Literature DB >> 19422920

Separating event-related BOLD components within trials: the partial-trial design revisited.

Hannes Ruge1, Thomas Goschke, Todd S Braver.   

Abstract

Many event-related fMRI designs involve multiple successive events occurring within a trial, spaced closely in time (e.g., in cued set-shifting paradigms). Yet, it is notoriously difficult to separate the activation components to these sequentially ordered events, given the long evolution time of the BOLD response. One approach to deal with this problem is to omit the second of two successive events (S1 and S2) in a certain proportion of 'partial S1-only' trials. The present article describes a novel method that extends the basic partial-trial design in several ways. As a central new feature it introduces two different delay intervals between S1 onset and S2 presentation, or, in case of S1-only trials, S2 omission. The analysis is based on three BOLD response regressors, one synchronized with S1 onset for short S1-S2 delay trials, another one synchronized with S1 onset for long S1-S2 delay trials, and a third synchronized with S2 onset. The two estimated S1-related activation time courses are then assessed by 'temporal profiling' based on the parameterization of onset latencies, peak latencies, and the area under the curves. Based on this information it is possible (1) to distinguish transient activity elicited with S1 onset from delay-related activity and (2) to identify the activation profile associated with possible 'nogo-type' activity caused by S2 omission. Despite these two new important possibilities, some caution is still advised when interpreting data from the proposed partial-trial design. Yet, in contrast to previous methods, it is possible to identify ambiguous data patterns and, by following an explicit decision scheme, to avoid erroneous conclusions.

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Year:  2009        PMID: 19422920      PMCID: PMC2791061          DOI: 10.1016/j.neuroimage.2009.04.075

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


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