Spatio-temporal information analysis of event-related BOLD responses

Galit Fuhrmann Alpert, Fellice T. Sun, Daniel Handwerker, Mark D'Esposito, Robert T. Knight

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


A new approach for analysis of event-related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task-related activity, as well as for extracting temporal information regarding the task-dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF) nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that, during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times.

Original languageEnglish
Pages (from-to)1545-1561
Number of pages17
Issue number4
StatePublished - 15 Feb 2007
Externally publishedYes


  • Hemodynamic response function
  • Information theory
  • Model-free analysis
  • Motor learning
  • fMRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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