Improved spatio-temporal measurements of visually evoked fields using optically-pumped magnetometers

Abstract

Recent developments in performance and practicality of optically-pumped magnetometers (OPMs) have enabled new capabilities in non-invasive brain function mapping through magnetoencephalography. In particular, the lack of cryogenic operating conditions allows for more flexible placement of sensor heads closer to the brain, leading to improved spatial resolution and source localisation capabilities. Through recording visually evoked brain fields (VEFs), we demonstrate that the closer sensor proximity can be exploited to improve temporal resolution. We use OPMs, and superconducting quantum interference devices (SQUIDs) for reference, to measure brain responses to flash and pattern reversal stimuli. We find highly reproducible signals with consistency across multiple participants, stimulus paradigms and sensor modalities. The temporal resolution advantage of OPMs is manifest in a twofold improvement, compared to SQUIDs. The capability for improved spatio-temporal signal tracing is illustrated by simultaneous vector recordings of VEFs in the primary and associative visual cortex, where a time lag on the order of 10–20 ms is consistently found. This paves the way for further spatio-temporal studies of neurophysiological signal tracking in visual stimulus processing, and other brain responses, with potentially far-reaching consequences for time-critical mapping of functionality in healthy and pathological brains.

Full text

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Improved spatio‑temporal
measurements of visually evoked
elds using optically‑pumped
magnetometers
Aikaterini Gialopsou1,2*, Christopher Abel1, T. M. James1, Thomas Coussens1,
Mark G. Bason1, Reuben Puddy1, Francesco Di Lorenzo2, Katharina Rolfs3, Jens Voigt3,
Tilmann Sander3, Mara Cercignani2,4 & Peter Krüger1
Recent developments in performance and practicality of optically‑pumped magnetometers
(OPMs) have enabled new capabilities in non‑invasive brain function mapping through
magnetoencephalography. In particular, the lack of cryogenic operating conditions allows for more
exible placement of sensor heads closer to the brain, leading to improved spatial resolution and
source localisation capabilities. Through recording visually evoked brain elds (VEFs), we demonstrate
that the closer sensor proximity can be exploited to improve temporal resolution. We use OPMs, and
superconducting quantum interference devices (SQUIDs) for reference, to measure brain responses to
ash and pattern reversal stimuli. We nd highly reproducible signals with consistency across multiple
participants, stimulus paradigms and sensor modalities. The temporal resolution advantage of OPMs
is manifest in a twofold improvement, compared to SQUIDs. The capability for improved spatio‑
temporal signal tracing is illustrated by simultaneous vector recordings of VEFs in the primary and
associative visual cortex, where a time lag on the order of 10–20 ms is consistently found. This paves
the way for further spatio‑temporal studies of neurophysiological signal tracking in visual stimulus
processing, and other brain responses, with potentially far‑reaching consequences for time‑critical
mapping of functionality in healthy and pathological brains.
Over the last century, outstanding advances in medical physics have led to the development of non-invasive
functional neuroimaging techniques1–3. is has provided signicant insights into brain function and connec-
tivity. Important improvements in modern neuroimaging techniques have allowed neural patterns associated
with specic stimulations to be investigated4, providing information about the signal’s spatial and temporal
characteristics5. Previous studies have shown that a spatio-temporal analysis of brain signals is not only essential
to understand the basic mechanisms of brain circuits, but would also provide reliable biomarkers for dieren-
tiating physiological and pathological brain activity in neurodegenerative diseases6,7. ere is even a potential
for predicting clinical progression or treatment responses8. e realisation of the full scope of temporal and
spatial localisation of brain signals, however, is hampered by the intrinsically low spatio-temporal resolution of
currently available methods9,10.
Functional Magnetic Resonance Imaging is capable of mapping activated brain regions with high spatial
resolution, but oers only low temporal resolutions (
∼1s
), as the local measured changes in blood ow are not
synchronized with neuronal activity11. Electroencephalography (EEG) is a real-time neuroimaging method, with
limited source localisation capability and spatial resolution (
∼10 mm
)12.
Magnetoencephalography (MEG) is an alternative real-time method with a theoretically possible improved
spatial resolution, able to measure postsynaptic potentials of tangential pyramidal cells at the surface of the
scalp12. Recent research has shown that MEG can be used for the evaluation of abnormal cortical signals in
patients with Alzheimer’s disease13, Parkinson’s disease14, autism spectrum disorder15, and in severe cases of post-
traumatic stress disorder16. However, MEG suers from low signal-to-noise ratio (SnR), and its use is conned to
OPEN
1Department of Physics and Astronomy, University of Sussex, Falmer, Brighton BN1 9HQ, UK. 2Clinical Imaging
Sciences Centre, University of Sussex, Falmer, Brighton BN1 9PH, UK. 3Physikalisch Technische Bundesanstalt,
10587 Berlin, Germany. 4Cardi University Brain Research Imaging Centre (CUBRIC), Cardi University,
Cardi CF24 4HQ, UK. *email: A.Gialopsou@sussex.ac.uk
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magnetically-shielded rooms (MSRs). e magnetically shielded environments are used to subdue environmental
magnetic noise, oen many orders of magnitude higher than neuromagnetic elds (fT to pT range).
Traditionally, MEG relies on an array of superconductive quantum interference devices (SQUIDs) to measure
the brain’s magnetic elds17.
With the sensor array being xed inside a required cryogenic dewar, the locations of the individual sensors
must be arranged to t a vast majority of head sizes and shapes18. e xed positions result in dierent radial
osets from a subject’s head. Coupled with tiny head movements from a subject during a measurement, the
osets and xed positions have a major impact on the potential cortical activity detection19. In particular, the
theoretically achievable precision of signal source localisation is lost. is makes SQUID-MEG impractical in
many cases, in particular in the clinical context.
Extremely sensitive spin-exchange relaxation-free (SERF) optically-pumped magnetometers (OPMs), devel-
oped at the turn of the millennium20, can help to overcome the SQUID-MEG limited spatial resolution21. With
the OPMs able to be xed to a subject’s head22, a smaller oset distance than SQUIDS, and the ability for simul-
taneous dual axis measurements, OPM-MEG has several advantages over SQUID-MEG, including its suitability
for applications within pediatric and clinical populations.
e aim of this study was to demonstrate the improved ability of OPM-MEG by recording spatio-temporal
characteristics of neurophysiological signals, and comparing them to conventional SQUID-MEG. As a prototypi-
cal test case we have chosen visual cortex responses to established standard visual stimulations, with the measured
responses evaluated in a well-characterised context. We nd that OPM-MEG is superior to SQUID-MEG in
brain signal tracking in space and time,making a suitable method to provide new information about propagating
signals, source localisation, neural speed, and brain circuits far beyond the processing of visual stimuli.
Theory considerations
Precisely determining the time onset of magnetic sources is equally important as localising their position when
tracking neural activity throughout the brain. e extent to which it is possible to distinguish both location and
timing of neurophysiological events is referred to as spatio-temporal resolution. Here we argue that OPM-MEG
can be advantageous in both necessary aspects, i.e. in terms of spatial and temporal resolution.
Spatial resolution. A general denition for spatial resolution in MEG is the minimum separation required
for two magnetic sources to be resolved. As the magnetic eld amplitude decays according to a power law with
the distance from a eld source, improved signal detection is achieved when sensors are moved closer to the
brain. e consequences of the eld decay law are that closer positioning of a sensor system provides improved
signal-to-noise, better spatial resolution and more precise source localisation, as shown formally for a generic
situation23 and conrmed through realistic brain anatomy simulations24,25. Quantifying this improvement is
only possible in specic situations where source distances and their magnetic eld characteristics (multipole
expansion) are known. In general, when applying the Rayleigh criterion for resolution, the maximum distance
at which two sources can be resolved is comparable to the distance between the two sources23. As OPMs can be
placed closer to the head than SQUID systems, the OPMs are able to achieve a higher spatial resolution.
Temporal resolution. We dene temporal resolution of an OPM-MEG system as the minimum time inter-
val between two neurophysiological events in the brain to be detected as distinct from each other. Note that this
is dierent from a sensor-level denition that would for example be based on the bandwidth of the device or the
sample frequency of the data acquisition system (although these measures can aect the temporal resolution of
the MEG system). A given detected event is associated with a magnetic eld pulse shape whose measured tem-
poral width, amplitude and signal uncertainty will determine the temporal resolution by the above denition.
e uncertainty of a typical response to a given brain stimulation in MEG is determined through measure-
ments of a series of pulses, commonly averaged over many trials. is averaging over uncorrelated measure-
ments enables association of a statistical standard error with the mean signal determined at each point in time.
A practical quantitative denition of temporal resolution is then the time that passes aer a characteristic feature
(typically a peak) before the signal signicantly diers from its value at that characteristic feature.
As a simple and typical example, consider a pulse with a Gaussian shape g(t):
where A is the amplitude of the pulse,
t0
is the time when the pulse maximum occurs,
2σ
is the width of the
pulse. e uncertainty of the signal is the standard error
ε
, assumed to be time-independent in this example. e
temporal resolution
tres
for this signal shape is then the time interval between the peak time
t0
and the time (aer
or before)
t0
by which the signal is signicantly, i.e. by an amount
ε
, smaller than the peak amplitude A, so that
We can solve for
tres
nding,
Note this only holds with
𝜀∕A<1
. e inverse ratio can be interpreted as the signal to noise ratio
SnR
=
A/ε
(with
SnR >1
). In rst order Taylor expansion (in the logarithmic term) Eq. (3) simplies to
(1)
g
(
t
)=
Ae
(t−t0)
2
2𝜎2
,
(2)
g(t0)=g(t0±tres)+𝜀
(3)
t
res =𝜎
√
−2 ln
(
1−
𝜀
A).
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is scaling of
tres ∝w∕√
SnR holds also for more general (not necessarily Gaussian) pulse shapes with width
w. For MEG signals comprising of multiple non-Gaussian pulses it is not always possible to achieve accurate
(Gaussian or other functional curve) ts. Within the work presented here, we hence dene the width w as the
time between the two local minima adjacent to a pulse’s maximum signal value, and the amplitude as the dier-
ence between the maximum and the mean of those two minima (see inset of Fig.2).
A consequence of the above scaling is that a measurement method that increases the measured amplitude of
a pulse whilst retaining a similar width and standard error, will improve the temporal resolution. OPMs have
recently been shown to approach noise oors similar to that of SQUIDs26,27, therefore similar values of
ε
can
be assumed for both technologies. e gure of merit for improving time resolution of MEG then becomes
𝜂=√A∕w
with higher values of
η
corresponding to better time resolution, i.e. shorter
tres
. OPMs due to their
closer proximity to the brain sources measure larger amplitude signals and are hence expected to achieve better
values of
η
than SQUIDs.
Vectoral measurements. In conventional MEG only one component of the vectorial magnetic eld is
measured. Most commercial setups for SQUID-MEG only measure magnetic eld gradients radial to the brain.
At the typical stand-o distances of several centimetres, the orthogonal components tend to be weak, so that the
radial eld (gradient) component approximates the total eld (gradient). With closer sensor proximity to the
brain, OPMs are able to measure multiple eld (gradient) components to extract additional spatial information28.
A vector measurement taken at short distances does not suer from the zone of a vanishing eld component in
the immediate vicinity of a current dipole, and is sensitive to volume currents in the brain.
Measuring both radial and tangential eld components also helps to improve signal temporal resolution. is
is a consequence of the ability to characterise the eld as a vector. At the sensor, the magnetic eld has a direction
and magnitude. A radial sensor measures the magnetic eld projected onto the radial direction. By measuring
in only the radial direction it is not possible to dierentiate between a rotation or a change in magnitude of the
magnetic eld vector. Worse still, if the magnetic eld vector simultaneously changes in both direction and
magnitude, then the time at which the magnetic eld reaches peak magnitude can be obscured. By measuring
a second component of the magnetic eld we can begin to dierentiate between a change in the magnitude of
the magnetic eld, and a change in magnetic eld direction. Sensors near the head are in a source-free region
(
J=0
) , therefore using Ampère’s Law
∇×B=
𝜇
0J=0
the third magnetic eld component can be calculated
from the other two magnetic eld components assuming the gradient of the magnetic eld can be calculated.
For a system with a low sensor count, all three magnetic eld components need to be measured to achieve full
characterisation of the magnetic eld.
Materials and methods
Participants and MRI. Visual evoked elds were studied in 3 healthy participants (2 men aged 26 and 30,
1 woman aged 47 years) with normal or corrected-to-normal vision. e 3 participants received a 3 T MRI scan
(Siemens Magnetom Prisma, Siemens Healthineers, Erlangen, Germany) at the University of Sussex, including
a high-resolution T1-weighted anatomical scan. For one participant a diusion-weighted scan was acquired for
reconstructing the optic radiations, with two diusion-weighting shells (b values = 1000 and 3000s/
mm2
). For
each b value, diusion gradients were applied along 60 non-collinear directions. Six images with no diusion
weighting (b = 0) were also collected. Image processing was performed using tools from the FMRIB’s Diusion
Toolbox5.0. First, data were corrected for involuntary motion and eddy currents using ane registration. BED-
POSTx was run with default settings to t a crossing bers model29, and nally, XTRACT was used to automati-
cally reconstruct the le and right optic radiations in native space by probabilistic tractography30. e results are
shown in Fig.1d,f.
Experimental design. e study was conducted in accordance with the Declaration of Helsinki Ethical
Principles, and was approved by the Brighton and Sussex Medical School Research Governance and Ethics Com-
mittee (ER/BSMS3100/1); all participants gave written informed consent to take part, aer explanation of the
procedure and purpose of the experiment. All MEG measurements, OPM-MEG and SQUID-MEG, were taken
in the Ak3b MSR (Vacuumschmelze, Hanau, Germany) at Physikalisch- Technische Bundesanstalt (PTB), Ber-
lin. e MSR is equipped with an external triaxial active shielding coil system controlled by uxgates. Inside the
MSR eld uctuations are suciently weak to allow OPM operation31,32.
Two standard full-eld visual stimulation protocols were employed during the MEG recording, a ash stimu-
lus (FS), and a pattern reversal stimulus (PR). e parameters used were based on standard guidelines for clini-
cally evoked potentials33. ese paradigms are widely used to evaluate early visual processing, and to detect
abnormalities in the visual pathways. e ash stimulus, shown in Fig.1a, consists of short white ashes of length
0.08 s (5 frames). To avoid participants from preempting the stimulus, each white ash was followed by a dark
period with the length varying pseudo-randomly between 0.92 s and 1.00 s (55 to 60 frames). e total duration
of a single FS measurement run was 300 s.
e pattern-reversal stimulus, Fig.1b, consisted of a black and white checkerboard (10 squares wide, 8 high)
with the colours inverting at 0.5 s (30 frame) intervals. Each run had a duration of 280 s. For both FS and PR, a
red dot was continuously projected onto the centre of the screen to act as a focal point for the participant. Before
each measurement run, whilst in position for the trial, the participants were exposed to a three-minute dark adap-
tation period. Measurements of the empty MSR were obtained in order to evaluate environmental noise levels.
(4)
t
res =𝜎
√
2𝜀
A.
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During the noise measurements, the OPMs were located in the same position and orientation as they would with
a participant wearing the sensors. During measurements, participants sat upright with the sensors mounted in a
3D-printed helmet (Fig.1c). A chin rest was used to help stabilise each subject’s head, reducing movement when
looking forward at a 50 cm
×
34 cm vertically orientated screen. e stimuli were projected on to the screen via a
mirror system using a 60 Hz LCD projector, positioned outside of the MSR. e SQUID-MEG system (Fig.1e)
accommodated participants in a horizontal position, with the same screen positioned horizontally above the
subject. e screen to eye distance was 53 cm for the OPM-MEG setup and 45 cm for the SQUID-MEG system.
MEG systems overview
OPM‑MEG. e OPM-MEG system consisted of two second-generation QuSpin zero-eld magnetometers
(QuSpin Inc., Louisville, CO, USA), with a specied typical sensitivity
<
15 fT
∕
Hz
1∕2
and magnetic eld meas-
urement bandwidth of 135 Hz in a 12.4 mm
×
16.6 mm
×
24.4 mm sensor head. e OPMs were mounted in
a 3D printed helmet (open-source design; OpenBCI Mark IV helmet) (https:// github. com/ OpenB CI/ Ultra cor-
tex/ tree/ master/ Mark_ IV/ MarkIV- FINAL) and positioned over the visual cortex at the Oz and POz positions,
according to the standard 10–10 system34. ese locations were chosen in accordance with each subject’s MRI
scan.
e Oz and POz positions correspond to the primary visual cortex (V1) and the associative visual cortex
(V2), respectively. Studies have shown the feed-forward and feedback interaction between the V1 and V2 areas
in response to visual stimulation35. More specically, there is an early activation at V1, known as the P1 or C1
component, which is then suppressed as the signal propagates to V2, aer which a reected wave is initiated
and propagates back to V136.
e design of the helmet and sensor head xes the scalp to sensor distance to
∼5 mm
. Python-based soware
was developed for the design and presentation of stimuli. e soware was directly connected and synchro-
nized with a main OPM-MEG data acquisition system (DAQ). e OPM-MEG system’s analogue outputs were
recorded at 1 kHz via a Labjack T7 pro (Labjack Corporation, Co, USA). All DAQ electronics, except the OPM
sensor-heads, were located outside the MSR and directly connected to the Labjack and control computer.
SQUID‑MEG. e SQUID-MEG system MEGvision (Yokogawa Electric Corporation, Japan) comprises of
125 axial gradiometers and 3 reference magnetometers. For the stimuli presentation the same soware was
used to prevent any bias in the stimulation delivery. Data from all sensors were recorded at a 2 kHz sampling
frequency, and the sensors located closest to the OPM positions were used for analysis. e xed positions of the
sensors result in a
∼50 mm
stando from the subject’s scalp. e SQUID-MEG used MEG Laboratory 2.004C
(Eagle Technology Corporation) data acquisition soware.
Figure1. (a) Flash and (b) pattern reversal stimulation protocols. (c) A participant in position with the 3D
printed helmet containing the OPM devices. Red highlighted cells show the sensor locations used for the study,
the Oz and POz. e sensors were placed over the primary visual cortex and the associative visual cortex,
respectively. (d) 3D rendering of the MRI scan of Participant 1 showing approximate locations of OPM sensors
1 & 2, and scalp-sensor separation of around 5 mm. e reconstructed optic radiation are also shown in red.
(e) e Yokogawa SQUID-MEG system. (f) Schematic of the SQUID-MEG system showing a sensor to scalp
separation of approximately 50 mm.
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Data analysis. e DAQ systems were synchronised with the presentation soware. Both OPM and SQUID
data analyses were performed using the FieldTrip toolbox37 and MATLAB. In order to isolate the frequencies of
interest with relevance in visually evoked elds, all data were ltered with a bandpass lter between 5 and 60 Hz.
A further bandstop lter was applied between 49 and 51 Hz to suppress 50 Hz line-noise. e epoched trials for
FS were
−45 ms
to 350 ms, and 0–250 ms for PR. Any trials with interrupted recordings were removed from the
analysis. All the time-locked averaged responses contain more than 380 trials for the FS stimulation, and more
than 280 for PR.
In the following sections the evoked elds are shown as the mean across all individual trials for a single run.
e uncertainties on the signal amplitudes are calculated as the standard error at each time point (with a 1 ms
time spacing for OPM-MEG and 0.5 ms for SQUID-MEG).
e resulting uncertainty band is then used to determine temporal uncertainties of signal features such as
amplitude peaks. e time error is set as the width of the uncertainty band at the amplitude feature, as outlined
in “Temporal resolution” section.
In order to compare the spatio-temporal response of OPM-MEG to SQUID-MEG we initially study the
temporal resolution of the two systems by measuring
η
in characteristic evoked magnetic eld peaks as dened
at the the theoretical considerations “Temporal resolution” section. e
η
uncertainty results as error propagated
from time and signal uncertainties, determined in the above described manner.
In a second step, the evoked potentials as measured at the two sensor locations are then compared. For the
OPM system, the simultaneously obtained individual eld component (radial
Bz
and axial
By
) data are further
compared to the resulting planar projection
Byz
, with
|
Byz
|
=
√
B2
y+B2
z . is reduces timing artefacts that can
occur in data restricted to a single component.
VEFs are characterized by three time components occurring at dierent times: the early component (P1), the
main component (P2 for ash stimuli and P100 for pattern reversal stimuli), and the late component (P3), where
we established the onset range for the main and late components based on previous studies10,38–42. e ash and
pattern reversal stimulus responses consist of an early component with peak onset between 35 and 60 ms, a main
component (P2) between 83 and 152 ms, and a late component (P3) between 160 and 230 ms.
Each participant had at least four FS runs and three PR runs with the OPM-MEG system, and a single run
for each stimuli with the SQUID-MEG.
e averaged responses were determined using the same method as detailed above.
Results
In total, 30 OPM-MEG runs were undertaken, with 12 FS, 9 PR, and 9 background runs. In addition, a single
FS and PR run was conducted with SQUID-MEG for each participant. e VEFs from all participants and all
modalities were consistent with patterns known from the literature.
We consider the
η
of the two systems and the higher SnR of OPM-MEG observed in other studies22,24 com-
pared to SQUID-MEG. Figure2 shows measurements from a single OPM sensor and the corresponding SQUID
sensor for FS. e OPM-MEG recorded signals with up to 4 times higher amplitude than the SQUID-MEG, with
the OPM and SQUID sensors recording a maximum amplitude of 480(46) fT and 126(4) fT, respectively. Along
with the increase in amplitude over the SQUIDs, we see the same activation patterns in both methods, further
Figure2. Averaged evoked eld recorded by OPM-MEG and SQUID-MEG for Participant 1 for ash
stimulation over a single measurement run (300 trials). VEF measured at Oz using OPM sensor (blue line) and
the corresponding SQUID sensor (red line).e shaded area shows the standard error. Inset: e signal height
or A (red line) is the amplitude dierence between the peak maximum and the mean of the two local minima
(blue line). e width or w is the time dierence between the two local minima (dashed lines). e
η
is the ratio
√
A
∕
w of two values.
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verifying the OPM’s recorded traces. e OPM
η
was found to be
0.5
(6)
√
fT∕
ms
, compared to the SQUID
η
of
0.25
(2)
√
fT∕
ms
. In spite of having a non-optimal noise oor at our OPM DAQ system, in our case the higher
η
still indicates a higher temporal resolution of the OPM-MEG neuroimaging system. e OPM VEF shows more
pronounced peaks, resulting in sharply dened maxima and minima, resulting in lower temporal uncertainties
compared to SQUID-MEG measurements.
In Fig.3 we plot all participant 1 PR and FS runs recorded by OPM-MEG at Oz, along with their average. e
individual runs illustrate the reproducibility of the activation patterns during both stimulations, with the main
(P2 or P100) and late components (P3) having similar time onsets across all runs. For FS, the main component
(P2) has an onset time between 135 ms and 100 ms and the late component (P3) between 180 ms and 190 ms.
For PR, the main component P100 occurs between 128 ms and 133 ms, while the late component (P3) occurs
between 210 ms and 214 ms.
Visually evoked responses recorded for FS and PR had similar activation patterns and onset delays across all
runs for each participant(Table1). Participant 1 displays a similar activation pattern for all the FS and PR runs.
Participant 2 had well dened and reproducible FS responses, while the PR responses showed slightly more
variation. Participant 3 displayed similar activation patterns for both FS and PR. Anatomical dierences of the
cortical surfaces of each participant could be the origin of the small variability of the evoked responses between
the participants and the inverse correlation (see Table2) between participant 2, participant 3 and participant
1. All participants had VEF with similar onset patterns and dierences for the signal times at the POz and Oz
sensor locations.
In order to quantify the within-participant reproducibility of VEFs qualitatively observed in Fig.3, we cal-
culate the Pearson correlation coecients between runs per participant 1. e correlation coecients for the
ash stimulus and pattern reversal were calculated to be 0.83 and 0.85 for participant 1, respectively, and 0.70
and 0.24 for participant 2, and 0.65 and 0.54 for participant 3.
In addition to this, we then calculate the correlation coecient between subjects for the same stimuli (Table2).
Moderate between-subject correlation coecients were found for participants 2 and 3, while Participant 1 showed
anti-correlated signal at both sensors. e dierent cortical folding of each participant could explain the anti-
correlation measured between the VEFs. Previous studies have shown an asymmetry in extracranial magnetic
eld measurements due to variabilities in cortical folding43,44.
Figure3. OPM VEF for (a) four FS runs and the associated mean. (b) ree PR runs along with the mean for
Participant 1. e individual runs (black) for both FS and PR show the same activation pattern as the associated
mean (red). e shaded area displays the standard error of the mean.
Table 1. Pearson correlation coecient across 4 FS runs and 3 PR runs for each participant. e bracketed
values are the standard error.
Stimulation Flash stimulus Pattern reversal
Participant 1 0.83 (4) 0.85 (2)
Participant 2 0.70 (7) 0.24 (8)
Participant 3 0.56 (3) 0.54 (6)
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Figure4 displays a single run recorded by OPM-MEG (a) and SQUID-MEG (b) systems during FS. Although
OPM-MEG shows the initial activation (P1) at the primary visual cortex, there is a signicant time dierence
between the arrival of the signal at POz and Oz for both the main and late components, with an earlier activa-
tion at POz. e vertical purple bands represent the time range of P2 and P3 components found in previous
studies for Oz EEG sensors9,10,38–42. e dominant peaks that fall within these boundaries are shown by bold
dashed lines, representing the peak times of the main (P2) and late (P3) components.
Δ𝜏1
and
Δ𝜏2
, dened as
the delay between signals arriving at POz and Oz for the main and late components, were measured as 10(7) ms
and 20(4) ms, respectively.
e earlier activation of POz compared to Oz for Participant 1 was observed in all four runs, with
Δ𝜏1=
8(1)
ms and
Δ𝜏2=
18(1) ms (see Fig.6).
e reproducibility of the time delay, and the small variations in
Δ𝜏1
and
Δ𝜏2
over multiple measurement runs
points to a neurophysiological origin of the delay, such as a timing dierence of signals arriving at dierent loca-
tions within the visual cortex. While the absolute timings dier from those determined with the OPMs, similar
time delays are also found for the SQUID-MEG measurement, where we nd
Δ𝜏1=
2(5) ms and
Δ𝜏2=
18(3)
ms (Fig.4b). e dierence in absolute timings between OPMs and SQUIDs is not unexpected, as the precise
positioning of the sensors dier both laterally and by distance from the scalp. Furthermore, the orientation of the
sensitive axis for the SQUIDs is not fully aligned with the radial component measured by the OPMs. Although
we recorded a higher SnR for SQUID, over OPM measurements, the timing uncertainties for
Δ
𝜏
1,2
are similar
in both modalities due the improved
η
achieved with OPMs.
We observe the same activation delay for the pattern reversal stimulation for participant 1 (Fig.5). e time
delays were measured as
Δ𝜏1PR =
28(16) ms and
Δ𝜏2PR =
45(8) ms. Although
Δ𝜏1
and
Δ𝜏2
are observed in all the
evoked responses during both FS and PR, the time delays during PR stimulation are signicantly longer than
the FS. Similar activation pattern and delays are observed across all the PR runs. SQUID-MEG recorded similar
time delays for the PR stimulus, with the
Δ𝜏1PR
as 30(6) and
Δ𝜏2PR
as 57(5). e observed activation patterns
were shown to be reproducible across all runs (Fig.6) and stimuli (Fig.4), with activation of POz before Oz
detected in all participants.
Compared to SQUIDs, the additional feature of OPM-MEG to simultaneously measure components along two
axes, in this case y and z, can be used to further support the neurophysiological origins of the delay phenomenon,
such as those observed here for the signal’s main and late components. Figure7 shows the two magnetic eld
Table 2. Pearson correlation coecient between participants for Flash and Pattern reversal stimulation
recorded at the Oz sensor. e bracketed values give the 95% condence interval.
Participants/stimuli Flash stimulation Pattern reversal
Participant 1–Participant 2 −0.53 (7) −0.45(9)
Participant 1–Participant 3 −0.54 (7) 0.49 (9)
Participant 2–Participant 3 0.38 (8) −0.35 (11)
Figure4. Visually evoked response during ash stimulation recorded by: (a) OPM-MEG and (b) SQUID-
MEG for Participant 1. e coloured areas indicate the limits where the peak onset for Oz is expected for each
stimulus10,38–42. e selected peaks for Oz (red) and POz (blue) sensors are marked with dashed lines for both
components P2 and P3.
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components
By
and
Bz
simultaneously measured by two OPMs, along with each sensor’s magnitude projected
in the yz-plane
|Byz |
.
In Fig.7a we show OPM-MEG
By
and
Bz
FS responses recorded simultaneously from a single run at POz and
Oz.
Bz
shows a VEF with higher amplitudes and more clearly discernable peak structure than that recorded by
By
.
In Fig.7b we show
|Byz |
. e characteristic components of the VEF recorded in the vector components persists,
including the timings and relative time delays of the main VEF features (previously negative peaks are now posi-
tive as the yz-plane projection is displayed as the modulus). Our result of a signicant and reproducible time delay
between signals arriving at POz and Oz (Figs.4 and 7) is consistently observed across participants and stimuli.
Discussion
In this study we used VEFs to assess and demonstrate the ability of MEG based on two types of highly sensitive
magnetometers (OPMs and SQUIDs) to detect neurophysiogical brain signals with simultaneously high spatial
and temporal resolution. We nd that both sensor modalities are suitable to reproduce characteristic brain sig-
natures known from well-established neurophysiological research and clinical practice.
Figure5. Visually evoked response during pattern reversal stimulation recorded by: (a) OPM-MEG and (b)
SQUID-MEG for Participant 1. e coloured areas indicate the limits where the peak onset for Oz is expected
for each stimulus10,38–42. e selected peaks for Oz (red) and POz (blue) sensors are marked with dashed lines for
both components P2 and P3. e selected peaks for Oz (red) and POz (blue) sensors are marked with dashed
lines for both components P2 and P3.
Figure6. OPM VEF between the POz and Oz sensor for four ash stimulation runs for Participant 1. All
four runs show similar peak onset and amplitude. e time lag between the POz (blue) and the Oz (red) OPM
sensors is consistent for the two components across the runs.
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e ability to track local brain responses in space and time can be quantied by determining the time interval
over which a signal rises and falls. We nd that the OPM
η
has a twofold increase over SQUID measurements,
conrming the expectation of the closer proximity of the OPMs to the visual cortex having such an eect.
Importantly, we were able to conrm that the OPM-MEG measurements are robust. Repeating the experiment
with two dierent visual stimuli (ash stimulus and a checker board pattern reversal stimulus) and with three
dierent participants, we observed good reproducibility over multiple repeated runs within each subject and each
stimulus. Dierences between subjects and type of stimulus are discernable, but the key signal characteristics
remain. Individual cortical folding variations could lead to dierent cancellation of the extracranial magnetic
eld43,44 which reects the asymmetry of the VEF and the anti-correlation between some of the participants’
responses.
Finally, we illustrate that OPM-MEG is capable of recording neurophysiological signals of a common origin
at dierent locations at dierent times. By measuring the arrival times of characteristic VEFs at two distinct
locations within the visual cortex. e temporal resolution is suciently high to determine signicant time dier-
ences between the primary visual cortex (Oz) and the associative visual cortex (POz). We measured a consistent
delayed response at the Oz position relative to the POz position on the order of 10 to 20 ms for the second (P2/
P100) and third (P3) components. is observation is again highly reproducible for dierent runs and is similar
across participants and both types of stimuli. It is conrmed by corresponding SQUID measurements. e time
delay uncertainties of the OPM data are comparable and even slightly lower than their SQUID counterparts. We
attribute this to the strongly enhanced
η
featured by the OPMs.
In order to verify the neuronal nature of the measured time delay, we analysed the recordings along the
OPM’s orthogonal y and z axes. Although we have already demonstrated that our analysis of
Bz
results in an
earlier activation of POz, the inclusion of a second axis, for which we now measure
|Byz |
, follows the same trend.
We can indicate that the observed activation pattern is more likely to be from neural activity than an artifact of
limited information. e future addition of a third orthogonal axis, to complement our two axis system, will be
benecial in order to fully validate the activation source observed. As we have demonstrated the reproducibility
of VEFs in separate runs, sometimes recorded over dierent days, acquiring three dimensional recordings by
rotating the OPM-MEG sensors between runs could be used in future experiments.
Although the VEFs are well dened in humans41, the spatio-temporal pattern of the propagating signal is
not well characterized. Studies have previously revealed the interaction of the primary visual cortex (V1) with
associative visual areas (V2, V3) using an invasive cortical feedback system in animal models35,36. e hierarchical
order and spatio-temporal processing of the signal in humans remains uncertain. Some studies have claimed P1
originates from the primary visual cortex10,45, while others indicate it originates from the extrastriate cortex38,46.
Additionally, the P2/P100 component appears to originate from the extrastriate cortex without a denite region10.
e widespread sensor positioning of electrodes or SQUIDs combined with the low spatio-temporal resolution
may not be able to record coincident responses from close cortical sources. Here, we introduce the OPM-MEG
system as a non-invasive investigational tool, with the potential to further detail and explore the structural and
functional connectivity of neighbouring cortical areas, with a higher spatio-temporal resolution than currently
available. Our initial experiments are consistent with the ndings in animal models35,36 being applicable also to
the human brain.
e benets of OPM-MEG could be important both at research and clinical levels: its higher spatio-temporal
resolution would allow to better investigate neural networks, shedding light on the relationships between the
connectivity of functionally related brain areas, along with their frequency synchronization. Moreover, this
Figure7. FS VEF recorded at Oz (blue) and POz (red) using OPM-MEG. (a) e averaged Oz and POz
response along the z (bold) and y (dashed) directions. (b) e Oz and POz magnitude projected into the y-z
plane. e bold black lines indicate the earlier activation of POz followed by the Oz.
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advancement could be applied in clinical populations at dierent stages, such as those with Alzheimer’s disease.
In patients with mild cognitive symptoms, topographical biomarkers based on the analyses of the frequency
domain might monitor the progression of the disease over years and help to evaluate therapy response. An even
higher impact could be achieved especially at a prodromal (or, even better, preclinical) stage, in which these
biomarkers could be used as “gatekeepers” for people at risk of developing Alzheimer’s disease47.
As this study’s small sample was limited, future research should aim to demonstrate the reproducibility of
our results with a larger population. Moreover, it is important to explore the high spatio-temporal resolution
of the OPM-MEG system using dierent stimuli and explore the propagating signals of dierent brain circuits.
Further research is needed to investigate other sensitive pathways in order to better establish the suitability of
OPM-MEG for its application in neurophysiological studies. In addition, we have subsequently shown that a
factor-six improvement in the DAQ noise oor can be achieved, increasing the SnR of the OPM-MEG for an
even higher spatio-temporal resolution.
Based on our observations, OPM-MEG could be a reliable neuroimaging method to identify the activation
patterns of close cortical regions in response to specic stimuli. It has the potential to enable reliable neural speed
measurements, and spatio-temporal tracking, of propagating signals, including more detailed investigations of
the visual pathway.
Received: 4 June 2021; Accepted: 1 November 2021
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Acknowledgements
We gratefully acknowledge insightful discussions with Chris Racey and Jamie Ward. is work was supported
by the UK Quantum Technologies Hub for Sensors and Timing (EPSRC Grant EP/T001046/1) and by the Core
Facility ‘Metrology of Ultra-Low Magnetic Fields’ at Physikalisch-Technische Bundesanstalt which receives
funding from the Deutsche Forschungsgemeinscha (DFG KO 5321/3-1 and TR 408/11- 1).
Author contributions
C.A., M.C., T.C., A.G. and P.K. conceived of the presented idea and the data collection. Data were analysed and
interpreted by F.D.L, A.G. and T.J. K.R., T.S., and J.V. oversaw the measurements at PTB, and the design of the
visual equipment. All authors discussed the results and contributed to the nal manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to A.G.
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