PyNets: Reproducible Ensemble Graph Analysis of Functional and Structural Connectomes

Derek Pisner Presenter
University of Texas at Austin
Austin, TX 
United States
Software Demonstrations 
Connectomics remains a nascent subfield of neuroscience with a constantly evolving set of methods. Although connectomes may afford us the ability to study fine-grained, high-dimensional individual differences, that gain would seem to come at additional costs to reproducibility (that we cannot afford). More specifically, in estimating a connectome from neuroimaging data, the researcher is forced to make many, often arbitrary methodological choices (e.g. parcellation scheme(s), connectivity model(s), tractography step size(s), etc.) that can greatly influence a network's configuration downstream. Because of the added model uncertainty that results, these untuned hyperparameters amount to a combinatorial explosion of 'hidden' researcher degrees of freedom that can easily distort statistical inference. Although perhaps previously thought to be an intractable problem, PyNets aims to directly address this methodological gap by offering a powerful computational framework for modeling individual structural and/or functional connectomes iteratively and with hyperparameter optimization.