Which brain regions fluctuate together?
Resting-state fMRI describes statistical relationships between BOLD time series. We use these patterns to characterise networks and identify candidate stimulation targets in individual participants.
What does resting-state fMRI measure?
In classical task-based fMRI, we study BOLD signal changes while a participant performs a well-defined task, such as making a decision. Resting-state fMRI (rsfMRI), on the other hand, measures these signals in the absence of a prescribed task, while the participant is awake and at rest. Depending on the protocol, they may look at a fixation cross throughout the scan.
Even though the participant is at rest, the brain shows ongoing spontaneous signal fluctuations. It is the analysis of these slow fluctuations that resting-state fMRI focuses on. Instead of comparing the signal time course with a task, we compare the time courses of different brain regions. Regions whose signals fluctuate together can be grouped into functional networks. This statistical relationship does not require a direct anatomical connection.
A hypothesis to test
Correlation alone does not establish the direction or mechanism of an interaction. Interleaved TMS-fMRI adds a controlled intervention: stimulate a target, then measure the response. Chronometric experiments ask whether that response depends on the cognitive state at the time of stimulation.
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From connectivity to perturbation
Our 2017 work measured changes in resting-state connectivity after a stimulation session. The 2023 interleaved study then related the acute response during stimulation to networks affected by offline stimulation. These approaches address different moments in the response to an intervention.
Towards understanding rTMS mechanism of action: Stimulation of the DLPFC causes network-specific increase in functional connectivity
Tik M and colleagues.
An earlier sham-controlled study of resting-state connectivity after stimulation in healthy participants.
Acute TMS/fMRI response explains offline TMS network effects – An interleaved TMS-fMRI study
Tik M et al.
Connecting the response during stimulation with network changes measured after stimulation.
In language research with Anna-Lisa Schuler and colleagues, connectivity also helps interpret why nearby stimulation targets can produce different naming effects: read the 2023 study.