Population imaging relates features of brain images to rich descriptions of the subjects such as behavioral and clinical assessments. We use predictive analysis pipelines to extract functional biomarkers of brain disorders from large-scale datasets of resting-state functional Magnetic Resonance Imaging (R-fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). We also built tools for automated data analysis which facilitate processing large datasets at scale. Some of our results are highlighted below.
The IMaging-PsychAtry Challenge (IMPAC) is a data challenge on Autism Sprectrum Disorder (ASD). ASD is a severe psychiatric disorder that affects 1 in 166 children.
There is evidence that ASD is reflected in individuals brain networks and anatomy. Yet, it remains unclear how systematic these effects are, and how large is their predictive remain unclear. The large cohort assembled here can bring some answers. Predicting autism from brain imaging will provide biomarkers and shed some light on the mechanisms of the pathology.
Here we propose to jointly predict behavioral scores that make up the individual profiles from neuroimaging data with multi-output models. This approach boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two rs-fMRI datasets targeting different brain disorders (Alzheimer’s Disease and schizophrenia).
Here we demonstrate the feasibility of inter-site classification of neuropsychiatric status from functional connectivity, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available.
Here we systematically study resting state functional-connectivity (FC)-based prediction across three different cohorts (ADNI, COBRE, ACPI). We explore various methodological choices: ROI set selection, FC metrics, and linear classifiers to compare and evaluate the dominant strategies for the sake of prediction accuracy. We observe that: (i) tangent embedding performs better than correlation or partial correlation in all datasets; (ii) l2 regularized classifiers SVC and Ridge are more accurate than SVC- l1 classifier; (iii) with regards to brain atlases, decomposition methods (ICA, DictLearn) are generally the best choices, though with striking cross-datasets differences.
Removing artifacts from EEG and MEG signals is a common and necessary step in data analysis and, unfortunately, has claimed significant investment of human attention in the past. I developed and evaluated a novel algorithm, termed autoreject, for detecting and handling contaminated MEG and EEG data segments. Autoreject is described in Jas et al 2017 and is readily usable in a “plug and play” manner in a wide array of situations and has been validated on more than 250 datasets featuring a reanalysis of the Human Connectome Project MEG data. Notably, its successful usage does not require deep understanding of the method as it uses machine learning technology to handle artifact rejection in a data-driven manner, hence, reducing human processing time. It will soon be disseminated through the MNE Software. The code is accessible on github.
Here we investigate scale-free dynamics in brain activity. The temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed. Relying on the wavelet-leader multifractal formalism, we estimated self-similarity and multifractal exponents from resting-state and task MEG recordings.