Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal.
J Neurosci Methods 2018;
308:74-87. [PMID:
29969602 PMCID:
PMC6447290 DOI:
10.1016/j.jneumeth.2018.06.029]
[Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/29/2018] [Accepted: 06/29/2018] [Indexed: 01/09/2023]
Abstract
Background:
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.
New Methods:
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.
Results:
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.
Comparison with existing methods:
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.
Conclusion:
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.
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