University of Oulu

Ville Raatikainen, Niko Huotari, Vesa Korhonen, Aleksi Rasila, Janne Kananen, Lauri Raitamaa, Tuija Keinänen, Jussi Kantola, Osmo Tervonen, Vesa Kiviniemi, Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data, NeuroImage, Volume 148, 2017, Pages 352-363, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2017.01.024

Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data

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Author: Raatikainen, Ville1,2; Huotari, Niko1,2; Korhonen, Vesa1,2;
Organizations: 1Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
2Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
3Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202001131992
Language: English
Published: Elsevier, 2017
Publish Date: 2020-01-13
Description:

Abstract

This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach.

A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs.

Mean time lag of 0.6 s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2 min window analysis, there was large variability over subjects as ranging between 1–10 sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes.

The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0 s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.

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Series: Neuroimage
ISSN: 1053-8119
ISSN-E: 1095-9572
ISSN-L: 1053-8119
Volume: 148
Pages: 352 - 363
DOI: 10.1016/j.neuroimage.2017.01.024
OADOI: https://oadoi.org/10.1016/j.neuroimage.2017.01.024
Type of Publication: A1 Journal article – refereed
Field of Science: 3112 Neurosciences
217 Medical engineering
Subjects:
Copyright information: © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/