Source Estimate

A plugin for Alakazam

Tools > 4. Frequency and Component Analysis > Source Estimate · averaged → averaged, with a stored source estimate · SourceEstimate

Inverts every bin of an averaged dataset onto a template cortical surface and stores the result on a node, so that a source cluster test (The same test in source space) and the 3D brain read it instead of computing it again. The inverse methods and their limits are described in Source estimation.

Figure 1: **Source Estimate**: the inverse method, the orientation, the cortical mesh, the time window, the stored sampling rate and the regularisation, with the disk space the node will take.

Table 1: Source Estimate’s options.

Option	Default	Meaning
Inverse method	dSPM	dSPM (Dale et al. 2000) or sLORETA (Pascual-Marqui 2002).
Orientation	Signed (cortical normal)	The dipole component along the cortical normal, keeping its sign, or the Magnitude of a free orientation.
Source space	20 484 vertices	The template cortical mesh: 20 484, 8 196 or 5 124 vertices.
Time window	the whole epoch	The samples stored.
Store at	200 Hz	The rate the estimate is stored at.
Noise covariance	From the baseline	What dSPM is normalised by: the baseline covariance Average stored, or White noise (identity) (The noise covariance). Only white noise is offered when the dataset carries no baseline covariance.
Signal-to-noise ratio	3	With the baseline covariance, sets the regularisation, λ = 1/SNR².
Regularisation	0.05	The Tikhonov regularisation with white noise, and for sLORETA.

A stored estimate is reused only when every one of these settings, the montage and the data themselves match what the later analysis needs, so they should be the same for every subject. The dialog shows the space the node will take, which for a fine mesh and a long window is much larger than a typical node.

Figure 2: The N400 difference at 400 ms as a dSPM estimate on the template cortex, drawn as a magnitude (the **Signed** box unticked). The fit line reports how much of the scalp data the estimate reproduces.

References

Dale, Anders M., Arthur K. Liu, Bruce R. Fischl, et al. 2000. ‘Dynamic Statistical Parametric Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity’. Neuron 26 (1): 55–67. https://doi.org/10.1016/S0896-6273(00)81138-1.

Pascual-Marqui, Roberto D. 2002. ‘Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA): Technical Details’. Methods and Findings in Experimental and Clinical Pharmacology 24 (Suppl D): 5–12.