Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

Vizioli, L., Bratch, A., Lao, J., Ugurbil, K., Muckli, L. and Yacoub, E. (2018) Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal. Journal of Neuroscience Methods, 308, pp. 74-87. (doi: 10.1016/j.jneumeth.2018.06.029) (PMID:29969602)

Abstract

fMRI provides spatial resolution that is unmatched by non-invasive neuroimaging techniques. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic signal. We present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed on single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect. We demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of mean BOLD amplitude differences. Using Monte-Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and single-subject levels, FWER was either at or significantly below 5%. We reached the desired power with 18 subjects and 12 trials for the group level, and with 14 trials in the single-subject scenario. We compare the tMVPA statistical evaluation to that of a linear support vector machine (SVM). SVM outperformed tMVPA with large N and trial numbers. Conversely, tMVPA, leveraging on single trials analyses, outperformed SVM in low N and trials and in a single-subject scenario. Recent evidence suggesting that the BOLD signal carries finer-grained temporal information than previously thought, advocates the need for analytical tools, such as tMVPA, tailored to investigate BOLD temporal dynamics. The comparable performance between tMVPA and SVM, a powerful and reliable tool for fMRI, supports the validity of our technique.

• Item Type: Articles
• Keywords: BOLD, MVPA, multivariate, mean BOLD amplitude, temporal analysis
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Muckli, Professor Lars
• Authors: Vizioli, L., Bratch, A., Lao, J., Ugurbil, K., Muckli, L., and Yacoub, E.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Journal of Neuroscience Methods
• Publisher: Elsevier
• ISSN: 0165-0270
• ISSN (Online): 1872-678X
• Published Online: 30 June 2018
• Copyright Holders: Copyright © 2018 Elsevier
• First Published: First published in Journal of Neuroscience Methods 308:74-87
• Publisher Policy: Reproduced under a Creative Commons License