New technologies for precision brain science: studying individuality and variability in large human populations

FRANCO PESTILLI; CESAR F. CAIAFA; HIROMASA TAKEMURA

Yokohama

Simposio; 39th Annual Meeting of the Japan Neuroscience Society; 2016

Japan Neuroscience Society

The fundamental inner workings of the brain cannot be simply understood at the level of single neurons or brain areas. To capture human behavior, we need to understand the brain's circuit behavior. One of the next frontiers in brain science will be to develop models and theories that allow investigators to understand how networks support brain function in individual brains.Diffusion-weighted magnetic resonance imaging (dMRI) and fiber tracking allow mapping the network of brain connections and white matter tracts in living brains; the connectome. By measuring in living brains these technologies allow charting the relation between human behavior, development and aging. Efforts are necessary to improve accuracy in connectome estimation. Recently we developed a method (LiFE, Linear Fascicle Evaluation; Pestilli et al., 2014) for computing connectome accuracy. LiFE computes how well a connectome predicts the measured diffusion signal. It can be used to identify the most accurate connectomes in individual brains.We present a factorized version of the LiFE model. We show that the factorized model has accuracy indistinguishable from the original LiFE model without requiring large computer memory. We combine the factorized LiFE with innovative ensemble tracking methods (Takemura, et al., 2016) to generate a new approach to individualized in-vivo tractography.We show that the new approach generates connectomes that are highly reproducible within an individual but maintain anatomical features that allow fine discrimination between individuals. To do so we use dMRI datasets with repeated measures and compare connectomes build using repeated measurements in single individuals to connectomes built in different individuals. Current best practice in the field is to generate connectomes using single tractography methods. Instead, we show that by combining multiple tracking methods - deterministic and probabilistic based on tensor as well as constrained spherical deconvolution models - we can achieve high within-individual reliability.Results presented here show how to generate customized connectomes using multiple tracking methods. We show that the approach is valuable to study the network of brain connections as well as human individuality and variability. Our results contribute to developing approaches to understand and predict how brain networks predict brain function and behavior in living human individuals.