We continue to both develop and apply brain signal variability methods, moving beyond general variance measures to ask more explicit questions about the nature of that variance (e.g., spectral content and structure, time delay embedding). We are also inherently interested in discrete patterns in brain signals (e.g., entropy), and how they relate to overall variance (*Grandy, *Garrett et al., 2016). For example, we continue to optimize the estimation of multi-scale entropy that effectively eliminates variance-based biases from computation (Kosciessa et al., in prep; Kloosterman et al., in prep), permitting entropy to be directly compared with oscillatory dynamics within-person (e.g., Kosciessa et al., 2019).
Statistical and Computational Modelling
We have a keen interest in multivariate models (e.g., singular value decomposition (SVD)-based techniques) that best enable various correlates of signal dynamics to be simultaneously examined. We are currently exploring how SVD models can be optimized within a mixed-modeling framework in an attempt to link latent-level parametric cognitive performance to parametric task-based brain dynamics (e.g., Garrett et al., 2013, Cereb Cortex; Garrett et al., 2015, PNAS). We also continue to develop whole-to-whole brain multivariate models linking different neuroimaging modalities (e.g., ASL and BOLD; dopamine PET and BOLD; EEG and BOLD; DWI and BOLD; e.g., Garrett et al., 2017, Sci Rep; *Burzynska, *Garrett, et al., 2013, JNeurosci).
We also utilize and develop various computational models of brain and behaviour. For example, we have recently leveraged the HMAX model of the ventral visual cortex to estimate how the complexity of visual input may drive neural variability during perception (Garrett et al., 2018). With regard to behaviour, we have also modelled within-person, reward-driven perceptual decision-making via hierarchical drift diffusion modelling (Kloosterman et al., 2019, eLife), and now deploy various reinforcement-learning models across a host of probabilistic reward-based paradigms.
An important goal of our proposed work is to support the future examination of brain signal variability in neuroimaging through open-source software. At present, no analysis package provides for a comprehensive brain signal variability analysis. As such, we continue to develop an event-related and block-design fMRI-based Variability Toolbox (VarTbx) for SPM. Mimicking a Level-1 analysis, our toolbox allows SPM users (or more generally, Matlab users) to import any preprocessed fMRI data, choose from a number of different variability measures, and to output resulting individual maps for further analysis within SPM’s standard Level-2 module (or elsewhere). The first version of VarTbx is now available; see our software page for details. We further support open science by making all code/algorithms freely available on our LNDG Github repository (https://github.com/LNDG).