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fncel-16-1034270 December 7, 2022 Time: 14:54 # 1
TYPE Perspective
PUBLISHED 13 December 2022
DOI 10.3389/fncel.2022.1034270
OPEN ACCESS
EDITED BY
Maesoon Im,
Korea Institute of Science
and Technology (KIST), South Korea
REVIEWED BY
Mohit Naresh Shivdasani,
University of New South Wales,
Australia
*CORRESPONDENCE
Eduardo Fernández
e.fernandez@umh.es
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This article was submitted to
Cellular Neurophysiology,
a section of the journal
Frontiers in Cellular Neuroscience
RECEIVED 01 September 2022
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PUBLISHED 13 December 2022
CITATION
Grani F, Soto-Sánchez C, Fimia A and
Fernández E (2022) Toward
a personalized closed-loop
stimulation of the visual cortex:
Advances and challenges.
Front. Cell. Neurosci. 16:1034270.
doi: 10.3389/fncel.2022.1034270
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and Fernández. This is an open-access
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not comply with these terms.
Toward a personalized
closed-loop stimulation of the
visual cortex: Advances and
challenges
Fabrizio Grani1, Cristina Soto-Sánchez1,2, Antonio Fimia 3
and Eduardo Fernández1,2*
1Institute of Bioengineering, Universidad Miguel Hernández de Elche, Elche, Spain, 2Biomedical
Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN),
Madrid, Spain, 3Departamento de Ciencia de Materiales, Óptica y Tecnología Electrónica,
Universidad Miguel Hernández de Elche, Elche, Spain
Current cortical visual prosthesis approaches are primarily unidirectional
and do not consider the feed-back circuits that exist in just about every
part of the nervous system. Herein, we provide a brief overview of
some recent developments for better controlling brain stimulation and
present preliminary human data indicating that closed-loop strategies could
considerably enhance the effectiveness, safety, and long-term stability of
visual cortex stimulation. We propose that the development of improved
closed-loop strategies may help to enhance our capacity to communicate
with the brain.
KEYWORDS
closed-loop stimulation, visual prostheses, neural interfaces, brain stimulation, local
field potentials
Introduction
Visual impairment has a profound impact on the lives of those who experience it
(Bourne et al.,2017). Although some novel clinical approaches are becoming available
(Higuchi et al.,2017;De Silva and Moore,2022;Panikker et al.,2022;Van Gelder et al.,
2022), unfortunately, there is no treatment for all causes of blindness (Fernandez,2018;
Fernandez et al.,2020). Thus, there are many blind patients for whom there is still no
medical treatment. As a consequence of this growing and clearly unmet need, numerous
groups worldwide are pursuing other approaches to provide at least a rudimentary sense
of vision to the blind.
Visual prostheses are promising solutions to restore functional vision (i.e., visual
percepts that could help blind people to recognize objects or to navigate in complex
environments). Retinal prostheses are the most successful approach in this field to date
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(Nowik et al.,2020;Picaud and Sahel,2020;Nanegrungsunk
et al.,2022), but patients with severe retinal degeneration,
glaucoma, or optic atrophy cannot get benefit from a retinal
prosthesis. Therefore, there are compelling reasons for the
development of alternative approaches that can bypass the retina
to restore a functional sense of vision.
In this framework, although the optic nerve or lateral
geniculate nucleus could be good targets (Nguyen et al.,
2016;Gaillet et al.,2020;Borda et al.,2022;Rassia et al.,
2022), several groups are trying to develop visual prostheses
designed to directly stimulate the visual cortex (Lee et al.,
2016;Beauchamp et al.,2020;Fernandez et al.,2021;Bosking
et al.,2022). However, the biological and engineering problems
for the success of cortical implants are much more complex
than originally believed and involve, for example, long-term
biocompatibility issues and challenges related to the encoding of
visual information and the delivery of information to implants
(Fernandez et al.,2020). In addition, we should be aware that
the human brain is arguably one of the most complex systems in
nature and that cortical stimulation should be safe, precise, and
effective.
To achieve the ambitious objectives envisioned by cortical
visual prostheses, we should be able to stimulate the occipital
cortex in a way as similar as possible to the physiological
response to visual stimuli, mimicking the human visual pathway
(Nirenberg and Pandarinath,2012;Qiao et al.,2019;Brackbill
et al.,2020;Li et al.,2022;Price and Gavornik,2022). In this
framework, we should consider that closed-loop circuits exist
in just about every part of the nervous system (Farkhondeh
Tale Navi et al.,2022;Khodagholy et al.,2022). However,
current cortical visual prosthesis approaches are primarily
unidirectional and do not incorporate any adaptive system for
the modulation of the electrical stimulation used to induce
visual perception. Herein, we briefly introduce some recent
advances for better control of brain stimulation and present
preliminary human data suggesting that a closed-loop approach
could significantly improve the performance, safety, and long-
term stability of the stimulation of visual cortex neurons.
Learning to control brain electrical
stimulation
Electrical stimulation of the brain is the basis of many
technologies for the restoration of sensory and motor functions.
Brain stimulation has been used for reducing tremors in
Parkinson’s patients, controlling epileptiform activity, and
improving mood in patients with severe depression (Lozano
et al.,2019). Additionally, it is now possible to create
artificial sensations, with unprecedented resolution, via delivery
of intracortical microstimulation (Fernandez et al.,2021;
Fernandez,2022;Fifer et al.,2022). However, most current brain
stimulation approaches cannot flexibly control the patterns of
activity because, for it to work, we need to know the activity
of the neurons surrounding the electrodes and modulate the
electrical stimulation in function of this neural activity.
Although stimulating electrodes allow control of the
dynamics of populations of neurons, they do not provide
insights into the electrophysiological activity of the neurons
surrounding the electrodes. Thus, a critical step in the
development of closed-loop approaches is the creation of
microelectrodes and technologies capable of performing
simultaneous stimulation and recording or neural activity.
Currently, bidirectional electrodes that allow stimulation
and recording of neural activity at the same time exist, but
are limited by the artifacts generated in the recordings by
the stimulation (Xu et al.,2018). The detailed description of
techniques and materials that allow for the recording of neural
activity has been described elsewhere (Stevenson and Kording,
2011;Chen et al.,2017;Hong et al.,2021), but extracellular
recordings are the more common type associated with in vivo
brain recordings. Briefly, electrodes of the order of microns are
implanted into the brain and positioned close enough to the
neurons of interest to detect the fluctuations in voltage across
their membranes. To record from several neurons, a series
of microelectrodes can be organized to form a microelectrode
array. The main advantage of these microelectrode arrays is that
by recording from a number of neurons simultaneously, we
can extract more accurately the complex patterns of neuronal
activity and get some insights into the information flow (Hong
et al.,2021).
Some recent works have led to the development of novel
forms of neuromodulation, which are facilitating the ability to
manipulate populations of neurons in near real-time. These
techniques are based on recording the neural activity around the
electrodes and adjusting the electrical stimulation in function
of the observed neural activity (closed-loop stimulation).
According to the use of the closed-loop approach, these
techniques can be divided in device fitting techniques and
therapy/efficacy techniques (Table 1).
This procedures allow an improved control of some
neurological conditions such as epilepsy (Ranjandish and
Schmid,2020;Farkhondeh Tale Navi et al.,2022), and can also
be used for better control of obsessive-compulsive disorders
and depression (Figee et al.,2022;Visser-Vandewalle et al.,
2022). Furthermore, it has been shown that the outcome of
brain stimulation to treat Parkinson’s disease can be improved
by recording brain activity and stimulating only when the
local field potentials collected by electrodes inserted in the
subthalamic nucleus exceed a certain threshold (Little et al.,
2013), or by associating the brain stimulation to specific phases
of patients’ tremor activity (Cagnan et al.,2017). Also, the
electrical stimulation of the spinal cord for pain therapy can
be adjusted based on the evoked compound action potential
(ECAP) (Mekhail et al.,2020), while the movement output in
spinal cord stimulation for motor recovery can be controlled in
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TABLE 1 Examples of closed-loop strategies for neural prostheses.
Aim Description Utility Research/Clinical References
Epilepsy treatment Electrical stimulation only when epileptic seizures
are detected
Therapy Clinical Ranjandish and Schmid,2020;
Farkhondeh Tale Navi et al.,2022
Obsessive-compulsive disorder
control
Biomarker-based deep brain stimulation Therapy Research Vissani et al.,2022
Depression control Biomarker-based deep brain stimulation Therapy Research Scangos et al.,2021
Parkinson’s disease control Electrical stimulation based on local field
potentials (LFP) power
Therapy Clinical Little et al.,2013
Parkinson’s disease control Electrical stimulation based on the phase of hands
tremor
Therapy Research Cagnan et al.,2017
Fitting of cochlear implants Fitting of stimulation threshold based on the
contraction of stapedius muscle
Fitting Research Weiss et al.,2021
Fitting of cochlear implants Fitting of stimulation threshold based on evoked
compound action potential (ECAP)
Fitting Research McKay et al.,2013
Fitting of cochlear implants Fitting of stimulation threshold based on
electrically evocated auditory brainstem response
(EABR)
Fitting Research Guenser et al.,2015
Fitting of cochlear implants Fitting of stimulation threshold based on cortical
auditory evoked potentials (CAEPs)
Fitting Research Visram et al.,2015
Spinal cord stimulation for pain
therapy
Adjusting stimulation current based on the
measured ECAP
Therapy Clinical Mekhail et al.,2020
Spinal cord stimulation for motor
control
Modulation of gait features through stimulation
parameters
Therapy Research Wenger et al.,2014
Retinal electrical stimulation for
visual restoration
Modulation of electrical stimulation based on
retinal ganglion cells response
Therapy Research Guo et al.,2018;Spencer et al.,
2019;Shah and Chichilnisky,
2020
Fitting of intracortical visual
prostheses
Measure the response of V4 neurons to V1
stimulation
Fitting Research Chen et al.,2020
Increase efficacy of stimulation in
intracortical visual prostheses
Increase efficacy of electrical stimulation in the
visual cortex by LFP phase-locked stimulation
Therapy Research Allison-Walker et al.,2020
Brain state dependent stimulation
in cortical visual prostheses
Look for a brain state in which stimulating is easier
to induce visual perception
Therapy Research van Vugt et al.,2018
closed-loop changing the stimulation parameters (Wenger et al.,
2014).
The above-mentioned approaches can also be applied to
the field of sensory prostheses. Thus, the automatic tuning of
stimulation thresholds in cochlear implants can be done by
measuring the contraction of the stapedius muscle. This muscle
contracts to protect the inner ear from very loud sounds (Borg
and Zakrisson,1973) and measuring its contraction provides
objective feedback on the loudness of the sound induced by
the electrical stimulation (Weiss et al.,2021). Other measures
like ECAP (McKay et al.,2013), electrically evocated auditory
brainstem response (EABR) (Guenser et al.,2015), cortical
auditory evoked potentials (CAEPs) (Visram et al.,2015) have
been studied to automatically fit cochlear implants, but none of
them reached a clinical application.
In retinal prosthesis, research on closed-loop stimulation
have been done to optimize the stimulation parameters to obtain
the desired retinal ganglion cells output in response to a given
visual input (Guo et al.,2018;Spencer et al.,2019;Shah and
Chichilnisky,2020). The same approach could be applied in
cortical visual prosthesis using the activity of cortical neurons
instead of retinal ganglion cells. For this to be optimal, a larger
part of the visual field should be covered by the electrodes in
cortical visual prostheses with respect to the current research
devices.
However, even with a smaller covering of the visual field it
should be feasible to design and develop similar approaches in
the field of cortical visual prosthesis for controlling the timing
of stimulation, reducing charge requirements, and fitting the
device faster. In this framework, a recent study in monkey visual
cortex shows that the activity of neurons in V4 provides direct
insight into the efficacy of the stimulation in V1 (Chen et al.,
2020). This suggests that neurons in higher visual areas could
be used, for example, to estimate and adjust V1 thresholds
on hundreds of electrodes. Furthermore, besides adjusting the
thresholds, the brain signals collected by the electrodes in
the visual cortex could reveal a brain state in which it is
easier to induce perception. Some preliminary studies in rats
support this point of view and show that it is possible to
use the information from the local field potentials (LFPs) as
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control signals to specify the precise timing of stimulation
to reduce charge requirements (Allison-Walker et al.,2020).
Moreover, it has been shown in humans that there is a
causal relationship between cortical excitation and phosphenes
perception so that the phase of pre-stimulus oscillatory activity
seems linked to visual perception (Dugue et al.,2011), and other
studies suggest that the power spectral density at low frequency
(f<30 Hz) contains information about visual perception (Gail
et al.,2004). Hence, we can hypothesize that the incorporation
of measures of neural activity around stimulating electrodes
could be helpful to enhance the effectiveness and safety of any
cortical visual prostheses. In addition, we could also incorporate
other measures such as linear combinations of brain signals
in different bandwidths and even information about the pupil
size and eye movements to improve the safety, robustness,
and reliability of conscious visual perceptions (van Vugt et al.,
2018). Table 1 presents some closed-loop neural stimulation
approaches currently used, specifying if they are in clinical or
research status.
Toward personalized closed-loop
stimulation in cortical visual
prostheses
Currently, most cortical visual prostheses are primarily
unidirectional or open-loop, passing the visual information
from the outside world captured by the image acquisition
sensors to the implanted microelectrode arrays. In the future,
it is expected that a high number of microelectrodes can
be implanted into the brain to provide a functional vision,
and such a large number of implanted electrodes may pose
several stimulation problems (Fernandez,2018;Rotermund
et al.,2019). Therefore, we have to start reconsidering and
improving our methods of cortical stimulation for example with
closed-loop approaches (Figure 1A;Rotermund et al.,2019).
It has been estimated that we need at least 625 electrodes
implanted in visual areas for reading (although at lower speeds)
and navigating through complex visual environments (Cha
et al.,1992). However, finding the lowest current thresholds
able to induce visual perceptions from each single electrode is
a time-consuming procedure that requires the user’s feedback.
Moreover, perception thresholds could vary over time, requiring
the users to calibrate each electrode many times. As the brain
signals surrounding the electrodes contain information about
the spread of currents and brain dynamics, we could determine
if the currents used are enough to induce perception simply
by measuring the brain response to electrical stimulation.
For this to be possible, the brain signals during (or after)
stimulation should have distinguishable features in case of
perception or no perception. Figure 1B shows an example from
our ongoing experiments with intracortical microelectrodes
in blind volunteers in which the neuronal activity after
stimulation increases when the stimulation intensity is enough
to induce perception (40 µA in this case). This approach
can also be used to construct psychometric curves (relation
between stimulus intensity and perception probability) that
are practically indistinguishable of the standard psychometric
curves using users’ feed-back. Figure 1C shows an example for
a single electrode using current intensities from 0 to 140 µA.
However, the most robust and reliable features to automatically
find perception thresholds still need to be investigated.
On the other hand, there is a need to reduce power
consumption and the charge required to elicit reliable
phosphenes. In this context, brain activity and other
physiological signals could be used to find a brain state in
which inducing visual perception is easier, thus decreasing
the currents needed to induce the visual perceptions. Using
the same stimulation parameters, a given pulse train might
produce a visual perception or not according to the LFP phase
at which the stimulation is sent (Figures 1D,E). This has been
reported in experiments in rats (Allison-Walker et al.,2020) and
using non-invasive transcranial magnetic stimulation (TMS)
in sighted humans (Dugue et al.,2011), but the feasibility of
LFP phase-locked stimulation with intracortical electrodes in
humans still remains unexplored. Nevertheless, targeting the
right LFP phase before stimulation could allow to reduce charge
requirements. Figure 1F shows an example.
Algorithms for closed-loop
stimulation
Figure 2 introduces some possible algorithms for closed-
loop stimulation in the framework of a cortical visual prosthesis.
Briefly, to search for perception thresholds (Figure 2A), a
stimulation with an initial current level I0is sent from one
electrode or a group of electrodes. Then, the brain signals during
and after the stimulation (up to 1 s) are recorded and used to
extract perception-related features. If perception is detected, the
current Iused to stimulate is set as the perception threshold
for that electrode or group of electrodes. If perception is not
detected from the extracted features, a new stimulation train is
sent with a higher current intensity I=I−1+1I, where I−1
is the previous current intensity and 1Iis the increment of
current intensity for each step. The velocity of this algorithm
to find perception thresholds depends on the initial current
intensity I0and on 1Isize. Bigger 1Ivalues will speed up the
threshold finding at the cost of reducing the precision of the
threshold. Furthermore, we can start with I0values close to the
last perception thresholds to improve processing speed.
To stimulate the desired LFP phase in real time, we can use
the approach proposed by Blackwood et al. (2018). First, we have
to record 1-second windows of the raw signal and then filter this
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FIGURE 1
Closed-loop stimulation of the visual cortex. (A) Diagram of open-loop versus closed-loop stimulation approaches. (B) Example of intracortical
brain signals. A clear difference between perception and no perception is needed to adjust the current to induce perception without the user’s
feedback. (C) Example of a psychometric curve obtained with user’s feedback (blue) and neural signals (orange). (D) Local field potentials (LFP)
phase dependent response to stimulation. Inducing perception might be easier by stimulating the right LFP phase. (E) Ideally, the distribution of
LFP phase should be different for perception and no perception. (F) Stimulating at the right LFP phase, we could decrease the charge required
to reliably evoke phosphenes.
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FIGURE 2
Flowcharts of possible closed-loop algorithms for a cortical visual prosthesis. (A) Automatic threshold adjustment for perception. (B) Local field
potentials (LFP)-phase locked stimulation.
signal between 4 and 15 Hz. As the phase estimation of the last
point in the window data is not accurate without knowing the
future behavior of the signal, an autoregressive model is fitted to
estimate the future trend. The Hilbert transform is then used to
estimate the current phase, and a stimulation train is sent only
if the current phase is the desired one (Figure 2B). We have
recently tested this algorithm using intracortical signals from
the visual cortex of a human blind volunteer at a sampling rate
of 30 kHz and we obtained an error of ±20◦in the LFP phase
estimation (Grani et al.,2022b).
Discussion
Research on real-time closed-loop neural systems has
built upon contributions from neuroscientists, engineers and
clinicians, and may prove essential for future cortical visual
prostheses, especially when high-number microelectrodes are
used. As a result, the next frontier in cortical visual prosthesis
may be the development of bidirectional implantable systems
with enhanced abilities to modulate and manipulate populations
of neurons in real-time. These closed-loop approaches could be
able to use information from neural recordings to adjust the
optimal stimulation, reduce charge requirements, and improve
stimulation performance. However, there are still a number of
important issues and challenges to overcome. For example, the
stability of signals over time, the influence of movements on
signal quality, and the higher energy consumption needed to
perform closed-loop stimulation.
Although closed-loop stimulation might increase the safety,
performance and usability of cortical visual prostheses, many
questions need to be solved before it can be implemented in
clinical devices able to continuously record and stimulate from
hundreds of electrodes. For instance, the battery of the system
needs to last at least for an entire day but adding a real-time
brain signal analysis processor to the device would increase the
energy consumption. This represents a significant challenge as
the sampling frequency often used to get reliable neural signals
is 30 kHz. Moreover, in order not to add complexity to the whole
device, many signals should be excluded from the closed-loop
approach. For example, perception could be inferred from EEG
signals in the occipital cortex (Gail et al.,2004), but adding a
standard EEG cap to the prosthesis will decrease the overall
wearability, and the users might not want to use it on a daily
basis.
Intracortical signals captured from the same electrodes used
to stimulate cortical areas are the best candidates to build
these closed-loop stimulation approaches. However, the signals
collected during electrical stimulation are usually corrupted by
the stimulation artifacts. Different signal processing techniques
and electronic front-end designs can be used to retrieve the
signals from stimulation artifacts (Wagenaar and Potter,2002;
Zhou et al.,2018) but these techniques do not work when the
amplifiers are saturated. If this is the case, the blanking or
exclusion of data during stimulation could be a good option.
In addition, a discrimination between perception features and
artifacts could be possible assuming that the artifact features
increase linearly with the current intensity, while features linked
to perception should have a different behavior, starting to
increase only after the perception threshold.
On the other hand, as the microelectrodes have to be
permanently implanted in the user’s brain, it is important that
the signals on which closed-loop stimulation is based are stable
over time. Some studies show that the number of reliable spikes
captured by intracortical electrodes decreases with time (Sharma
et al.,2015), while LFPs are more stable (Grani et al.,2022a).
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Therefore, closed-loop approaches based on LFP recordings
could be more stable over time than approaches based on single
neuron spikes and become the basis for future closed-loop
approaches.
Datasets using current intensities able to induce perception
50% of the time, could be of great help to get better insight
into these issues and help to investigate in which brain state
it is easier to induce visual perceptions. Ideally, the LFP phase
before stimulation could be significantly different in case of
perception and no perception as it is shown in Figure 1E.
However, we do not yet know if the same phase of the LFP is
valid for all the electrodes. Thus, the neural population around
each electrode could determine the preferred LFP phase, which
means that to modulate the neural response to stimulation,
perhaps we should consider the specific dynamics of every
single electrode. In addition, the real-time detection of this
parameter can also be associated with uncertainties that reduce
the accuracy of phase estimation. Further, although different
algorithms have been proposed for continuous phase estimation
in real-time (Kim et al.,2016;Blackwood et al.,2018), this
locked phase stimulation can also limit the time resolution of
the stimulation. Thus, it seems that the frequency of ongoing
oscillations is around 10 Hz (Dugue et al.,2011), which means
that the preferred phase should appear approximately every
100 ms, limiting to this time the refresh rate of the visual
prosthesis. Therefore, all these results must be confirmed in
real-life environments, and there is still not enough information
about the period of the local field potential that corresponds to
maximum excitability nor about how many feedback channels
can be reliably provided in parallel.
Another complementary and not mutually exclusive
approach could be to reproduce the responses of cortical
neurons to different visual stimuli (Guo et al.,2018;Spencer
et al.,2019;Shah and Chichilnisky,2020). Sighted animal
models with intracortical electrodes in the visual cortex could
be used to obtain visual cortex responses to different visual
patterns. Then, knowing the neural activity elicited by a visual
stimulus and the neural activity elicited by each electrical
stimulation parameter, the stimulation parameters could be
shaped to obtain the desired neural activity for a certain visual
perception. However, there is no guarantee that eliciting with
electrical stimulation the same activity of a natural image in
V1 creates the same image perception. In addition, we have to
consider that the perception experience is modulated by higher
cortical areas (van Vugt et al.,2018) and could also be different
in a brain deprived of visual information (Merabet et al.,2007;
Fernandez,2018).
All the progress in neural technologies, neuroscience,
electronics, and bioengineering together with increased
intelligence in neural systems can help to foster the development
of improved custom-tailored devices, which will incorporate
advanced closed-loop algorithms for restoring some functional
sight to blind people. Therefore, we expect that in the future,
closed-loop stimulation will offer more safety, precision, and
personalization of cortical visual neuroprostheses approaches.
Data availability statement
The original contributions presented in this study are
included in the article/supplementary material, further inquiries
can be directed to the corresponding author.
Ethics statement
The studies involving human participants
were reviewed and approved by Hospital General
Universitario de Elche Clinical Research Committee. The
patients/participants provided their written informed consent
to participate in this study.
Author contributions
FG, CS-S, AF, and EF contributed to the design,
implementation of the research, and writing of the manuscript.
All authors contributed to the article and approved the
submitted version.
Funding
Funding was provided by grant PROMETEO/2019/119
from the Generalitat Valenciana (Spain), by the European
Union’s Horizon 2020 Research and Innovation Programme
under grant agreement No. 899287 (project NeuraViPer) and
under the Marie Skłodowska-Curie grant agreement No. 861423
(enTRAIN Vision), and by Ministerio de Ciencia e Innovación
(Spain) by grant PDC2022-133952-I00.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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References
Allison-Walker, T. J., Ann Hagan, M., Chiang Price, N. S., and Tat Wong, Y.
(2020). Local field potential phase modulates neural responses to intracortical
electrical stimulation. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020, 3521–3524.
doi: 10.1109/EMBC44109.2020.9176186
Beauchamp, M. S., Oswalt, D., Sun, P., Foster, B. L., Magnotti, J. F., Niketeghad,
S., et al. (2020). Dynamic stimulation of visual cortex produces form vision in
sighted and blind humans. Cell 181, 774.e5–783.e5. doi: 10.1016/j.cell.2020.04.033
Blackwood, E., Lo, M. C., and Alik Widge, S. (2018). Continuous phase
estimation for phase-locked neural stimulation using an autoregressive model for
signal prediction. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018, 4736–4739.
doi: 10.1109/EMBC.2018.8513232
Borda, E., Gaillet, V., Airaghi Leccardi, M. J. I., Zollinger, E. G., Moreira, R. C.,
and Ghezzi, D. (2022). Three-dimensional multilayer concentric bipolar electrodes
restrict spatial activation in optic nerve stimulation. J. Neural Eng. 19:036016.
doi: 10.1088/1741-2552/ac6d7e
Borg, E., and Zakrisson, J. E. (1973). Letter: stapedius reflex and speech features.
J. Acoust. Soc. Am. 54, 525–527. doi: 10.1121/1.1913610
Bosking, W. H., Oswalt, D. N., Foster, B. L., Sun, P., Beauchamp, M. S., and
Yoshor, D. (2022). Perceptsevoked by multi-ele ctrode stimulation of human visual
cortex. Brain Stimul. 15, 1163–1177. doi: 10.1016/j.brs.2022.08.007
Bourne, R. R. A., Flaxman, S. R., Braithwaite, T., Cicinelli, M. V., Das, A., Jonas,
J. B., et al. (2017). Magnitude, temporal trends, and projections of the global
prevalence of blindness and distance and near vision impairment: a systematic
review and meta-analysis. Lancet Glob. Health 5, e888–e897. doi: 10.1016/S2214-
109X(17)30293-0
Brackbill, N., Rhoades, C., Kling, A., Shah, N. P., Sher, A., Litke, A. M.,
et al. (2020). Reconstruction of natural images from responses of primate retinal
ganglion cells. eLife 9:e58516. doi: 10.7554/eLife.58516
Cagnan, H., Pedrosa, D., Little, S., Pogosyan, A., Cheeran, B., Aziz, T., et al.
(2017). Stimulating at the right time: phase-specific deep brain stimulation. Brain
140, 132–145. doi: 10.1093/brain/aww286
Cha, K., Horch, K. W., and Normann, R. A. (1992). Mobility performance with
a pixelized vision system. Vision Res. 32, 1367–1372.
Chen, R., Canales, A., and Anikeeva, P. (2017). Neural recording and
modulation technologies. Nat. Rev. Mater. 2:16093. doi: 10.1038/natrevmats.20
16.93
Chen, X., Wang, F., Fernandez, E., and Roelfsema, P. R. (2020). Shape
perception via a high-channel-count neuroprosthesis in monkey visual cortex.
Science 370, 1191–1196. doi: 10.1126/science.abd7435
De Silva, S. R., and Moore, A. T. (2022). Optogenetic approaches to therapy for
inherited retinal degenerations. J. Physiol. 600, 4623–4632. doi: 10.1113/JP282076
Dugue, L., Marque, P., and VanRullen, R. (2011). The phase of ongoing
oscillations mediates the causal relation between brain excitation and visual
perception. J. Neurosci. 31, 11889–11893. doi: 10.1523/JNEUROSCI.1161-11.2011
Farkhondeh Tale Navi, F., Heysieattalab, S., Ramanathan, D. S., Raoufy,
M. R., and Nazari, M. A. (2022). Closed-loop modulation of the self-regulating
brain: a review on approaches, emerging paradigms, and experimental designs.
Neuroscience 483, 104–126. doi: 10.1016/j.neuroscience.2021.12.004
Fernandez, E. (2018). Development of visual Neuroprostheses: trends and
challenges. Bioelectron. Med. 4:12. doi: 10.1186/s42234-018- 0013-8
Fernandez, E. (2022). Selective induction of fingertip sensations for
better neuroprosthetic control. Neurology 98, 261–262. doi: 10.1212/WNL.
0000000000013177
Fernandez, E., Alfaro, A., Soto-Sanchez, C., Gonzalez-Lopez, P., Lozano,
A. M., Pena, S., et al. (2021). Visual percepts evoked with an intracortical 96-
channel microelectrode array inserted in human occipital cortex. J. Clin. Invest.
131:e151331. doi: 10.1172/JCI151331
Fernandez, E., Alfaro-Saez, A., and Gonzalez-Lopez, P. (2020). Toward long-
term communication with the brain in the blind by intracortical stimulation:
challenges and future prospects. Front. Neurosci. 11:681. doi: 10.3389/fnins.2020.
00681
Fifer, M. S., McMullen, D. P., Osborn, L. E., Thomas, T. M., Christie, B., Nickl,
R. W., et al. (2022). Intracortical somatosensory stimulation to elicit fingertip
sensations in an individual with spinal cord injury. Neurology 98, e679–e687.
doi: 10.1212/WNL.0000000000013173
Figee, M., Riva-Posse, P., Choi, K. S., Bederson, L., Mayberg, H. S., and Kopell,
B. H. (2022). Deep brain stimulation for depression. Neurotherapeutics 19, 1229–
1245. doi: 10.1007/s13311-022- 01270-3
Gail, A., Brinksmeyer, H. J., and Eckhorn, R. (2004). Perception-related
modulations of local field potential power and coherence in primary visual cortex
of awake monkey during binocular rivalry. Cereb. Cortex 14, 300–313. doi: 10.
1093/cercor/bhg129
Gaillet, V., Cutrone, A., Artoni, F., Vagni, P., Mega Pratiwi, A., Romero, S. A.,
et al. (2020). Spatially selective activation of the visual cortex via intraneural
stimulation of the optic nerve. Nat. Biomed. Eng. 4, 181–194. doi: 10.1038/s41551-
019-0446- 8
Grani, F., Soto-Sanchez, C., Farfan, F. D., Alfaro, A., Grima, M. D., Rodil
Doblado, A., et al. (2022a). Time stability and connectivity analysis with an
intracortical 96-channel microelectrode array inserted in human visual cortex.
J. Neural Eng. 19:045001. doi: 10.1088/1741-2552/ac801d
Grani, F., Soto-Sanchez, C., Rodil, A., Grima, M. D., Farfan, F., Calvo, M.,
et al. (2022b). “Performance evaluation of a real-time phase estimation algorithm
applied to intracortical signals from human visual cortex,” in Artificial Intelligence
in Neuroscience: Affective Analysis and Health Applications, eds J. M. Ferrández
Vicente, J. R. Álvarez-Sánchez, F. de la Paz López, and H. Adeli (Cham: Springer),
516–525.
Guenser, G., Laudanski, J., Phillipon, B., Backus, B. C., Bordure, P., Romanet,
P., et al. (2015). The relationship between electrical auditory brainstem responses
and perceptual thresholds in Digisonic(R) SP cochlear implant users. Cochlear
Implants Int. 16, 32–38. doi: 10.1179/1754762814Y.0000000082
Guo, T., Yang, C. Y., Tsai, D., Muralidharan, M., Suaning, G. J., Morley, J. W.,
et al. (2018). Closed-loop efficient searching of optimal electrical stimulation
parameters for preferential excitation of retinal ganglion cells. Front. Neurosci.
12:168. doi: 10.3389/fnins.2018.00168
Higuchi, A., Kumar, S. S., Benelli, G., Alarfaj, A. A., Munusamy, M. A.,
Umezawa, A., et al. (2017). Stem cell therapies for reversing vision loss. Trends
Biotechnol. 35, 1102–1117. doi: 10.1016/j.tibtech.2017.06.016
Hong, J. W., Yoon, C., Jo, K., Won, J. H., and Park, S. (2021). Recent advances
in recording and modulation technologies for next-generation neural interfaces.
iScience 24:103550. doi: 10.1016/j.isci.2021.103550
Khodagholy, D., Ferrero, J. J., Park, J., Zhao, Z., and Gelinas, J. N. (2022). Large-
scale, closed-loop interrogation of neural circuits underlying cognition. Trends
Neurosci. 45, 968–983. doi: 10.1016/j.tins.2022.10.003
Kim, L., Harer,J., R angamani, A., Moran, J., Parks,P. D., Widge, A., et al. (2016).
Predicting local field potentials with recurrent neural networks. Annu. Int. Conf.
IEEE Eng. Med. Biol. Soc. 2016, 808–811. doi: 10.1109/EMBC.2016.7590824
Lee, S. W., Fallegger, F., Casse, B. D., and Fried, S. I. (2016). Implantable
microcoils for intracortical magnetic stimulation. Sci. Adv. 2:e1600889. doi: 10.
1126/sciadv.1600889
Li, Y., Tan, Z., Wang, J., Wang, M., and Wang, L. (2022). Neural substrates of
external and internal visual sensations induced by human intracranial electrical
stimulation. Front. Neurosci. 16:918767. doi: 10.3389/fnins.2022.918767
Little, S., Pogosyan, A., Neal, S., Zavala, B., Zrinzo, L., Hariz, M., et al. (2013).
Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol. 74,
449–457. doi: 10.1002/ana.23951
Lozano, A. M., Lipsman, N., Bergman, H., Brown, P., Chabardes, S., Chang,
J. W., et al. (2019). Deep brain stimulation: current challenges and future
directions. Nat. Rev. Neurol. 15, 148–160. doi: 10.1038/s41582-018-0128- 2
McKay, C. M., Chandan, K., Akhoun, I., Siciliano, C., and Kluk, K. (2013). Can
ECAP measures be used for totally objective programming of cochlear implants?
J. Assoc. Res. Otolaryngol. 14, 879–890. doi: 10.1007/s10162-013-0417-9
Mekhail, N., Levy, R. M., Deer, T. R., Kapural, L., Li, S., Amirdelfan, K., et al.
(2020). Long-term safety and efficacy of closed-loop spinal cord stimulation to
treat chronic back and leg pain (Evoke): a double-blind, randomised, controlled
trial. Lancet Neurol. 19, 123–134. doi: 10.1016/S1474-4422(19)30414- 4
Merabet, L. B., Rizzo, J. F. III, Pascual-Leone, A., and Fernandez, E. (2007). ’Who
is the ideal candidate?’: decisions and issues relating to visual neuroprosthesis
development, patient testing and neuroplasticity. J. Neural Eng. 4, S130–S135.
doi: 10.1088/1741-2560/4/1/S15
Nanegrungsunk, O., Au, A., Sarraf, D., and Sadda, S. R. (2022). New frontiers of
retinal therapeutic intervention: a critical analysis of novel approaches. Ann. Med.
54, 1067–1080. doi: 10.1080/07853890.2022.2066169
Nguyen, H. T., Tangutooru, S. M., Rountree, C. M., Kantzos, A. J., Tarlochan, F.,
Yoon, W. J., et al. (2016). Thalamic visual prosthesis. IEEE Trans. Biomed. Eng. 63,
1573–1580. doi: 10.1109/TBME.2016.2567300
Frontiers in Cellular Neuroscience 08 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 9
Grani et al. 10.3389/fncel.2022.1034270
Nirenberg, S., and Pandarinath, C. (2012). Retinal prosthetic strategy with the
capacity to restore normal vision. Proc. Natl. Acad. Sci. U.S.A. 109, 15012–15017.
doi: 10.1073/pnas.1207035109
Nowik, K., Langwinska-Wosko, E., Skopinski, P., Nowik, K. E., and Szaflik, J. P.
(2020). Bionic eye review - An update. J. Clin. Neurosci. 78, 8–19. doi: 10.1016/j.
jocn.2020.05.041
Panikker, P., Roy, S., Ghosh, A., Poornachandra, B., and Ghosh, A. (2022).
Advancing precision medicines for ocular disorders: diagnostic genomics to
tailored therapies. Front. Med. 9:906482. doi: 10.3389/fmed.2022.906482
Picaud, S., and Sahel, J. A. (2020). [Vision restoration: science fiction or reality?].
Med. Sci. 36, 1038–1044. doi: 10.1051/medsci/2020213
Price, B. H., and Gavornik, J. P. (2022). Efficient temporal coding in the early
visual system: existing evidence and future directions. Front. Comput. Neurosci.
16:929348. doi: 10.3389/fncom.2022.929348
Qiao, K., Chen, J., Wang, L., Zhang, C., Zeng, L., Tong, L., et al. (2019). Category
decoding of visual stimuli from human brain activity using a bidirectional
recurrent neural network to simulate bidirectional information flows in human
visual cortices. Front. Neurosci. 13:692. doi: 10.3389/fnins.2019.00692
Ranjandish, R., and Schmid, A. (2020). A review of microelectronic systems
and circuit techniques for electrical neural recording aimed at closed-loop epilepsy
control. Sensors 20:5716. doi: 10.3390/s20195716
Rassia, K. E. K., Moutoussis, K., and Pezaris, J. S. (2022). Reading text works
better than watching videos to improve acuity in a simulation of artificial vision.
Sci. Rep. 12:12953. doi: 10.1038/s41598-022- 10719-6
Rotermund, D., Ernst, U. A., and Pawelzik, K. R. (2019). Open Hardware for
neuro-prosthesis research: a study about a closed-loop multi-channel system for
electrical surface stimulations and measurements. HardwareX 6:e00078. doi: 10.
1016/j.ohx.2019.e00078
Scangos, K. W., Khambhati, A. N., Daly, P. M., Makhoul, G. S., Sugrue, L. P.,
Zamanian, H., et al. (2021). Closed-loop neuromodulation in an individual with
treatment-resistant depression. Nat Med. 27, 1696–1700. doi: 10.1038/s41591-
021-01480- w
Shah, N. P., and Chichilnisky, E. J. (2020). Computational challenges and
opportunities for a bi-directional artificial retina. J. Neural Eng. 17:055002. doi:
10.1088/1741-2552/aba8b1
Sharma, G., Anneta, N., Fridenberg, D., Blanco, T., Vasconcelos,D., Shaikhouni,
A., et al. (2015). Time stability and coherence analysis of multiunit, single-unit
and local field potential neuronal signals in chronically implanted brain electrodes.
Bioelectron. Med. 2, 63–71. doi: 10.15424/bioelectronmed.2015.00010
Spencer, M. J., Kameneva, T., Grayden, D. B., Meffin, H., and Burkitt, A. N.
(2019). Global activity shaping strategies for a retinal implant. J. Neural Eng.
16:026008. doi: 10.1088/1741-2552/aaf071
Stevenson, I. H., and Kording, K. P. (2011). How advances in neural recording
affect data analysis. Nat. Neurosci. 14, 139–142. doi: 10.1038/nn.2731
Van Gelder, R. N., Chiang, M. F., Dyer, M. A., Greenwell, T. N., Levin, L. A.,
Wong, R. O., et al. (2022). Regenerative and restorative medicine for eye disease.
Nat. Med. 28, 1149–1156. doi: 10.1038/s41591-022-01862- 8
van Vugt, B., Dagnino, B., Vartak, D., Safaai, H., Panzeri, S., Dehaene, S., et al.
(2018). The threshold for conscious report: signal loss and response bias in visual
and frontal cortex. Science 360, 537–542. doi: 10.1126/science.aar7186
Visram, A. S., Innes-Brown, H., El-Deredy, W., and McKay, C. M. (2015).
Cortical auditory evoked potentials as an objective measure of behavioral
thresholds in cochlear implant users. Hear. Res. 327, 35–42.
Vissani, M., Nanda, P., Bush, A., Neudorfer, C., Dougherty, D., and Richardson,
R. M. (2022). Toward closed-loop intracranial neurostimulation in obsessive-
compulsive disorder. Biol. Psychiatry 16:S0006-3223(22)01432-9. doi: 10.1016/j.
biopsych.2022.07.003
Visser-Vandewalle, V., Andrade, P., Mosley, P. E., Greenberg, B. D., Schuurman,
R., McLaughlin, N. C., et al. (2022). Deep brain stimulation for obsessive-
compulsive disorder: a crisis of access. Nat. Med. 28, 1529–1532. doi: 10.1038/
s41591-022- 01879-z
Wagenaar, D. A., and Potter, S. M. (2002). Real-time multi-channel stimulus
artifact suppression by local curve fitting. J. Neurosci. Methods 120, 113–120.
doi: 10.1016/s0165-0270(02)00149- 8
Weiss, N. M., Ovari, A., Oberhoffner, T., Demaret, L., Bicer, A., Schraven, S.,
et al. (2021). Automated detection of electrically evoked stapedius reflexes (eSR)
during cochlear implantation. Eur. Arch. Otorhinolaryngol. 278, 1773–1779. doi:
10.1007/s00405-020- 06226-x
Wenger, N., Moraud, E. M., Raspopovic, S., Bonizzato, M., DiGiovanna, J.,
Musienko, P., et al. (2014). Closed-loop neuromodulation of spinal sensorimotor
circuits controls refined locomotion after complete spinal cord injury. Sci. Transl.
Med. 6:255ra133. doi: 10.1126/scitranslmed.3008325
Xu, J., Guo, H., Nguyen, A. T., Lim, H., and Yang, Z. (2018). A bidirectional
neuromodulation technology for nerve recording and stimulation. Micromachines
9:538. doi: 10.3390/mi9110538
Zhou, A., Johnson, B. C., and Muller, R. (2018). Toward true closed-
loop neuromodulation: artifact-free recording during stimulation. Curr. Opin.
Neurobiol. 50, 119–127. doi: 10.1016/j.conb.2018.01.012
Frontiers in Cellular Neuroscience 09 frontiersin.org