TY - JOUR
T1 - Temporal multivariate pattern analysis (tMVPA)
T2 - A single trial approach exploring the temporal dynamics of the BOLD signal
AU - Vizioli, Luca
AU - Bratch, Alexander
AU - Lao, Junpeng
AU - Ugurbil, Kamil
AU - Muckli, Lars
AU - Yacoub, Essa
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
KW - BOLD
KW - MVPA
KW - Mean BOLD amplitude
KW - Multivariate
KW - Temporal analysis
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U2 - 10.1016/j.jneumeth.2018.06.029
DO - 10.1016/j.jneumeth.2018.06.029
M3 - Article
C2 - 29969602
AN - SCOPUS:85050664996
SN - 0165-0270
VL - 308
SP - 74
EP - 87
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
ER -