PT  - JOURNAL ARTICLE
AU  - Luca Vizioli
AU  - Alexander Bratch
AU  - Junpeng Lao
AU  - Kamil Ugurbil
AU  - Lars Muckli
AU  - Essa Yacoub
TI  - Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal
AID  - 10.1101/273110
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 273110
4099  - http://biorxiv.org/content/early/2018/02/27/273110.short
4100  - http://biorxiv.org/content/early/2018/02/27/273110.full
AB  - Background fMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.New Methods We present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of 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 univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.Comparison with existing methods tMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.Conclusion The growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.