Comparison of continuously acquired resting state and extracted analogues from active tasks

S. Ganger, A. Hahn, M. Küblböck, Georg Kranz, M. Spies, T. Vanicek, R. Seiger, R. Sladky, C. Windischberger, S. Kasper, R. Lanzenberger

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


© 2015 Wiley Periodicals, Inc.Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R2) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information.
Original languageEnglish
Pages (from-to)4053-4063
Number of pages11
JournalHuman Brain Mapping
Issue number10
Publication statusPublished - 1 Oct 2015
Externally publishedYes


  • Brain network
  • Resting-state fMRI
  • Task regression
  • Task-derived resting state

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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