Relating EEG power to laminar specific fMRI connectivity.

Poster No:

1220 

Submission Type:

Abstract Submission 

Authors:

Rene Scheeringa1, Tim van Mourik2, Mathilde Bonnefond3, David G. Norris4, Peter Koopmans1

Institutions:

1University of Duisburg-Essen, Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany, 2Radboud University Nijmegen, Donders Institute, Nijmegen, The Netherlands, 3INSERM, Lyon, France, 4Radboud University Nijmegen, Donders Institute, Nijmegen, The Netherlands

First Author:

Rene Scheeringa  
University of Duisburg-Essen, Erwin L. Hahn Institute for Magnetic Resonance Imaging
Essen, Germany

Co-Author(s):

Tim van Mourik  
Radboud University Nijmegen, Donders Institute
Nijmegen, The Netherlands
Mathilde Bonnefond  
INSERM
Lyon, France
David G. Norris  
Radboud University Nijmegen, Donders Institute
Nijmegen, The Netherlands
Peter Koopmans  
University of Duisburg-Essen, Erwin L. Hahn Institute for Magnetic Resonance Imaging
Essen, Germany

Introduction:

The different cortical layers have layer specific anatomical connections within and between brain regions (Douglas and Martin, 2004), while alpha, beta and gamma oscillations have been related to laminar specific feedforward and feedback projections between brain regions (Bastos et al., 2015; van Kerkoerle et al., 2014). With laminar fMRI we can now non-invasively study brain activation and connectivity at the laminar level in humans. In a previous experiment we recorded simultaneous laminar fMRI & EEG (Scheeringa et al., 2016). We observed that attention effects in alpha, beta and gamma band EEG power relate to attention effects in fMRI activation in V1/V2/V3 at distinct cortical depths: alpha and gamma band EEG attention effects related to fMRI effects in superficial layers, beta attention effects related to deep layers. In this study we further analyse this data to investigate how these EEG-attention effects relate to changes in laminar resolved fMRI connectivity.

Methods:

The task consisted of centrally presented inward contracting circular gratings for which subjects were instructed to respond to a speed-increase in the contraction. A cue indicated whether a subsequent speed increase was likely to occur (67% valid) or would not occur (100% valid). The attention contrast was defined as the difference between trials without a speed increase where the cue indicated an increase or no increase would happen ("attention on" versus "attention off"). For the extracted layer specific BOLD signals in (bilateral) V1/V2/V3, amplitude estimates of the single trial BOLD responses were computed for both conditions. For each condition separately the correlation between all layers of all the included regions were computed. The attention effect in laminar specific connectivity between and within brain regions was defined as the difference between the Fisher Z-transformed correlations in both conditions. We averaged attention effects in connectivity over region combinations within a hemisphere (I), between region combinations in different hemispheres (II) and over regions the intra-regional laminar connectivity effects (III). See Figure 1 for a graphical depiction. Subsequently, the correlation of the layer specific attention effects were correlated (partial Spearman correlation) with the attention effects in the alpha, beta and gamma bands that were previously observed in the simultaneously recorded EEG.
Supporting Image: fig-1.png
   ·Figure 1
 

Results:

We observed that especially the beta band relates to strongly to laminar specific changes in laminar connectivity. We observed a negative correlation between the attention effect in beta power and the attention effect in deep-to-deep layer connectivity between regions within a hemisphere, superficial-to-superficial connectivity between hemispheres and middle to superficial layer connectivity within brain regions. For alpha power we observed a positive correlation with the attention effects in connectivity between deep and superficial layers within brain regions and middle/deep layers between regions within hemispheres. We observed no strong relation between laminar connectivity and gamma band oscillations. These results are depicted in Figure 1.

Conclusions:

This study explored the relation between neural oscillations and laminar level fMRI connectivity with simultaneously recorded EEG. We observed that in particular the beta band and also the alpha band oscillations relate to laminar specific connectivity. Although we observed a decrease in both alpha and beta power when the stimulus was attended, they related in opposite directions to laminar resolved fMRI connectivity. Alpha was found to positively correlate with laminar specific fMRI based connectivity, while beta oscillations negatively correlate with laminar specific connectivity. This suggests distinctly different roles of alpha and beta oscillations in attentional information processing as well as a necessity to reevaluate the interpretation of changes in (laminar) fMRI connectivity.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) 1
EEG/MEG Modeling and Analysis 2
fMRI Connectivity and Network Modeling

Novel Imaging Acquisition Methods:

Multi-Modal Imaging

Perception, Attention and Motor Behavior:

Attention: Visual

Keywords:

Cortical Layers
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
FUNCTIONAL MRI
Vision
Other - Neural Oscillations

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
EEG/ERP
Structural MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

SPM
FSL
Free Surfer

Provide references using author date format

Bastos, A.M., Vezoli, J., Bosman, C.A., Schoffelen, J.M., Oostenveld, R., Dowdall, J.R., De Weerd, P., Kennedy, H., Fries, P., 2015. Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels. Neuron 85, 390-401.
Douglas, R.J., Martin, K.A., 2004. Neuronal circuits of the neocortex. Annu Rev Neurosci 27, 419-451.
Scheeringa, R., Koopmans, P.J., van Mourik, T., Jensen, O., Norris, D.G., 2016. The relationship between oscillatory EEG activity and the laminar-specific BOLD signal. Proc Natl Acad Sci U S A 113, 6761-6766.
van Kerkoerle, T., Self, M.W., Dagnino, B., Gariel-Mathis, M.A., Poort, J., van der Togt, C., Roelfsema, P.R., 2014. Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proc Natl Acad Sci U S A 111, 14332-14341.