Wednesday, June 28, 2017: 12:45 PM
Wednesday, June 28 & Thursday, June 29
Viola Borchardt1,2, Galina Surova3, Johan van der Meer4,1, Michal Bola5, Yan Fan1,6, Anna Linda Krause1,7, Jörg Frommer8, Meng Li1, Sebastian Olbrich9,10, Martin Walter1,2,11
1Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany, 2Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany, 3Clinic for Psychiatry and Psychotherapy, Leipzig, Germany, 4QIMR Berghofer Medical Research Institute, Brisbane, Australia, 5Laboratory of Brain Imaging, Neurobiology Center, Nencki Institute of Experimental Biology of Polish, Warsaw, Poland, 6Department of Psychiatry, CBF, Charité, Berlin, Germany, 7Clinic for Psychiatry and Psychotherapy, Otto-von Guericke University Magdeburg, Magdeburg, Germany, 8Clinic for Psychosomatic Medicine and Psychotherapy, University Clinic Magdeburg, Magdeburg, Germany, 9Clinic for Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany, 10Clinic for Psychiatry, Psychotherapy and Psychosomatic, University Clinic Zurich, Zurich, Switzerland, 11Clinic for Psychiatry and Psychotherapy, Eberhard-Karls University, Tuebingen, Germany
Clinical Affective Neuroimaging Laboratory|Department of Behavioral Neurology, Leibniz Institute for Neurobiology
Magdeburg, Germany|Magdeburg, Germany
Based on EEG acquisitions at resting state, it is possible to differentiate stages of wakefulness regulation [Olbrich et al., 2009].
A region that has a central role in supporting internally-directed cognition and showed both reduced neural activity and altered interaction with other brain regions in states of low arousal and awareness, is the posterior cingulate cortex (PCC) [Fox et al., 2005; Sämann et al., 2011]. It's dorsal and ventral subregions show heterogeneity in tuning functional brain states depending on environmental demands [Leech and Sharp, 2014; Liang et al., 2015].
To investigate the interplay between individual variations in tonic arousal level and network connectivity patterns, we analyzed how functional coupling between subregions of the PCC and their patterns of interconnection with major intrinsic connectivity networks (ICN) are modulated by vigilance.
16 neurotypical subjects (8 males, age: 28.1±7.4) underwent a simultaneous resting-state EEG-fMRI scan for ten minutes with closed eyes (Magdeburg, 3T Siemens Verio).
EEG signals from 28 channels were preprocessed in EEGLab using FIR filter (0.5-125Hz), correction for gradient artifacts [Moosmann et al., 2009], carbon-wire based artifact correction [van der Meer et al., 2016], removal of additional artifact components by ICA, FIR filter (0.5-70Hz), downsampling to 500Hz and referenced to the FCz position with a ground electrode located at the AFz position. Using the VIGALL 2.0 algorithm, vigilance stages (high: A1, A2, A3, low: B2/3) in consecutive EEG segments (duration: 2.4s=1 TR) were classified [Sander et al., 2015].
FMRI images were preprocessed using slicetime correction, coregistration, segmentation, normalization, regression of signals from 6 motion regressors, white matter, CSF and a linear trend, bandpass filter (0.01-0.1 Hz), and scrubbing by interpolation in DPARSFA toolbox.
To analyze functional coupling of PCC subregions, extracted timecourses of bilateral spherical seeds were correlated (dPCC: [±2, -34, 40], vPCC [±2, -58, 28], radius 6mm) [Leech et al., 2011].
To analyze the changes in between-network FC patterns, a network of 42 nodes placed in Default Mode Network (DMN), Dorsal Attention Network and Central Executive Network (CEN) [Spreng et al., 2013] and the four PCC nodes was created and the functional connectivity strength (FCS) of each PCC ROI to each ICN was calculated separately.
EEG and fMRI metrics were correlated using Pearson correlation coefficient.
PCC subregions were functionally coupled, but dPCC and vPCC had strongest FCS to CEN and DMN, respectively.
The percentage of subjects in low vigilance stages was constantly high and stable over time.
Intra-PCC coupling was stronger if a subject spent more time in low vigilance stages (right hemisphere: p=0.026, r=-0.55, left hemisphere: p=0.019, r=-0.5) and if changes vigilance state were seldom (right hemisphere: p=0.027, r=-0.55, left hemisphere: p=0.035, r=-0.53). FCS between right dPCC and DMN was stronger, if the number of TR spent in high vigilance stages was low (p=0.038, r=-0.52) and if vigilance state changes were seldom (right PCC: p=0.02, r=-0.57, left PCC: p=0.049, r=-0.49).
Existence of differential canonic FC to major ICN provides further evidence for functional heterogeneity of dPCC and vPCC.
EEG-informed analysis of intra-PCC coupling revealed a modulating effect of vigilance in that coupling was strongest during low and stable vigilance.
Vigilance predicted network interaction between dPCC and its non-canonical network DMN.
Using combined EEG-fMRI data we showed that variations in arousal modulate functional coupling in the PCC and specifically interaction of the dorsal subregion to the DMN at rest.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
fMRI Connectivity and Network Modeling 1
Poster Session - Wednesday
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Fox, M.D. (2005): The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102:9673–8.
Leech, R. (2011): Fractionating the default mode network: distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. J Neurosci 31:3217–24.
Leech, R. (2014): The role of the posterior cingulate cortex in cognition and disease. Brain 137:12–32.
Liang, X. (2015): Topologically Reorganized Connectivity Architecture of Default-Mode , Executive-Control , and Salience Networks across Working Memory Task Loads. Cereb Cortex:1–11.
van der Meer, JN. (2016): Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections-A validation of a real-time simultaneous EEG/fMRI correction method. Neuroimage 125:880–894.
Moosmann, M. 2009): Realignment parameter-informed artefact correction for simultaneous EEG–fMRI recordings. Neuroimage 45:1144–1150.
Olbrich, S. (2009): EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement. Neuroimage 45:319–332.
Sämann, P.G. (2011): Development of the Brain’s Default Mode Network from Wakefulness to Slow Wave Sleep. Cereb Cortex 21:2082–2093.
Sander, C. (2015): Assessment of Wakefulness and Brain Arousal Regulation in Psychiatric Research. Neuropsychobiology 72:195:205.
Spreng, R. (2013): Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. J Cogn Neurosci 25.