Benchmarking functional connectome-based predictive models for resting-state fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 192; no. 192; pp. 115 - 134
• Main Authors: Dadi, Kamalaker, Rahim, Mehdi, Abraham, Alexandre, Chyzhyk, Darya, Milham, Michael, Thirion, Bertrand, Varoquaux, Gaël
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2019; Elsevier Limited; Elsevier
• Subjects: Accuracy; Alzheimer's disease; Autism; Benchmarking - methods; Biomarkers; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Cannabis; Classification; Computer Science; Connectome - methods; Connectome - standards; Datasets; Dictionaries; Functional connectomes; Functional magnetic resonance imaging; Humans; Intelligence; Machine Learning; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Medical Imaging; Mental disorders; Models, Neurological; Neurodegenerative diseases; Neuroimaging; Population study; Post traumatic stress disorder; Prediction models; Predictive modeling; Rest; Resting-state fMRI; Schizophrenia; Trends; Resting-state fMRI; Predictive modeling; Population study; Functional connectomes; Classification
• DOI: 10.1016/j.neuroimage.2019.02.062
• ISSN: 1053-8119; 1095-9572

Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions –either pre-defined or generated from the rest-fMRI data– 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings. [Display omitted] •We evaluate methods for prediction from resting-state fMRI on 6 cohorts.•A prediction pipeline needs brain regions, a connectome, and a supervised predictor.•Regions defined functionally (with dictionary learning or ICA) give best prediction.•Prefer tangent-space parametrization of connectomes to full or partial correlation.•Non-sparse linear classifiers are best for supervised learning.