Canonical template tracking: Measuring the activation state of specific neural representations - Cognition, Langues, Langage, Ergonomie
Article Dans Une Revue Frontiers in Neuroimaging Année : 2023

Canonical template tracking: Measuring the activation state of specific neural representations

Traçage de modèles canoniques : mesure de l'état d'activation de représentations neuronales spécifiques

Résumé

Multivariate analyses of neural data have become increasingly influential in cognitive neuroscience since they allow to address questions about the representational signatures of neurocognitive phenomena. Here, we describe Canonical Template Tracking: a multivariate approach that employs independent localizer tasks to assess the activation state of specific representations during the execution of cognitive paradigms. We illustrate the benefits of this methodology in characterizing the particular content and format of task-induced representations, comparing it with standard (cross-)decoding and representational similarity analyses. Then, we discuss relevant design decisions for experiments using this analysis approach, focusing on the nature of the localizer tasks from which the canonical templates are derived. We further provide a step-by-step tutorial of this method, stressing the relevant analysis choices for functional magnetic resonance imaging and magneto/electroencephalography data. Importantly, we point out the potential pitfalls linked to canonical template tracking implementation and interpretation of the results, together with recommendations to mitigate them. To conclude, we provide some examples from previous literature that highlight the potential of this analysis to address relevant theoretical questions in cognitive neuroscience.
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hal-04852674 , version 1 (06-01-2025)

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Ana F Palenciano, Mehdi Senoussi, Silvia Formica, Carlos González-García. Canonical template tracking: Measuring the activation state of specific neural representations. Frontiers in Neuroimaging, 2023, 1, pp.363 - 373. ⟨10.3389/fnimg.2022.974927⟩. ⟨hal-04852674⟩
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