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<p>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 <i>k</i> nearest-neighbour memberships. Inter-parcel crosstalk was minimized by assigning <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_90166_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="86" /> </InlineMediaObject> <EquationSource Format="TEX">\(k=20-30\)</EquationSource> </InlineEquation> 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.</p>

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Cortical parcellation optimized for magnetoencephalography with a clustering technique

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月21日

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  • Sara Sommariva,
  • Narayan Puthanmadam Subramaniyam,
  • Lauri Parkkonen

摘要

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.