We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.
Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or “brain movies”) related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.
1. Anatomical Data Acquisition and Processing
2. M/EEG Data Acquisition
3. M/EEG Co-registration with MRI Scan and Data Processing
4. Statistical Inference Based on a Common Surface-based Coordinate System
5. Representative Results
Figure 6 shows a set of representative results using the behavioral paradigm outlined in Figure 4. Using the non-parametric spatiotemporal clustering procedure (4.5), the right FEF is found to be significant when a subject is performing a reorientation task compared to a standard task (Figure 6 left). Using the ROI approach (4.3), the time course of the right FEF is shown, along with the time period that these two conditions are significantly different.
Figure 1. Workflow for generating a “brain movie” using cortically-constrained minimum-norm dipole estimates (cf., Figure 1 in Liu et al., 2010).
Figure 2. MNE software used to facilitate EEG channels and HPI locations co-registration onto one subject’s MRI co-ordinate space.
Figure 3. MEG data before and after using SSP to remove cardiac (highlighted in orange) and eye-blinks (highlighted in blue-green) artifacts and lowpass filtering to remove line-frequency. Click here to view larger figure.
Figure 4. A “brain movie” on subject’s native cortical space and the timing of the audio-visual presentation (with auditory stimuli presented at 600 ms and a visual stimulus presented at -600 ms) in one experimental paradigm (Note: this will be presented as a movie in the final movie clip)
Figure 5. Comparison between a hypothetical ROI mapped on a subject’s native cortical space and after morphed onto a common cortical space.
Figure 6. Representative spatio-temporal cluster and time-course associated with the two experimental conditions tested.
In order to estimate the dipole activation on the cortex from the acquired MEG/EEG data, we need to solve an inverse problem, which does not have a unique stable solution unless appropriate anatomically and physiologically sound constraints are applied. Using the anatomical constraint acquired for individual subjects using MRI and adopting the minimum-norm as our estimation criterion, we can arrive at an inverse cortical current source estimate that agrees with the sensor measurements. This approach has proved useful in studies of not only auditory processing 14 but also other domains such as visual 15 and language processing 16 .
There are many other inverse approaches. However, all these methods can be summarized into two categories: localization (e.g., equivalent current dipole modeling) or imaging (e.g., MNE, beamforming techniques). Furthermore, each inverse approach has its tradeoff (see 17 for an in-depth discussion). For example, the current estimate using the approach presented here must necessarily be distributed in space due to its minimum-norm constraint. This minimum-norm estimate approach is well suited for tasks that recruit a distributed cortical network. Mapping early responses to stimuli that evoke focal source activity, such as those in audition that are believed to be localized in and around bilateral auditory cortex (e.g., N1m and awareness related negativity 18), can also be improved by using fMRI co-constraints 14.
Spectral domain analysis, e.g., investigating the role of different cortical rhythms involved in attention, across the cortex can also be performed after using any of the aforementioned inverse techniques. Furthermore, this type of analysis can easily be extended to address questions related to functional connectivity between distinct regions in the brain.
The authors have nothing to disclose.
The authors would like to thank Matti S. Hämäläinen, Lilla Zöllei and three anonymous reviewers for their helpful comments. Funding sources: R00DC010196 (AKCL); T32DC000018 (EDL); T32DC005361 (RKM).
Name of equipment / software | Company / source | ||
306-channel Vectorview MEG system | Eleka-Neuromag Ltd, | ||
1.5-T Avanto MRI scanner | Siemens Medical Solutions | ||
FreeSurfer | http://freesurfer.net/ | ||
MNE software | http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php | ||
EEG electrodes | Brain Products, Easycap GmbH | ||
3Space Fastrak system | Polhemus | ||
Optical button box (FIU-932) | Current Designs |