# HGA-MarsAtlas: a toolbox for the estimate of high-resolution, single-trial source-level high-gamma activity from MEG data

Christelle Zielinski, Valerie Chanoine, Guillaume Auzias, Olivier Coulon, Andrea Brovelli

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To cite this version:

Christelle Zielinski, Valerie Chanoine, Guillaume Auzias, Olivier Coulon, Andrea Brovelli. HGA-MarsAtlas: a toolbox for the estimate of high-resolution, single-trial source-level high-gamma activity from MEG data. Journées de l'ILCB / BLRI, May 2019, Porquerolles, France. hal-03979007v2

HAL Id: hal-03979007

https://hal.science/hal-03979007v2

Submitted on 16 Feb 2023

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Distributed under a Creative Commons Attribution 4.0 International License
# HGA-MarsAtlas: a toolbox for the estimate of high-resolution, single-trial source-level high-gamma activity from MEG data

Christelle Zielinski¹, Valérie Chanoine¹, Guillaume Auzias², Olivier Coulon², Andrea Brovelli²

(1) ILCB CREx (2) Institut de Neurosciences de la Timone, AMU-CNRS

## Background

High-Gamma band activity (from 50 to 150 Hz) can be used to unravel the dynamics of functional connectivity (FC) mediating human cognitive functions [1, 2].

The approach is based on the combinaison of MEG data with a single-subject cortical parcellation scheme from individual anatomical MRI produced by the MarsAtlas model (available in the BrainVisa software) [3].

The resulting single-trial HGA from all cortical parcels can be used for functional mapping and FC analyses.

More recently, the approach has been generalized to high-resolution source models based on cortical meshes containing up to 8000 sources. Importantly, the sources within these meshes preserve inter-individual correspondences, allowing group-level analyses.

The HGA-MarsAtlas toolbox has been developed to enable automated extraction of the single-trial HGA by linking the results of anatomical analyses performed with BrainVisa and exploiting the functionalities of FieldTrip software for MEG analysis.

### Specifications

* ✓ Matlab

* ✓ parametrizable

* ✓ batch processing

* ✓ optimized processing speed

We ran the HGA-MarsAtlas pipeline on the dataset from a visuomotor task analyzed in [1, 2], giving the results shown here.

Thanks to the partners!

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MEG
MARSEILLE

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Institut de
Neurosciences des
Systèmes

## References

[1] Brovelli et al., 2015, Characterization of cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping × 10.1523/JNEUROSCI.4892-14.2015 [2] Brovelli et al., 2017, Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks × 10.1523/JNEUROSCI.1672-16.2017 [3] Auzias et al., 2016, MarsAtlas: A cortical parcellation atlas for functional mapping × 10.1002/hbm.23121 [4] Gross et al., 2001, Dynamic imaging of coherent sources: Studying neural interactions in the human brain × 10.1073/pnas.98.2.694 [BrainVISA: brainvisa.info] [FieldTrip: www.fieldtriptoolbox.org]

## High-resolution trial-averaged HGA

<figure><img src="image_5.png" /><figcaption>t= 0 s: participant's response (finger movement) to the visual stimulus</figcaption></figure>

## Single-trial source-level HGA

<figure><img src="image_6.png" /></figure>

## Trial-averaged HGA at parcel-level

<figure><img src="image_7.png" /><figcaption>Number of sources in parcel with Z > localFDR threshold</figcaption></figure>

<caption>Equivalent proportion of sources relative to the total number in parcel</caption>

## Single-trial HGA at parcel-level

<figure><img src="image_8.png" /></figure>

Beta version

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

## HGA-MA pipeline

### MarsAtlas

<figure><img src="image_12.png" /></figure>

-1

BrainVisa/FreeSurfer pipeline from individual MRI

* MarsAtlas parcellation (82 regions)

* cortical surfaces decimation (up to 8000 sources)

* ✓ correspondences across participants

### Initialization

0

* Parameter initialization, data importation (once):

* ✓ MRI, MA surface & volume from BV and FS databases

* ✓ pointers to MEG data in MEG database

### Preprocessing

1

### Anatomical data

1. Fiducial identification

* for coregistration [ft-manual]

2. Inner skull segmentation

* (conduction volume) [ft-SPM]

3. Sources preparation

* ✓ cortical: surface normals

* ✓ subcortical: k-mean in MA parcels

4. Coregistration check (Left/Right flip correction)

### MEG data

<figure><img src="image_13.png" /></figure>

* From a resampled data version (400 Hz)

1. Strong artefact detection

2. Bad channels

3. Artefact-linked ICA components

4. Bad trials [ft-browser]

5. Final review

* Data cleaning and backup

## Source imaging

### Forward model

<figure><img src="image_14.png" /></figure>

2

### Inverse modeling

### Power matrix
[sourcelabourcetrialtime]

1. Time-frequency analysis

* using multitapers [ft]

2. Dynamic Imaging of Coherent Sources beamformer

3. Baseline normalization (z-transform)

## Next steps

* Broad-band source-level MEG activity (LCMV beamformer)

* Cluster-based statistics to find spatio-temporal clusters of activation

* Functional connectivity dynamics

* Comparison across modalities (simultaneous MEG/SEEG)