ArticleJournal of neuroscience methods2018
Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal.
Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mindful minds: How group identity shapes brain and behavior in social decision-making.Cognitive, affective & behavioral neuroscience · 2026Article
- Action-type mapping principles extend beyond evolutionarily conserved actions, even in people born without hands.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The roles of amygdala subnuclei in processing of approaching in- and outgroup others in virtual space.Social cognitive and affective neuroscience · 2025Article
- Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing.eLife · 2023Article
- Grasping with a Twist: Dissociating Action Goals from Motor Actions in Human Frontoparietal Circuits.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing.bioRxiv : the preprint server for biology · 2023Article
- Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response.Progress in neurobiology · 2021Review
- Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI.Progress in neurobiology · 2021Review
- Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy.Journal of neuroscience methods · 2020Article
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Authors and funding
6 authors.
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Abstract
backgroundfMRI 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
methodsWe 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.
resultsWe 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
methodsWe 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.
conclusionRecent 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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