Large volumes of high-quality and diverse open neuroimaging datasets continue to be measured and shared, from microscopic single-neuron spike recordings to macroscopic whole-brain fMRI data. These incredible time-series datasets encapsulate the rich temporal dynamics underlying our ability to process information around us but the dominant analytic tools we use to analyze them are mostly very simple. For example, time series are most commonly analyzed using features derived from the Fourier transform, despite many more sophisticated analysis methods being developed over the past decades across a range of scientific disciplines. There is a real need to unleash more sophisticated analyses on these data, but there are challenges in selecting suitable methods and implementing them efficiently in open-source environments to enable their application to large time-series datasets.
Aims: In this project, we will leverage a comprehensive library of time-series analysis methods that we recently developed (Matlab-based hctsa, https://github.com/benfulcher/hctsa), to formulate a new reduced feature set tailored to neuroimaging data. We recently showed that this library of >7000 features can be reduced to just 22 with minimal loss in accuracy: the C-coded catch22 feature set (https://github.com/chlubba/catch22). This project will follow a similar methodology to produce an efficiently coded set of time-series features for neuroimaging data, enabling the diversity and sophistication of the time-series analysis literature to be leveraged by the neuroscience community. As with catch22, the features will be coded in C, with wrappers for other open coding languages including python.
Skills: Python and C coding, familiarity with Matlab (to run existing code), and familiarity with statistical analyses.
Mentors: Ben Fulcher (firstname.lastname@example.org) and Joseph Lizier (email@example.com)