In SLAB, we developed dedicated machine learning and statistical signal processing methods and favored the emergence of new challenges for these fields focussing on five open problems:
1) source localization with M/EEG for brain imaging at high temporal resolution: we have developed state-of-the-art optimization methods exploiting the sparsity of solutions. The intuition is that few brain regions are engaged at a given millisecond on one cognitive task. The techniques based on active set techniques have enabled significant speed ups. New models taking into account heteroscedastic noise models have been proposed and very importantly automatic model selection procedures have been proposed and made available to the neuroscience community via the MNE-Python software.
2) unsupervised representation learning from multivariate (M/EEG) signals: state-of-the-art convolutional sparse coding (CSC) distributed algorithms have been proposed. While this technique starts only to be of interest in the neuroscience community, we have also tackled the acceleration of widely employed mining techniques, namely Independent Component Analysis (ICA) for which we have proposed two major contributions that accelerate inference of independent sources from EEG signals (the PICARD algorithm and the QNDIAG algorithm for joint diagonalization).
3) fusion of heterogeneous neuroimaging data: we have explored the use of predictive stacking models to evaluate cognitive decline from combined anatomical MRI, functional MRI and MEG. This work built on the concept of "brain age" was published in eLife and subsequently led to a public and reproducible benchmark on multiple EEG or MEG population datasets.
4) modelling of non-stationary spectral interactions to identify functional coupling between neural ensembles: to address this very timely issue we proposed the Driven Autoregressive (DAR) models which offer more statistical power (detect coupling with shorter signals) and come with a built-in automatic model selection procedure.
5) development of algorithms tractable on large datasets and easy to use by non-experts: we have contributed and extended numerous open-source Python packages, to name a few scikit-learn, MNE-Python, but also the new Pactools, Picard and the Celer packages.
SLAB has strengthened the mathematical and computational foundations of neuroimaging data analysis. The methods developed have applications across fields (e.g. computational biology, astronomy, econometrics). Yet, the primary users of the technologies developed are cognitive and clinical neuroscientists.
