ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
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
SPECIALTY SECTION
This article was submitted to
Cellular Neurophysiology,
a section of the journal
Frontiers in Cellular Neuroscience
RECEIVED 01 September 2022
ACCEPTED 24 November 2022
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
COPYRIGHT
© 2022 Grani, Soto-Sánchez, Fimia
and Fernández. This is an open-access
article distributed under the terms of
the Creative Commons Attribution
License (CC BY). The use, distribution
or reproduction in other forums is
permitted, provided the original
author(s) and the copyright owner(s)
are credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does
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
Frontiers in Cellular Neuroscience 01 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 2
Grani et al. 10.3389/fncel.2022.1034270
(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
Frontiers in Cellular Neuroscience 02 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 3
Grani et al. 10.3389/fncel.2022.1034270
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
Frontiers in Cellular Neuroscience 03 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 4
Grani et al. 10.3389/fncel.2022.1034270
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=I1+1I, where I1
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
Frontiers in Cellular Neuroscience 04 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 5
Grani et al. 10.3389/fncel.2022.1034270
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.
Frontiers in Cellular Neuroscience 05 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 6
Grani et al. 10.3389/fncel.2022.1034270
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 ±20in 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).
Frontiers in Cellular Neuroscience 06 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 7
Grani et al. 10.3389/fncel.2022.1034270
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.
Frontiers in Cellular Neuroscience 07 frontiersin.org
fncel-16-1034270 December 7, 2022 Time: 14:54 # 8
Grani et al. 10.3389/fncel.2022.1034270
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
... requiring extensive training to learn to interpret the evoked percepts, which are typically described as "fundamentally different" from natural vision [7]. Moreover, phosphene appearance varies widely across patients [8], making personalized stimulus optimization a key open challenge [9]. ...
... These spirals follow a simulated axon map [44] based on tracings of axon trajectories in 55 human eyes. In summary, phosphene size, eccentricity, brightness, and orientation are modulated based on stimulus parameters and implant location according to the following equations: b e = a 0 (amp e ) a1 + a 2 (f req e ) (8) ρ e = ρ * a 3 * amp e (9) λ e = λ pdur 0.45 a4 (10) ...
Preprint
Full-text available
Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be used to optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to optimize patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies.
... Furthermore, we should consider the development of new tools to enhance bidirectional interaction with the targeted neurons. This approach has been successfully used to optimize stimulation parameters in retinal and cortical prostheses and could be decisive for the development of next generation visual prostheses [10]. This unit wirelessly transmits power and data via a radiofrequency (RF) link to the internal implanted system, which decodes the signals, identifies the target electrodes, and generates the final stimulation waveforms. ...
Article
Full-text available
The past 20 years have witnessed significant advancements in the field of visual prostheses, with developments spanning from early retinal implants to recent cortical approaches. This Perspective looks at some of the remaining challenges to achieve the ambitious clinical goals that these technologies could enable.
Article
The prospect of direct interaction between the brain and computers has been investigated in recent decades, revealing several potential applications. One of these is sight restoration in profoundly blind people, which is based on the ability to elicit visual perceptions while directly stimulating the occipital cortex. Technological innovation has led to the development of microelectrodes implantable on the brain surface. The feasibility of implanting a microelectrode on the visual cortex has already been shown in animals, with promising results. Current research has focused on the implantation of microelectrodes into the occipital brain of blind volunteers. The technique raises several technical challenges. In this technical note, the authors suggest a safe and effective approach for robot-assisted implantation of microelectrodes in the occipital lobe for sight restoration.
Article
Full-text available
Cognitive functions are increasingly understood to involve coordinated activity patterns between multiple brain regions, and their disruption by neuropsychiatric disorders is similarly complex. Closed-loop neurostimulation can directly modulate neural signals with temporal and spatial precision. How to leverage such an approach to effectively identify and target distributed neural networks implicated in mediating cognition remains unclear. We review current conceptual and technical advances in this area, proposing that devices that enable large-scale acquisition, integrated processing, and multiregion, arbitrary waveform stimulation will be critical for mechanistically driven manipulation of cognitive processes in physiological and pathological brain networks.
Article
Full-text available
Background Direct electrical stimulation of early visual cortex evokes the perception of small spots of light known as phosphenes. Previous studies have examined the location, size, and brightness of phosphenes evoked by stimulation of single electrodes. While it has been envisioned that concurrent stimulation of many electrodes could be used as the basis for a visual cortical prosthesis, the percepts resulting from multi-electrode stimulation have not been fully characterized. Objective To understand the rules governing perception of phosphenes evoked by multi-electrode stimulation of visual cortex. Methods Multi-electrode stimulation was conducted in human epilepsy patients. We examined the number and spatial arrangement of phosphenes evoked by stimulation of individual multi-electrode groups (n = 8), and the ability of subjects to discriminate between the pattern of phosphenes generated by stimulation of different multi-electrode groups (n = 7). Results Simultaneous stimulation of pairs of electrodes separated by greater than 4 mm tended to produce perception of two distinct phosphenes. Simultaneous stimulation of three electrodes gave rise to a consistent spatial pattern of phosphenes, but with significant variation in the absolute location, size, and orientation of that pattern perceived on each trial. Although multi-electrode stimulation did not produce perception of recognizable forms, subjects could use the pattern of phosphenes evoked by stimulation to perform simple discriminations. Conclusions The number of phosphenes produced by multi-electrode stimulation can be predicted using a model for spread of activity in early visual cortex, but there are additional subtle effects that must be accounted for.
Article
Full-text available
Inherited retinal degenerations such as retinitis pigmentosa (RP) affect around one in 4000 people and are the leading cause of blindness in working age adults in several countries. In these typically monogenic conditions, there is progressive degeneration of photoreceptors; however, inner retinal neurons such as bipolar cells and ganglion cells remain largely structurally intact, even in end‐stage disease. Therapeutic approaches aiming to stimulate these residual cells, independent of the underlying genetic cause, could potentially restore visual function in patients with advanced vision loss, and benefit many more patients than therapies directed at the specific gene implicated in each disorder. One approach investigated for this purpose is that of optogenetics, a method of neuromodulation that utilises light to activate neurons engineered to ectopically express a light‐sensitive protein. Using gene therapy via adeno‐associated viral vectors, a range of photosensitive proteins have been expressed in remaining retinal cells in advanced retinal degeneration with in vivo studies demonstrating restoration of visual function. Developing an effective optogenetic strategy requires consideration of multiple factors, including the light‐sensitive protein that is used, the vector and method for gene delivery, and the target cell for expression because these in turn may affect the quality of vision that can be restored. Currently, at least four clinical trials are ongoing to investigate optogenetic therapies in patients, with the ultimate aim of reversing visual loss in end‐stage disease. image
Article
Full-text available
Simulated artificial vision is used in visual prosthesis design to answer questions about device usability. We previously reported a striking increase in equivalent visual acuity with daily use of a simulation of artificial vision in an active task, reading sentences, that required high levels of subject engagement, but passive activities are more likely to dominate post-implant experience. Here, we investigated the longitudinal effects of a passive task, watching videos. Eight subjects used a simulation of a thalamic visual prosthesis with 1000 phosphenes to watch 23 episodes of classic American television in daily, 25-min sessions, for a period of 1 month with interspersed reading tests that quantified reading accuracy and reading speed. For reading accuracy, we found similar dynamics to the early part of the learning process in our previous report, here leading to an improvement in visual acuity of 0.15 ± 0.05 logMAR. For reading speed, however, no change was apparent by the end of training. We found that single reading sessions drove about twice the improvement in acuity of single video sessions despite being only half as long. We conclude that while passive viewing tasks may prove useful for post-implant rehabilitation, active tasks are likely to be preferable.
Article
Full-text available
While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.
Article
Full-text available
Offline perceptions are self-generated sensations that do not involve physical stimulus. These perceptions can be induced by external hallucinated objects or internal imagined objects. However, how the brain dissociates these visual sensations remains unclear. We aimed to map the brain areas involved in internal and external visual sensations induced by intracranial electrical stimulation and further investigate their neural differences. In this study, we collected subjective reports of internal and external visual sensations elicited by electrical stimulation in 40 drug-refractory epilepsy during presurgical evaluation. The response rate was calculated and compared to quantify the dissociated distribution of visual responses. We found that internal and external visual sensations could be elicited when different brain areas were stimulated, although there were more overlapping brain areas. Specifically, stimulation of the hippocampus and inferior temporal cortex primarily induces internal visual sensations. In contrast, stimulation of the occipital visual cortex mainly triggers external visual sensations. Furthermore, compared to that of the dorsal visual areas, the ventral visual areas show more overlap between the two visual sensations. Our findings show that internal and external visual sensations may rely on distinct neural representations of the visual pathway. This study indicated that implantation of electrodes in ventral visual areas should be considered during the evaluation of visual sensation aura epileptic seizures.
Article
Full-text available
Successful sequencing of the human genome and evolving functional knowledge of gene products has taken genomic medicine to the forefront, soon combining broadly with traditional diagnostics, therapeutics, and prognostics in patients. Recent years have witnessed an extraordinary leap in our understanding of ocular diseases and their respective genetic underpinnings. As we are entering the age of genomic medicine, rapid advances in genome sequencing, gene delivery, genome surgery, and computational genomics enable an ever-increasing capacity to provide a precise and robust diagnosis of diseases and the development of targeted treatment strategies. Inherited retinal diseases are a major source of blindness around the world where a large number of causative genes have been identified, paving the way for personalized diagnostics in the clinic. Developments in functional genetics and gene transfer techniques has also led to the first FDA approval of gene therapy for LCA, a childhood blindness. Many such retinal diseases are the focus of various clinical trials, making clinical diagnoses of retinal diseases, their underlying genetics and the studies of natural history important. Here, we review methodologies for identifying new genes and variants associated with various ocular disorders and the complexities associated with them. Thereafter we discuss briefly, various retinal diseases and the application of genomic technologies in their diagnosis. We also discuss the strategies, challenges, and potential of gene therapy for the treatment of inherited and acquired retinal diseases. Additionally, we discuss the translational aspects of gene therapy, the important vector types and considerations for human trials that may help advance personalized therapeutics in ophthalmology. Retinal disease research has led the application of precision diagnostics and precision therapies; therefore, this review provides a general understanding of the current status of precision medicine in ophthalmology.
Article
Deep brain stimulation is an effective treatment for obsessive–compulsive disorder but is rarely used. Action is needed by psychologists, psychiatrists and insurers so that patients with otherwise intractable cases can receive this therapy to improve their mental health.
Article
Deep brain stimulation has been extensively studied as a therapeutic option for treatment-resistant depression (TRD). DBS across different targets is associated with on average 60% response rates in previously refractory chronically depressed patients. However, response rates vary greatly between patients and between studies and often require extensive trial-and-error optimizations of stimulation parameters. Emerging evidence from tractography imaging suggests that targeting combinations of white matter tracts, rather than specific grey matter regions, is necessary for meaningful antidepressant response to DBS. In this article, we review efficacy of various DBS targets for TRD, which networks are involved in their therapeutic effects, and how we can use this information to improve targeting and programing of DBS for individual patients. We will also highlight how to integrate these DBS network findings into developing adaptive stimulation and optimal trial designs.