2018/05/20 by Peter Zeidman, Samira M Kazan, Samira M. Kazan +5 · 20 citations
Engineering · Mathematics · Neuroscience · Psychology · #Computer science #Control Systems and Identification #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neuroscience #Psychology #stat.AP
paper · pdf · doi:10.3389/fnins.2018.00986
published in Frontiers in Neuroscience 12, 986 (Frontiers Media) · Equal contribution by Peter Zeidman and Samira M Kazan
arxiv created 2018/05/20 · openalex publication_date 2019/01/09 · arxiv updated 2019/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol-and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.