An Analysis of How Spatiotemporal Dynamic Models of Brain Activity Could Improve MEG/EEG Inverse Solutions

TL;DR

This paper demonstrates that incorporating brain connectivity and spatiotemporal dynamics into MEG/EEG inverse solutions can significantly enhance source localization accuracy, especially for deep cortical sources, by developing the dynamic lead field mapping.

Contribution

The paper introduces the concept of dynamic lead field mapping, showing how modeling brain dynamics can improve the number of recoverable sources in MEG/EEG analysis.

Findings

Source recovery could increase by up to 20 times.

Deep cortical sources benefit most from dynamic modeling.

Spatiotemporal models can dramatically improve source resolution.

Abstract

MEG and EEG are noninvasive functional neuroimaging techniques that provide recordings of brain activity with high temporal resolution, and thus provide a unique window to study fast time-scale neural dynamics in humans. However, the accuracy of brain activity estimates resulting from these data is limited mainly because 1) the number of sensors is much smaller than the number of sources, and 2) the low sensitivity of the recording device to deep or radially oriented sources. These factors limit the number of sources that can be recovered and bias estimates to superficial cortical areas, resulting in the need to include a priori information about the source activity. The question of how to specify this information and how it might lead to improved solutions remains a critical open problem. In this paper we show that the incorporation of knowledge about the brain's underlying connectivity and spatiotemporal dynamics could dramatically improve inverse solutions. To do this, we develop the concept of the \textit{dynamic lead field mapping}, which expresses how information about source activity at a given time is mapped not only to the immediate measurement, but to a time series of measurements. With this mapping we show that the number of source parameters that can be recovered could increase by up to a factor of ${\sim20}$, and that such improvement is primarily represented by deep cortical areas. Our result implies that future developments in MEG/EEG analysis that model spatialtemporal dynamics have the potential to dramatically increase source resolution.