Abstract:
:Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time-series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time-series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task-dependent changes of connectivity). We present a fully data-driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time-windows was used to identify if/when any area showed stimulus/task-related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time-series (stimulus/task-related networks). Finally, for each network, a second clustering step grouped together all the time-windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task-related activity at specific time-points during the fMRI time-series. We label these configurations: "brain modes" (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli.
journal_name
Hum Brain Mappjournal_title
Human brain mappingauthors
Bordier C,Macaluso Edoi
10.1002/hbm.22852subject
Has Abstractpub_date
2015-09-01 00:00:00pages
3404-25issue
9eissn
1065-9471issn
1097-0193journal_volume
36pub_type
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pub_type: 临床试验,杂志文章
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pub_type: 杂志文章,评审
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