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.
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.