Cortical parcellation optimized for magnetoencephalography with a clustering technique
Abstract A typical approach to estimate connectivity from magnetoencephalographic (MEG) data consists of 1) computing a cortically-constrained, distributed source estimate, 2) dividing the cortex into parcels according to an anatomical atlas, 3) combining the source time courses within each parcel,...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-90166-1 |
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| author | Sara Sommariva Narayan Puthanmadam Subramaniyam Lauri Parkkonen |
| author_facet | Sara Sommariva Narayan Puthanmadam Subramaniyam Lauri Parkkonen |
| author_sort | Sara Sommariva |
| collection | DOAJ |
| description | Abstract A typical approach to estimate connectivity from magnetoencephalographic (MEG) data consists of 1) computing a cortically-constrained, distributed source estimate, 2) dividing the cortex into parcels according to an anatomical atlas, 3) combining the source time courses within each parcel, and 4) computing a connectivity metric between these combined time courses. However, combining MEG signals to spatial mean activities of anatomically-defined parcels often leads to cancellation within and crosstalk between parcels. We present a method that divides the cortex into parcels whose activity can be faithfully represented by a single dipolar source while minimizing inter-parcel crosstalk. The method relies on unsupervised clustering of the MEG leadfields, also accounting for distances between the cortically-constrained sources to promote spatially contiguous parcels. The cluster each source point belongs to is determined by its k nearest-neighbour memberships. Inter-parcel crosstalk was minimized by assigning $$k=20-30$$ and a weight of 20%-40% to the spatial distances, leading to 60–120 parcels. Our approach, available through the Python package “megicparc”, enables a compact yet anatomically-informed source-level representation of MEG data with a similar dimensionality as in the original sensor-level data. Such representation should enable significant improvements in source-space visualization of MEG features or in estimating functional connectivity. |
| format | Article |
| id | doaj-art-a47c4a2cbb0344b8893235d4a47dad9e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a47c4a2cbb0344b8893235d4a47dad9e2025-08-20T02:15:10ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-90166-1Cortical parcellation optimized for magnetoencephalography with a clustering techniqueSara Sommariva0Narayan Puthanmadam Subramaniyam1Lauri Parkkonen2Department of Neuroscience and Biomedical Engineering, Aalto University School of ScienceDepartment of Neuroscience and Biomedical Engineering, Aalto University School of ScienceDepartment of Neuroscience and Biomedical Engineering, Aalto University School of ScienceAbstract A typical approach to estimate connectivity from magnetoencephalographic (MEG) data consists of 1) computing a cortically-constrained, distributed source estimate, 2) dividing the cortex into parcels according to an anatomical atlas, 3) combining the source time courses within each parcel, and 4) computing a connectivity metric between these combined time courses. However, combining MEG signals to spatial mean activities of anatomically-defined parcels often leads to cancellation within and crosstalk between parcels. We present a method that divides the cortex into parcels whose activity can be faithfully represented by a single dipolar source while minimizing inter-parcel crosstalk. The method relies on unsupervised clustering of the MEG leadfields, also accounting for distances between the cortically-constrained sources to promote spatially contiguous parcels. The cluster each source point belongs to is determined by its k nearest-neighbour memberships. Inter-parcel crosstalk was minimized by assigning $$k=20-30$$ and a weight of 20%-40% to the spatial distances, leading to 60–120 parcels. Our approach, available through the Python package “megicparc”, enables a compact yet anatomically-informed source-level representation of MEG data with a similar dimensionality as in the original sensor-level data. Such representation should enable significant improvements in source-space visualization of MEG features or in estimating functional connectivity.https://doi.org/10.1038/s41598-025-90166-1 |
| spellingShingle | Sara Sommariva Narayan Puthanmadam Subramaniyam Lauri Parkkonen Cortical parcellation optimized for magnetoencephalography with a clustering technique Scientific Reports |
| title | Cortical parcellation optimized for magnetoencephalography with a clustering technique |
| title_full | Cortical parcellation optimized for magnetoencephalography with a clustering technique |
| title_fullStr | Cortical parcellation optimized for magnetoencephalography with a clustering technique |
| title_full_unstemmed | Cortical parcellation optimized for magnetoencephalography with a clustering technique |
| title_short | Cortical parcellation optimized for magnetoencephalography with a clustering technique |
| title_sort | cortical parcellation optimized for magnetoencephalography with a clustering technique |
| url | https://doi.org/10.1038/s41598-025-90166-1 |
| work_keys_str_mv | AT sarasommariva corticalparcellationoptimizedformagnetoencephalographywithaclusteringtechnique AT narayanputhanmadamsubramaniyam corticalparcellationoptimizedformagnetoencephalographywithaclusteringtechnique AT lauriparkkonen corticalparcellationoptimizedformagnetoencephalographywithaclusteringtechnique |