Multidimensional encoding of brain connectomes: building quantitative biological networks with preserved edge properties to study the visual white matter and brain aging

FRANCO PESTILLI; BRENT MCPHERSON; DANIEL BULLOCK; ANDREA AVENA-KOENIGSBERGER; JOEY CONTRERAS; CESAR F. CAIAFA; OLAF SPORNS; ANDREW SAYKIN

Washington

Conferencia; Society for Neuroscience 2017; 2017

SfN

The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between brain and behavior in health and disease. We present a framework to encode brain connectomes and diffusion-weighted magnetic resonance data into multidimensional arrays. The framework goes beyond current methods by integrating the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating more than 3,000 connectomes across thirteen tractography methods and four data sets in normal and clinical populations.We show that this framework allows mapping connectivity matrices, edge anatomy, and microstructural properties of the white matter tissue in each connectome edge. The framework is based on statistical evaluation principles introduced with the Linear Fascicle Evaluation and virtual lesions methods (LiFE; Pestilli et al., 2014). In short, instead of building networks by relying uniquely on the terminations of fascicles into the cortex, we exploit the full measured signal available for each connectome edge by extracting a forward-prediction of the biological tissue properties of the edge. We validated the framework by comparing results with standard connectome measures (fiber count and density). To do so, we generated ten repeated-measures connectomes in each individual brain in various datasets, using different tracking methods. For each connectome estimated in an individual, we computed the mean network clustering coefficient across repeated measures. We demonstrate high reliability of the clustering coefficients. We also demonstrate profound differences in connectomes across brains, beyond what can be captured using standard measures (fiber density). We also show that the proposed method is highly sensitive to differences between individuals by improving subject classification into various diagnostic groups. Finally, we show that the framework is useful in clarifying fundamental properties of the human visual white matter as well as identifying useful network science biomarkers for predicting degenerative changes in the Alzheimer´s brain.We publish the method with software compatible with data from the Human Connectome Project, the Alzheimer Disease Neuroimaging Initiative, and Indiana Alzheimer Disease Center Data. The software integrates the Brain Connectivity Toolbox and is available open source GitHub.com/brain-life and stand-alone at hub.docker.com/u/brainlife.