Franco Pestilli: “Quantifying white matter structure by multidimensional encoding of brain connectomes”
CiNet 1Ｆ 大会議室
Department of Psychological and Brain Sciences
Indiana University Bloomington
Network Science Institute
Diffusion weighted Magnetic Resonance Imaging (dMRI) combined with fiber tracking algorithms enables the measuring of anatomy and tissue properties of the human white-matter in living brains (Wandell 2016; Jbabdi et al. 2015). By measuring living brains, this technology makes it possible to correlate white matter properties with human behavior and cognition, as well as development and aging processes. The availability of these modern measurements has the potential to allow for the transition from simple qualitative descriptions of white matter to full quantitative models of tissue organization and to enhance the renewed interest in mapping human connectomes – the full map of brain connections.
We present a computational framework to represent brain connectomes efficiently using sparse-tensors (multidimensional arrays). The framework takes as input the anatomy of a full set of brain connections generated using any dMRI data and tractography method and returns as output a tensor representing fundamental fascicles properties, such as position, identity and angle of curvature. We describe the computational framework and show applicability to the analysis of in-vivo human brain measurements using several brains from multiple data sets.
We report results on 2,000 connectomes from three dMRI datasets (Van Essen et al. 2013; Pestilli et al. 2014). Ten connectomes were generated for each brain using multiple tractography methods (constrained-spherical deconvolution (Tournier et al. 2012; Descoteaux et al. 2009) and the tensor (Basser et al. 2000) models).
Results describe computational means of performing fundamental anatomical operations on white matter connectomes. More precisely, we describe mechanisms to perform the following operations on brain tissue using multidimensional arrays: (1) we show that the framework can efficiently implement forward models of diffusion signal (Pestilli et al. 2014), and (2) identify brain connections and establish their statistical evidence in individual brains.
Our results show that mapping white matter fascicles using multidimensional tensors allows to study connectome anatomy efficiently and at scale. Because of its computational efficiency, the framework opens new avenues of investigation to understand the white matter structure in individual brains and across large populations of brains.