ERG responses were examined with respect to the corresponding responses of thalamus and visual cortex, as reconstructed with MEG. We implemented a novel neuroimaging strategy, combining beamforming with the Hilbert transform to examine high-frequency response in bands ranging from 55 Hz to 145 Hz. The first cortical responses appear at 27 ms at ~115 Hz, lagging the corresponding retinal oscillatory potential by 8 ms.
Some studies suggest that the processing of dark stimuli may occur more quickly by taking advantage of greater neural resources in the visual system. In a second experiment, longer light pulses of about half a second were employed. In the cortex but not in the retina, high frequency responses occurred more quickly with transitions from light to dark compared to transitions from dark to light (Figure 1). Interestingly, while dark-to-light transitions involved a wide range of frequencies (55-195 Hz in the retina, and 55-145 Hz in the cortex), light-to-dark transitions were restricted to the 75-95 Hz frequency band in both retina and cortex (Westner et al., 2019).
We next investigated how perception of visual motion may begin in the retina. Experiments measured retinal and cortical responses to expanding and contracting annular gratings. Motion onset elicited low-frequency phase synchrony in the electroretinogram, demonstrating for the first time that human retinal oscillations respond to motion stimuli (Alves, 2022).
We demonstrated that OPMs can capture the same activity without even touching the participant (Figure 2), opening up retinal measurements to OPM-MEG labs that are rapidly proliferating with the potential to serve as an alternative diagnostic in eye clinic patients (Westner et al., 2021). In a subsequent investigation, we furthermore found that OPMs can also detect high-frequency oscillations in the retina up to 150 Hz (Lubell et al., in prep).
We have also made significant strides in contributing to open source software for MEG/EEG analysis, particularly MEG/EEG source reconstruction methods in MNE-Python, a rapidly growing open source toolbox (
https://mne.tools(öffnet in neuem Fenster)). This included several beamformer variants, including the Hilbert beamformer method developed in our group to reconstruct amplitude and phase information across frequency bands. We have found it is particularly well-suited to high gamma band responses (75-150 Hz).
We furthermore developed a new method for removing electrical interference from our MEG/EEG recordings (Leske & Dalal, 2019). We contributed our method to a leading open source toolbox, FieldTrip (fieldtriptoolbox.org) for the immediate benefit of the MEG/EEG community.
We also developed approaches to improve MEG/EEG acquisition, including design of a camera array and associated software for photogrammetric reconstruction of research participants’ heads together with MEG/EEG sensor positions (Clausner et al., 2017). This involved the construction of a geodesic dome that simultaneously photographs the head from multiple angles while the volunteer wears either an EEG cap or MEG fiducial markers. The software, Janus3D (
https://janus3d.github.io/janus3D_toolbox/(öffnet in neuem Fenster)) then derives the positions of the MEG/EEG sensors relative to the volunteer’s head and matches it to their MRI. This ultimately increases the accuracy of MEG/EEG source activity and its correspondence with an individual’s brain anatomy
