivity network of a subject population. Extracting this core common network across subjects is a main problem in current neuroscience. Such network facilitates cognitive and clinical analyses by reducing the number of connections that need to be explored. Furthermore, insights into the human brain structure can be gained by comparing core networks of different populations. We show that our novel algorithm has theoretical and practical advantages. First, contrary to the current approach our algorithm guarantees that the extracted core subnetwork is connected agreeing with current evidence that the core structural network is tightly connected. Second, our algorithm shows enhanced performance when used as feature selection approach for connectivity analysis on populations.

Mots clés: Graph Theory; Connectivity; Diffusion MRI; Structural Connectivity; Extrinsic Connectivity

Domaines: Imagerie médicale; Neurosciences [q-bio.NC]

Demian Wassermann, Dorian Mazauric, Guillermo Gallardo-Diez, Rachid Deriche. Extracting the Core Structural Connectivity Network: Guaranteeing Network Connectedness Through a Graph-Theoretical Approach. MICCAI 2016, Sep 2016, Athens, Greece. ⟨hal-01333301v2⟩