The authors would like to thank Matti S. H&auml;m&auml;l&auml;inen, Lilla Z&ouml;llei and three anonymous reviewers for their helpful comments. Funding sources: R00DC010196 (AKCL); T32DC000018 (EDL); T32DC005361 (RKM).

1. Anatomical Data Acquisition and Processing
<ol>
 <li>Acquire one magnetization-prepared rapid gradient echo (MPRAGE) MRI scan of the subject. This may take 5-10 min depending on which specific scanning protocol is used.</li>
 <li>Acquire two additional fast low-angle shot (FLASH) MRI scans (flip angles = 5&deg; and 30&deg;) if EEG data are used for inverse imaging analysis, as FLASH sequences provide different tissue contrast from the standard MPRAGE sequences 1.</li>
 <li>Use FreeSurfer software (see Table) 2, 3 to reconstruct the cortical surface and to set up individual M/EEG dipole source space.
 <ol>
 <li>This source space is constrained to the grey/white matter boundary segmented from the MPRAGE scan. Each hemisphere contains roughly 100,000 potential vertices, spaced at ~1 mm. For dipole amplitude estimation (see below), use a grid spacing of 7 mm, which yields ~3,000 dipoles per hemisphere.</li>
 </ol>
 </li>
 <li>Reconstruct the skin, outer skull and inner skull surfaces from the MPRAGE and FLASH images using MNE (see Table) and FreeSurfer. Use these surfaces to generate a three-layer boundary element model (BEM).</li>
</ol>
2. M/EEG Data Acquisition
<ol>
 <li>Prepare subject for M/EEG recording.
 <ol>
 <li>Refer to Liu et al 4 for details of electrooculogram and reference electrode preparation as well as digitization of the subject's fiducial landmarks, head-position indicator coils (HPI) and EEG electrodes.</li>
 </ol>
 </li>
 <li>Once subject is seated in the MEG, measure the head position using the HPI coils.</li>
 <li>Start recording. Begin presentation of auditory and visual stimuli.
 <ol>
 <li>Many hardware and software solutions are available to perform stimulus presentation (e.g., Presentation, E-Prime). We use a Tucker-Davis Technologies RZ6 for auditory stimulus presentation and trigger stamping, with Psychtoolbox 5 for visual stimulus presentation, both controlled by MATLAB. Testing the auditory and visual latencies using a microphone and photodiode attached to the screen, and subsequently ensuring there is no observable jitter (which may necessitate setting the presentation projector to its native resolution) prior to the experiment helps ensure timing integrity.</li>
 </ol>
 </li>
 <li>Subject responds to auditory and visual stimuli via an optical button box while performing behavioral task.</li>
 <li>Save all stimuli, experimental parameters and data files for off-line analysis.</li>
</ol>
3. M/EEG Co-registration with MRI Scan and Data Processing
<ol>
 <li>Using the MNE software, load digitizer data and subject's reconstructed MRI head model. Pick fiducial landmarks to initiate co-registration process and proceed to use automatic alignment procedure to complete coordinate transformation (Figure 2).</li>
 <li>To relate the location of each dipole in the source space with the location of each sensor, combine recorded HPI data (see 2.2) to compute a forward solution with the three-layer BEM (see 1.4)</li>
 <li>Inspect all recorded M/EEG data and identify channels that have exceptionally high variance or are completely flat. Set these channels as bad channels.</li>
 <li>Use signal-space projection 6 or other noise reduction techniques (such as signal space separation 7) to project or separate out spatial field patterns originated from ambient environmental field contamination or other undesirable physiological signals, such as those associated with eye-blinks and cardiac artifacts (Figure 3).
 <ol>
 <li>Apply time-domain artifact removal (e.g., removing epochs containing abnormally high amplitude signals due to spiking of a channel) and frequency-domain artifact removal (e.g., band-notch filtering at 50 or 60 Hz line-frequency) to further increase signal-to-noise ratio.</li>
 </ol>
 </li>
 <li>Identify a baseline period in which the subject was not performing any task (e.g., 200 ms period prior to the onset of each trial). Generate an average of these baseline epochs in order to obtain a noise estimate (also known as the covariance matrix).</li>
 <li>Identify epochs of interest (e.g., only collecting epochs with correct behavioral responses) and define conditions for experimental contrasts (e.g., epochs associated with subject having switched their auditory attention to the opposite hemifield as originally cued - "Switch" condition - versus subject maintaining attention on the original hemifield - "Hold" condition). Generate an average response for each of the condition defined.
 <ol>
 <li>These averages can be baseline-corrected or not depending on experimental parameters (see 8); data shown here are baseline-corrected.</li>
 </ol>
 </li>
 <li>Combine the covariance matrix (3.5) and the computed forward solution (3.2) to obtain a distributed cortically constrained minimum-norm inverse operator that relates the sensor measurements to dipole current estimates in the source space.
 <ol>
 <li>You can either approximately constrain or fix the dipole orientation to the cortical normal direction 9.</li>
 </ol>
 </li>
 <li>Generate a "brain movie" of the distributed dipole estimate (i.e., the current estimate at each dipole location in the source space in time) for each experimental condition (Figure 4).
 <ol>
 <li>Depending on the temporal characteristics of your experimental design, you can bin your data in time by averaging current estimates using non-overlapping temporal windows.</li>
 </ol>
 </li>
</ol>
4. Statistical Inference Based on a Common Surface-based Coordinate System
<ol>
 <li>Morph the "brain movies" for each subject onto a common (average) cortical space based on a surface-based coordinate system that optimally aligns individual sulcal-gyral patterns 3. This allows us to compare or average cortical activities across all subjects. (Figure 5).</li>
 <li>There are many different statistical inference approaches. We will highlight three possible approaches here. Approaches that are not implemented in software package can be written using custom software-in our examples we use MATLAB to perform the non-parametric spatio-temporal clustering permutation test. Despite the high dimensionality (Space x Time x Subjects) of these data, all of these approaches can be performed using standard modern desktop computer hardware in seconds (ROI; 4.3 approach) to hours (non-parametric clustering; 4.5).</li>
 <li>Region-of-interest (ROI) approach
 <ol>
 <li>You can define the ROI anatomically (e.g., defined by automatic parcellation algorithm 1) and / or functionally (e.g., by recording a functional localizing task, such as a Go/No Go saccade task to identify the oculomotor regions).</li>
 <li>You can further constrain your analysis to a specific time-of-interest that is appropriate to your experimental paradigm (e.g., a time period immediately before and after the onset of the sound stimuli). You can also use other statistical inference associated with time-series analysis.</li>
 </ol>
 </li>
 <li>Whole-brain Bonferroni or False-Discovery-Rate (FDR) correction
 <ol>
 <li>Employ Bonferroni or FDR correction if you require whole-brain, all-time analysis.</li>
 <li>Generate a statistical map at each dipole location and each time point using appropriate test statistics, such as a t-test or within-subjects ANOVA for approximately normally-distributed data. For example, z-scores from dynamic statistical parameter mapping of the MNE estimates for fixed dipole sources 10 can be used when paired with a correction for correlations in the estimates (such as the conservative Greenhouse-Geisser correction).</li>
 <li>For Bonferroni correction, obtain significant space-time points by thresholding at a significance level of 0.05 divided by the number of comparisons (number of dipoles multiplied by number of time points). For a less conservative approach, use FDR p-value correction 11.</li>
 </ol>
 </li>
 <li>Non-parametric spatiotemporal clustering 
 <ol>
 <li>Use this method (based on a simple extension of 12) to find regions of large, consistent spatial and temporal activation while being less conservative than Bonferroni correction, and less prone to Type I statistical errors than FDR by controlling for the family-wise error rate.
 <ol>
 <li>Because this approach uses permutation or Monte Carlo resampling techniques, it does not rely on assumptions of normality of the data, and only assumes that the condition labels are exchangeable under the null hypothesis. Although it is more computationally intensive than the previous two approaches, it can still be performed in hours on a single machine using modern desktop computer hardware.</li>
 </ol>
 </li>
 <li>Generate a statistical map at each dipole location and each time point using the appropriate test statistics, such as a t-test.</li>
 <li>Threshold this map at a preliminary significance threshold, e.g. p &lt; 0.05.</li>
 <li>Cluster these putative significant points based on spatio-temporal proximity, e.g. significant points within 5 ms and 5 mm geodesic distance of one another are put in the same cluster. Score each resulting cluster using hypervolume or total significance (e.g. sum of t-scores of points in the cluster).</li>
 <li>Perform a standard permutation resampling (or Monte Carlo resampling for larger datasets, e.g. number of subjects N &gt; 10, to save on computation) test with a maximal statistic (see 12 for permutation test examples). Briefly, for a random subset of subjects (choosing anywhere from 0 to N subjects), relabel the conditions being compared before obtaining the statistical map, perform clustering on the new statistical map, and obtain the maximal cluster score for that relabeling. Perform this procedure on new random relabelings for up to 2N permutations to obtain a distribution of the maximal statistic; performing all 2N possible relabelings yields the permutation test and utilizing a random subset of fewer than 2N relabelings yields a Monte Carlo (or random) permutation test.</li>
 <li>Obtain the significance of a given original cluster (from the original labeling) by determining the proportion of time the maximal cluster sizes were greater than that of the original cluster, e.g. clusters that were larger than 95% of the maximal statistic clusters can be declared significant.
 <ol>
 <li>For an in-depth discussion on statistical inference in MEG distributed source imaging, see 13.</li>
 </ol>
 </li>
 </ol>
 </li>
 <li>The resulting data files can be visualized in many ways, including using the formats natively utilized by MNE software to store spatio-temporal cortical estimates, namely STC files. These, alongside labels that can be produced corresponding to the significant regions, can be generated using MNE toolboxes provided for MATLAB and Python.</li>
</ol>
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.
<img alt="Figure 1" src="/files/ftp_upload/4262/4262fig1.jpg" /> 
 Figure 1. Workflow for generating a "brain movie" using cortically-constrained minimum-norm dipole estimates (cf., Figure 1 in Liu et al., 2010).
<img alt="Figure 2" src="/files/ftp_upload/4262/4262fig2.jpg" /> 
 Figure 2. MNE software used to facilitate EEG channels and HPI locations co-registration onto one subject's MRI co-ordinate space.
<img alt="Figure 3" src="/files/ftp_upload/4262/4262fig3.jpg" /> 
 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. <a href="https://www.jove.com/files/ftp_upload/4262/4262fig3large.jpg" target="_blank"> Click here to view larger figure</a>.
<img alt="Figure 4" src="/files/ftp_upload/4262/4262fig4.jpg" /> 
 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)
<img alt="Figure 5" src="/files/ftp_upload/4262/4262fig5.jpg" /> 
 Figure 5. Comparison between a hypothetical ROI mapped on a subject's native cortical space and after morphed onto a common cortical space.
<img alt="Figure 6" src="/files/ftp_upload/4262/4262fig6.jpg" /> 
 Figure 6. Representative spatio-temporal cluster and time-course associated with the two experimental conditions tested.

<ol>
	<li>Fischl, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatically+parcellating+the+human+cerebral+cortex.">Automatically parcellating the human cerebral cortex.</a> Cerebral Cortex. 14, 11-22 (2004).</li><li>Dale, A., Sereno, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+localization+of+cortical+activity+by+combining+EEG+and+MEG+with+MRI+cortical+surface+reconstruction:+A+linear+approach.">Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach.</a> Journal of Cognitive Neuroscience. 5, 162-176 (1993).</li><li>Fischl, B., Sereno, M. I., Dale, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.</a> NeuroImage. 9, 195-207 (1999).</li><li>Liu, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+Mapping+with+Simultaneous+MEG+and+EEG.">Functional Mapping with Simultaneous MEG and EEG.</a> Journal of Visualized Experiments. (40), e1668 (2010).</li><li>Brainard, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Psychophysics+Toolbox.">The Psychophysics Toolbox.</a> Spatial Vision. 10, 433-436 (1997).</li><li>Uusitalo, M. A., Ilmoniemi, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signal-space+projection+method+for+separating+MEG+or+EEG+into+components.">Signal-space projection method for separating MEG or EEG into components.</a> Med. Biol. Eng. Comput. 35, 135-140 (1997).</li><li>Taulu, S., Simola, J., Kajola, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+the+signal+space+separation+method.">Applications of the signal space separation method.</a> IEEE Transactions on Signal Processing. 53, 3359-3372 (2005).</li><li>Urbach, T. P., Kutas, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpreting+event-related+brain+potential+(ERP)+distributions:+Implications+of+baseline+potentials+and+variability+with+application+to+amplitude+normalization+by+vector+scaling.">Interpreting event-related brain potential (ERP) distributions: Implications of baseline potentials and variability with application to amplitude normalization by vector scaling.</a> Biological Psychology. 72, 333-343 (2006).</li><li>Lin, F. -. H., Belliveau, J. W., Dale, A. M., H&#228;m&#228;l&#228;inen, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distributed+current+estimates+using+cortical+orientation+constraints.">Distributed current estimates using cortical orientation constraints.</a> Human Brain Mapping. 27, 1-13 (2006).</li><li>Dale, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+statistical+parametric+mapping:+combining+fMRI+and+MEG+for+high-resolution+imaging+of+cortical+activity.">Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity.</a> Neuron. 26, 55-67 (2000).</li><li>Nichols, T., Hayasaka, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Controlling+the+familywise+error+rate+in+functional+neuroimaging:+a+comparative+review.">Controlling the familywise error rate in functional neuroimaging: a comparative review.</a> Statistical Methods in Medical Research. 12, 419-446 (2003).</li><li>Nichols, T. E., Holmes, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonparametric+permutation+tests+for+functional+neuroimaging:+a+primer+with+examples.">Nonparametric permutation tests for functional neuroimaging: a primer with examples.</a> Human Brain Mapping. 15, 1-25 (2001).</li><li>Pantazis, D., Leahy, R. M., Hansen, P., Kringelbach, M., Salmelin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+Inference+in+MEG+Distributed+Source+Imaging.">Statistical Inference in MEG Distributed Source Imaging.</a> MEG: An Introduction to Methods. , 245-272 (2010).</li><li>Ahveninen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attention-driven+auditory+cortex+short-term+plasticity+helps+segregate+relevant+sounds+from+noise.">Attention-driven auditory cortex short-term plasticity helps segregate relevant sounds from noise.</a> Proceedings of the National Academy of Sciences. , 1-6 (2011).</li><li>Sharon, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+advantage+of+combining+MEG+and+EEG:+comparison+to+fMRI+in+focally+stimulated+visual+cortex.">The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex.</a> Neuroimage. 36, 1225-1235 (2007).</li><li>Herrmann, B., Maess, B., Hasting, A. S., Friederici, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Localization+of+the+syntactic+mismatch+negativity+in+the+temporal+cortex:+An+MEG+study.">Localization of the syntactic mismatch negativity in the temporal cortex: An MEG study.</a> NeuroImage. 48, 590-600 (2009).</li><li>Baillet, S., Hansen, P., Kringelbach, M., Salmelin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Dowser+in+the+Fields:+Searching+for+MEG+Sources.">The Dowser in the Fields: Searching for MEG Sources.</a> MEG: An Introduction to Methods. , 83-123 (2010).</li><li>Gutschalk, A., Micheyl, C., Oxenham, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+correlates+of+auditory+perceptual+awareness+under+informational+masking.">Neural correlates of auditory perceptual awareness under informational masking.</a> PLoS Biology. 6, e138 (2008).</li></ol>

No conflicts of interest declared.

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 ...

<table border="1">
 <tbody>
 <tr>
 <td>Name of equipment / software</td>
 <td>Company / source</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>306-channel Vectorview MEG system</td>
 <td>Eleka-Neuromag Ltd,</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>1.5-T Avanto MRI scanner</td>
 <td>Siemens Medical Solutions</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>FreeSurfer</td>
 <td></td>
 <td></td>
 <td><a href="http://freesurfer.net/" target="_blank">http://freesurfer.net/</a></td>
 </tr>
 <tr>
 <td>MNE software</td>
 <td></td>
 <td></td>
 <td><a href="http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php" target="_blank">http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php</a></td>
 </tr>
 <tr>
 <td>EEG electrodes</td>
 <td>Brain Products, Easycap GmbH</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>3Space Fastrak system</td>
 <td>Polhemus</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>Optical button box (FIU-932)</td>
 <td>Current Designs</td>
 <td></td>
 <td></td>
 </tr>
 </tbody>
</table>

mapping cortical dynamics using simultaneous meg/eeg and anatomically-constrained minimum-norm estimates: an auditory attention example

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.

Watch this Scientific Journal Video about Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example at JoVE.com

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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 ...

mapping-cortical-dynamics-using-simultaneous-megeeg-anatomically

Research

JoVE Journal

Neuroscience

14.6K vistas.  University of Washington. Usamos magneto-y electroencefalografía (MEG / EEG), combinada con la información anatómica capturado por resonancia magnética (MRI), para asignar la dinámica de la red cortical asociada con la atención auditiva.

Video: Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to map brain areas involved in the processing of simple sensory stimuli.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the ...

Preparation and Culture of Chicken Auditory Brainstem Slices

The chicken auditory brainstem is a well-established model system that has been widely used to study the anatomy and physiology of auditory processing at discreet periods of development 1-4 as well as mechanisms for temporal coding in the central nervous system 5-7.
Here we present a method to prepare chicken auditory brainstem slices that can be used for acute experimental procedures or to culture organotypic slices for long-term experimental manipulations.
The chicken auditory brainstem is composed of nucleus angularis, magnocellularis, laminaris and superior olive. These nuclei are responsible for binaural sound processing and single coronal slice preparations preserve the entire circuitry. Ultimately, organotypic slice cultures can provide the opportunity to manipulate several developmental parameters such as synaptic activity, expression of pre and postsynaptic components, expression of aspects controlling excitability and differential gene expression
This approach can be used to broaden general knowledge about neural circuit development, refinement and maturation.

The chicken auditory brainstem is comprised of nuclei responsible for binaural sound processing. A single coronal slice preparation maintains the entire circuitry while the cultured approach provides a unique preparation to study the development of neuronal structure and auditory function at the molecular, cellular and network levels.

The chicken auditory brainstem is a well-established model system that has been widely used to study the anatomy and physiology of auditory processing at ...

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., &lt;1 cm/&lt;1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning1, auditory processing2 and visual-spatial attention3. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication6 and control7. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform8,9 and the SIGFRIED10 method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.
Prerequisites and Planning:
Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.
In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.
At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.
Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.
A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).
Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping10.
The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

We present a method for collecting electrocorticographic signals for research purposes from humans who are undergoing invasive epilepsy monitoring. We show how to use the BCI2000 software platform for data collection, signal processing and stimulus presentation. Specifically, we demonstrate SIGFRIED, a BCI2000-based tool for real-time functional brain mapping.

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), ...

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Current knowledge of sensory processing in the mammalian auditory system is mainly derived from electrophysiological studies in a variety of animal models, including monkeys, ferrets, bats, rodents, and cats. In order to draw suitable parallels between human and animal models of auditory function, it is important to establish a bridge between human functional imaging studies and animal electrophysiological studies. Functional magnetic resonance imaging (fMRI) is an established, minimally invasive method of measuring broad patterns of hemodynamic activity across different regions of the cerebral cortex. This technique is widely used to probe sensory function in the human brain, is a useful tool in linking studies of auditory processing in both humans and animals and has been successfully used to investigate auditory function in monkeys and rodents. The following protocol describes an experimental procedure for investigating auditory function in anesthetized adult cats by measuring stimulus-evoked hemodynamic changes in auditory cortex using fMRI. This method facilitates comparison of the hemodynamic responses across different models of auditory function thus leading to a better understanding of species-independent features of the mammalian auditory cortex.

Functional studies of the auditory system in mammals have traditionally been conducted using spatially-focused techniques such as electrophysiological recordings. The following protocol describes a method of visualizing large-scale patterns of evoked hemodynamic activity in the cat auditory cortex using functional magnetic resonance imaging.

Current knowledge of sensory processing in the mammalian auditory system is mainly derived from electrophysiological studies in a variety of animal ...

Optogenetic Stimulation of the Auditory Nerve

Direct electrical stimulation of spiral ganglion neurons (SGNs) by cochlear implants (CIs) enables open speech comprehension in the majority of implanted deaf subjects1-6. Nonetheless, sound coding with current CIs has poor frequency and intensity resolution due to broad current spread from each electrode contact activating a large number of SGNs along the tonotopic axis of the cochlea7-9. Optical stimulation is proposed as an alternative to electrical stimulation that promises spatially more confined activation of SGNs and, hence, higher frequency resolution of coding. In recent years, direct infrared illumination of&#160;the cochlea has been used to evoke responses in the auditory nerve10. Nevertheless it requires higher energies than electrical stimulation10,11 and uncertainty remains as to the underlying mechanism12. Here we describe a method based on optogenetics to stimulate SGNs with low intensity blue light, using transgenic mice with neuronal expression of channelrhodopsin 2 (ChR2)13 or virus-mediated expression of the ChR2-variant CatCh14. We used micro-light emitting diodes (&#181;LEDs) and fiber-coupled lasers to stimulate ChR2-expressing SGNs through a small artificial opening (cochleostomy) or the round window. We assayed the responses by scalp recordings of light-evoked potentials (optogenetic auditory brainstem response: oABR) or by microelectrode recordings from the auditory pathway and compared them with acoustic and electrical stimulation.

Cochlear implants (CIs) enable hearing by direct electrical stimulation of the auditory nerve. However, poor frequency and intensity resolution limits the quality of hearing with CIs. Here we describe optogenetic stimulation of the auditory nerve in mice as an alternative strategy for auditory research and developing future CIs.

Direct electrical stimulation of spiral ganglion neurons (SGNs) by cochlear implants (CIs) enables open speech comprehension in the majority of implanted ...

Touchscreen Sustained Attention Task (SAT) for Rats

Sustained attention is the ability to monitor intermittent and unpredictable events over a prolonged period of time. This attentional process subserves other aspects of cognition and is disrupted in certain neurodevelopmental, neuropsychiatric, and neurodegenerative disorders. Thus, it is clinically important to identify mechanisms that impair and improve sustained attention. Such mechanisms are often first discovered using rodent models. Therefore, several behavior procedures for testing aspects of sustained attention have been developed for rodents. One, first described by McGaughy and Sarter (1995), called the sustained attention task (SAT), trains rats to distinguish between signal (i.e., brief light presentation) and non-signal trials. The signals are short and thus require careful attention to be perceived. Attentional demands can be increased further by introducing a distractor (e.g., flashing houselight). We have modified this task for touchscreen operant chambers, which are configured with a touchscreen on one wall that can present stimuli and record responses. Here we detail our protocol for SAT in touchscreen chambers. Additionally, we present standard measures of performance in male and female Sprague-Dawley rats. Comparable performance on this task in both sexes highlights its use for attention studies, especially as more researchers are including female rodents in their experimental design. Moreover, the easy implementation of SAT for the increasingly popular touchscreen chambers increases its utility.

Touchscreen Sustained Attention Task SAT for Rats

Sustained attention, or continuously monitoring situations for intermittent and unpredictable events, is a critical aspect of cognition. Here we detail how to test sustained attention in rats using touchscreen operant chambers. We demonstrate comparable performance in male and female rats, making this task useful for studying attention in both sexes.

Sustained attention is the ability to monitor intermittent and unpredictable events over a prolonged period of time. This attentional process subserves ...

Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat

Monitoring the physiological dynamics of the brain and peripheral tissues is necessary for addressing a number of questions about how the brain controls body functions and internal organ rhythms when animals are exposed to emotional challenges and changes in their living environments. In general experiments, signals from different organs, such as the brain and the heart, are recorded by independent recording systems that require multiple recording devices and different procedures for processing the data files. This study describes a new method that can simultaneously monitor electrical biosignals, including tens of local field potentials in multiple brain regions, electrocardiograms that represent the cardiac rhythm, electromyograms that represent awake/sleep-related muscle contraction, and breathing signals, in a freely moving rat. The recording configuration of this method is based on a conventional micro-drive array for cortical local field potential recordings in which tens of electrodes are accommodated, and the signals obtained from these electrodes are integrated into a single electrical board mounted on the animal's head. Here, this recording system was improved so that signals from the peripheral organs are also transferred to an electrical interface board. In a single surgery, electrodes are first separately implanted into the appropriate body parts and the target brain areas. The open ends of all of these electrodes are then soldered to individual channels of the electrical board above the animal's head so that all of the signals can be integrated into the single electrical board. Connecting this board to a recording device allows for the collection of all of the signals into a single device, which reduces experimental costs and simplifies data processing, because all data can be handled in the same data file. This technique will aid the understanding of the neurophysiological correlates of the associations between central and peripheral organs.

This study introduces a method for the simultaneous recording of local field potentials in the brain, electrocardiograms, electromyograms, and breathing signals of a freely moving rat. This technique, which reduces experimental costs and simplifies data analysis, will contribute to the understanding of the interactions between the brain and peripheral organs.

Monitoring the physiological dynamics of the brain and peripheral tissues is necessary for addressing a number of questions about how the brain controls ...

Visualization of Motor Axon Navigation and Quantification of Axon Arborization In Mouse Embryos Using Light Sheet Fluorescence Microscopy

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in vertebrates. Thus, it is critical to uncover the molecular mechanisms of how motor axons navigate to, arborize, and innervate their peripheral muscle targets during development and degeneration. Although transgenic Hb9::GFP mouse lines have long served to visualize motor axon trajectories during embryonic development, detailed descriptions of the full spectrum of axon terminal arborization remain incomplete due to the pattern complexity and limitations of current optical microscopy. Here, we describe an improved protocol that combines light sheet fluorescence microscopy (LSFM) and robust image analysis to qualitatively and quantitatively visualize developing motor axons. This system can be easily adopted to cross genetic mutants or MN disease models with Hb9::GFP lines, revealing novel molecular mechanisms that lead to defects in motor axon navigation and arborization.

Here, we describe a protocol for visualizing motor neuron projection and axon arborization in transgenic Hb9::GFP mouse embryos. After immunostaining for motor neurons, we used light sheet fluorescence microscopy to image embryos for subsequent quantitative analysis. This protocol is applicable to other neuron navigation processes in the central nervous system.

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in ...

Rat Model of Widespread Cerebral Cortical Demyelination Induced by an Intracerebral Injection of Pro-Inflammatory Cytokines

Multiple sclerosis (MS) is the most common immune-mediated disease of the central nervous system (CNS) and progressively leads to physical disability and death, caused by white matter lesions in the spinal cord and cerebellum, as well as by demyelination in grey matter. Whilst conventional models of experimental allergic encephalomyelitis are suitable for the investigation of the cell-mediated inflammation in the spinal and cerebellar white matter, they fail to address grey matter pathologies. Here, we present the experimental protocol for a novel rat model of cortical demyelination allowing the investigation of the pathological and molecular mechanisms leading to cortical lesions. The demyelination is induced by an immunization with low-dose myelin oligodendrocyte glycoprotein (MOG) in an incomplete Freund&#39;s adjuvant followed by a catheter-mediated intracerebral delivery of pro-inflammatory cytokines. The catheter, moreover, enables multiple rounds of demyelination without causing injection-induced trauma, as well as the intracerebral delivery of potential therapeutic drugs undergoing a preclinical investigation. The method is also ethically favorable as animal pain and distress or disability are controlled and relatively minimal. The expected timeframe for the implementation of the entire protocol is around 8 - 10 weeks.

The protocol presented here allows the reproduction of a widespread grey matter demyelination of both cortical hemispheres in adult male Dark Agouti rats. The method comprises of intracerebral implantation of a catheter, subclinical immunization against myelin oligodendrocyte glycoprotein, and intracerebral injection of a pro-inflammatory cytokine mixture through the implanted catheter.

Multiple sclerosis (MS) is the most common immune-mediated disease of the central nervous system (CNS) and progressively leads to physical disability and ...

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging.
Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks.
This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional &#34;maps&#34; of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques.
This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, ...