An empirical evaluation of functional alignment using inter-subject decoding

• Published in: NeuroImage (Orlando, Fla.) Vol. 245; p. 118683
• Main Authors: Bazeille, Thomas, DuPre, Elizabeth, Richard, Hugo, Poline, Jean-Baptiste, Thirion, Bertrand
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.12.2021; Elsevier Limited; Elsevier
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Bioinformatics; Brain - diagnostic imaging; Brain architecture; Brain Mapping - methods; Brain research; Computational neuroscience; Computer Science; Datasets; fMRI; Functional alignment; Functional morphology; Humans; Inter-subject variability; Magnetic Resonance Imaging - methods; Mathematics; Neural coding; Performance evaluation; Predictive modeling; Statistics; Structure-function relationships; Usability; Functional alignment; fMRI; Predictive modeling; Inter-subject variability; inter-subject variability; predictive modeling; functional alignment

•Methods that improve inter-subject decoding accuracy reduce inter-individual variability without losing signal specificity.•Functional alignment methods consistently improve inter-subject decoding on several datasets, with the best methods recovering half of the signal lost in anatomical-only align...