Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines

Abstract

The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.

Keywords: MEG; beamforming; deep sources; deep structures; hippocampus; magnetoencephalography; neural oscillations; source localization; theta.

Figure 1

Schematic depictions of the location of the hippocampal formation relative to other brain structures (left panel) and transverse section of the medial temporal lobe with the main hippocampal subfields and parahippocampal cortex in a coronal view (right panel)

Figure 2

Simulated data of magnetic fields indicate that hippocampal volumes produce a signal that, while lower in magnitude than that of the neocortex, should nonetheless be robust enough for detection with MEG. These simulations take into account a number of variables including anatomical geometry, source‐to‐sensor gain matrix, and current dipole moment density (adapted from “Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: A MEG study,” by Attal & Schwartz, 2013, PLoS One, 8, e59856) [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

Selection process for review: Studies were screened to determine that basic requirements were met and then selected based on an independent quality assessment that used prespecified inclusion and exclusion criteria