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Neuroimage. 2017 Dec;163:319-341. doi: 10.1016/j.neuroimage.2017.09.014. Epub 2017 Sep 9.

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks.

Author information

1
MR Research Center, Semmelweis University, 1085, Budapest, Hungary. Electronic address: lrkozak@gmail.com.
2
Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, WC1N 3BG, London, UK; Epilepsy Society, SL9 0RJ Chalfont St. Peter, Buckinghamshire, UK. Electronic address: louis.graan.12@ucl.ac.uk.
3
Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, WC1N 3BG, London, UK; Epilepsy Society, SL9 0RJ Chalfont St. Peter, Buckinghamshire, UK. Electronic address: umair.chaudhary@alumni.ucl.ac.uk.
4
MR Research Center, Semmelweis University, 1085, Budapest, Hungary. Electronic address: szabadam@gmail.com.
5
Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, WC1N 3BG, London, UK; Epilepsy Society, SL9 0RJ Chalfont St. Peter, Buckinghamshire, UK. Electronic address: louis.lemieux@ucl.ac.uk.

Abstract

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.

KEYWORDS:

Brain atlas; Data analysis; Functional characterization; Functional magnetic resonance imaging; Intrinsic connectivity networks; Resting-state networks

PMID:
28899742
PMCID:
PMC5725313
DOI:
10.1016/j.neuroimage.2017.09.014
[Indexed for MEDLINE]
Free PMC Article

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