Improving the accuracy of single-trial fMRI response estimates using GLMsingle

• Published in: eLife 2022-11, Vol.11
• Main Authors: Prince, Jacob S, Charest, Ian, Kurzawski, Jan W, Pyles, John A, Tarr, Michael J, Kay, Kendrick N
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Brain mapping; Cognitive ability; Datasets; denoising; Design; Estimates; fMRI pre-processing; Functional magnetic resonance imaging; GLM; Hemodynamics; Humans; large-scale datasets; Libraries; Magnetic Resonance Imaging; Nervous system; Neuroimaging; Neuroscience; Neurosciences; Reproducibility of Results; RSA; Signal-To-Noise Ratio; Tools and Resources; Visual stimuli; voxel reliability
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.77599

Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing , a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.