*For correspondence:
andersen@vis.caltech.edu
†
These authors contributed
equally to this work
Competing interests:
The
authors declare that no
competing interests exist.
Funding:
See page 9
Received:
18 October 2017
Accepted:
20 February 2018
Published:
10 April 2018
Reviewing editor:
Ranulfo
Romo, Universidad Nacional
Auto ́ noma de Me ́xico, Mexico
Copyright Armenta Salas et al.
This article is distributed under
the terms of the
Creative
Commons Attribution License,
which permits unrestricted use
and redistribution provided that
the original author and source are
credited.
Proprioceptive and cutaneous sensations
in humans elicited by intracortical
microstimulation
Michelle Armenta Salas
1,2†
, Luke Bashford
1,2†
, Spencer Kellis
1,2,3,4†
,
Matiar Jafari
1,2,5
, HyeongChan Jo
1,2
, Daniel Kramer
3,4
, Kathleen Shanfield
6
,
Kelsie Pejsa
1,2
, Brian Lee
3,4
, Charles Y Liu
3,4,6
, Richard A Andersen
1,2
*
1
Department of Biology and Biological Engineering, California Institute of
Technology, Pasadena, United States;
2
T & C Chen Brain-Machine Interface Center,
California Institute of Technology, Pasadena, United States;
3
USC Neurorestoration
Center, Keck School of Medicine of USC, Los Angeles, United States;
4
Department
of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, United
States;
5
UCLA-Caltech Medical Scientist Training Program, Los Angeles, United
States;
6
Rancho Los Amigos National Rehabilitation Center, Downey, United States
Abstract
Pioneering work with nonhuman primates and recent human studies established
intracortical microstimulation (ICMS) in primary somatosensory cortex (S1) as a method of inducing
discriminable artificial sensation. However, these artificial sensations do not yet provide the
breadth of cutaneous and proprioceptive percepts available through natural stimulation. In a
tetraplegic human with two microelectrode arrays implanted in S1, we report replicable elicitations
of sensations in both the cutaneous and proprioceptive modalities localized to the contralateral
arm, dependent on both amplitude and frequency of stimulation. Furthermore, we found a subset
of electrodes that exhibited multimodal properties, and that proprioceptive percepts on these
electrodes were associated with higher amplitudes, irrespective of the frequency. These novel
results demonstrate the ability to provide naturalistic percepts through ICMS that can more closely
mimic the body’s natural physiological capabilities. Furthermore, delivering both cutaneous and
proprioceptive sensations through artificial somatosensory feedback could improve performance
and embodiment in brain-machine interfaces.
DOI: https://doi.org/10.7554/eLife.32904.001
Introduction
The absence of somatosensation profoundly diminishes a person’s ability to move and interact within
their environment (
Cole and Cole, 1995
;
Sainburg et al., 1993
). Even with intact vision and hearing,
which can provide sensory information about body position, movement, and interaction, basic
behaviors such as walking or reach-and-grasp require substantially greater cognitive load without
somatosensory feedback. The severity of these deficits underscores how deeply integrated cutane-
ous and proprioceptive somatosensations are in the neural control of movement, and motivates the
problem of restoring sensation when it is missing. However, the complexity of the somatosensory cir-
cuit, and the difficulty of writing information into this circuit with sufficient integrity, have posed sig-
nificant challenges. Recent advances in brain-machine interface (BMI) technology have led to
renewed efforts in this area, under the hypothesis that providing closed-loop motor-sensory control
and feedback pathways could lead to vital increases in performance (
Bensmaia and Miller, 2014
).
Intracortical microstimulation (ICMS) is a promising technique for implementing a return path in
which electrical stimuli are written directly into the somatosensory cortex through implanted
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electrode arrays. Non-human primates (NHPs) successfully incorporated ICMS information to per-
form discrimination, detection tasks (
Romo et al., 1998
;
Romo et al., 2000
;
Tabot et al., 2013
;
Dadarlat et al., 2015
) and as sensory feedback for brain control in BMI tasks (
O’Doherty et al.,
2011
;
Klaes et al., 2014
), and recent human studies have provided insight into the feeling and per-
ception of the sensations produced through ICMS (
Flesher et al., 2016
). However, qualities ascribed
by human subjects to these sensations (e.g., ‘tingling’ or ‘buzzing’) have been mostly artificial in
nature (
Johnson et al., 2013
;
Flesher et al., 2016
), and it is as yet unclear what range of sensations
could be elicited through ICMS. Here, we present novel findings from two experiments: one which
tested each electrode over a range of amplitudes with fixed frequency, and one which tested a sub-
set of electrodes over a range of amplitudes and frequencies. We found reliable elicitation of natural
cutaneous and proprioceptive sensations spanning a range of stimulus amplitudes and frequencies,
obtained from stimulation in S1 of a single human subject (participant FG,
Figure 1
; see Materials
and methods) with a C5-level spinal cord injury. We further show that current amplitude, not fre-
quency, of the electrical stimulus differentiates the modality (i.e., cutaneous or proprioceptive) of the
elicited percept at some stimulation sites.
Results and discussion
In Experiment 1, over an eight-week period, electrical stimuli were tested across a range of current
amplitudes between 20–100
m
A, with pulse frequency held constant at 150 Hz (see Materials and
methods). Stimulation through 46/96 electrodes (48%) prompted at least one response, and there
were in total 381 reported sensations out of 1229 non-catch trials (see Materials and methods).
There was weak correlation between the number of electrodes that elicited a sensation and the cur-
rent amplitude (r = 0.34, p=0.42, Pearson linear correlation). Additionally, we found no correlation
between electrode impedance and the likelihood of elicited percepts (p=0.80, Pearson linear corre-
lation coefficient), pooling all electrode responses over all days. Furthermore, there was no signifi-
cant difference in the aggregate impedances of either electrodes that produced or did not produce
percepts (p=0.707, Kolmogorov-Smirnov two-sample test). No false positives were reported in any
eLife digest
Nerves throughout the body send information about touch, temperature, body
position and pain through the spinal cord to the brain. A part of the brain called the somatosensory
cortex processes this information. Spinal cord injuries disrupt these messages. Even though the
somatosensory cortex has not been damaged, sensation is lost for the affected body areas. No
treatment exists to repair the spinal cord so the loss of sensation is permanent.
Applying electricity to the somatosensory cortex can produce artificial sensations. Scientists are
testing this approach to restore a sense of touch for people with spinal cord injury. Early
experiments show that using different patterns of electrical stimulation generates unnatural
sensations in different body parts. People receiving the stimulation describe it as tingling or shocks.
Scientists wonder if they can improve the technique to mimic feelings like touch or body position to
make it easier for people with a spinal injury to move or use prostheses.
Now, Armenta Salas et al. generated more natural sensations in a person with a spinal cord
injury. Instead of taking the usual approach of delivering large currents to the surface of cortex, they
inserted small electrodes into the inside of the cortex to stimulate it with small currents. In the
experiments, electrodes were implanted in the somatosensory cortex of a volunteer who had lost
the use of his limbs and torso because of a spinal injury. Armenta Salas et al. applied different
patterns of electrical stimuli and the volunteer reported what they felt like. The patient described
sensations like a pinch or squeeze in the forearm or upper arm with certain patterns. In some cases,
the patient reported the sensation of the arm moving with stronger electrical currents.
The experiments show that electrical stimulation of the brain can recreate some natural
sensations. These sensations could help patients using robotic or prosthetic arms become more
dexterous. It might also help patients view artificial limbs as part of their bodies, which could
improve their sense of wellbeing.
DOI: https://doi.org/10.7554/eLife.32904.002
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catch trials, and we found no effect of trial history in the proportion of reported sensations during
stimulation (see Materials and methods). The stimulation did not trigger any painful sensations, and
no adverse events occurred during any of the sessions.
Receptive fields along the upper arm, forearm and hand corresponded to coarse somatotopical
organization in the corresponding stimulation sites.
Figure 2
shows the most frequently reported
receptive field and sensation modality for each electrode across all trials. Of the 46 electrodes with
responses, 32 evoked percepts in the upper arm, 18 in the forearm, and two in the hand (palmar sur-
face of digits and a finger pad). In agreement with previous reports, stimulation could produce per-
cepts with variably-sized receptive fields in different electrodes (
Flesher et al., 2016
). For the
majority of electrodes (24/46), receptive fields were reported in the same body region (i.e. upper
arm or forearm) or in the same plane (i.e. anterior or posterior) across all tested amplitudes. Coarse
somatotopy was present between the medial and lateral arrays (
Figure 2B
); the medial array was
Figure 1.
Array implant locations on rendered MRI image of the left hemisphere of FG. 96-channel microelectrode arrays were implanted into ventral
premotor cortex (PMv) and supramarginal gyrus (SMG), and two 48-channel stimulating arrays were implanted into primary somatosensory cortex (S1).
The insert shows the in situ array locations.
DOI: https://doi.org/10.7554/eLife.32904.003
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more likely to have reliable receptive fields in the anterior upper arm (46% of medial-array receptive
fields), while stimulation on the lateral array induced sensation more frequently on the posterior fore-
arm (51% of lateral-array receptive fields). However, there was no clear somatotopical organization
within each array as previously reported (
Kim et al., 2015a
;
Kaas, 1983
;
Flesher et al., 2016
). The
coarse somatotopy found across arrays but not within arrays, could be due to the small area of cor-
tex sampled by the implants, and the fact that the implants predominantly covered upper arm and
forearm, areas with a less established somatotopic map (
Kaas et al., 1979
;
Kaas, 1983
). Another
plausible explanation is that the topography in somatosensory cortex has been remapped after
injury (
Kaas et al., 1983
;
Florence et al., 1998
;
Moore et al., 2000
)
FG has reported a wealth of qualitative sensations induced by ICMS (
Table 1
). Unlike paresthetic
sensations experienced post-injury, these naturalistic responses were broadly characterized as
cuta-
neous
(e.g. squeeze) or
proprioceptive
(e.g. rightward movement), and as being subjectively similar
to sensations experienced prior to injury. At his own discretion, the subject used single-word
descriptors to characterize the perceived sensations as accurately as possible. Single-word descrip-
tors have the advantage that they can be compared across large data sets or subjects. However, as
experimental advances continue to push the capabilities of ICMS, responses could become more
complex and future studies might benefit from more structured descriptors, which take into consid-
eration the complexity of these sensory experiences (
Darie et al., 2017
).
Anterior Posterior
Wire
bundle
Wire
bundle
medial array
lateral array
A
B
*
Receptive Fields
Sensation modality
Cutaneous
Proprioception
*
*
*
Figure 2.
Receptive fields and sensation modality across all amplitude mapping experiments. (
A
) Receptive field location on anterior (lighter shades)
and posterior (darker shades) planes of the right upper arm (green), forearm (pink), and hand (cyan). Grid is the same that the subject referenced during
the experiment. (
B
) Schematic of the two electrode arrays implanted over S1 (
Figure 1
). Left side panels display the reported receptive fields at each
electrode location, and right side panels display the sensation modality (cutaneous - red, proprioceptive - blue). Light gray boxes show electrodes with
no reported sensation, while dark gray boxes represent reference channels which are not used in recording. The five electrodes with a thick black
outline represent the subset tested in the additional parameter-wide mapping task. Yellow and magenta asterisks mark the inferior-posterior corner of
the implants, for the medial and lateral arrays respectively.
DOI: https://doi.org/10.7554/eLife.32904.004
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We found that 18 electrodes had cutaneous-only responses across all tested current amplitudes,
while six electrodes had proprioceptive-only responses; the rest of the electrodes (22/46) had mixed
responses, where the perceived modality (cutaneous or proprioceptive) varied as stimulus parame-
ters changed. Of these mixed-response electrodes, 45% evoked mostly cutaneous sensations, 32%
evoked mostly proprioceptive sensations, and 23% had an equal number of cutaneous and proprio-
ceptive sensations (
Figure 2B
). This pattern of cutaneous and proprioceptive evoked sensations
complements recent reports of multimodal (i.e. cutaneous and proprioceptive) neurons throughout
S1 (
Yau et al., 2016
;
Kim et al., 2015b
). While prior single-unit experiments have defined maps
from single neurons to specific unimodal receptive fields (
Kaas et al., 1979
;
Kaas, 1983
;
Friedman et al., 2004
;
Romo et al., 2000
), the above results suggest that more than one variable
may be represented when mapping with ICMS. This finding may be the product of different mecha-
nisms by which receptive fields are observed through recording versus stimulation, and could be an
important topic for future work. We found a significant difference between the amplitudes that eli-
cited cutaneous or proprioceptive responses, with the distribution of proprioceptive responses
skewed towards higher amplitudes (
Figure 3A
), when pooling across all electrodes and amplitudes
that produced a sensation (p=0.039, Kruskal-Wallis nonparametric ANOVA,
c
2
(1,378)=4.41, proprio-
ceptive responses N = 79, cutaneous responses N = 302). To assess consistent current delivery
across all electrodes, we measured electrode impedance at the beginning of every session and
found no significant difference when comparing proprioceptive or cutaneous responses (p=0.237,
c
2
(1,378)=1.39) and, furthermore, we found no significant difference between the impedance of pro-
prioceptive- and cutaneous-only (p=0.922,
c
2
(1,155)=0.01) or mixed-response electrodes (p=0.372,
c
2
(1,221)=0.8). To account for potential bias from an uneven distribution of responses across ampli-
tudes, we compared the proportion of proprioceptive and cutaneous responses in a bootstrapped
resampling (N = 10000), in which each repetition drew 15 responses at each amplitude from all data
pooled across days (
Figure 3B
). We observed a clear relationship between the number of proprio-
ceptive and cutaneous responses and stimulation amplitudes, measured through overall positive
slopes in the 1st-order polynomial fit at each iteration for proprioceptive responses, and negative
slopes for cutaneous responses (
Figure 3C
).
Experiment 2 tested a subset of 5 electrodes with robust responses across all tested amplitudes
in Experiment 1 (
Figure 2B
,
Figure 3D
). In a pseudorandomly-interleaved fashion, we stimulated
each electrode with five amplitudes (range 20 to 100
m
A) at six different frequencies (range 50 to
300 Hz) over the course of three consecutive days (see Materials and methods). We reproduced the
effect of amplitude on sensation modality, either proprioceptive or cutaneous, when pooling across
all responses (p=2
10
5
,
c
2
(
1323
)
=
18.17,
Figure 3E
). Similar to the main mapping task, we did not
Table 1.
Descriptions of the most prevalent sensations by percentage of total responses.
Entries cover 90% of 381 reported sensations, with the final 10% comprising a mixture of other naturalistic cutaneous and propriocep-
tive descriptors. Each sensation is accompanied by the mode and 25th-75th percentiles in the distribution of amplitudes that elicited
each sensation, and by the same quantities for the perceived reported intensities (on a scale of 1 [weak] to 10 [strong]).
Description
% Total Sensations (381 total)
Amplitude
m
A
(mode)
Amplitude
m
A
(25th, 75th percentile)
Intensity
(mode)
Intensity
(25th, 75th percentile)
Squeeze
24.9
40
40, 87.5
7
4, 7
Tap
17.3
70
40, 80
1
1, 4
Right movement
9.7
90
55, 90
1
1, 3
Vibration
8.1
40
40, 90
2
2, 3
Blowing
6.6
60
30, 80
1
1, 2
Forward Movement
5.8
70
40, 80
1
1, 4
Pinch
5.5
40
40, 90
3
3, 6
Press
5.0
40
40, 70
7
4, 7
Upward Movement
3.9
70
70, 85
1
1.25, 4
Goosebumps
3.1
100
60, 90
5
2, 5
DOI: https://doi.org/10.7554/eLife.32904.005
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find any significant effect on modality due to electrode impedance (p=0.305,
c
2
(
1323
)=0.8). Further-
more, there was no significance when testing the effect of frequency in eliciting proprioceptive or
cutaneous responses (p=0.22,
c
2
(
1323
)
=
1.48).
This amplitude-specific effect on sensation modality is perhaps surprising given the more com-
monly observed effect of frequency and pulse-width modulation on sensation in the periphery
(
Graczyk et al., 2016
). Although there is evidence of tactile and proprioceptive inputs co-modulat-
ing S1 firing activity (
Kim et al., 2015b
), we are unaware of any reported effect of amplitude or fre-
quency thresholding for different sensory modalities in the CNS. Proprioceptive sensations are
commonly thought to derive from activity in areas 2 or 3a, while cutaneous sensations more likely
correspond to activity in areas 3b and 1. From topographical features, we estimate our implants lie
in area 1; however, with evidence of interindividual variability in the microstructural organization
within S1 (
Geyer et al., 1999
), and the potential for functional reorganization after injury
(
Kaas et al., 1983
;
Florence et al., 1998
), it is possible that higher current amplitudes could
increase the effective range of stimulation to include sensory areas 3a or 2. Moreover, given the
Figure 3.
Proprioceptive and cutaneous responses. (
A
) Kernel density estimate and box plot showing the difference in the distribution of amplitudes
associated with each report of proprioceptive (blue) or cutaneous (red) responses. (
B
) The median percentage of responses in the bootstrapped sample
(solid line) for proprioceptive and cutaneous responses at each amplitude tested. Dashed line shows 1st-order polynomial fit. (
C
) Kernel density
estimates of the distribution of slopes from 1st-order polynomial fits in each bootstrap iteration. (
D
) Pie charts show the percentage of total stimulations
of responses for the subset of electrodes tested over a range of both current amplitudes and pulse frequencies. The left panel shows an individual
example electrode (six trials per combination of amplitude and frequency) and the right panel shows data pooled over all five electrodes (30 total
stimulations per combination). The percentage of no response (white), proprioceptive (blue) or cutaneous (red) are shown. (
E
) A normalized histogram
of proprioceptive (blue) and cutaneous (red) responses at each of the amplitudes tested in experiment 2.
DOI: https://doi.org/10.7554/eLife.32904.006
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receptive fields activated during stimulation, the two implants are well within the arm and forearm
regions of S1, which might receive a larger ratio of proprioceptive-to-cutaneous signals than hand
regions (
McKenna et al., 1982
), making it more likely to activate these different modalities with
ICMS.
FG also provided subjective measures of sensation intensity and duration. Sensation intensity was
ranked from 1 to 10 (weakest to strongest). In Experiment 1, we found a strong positive correlation
between intensity and amplitude (r = 0.2, p=2.1
10
5
, Pearson linear correlation coefficient), with
an intensity of 2.4
±
1.9 a.u. (mean
±
s.d.) for 20
m
A and 4.0
±
2.1 a.u. for 100
m
A, with a slope of 0.02
(1st-order polynomial, least squares fitting). As subjective measures of intensity are most likely sensi-
tive to day-to-day variability, in post-hoc analysis we also normalized intensity values within each ses-
sion (see Materials and methods). We measured a negative correlation between the current
amplitude and the standard deviation of the intensity (r =
0.6, p=0.12). Duration of the percept
was recorded for each response as either
short
(sensation lasts only briefly at the onset of stimula-
tion),
medium
(sensation persists throughout the stimulation but not for the full length of the stimu-
lation) or
long
(sensation lasts the full duration of the stimulation). The majority of responses were
short (N = 225), followed by medium (N = 122) with very few long responses (N = 12). Stimulus dura-
tion was not recorded for 22 responses of the 381 responses. For Experiment 2 this trend was repli-
cated (N = 268, 55 and 1. Short, medium and long, respectively). There was no relationship between
duration of the sensation and either amplitude of stimulation (p=0.1,
c
2
(
1323
)
=4.53
) or frequency of
stimulation (p=0.2,
c
2
(
1323
)
=2.83
).
To our knowledge, this is the first report in human of replicable, purely naturalistic proprioceptive
and cutaneous sensations induced through ICMS. Stimulation over a wide range of amplitudes and
frequencies generated qualitatively diverse sensations, although percept modality was strongly
linked to variations in amplitude. Pairing these natural sensations with BMIs create a unique opportu-
nity to explore how effectively they can be incorporated in a closed-loop BMI system. For example,
the ability to evoke proprioceptive sensations could allow the subject to interpret position- or move-
ment-related information, as previously reported in primate studies (
Tomlinson and Miller, 2016
;
Dadarlat et al., 2015
), while eliciting cutaneous sensations could improve our ability to deliver richer
somatosensory feedback for object manipulation. Together these somatosensory signals have the
potential to improve performance and embodiment when using a BMI-controlled external device.
Materials and methods
Subject
We recruited and consented a 32-year-old male participant (FG) with C5-level complete spinal cord
injury, 1.5 years post-injury, to participate in a clinical trial of a BMI system with intracortical record-
ing and stimulation. The subject has residual sensation in the anterior-radial section of his upper
arm, and some residual sensation in the posterior-radial section of his upper arm and forearm, which
present as paresthesias. All procedures were approved by the Institutional Review Boards (IRB) of
the University of Southern California (USC) and Rancho Los Amigos National Rehabilitation Hospital
(RLA). The implant procedure occurred at Keck Hospital of USC, and study sessions took place at
RLA.
Surgical planning and implantation
Surgical planning followed the protocols described in (
Aflalo et al., 2015
), with an additional task
for identifying an implant location within somatosensory cortex. In this task, a visual cue prompted
the experimenter, who was standing next to the MRI, to reach into the MRI machine with a wooden
pole and repeatedly press at one of three points on the subjects right upper limb where he previ-
ously reported residual paresthetic sensation; biceps, forearm and thenar eminence. The subject was
instructed to attend to any residual sensation he felt at each location and report the number of times
the experimenter touched him on the cued location (
Kastner et al., 1998
;
Staines et al., 2002
).
After functional imaging, three target locations for electrode placement were identified; supramargi-
nal gyrus (SMG), ventral premotor cortex (PMv) and primary somatosensory cortex (S1). One 96-
channel, platinum-tipped Neuroport microelectrode recording array (Blackrock Microsystems, Salt
Lake City, UT) was implanted in each of SMG and PMv. Two 7
7 SIROF (sputtered iridium oxide
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film)-tipped microelectrode arrays (with 48 physically-connected channels each) were implanted in
S1. The SIROF-tipped electrodes have lower impedance than the platinum-tipped electrodes, and
thus are better suited to stimulation.
Stimulation and recording parameters
All stimuli consisted of biphasic, charge-balanced, cathodic-leading pulses, with 200
m
s width per
phase, 53
m
s interphase interval, and one-second stimulus duration delivered to a single electrode
on the S1 array only. The maximum charge delivered per phase was 20 nC. We selected these
parameters, and set electric charge limits according to safe ranges shown in ICMS studies with NHPs
(
Kim et al., 2015a
). Stimulation was delivered with a Blackrock CereStim device, and stimulation
parameters were set and delivered using the CereStim API through MATLAB (The Mathworks Inc,
Natick, MA) software (MATLAB code in Source code file 1).
Task
Experiment 1: After initial assessment of implant viability, we evaluated the effects of stimulation
parameters through a percept-detection task. For this primary mapping task, each of the 96 stimula-
tion electrodes were evaluated at eight amplitudes: 20, 30, 40, 60, 70, 80, 90, and 100
m
A, at 150
Hz. The subject was seated in a wheelchair approximately 1.5 meters from a TV screen. The subject
was instructed to look at a fixation point in the middle of the screen throughout the experiment. In
each trial, after a three-second inter-trial interval, the subject was presented with a large purple cir-
cle on the screen indicating that an electric stimulus was being delivered. Then, after a one-second
delay, an auditory cue signaled the subject to report whether he felt any sensation. When a sensa-
tion was perceived, the subject reported its location on a body and hand map, with anterior and
posterior views, by referencing a fine overlaying grid (
Figure 1
). The subject also reported qualita-
tive characteristics including the perceived stimulus intensity, the perceived duration of stimulation,
and a description of the sensation (
Table 1
). Sensations closer in nature to tactile stimuli were classi-
fied as
cutaneous
, and those triggering a feeling of movement or change in position were classified
as
proprioceptive
. To complete the mapping of amplitude, we ran trial blocks where we randomly
selected a subset of electrodes. Each block contained three replicates of stimulation per parameter,
per electrode. An additional set of trials, numbering 10% of the total trials in a block, were added as
‘catch’ trials, where the visual stimuli on the screen and auditory response cue remained identical
but the stimulation did not occur. Catch trials were randomly interleaved among the normal trials. In
each block, trials were ordered such that stimulation did not occur to the same or adjacent electro-
des concurrently.
Experiment 2: For the second mapping task, five electrodes were selected for further evaluation
at different amplitudes and frequencies. All the phases of the task and other stimulation parameters
were the same as in the previous mapping task. The subset of electrodes selected for this task were
those that exhibited the most reliable responses in the first mapping task. We varied the current
amplitude (20, 40, 60, 80, 100
m
A) and pulse frequency (50, 100, 150, 200, 250, 300 Hz), and tested
each amplitude-frequency combination six times per electrode. The full dataset was obtained over
three consecutive days. In each day, each of the five electrodes received two replicates of all possi-
ble amplitude and frequency combinations. The order of electrode stimulation was determined
pseudorandomly.
Statistics and analysis methods
Throughout the analysis we used the Kruskal-Wallis nonparametric ANOVA statistical test. We calcu-
lated correlations between responses using the Pearson linear correlation coefficient.
To examine whether response history had a significant effect on the proportion of reported sen-
sations (
de Lafuente and Romo, 2005
), we looked at differences between the distribution of
reported sensations during stimulation for three conditions: all trials, trials after a reported sensation
(
hit
) and trials after no reported sensation (
miss
). We estimated these distributions for each ampli-
tude in a given experimental session across all tested electrodes, and used Kruskal-Wallis nonpara-
metric ANOVA with Dunn-Sidak multiple comparisons correction to test for significance at each
amplitude. Furthermore, we generated a shuffle distribution of probabilities with N = 10,000 permu-
tations for hits following a hit or a miss for each amplitude. We found no significant difference
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between the shuffle distributions and the empirical data, with the actual proportion being within the
5th-95th percentile range of the shuffle distribution. For the bootstrapped resampling of proprio-
ceptive and cutaneous responses in Experiment 1, we drew 15 samples at each iteration from the
total responses at each amplitude (range 21–93 responses across all amplitudes). Where normalized
intensity data are reported, we rescaled the raw intensity (range 1–10) to a normalized scale (range
0–1) for each day by subtracting the minimum and then dividing by the maximum.
Raw data for all analysis presented in this manuscript can be found as downloadable source data
‘Responses to single-electrode stimulation’. Specific details can also be found in the first sheet of
the raw data file.
Acknowledgements
We would like to thank FG for his efforts and engagement in the clinical study, and the clinical staff
at Rancho Los Amigos for their work and dedication during the experimental sessions.
Additional information
Funding
Funder
Grant reference number Author
National Institute of Neurolo-
gical Disorders and Stroke
5U01NS098975-02
Michelle Armenta Salas
Luke Bashford
Spencer Kellis
Kelsie Pejsa
Brian Lee
Charles Y Liu
Richard A Andersen
Della Martin Foundation
Michelle Armenta Salas
David Geffen School of Medi-
cine, University of California,
Los Angeles
David Geffen Medical
Scholarship
Matiar Jafari
James G. Boswell Foundation
HyeongChan Jo
Richard A Andersen
National Science Foundation 1028725
HyeongChan Jo
National Institute of Neurolo-
gical Disorders and Stroke
NS099008-01
Daniel Kramer
T & C Chen Brain-machine
Interface Center
Richard A Andersen
The funders had no role in study design, data collection and interpretation, or the
decision to submit the work for publication.
Author contributions
Michelle Armenta Salas, Luke Bashford, Resources, Data curation, Software, Formal analysis, Valida-
tion, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing;
Spencer Kellis, Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision,
Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft,
Writing—review and editing; Matiar Jafari, Software, Investigation, Methodology; HyeongChan Jo,
Methodology; Daniel Kramer, Resources, Investigation; Kathleen Shanfield, Resources; Kelsie Pejsa,
Resources, Project administration; Brian Lee, Resources, Methodology; Charles Y Liu, Conceptualiza-
tion, Resources, Funding acquisition, Methodology; Richard A Andersen, Conceptualization, Resour-
ces, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing—
review and editing
Armenta Salas
etal
. eLife 2018;7:e32904.
DOI: https://doi.org/10.7554/eLife.32904
9 of 11
Short report
Neuroscience
Author ORCIDs
Michelle Armenta Salas
http://orcid.org/0000-0002-0634-2891
Luke Bashford
http://orcid.org/0000-0003-4391-2491
Spencer Kellis
http://orcid.org/0000-0002-5158-1058
Richard A Andersen
http://orcid.org/0000-0002-7947-0472
Ethics
Clinical trial registration: NCT01964261
Human subjects: This study was conducted in accordance with a protocol reviewed and approved by
the FDA as well as Institutional Review Boards at Rancho Los Amigos National Rehabilitation Center
and the University of Southern California (associated protocol numbers: Caltech IRB #15-0501, USC
IRB #HS-13-00492 and RLA IRB #154). The subject provided informed consent to participate in the
study, and also gave informed consent to publish.
Decision letter and Author response
Decision letter
https://doi.org/10.7554/eLife.32904.010
Author response
https://doi.org/10.7554/eLife.32904.011
Additional files
Supplementary files
.
Source code 1. Stimulation commands.
DOI: https://doi.org/10.7554/eLife.32904.007
.
Transparent reporting form
DOI: https://doi.org/10.7554/eLife.32904.008
References
Aflalo T
, Kellis S, Klaes C, Lee B, Shi Y, Pejsa K, Shanfield K, Hayes-Jackson S, Aisen M, Heck C, Liu C, Andersen
RA. 2015. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic
human.
Science
348
:906–910.
DOI: https://doi.org/10.1126/science.aaa5417
,
PMID: 25999506
Bensmaia SJ
, Miller LE. 2014. Restoring sensorimotor function through intracortical interfaces: progress and
looming challenges.
Nature Reviews Neuroscience
15
:313–325.
DOI: https://doi.org/10.1038/nrn3724
,
PMID: 24739786
Cole J
, Cole JO. 1995.
Pride and a Daily Marathon
. MIT Press.
Dadarlat MC
, O’Doherty JE, Sabes PN. 2015. A learning-based approach to artificial sensory feedback leads to
optimal integration.
Nature Neuroscience
18
:138–144.
DOI: https://doi.org/10.1038/nn.3883
,
PMID: 25420067
Darie R
, Powell M, Borton D. 2017. Delivering the sense of touch to the human brain.
Neuron
93
:728–730.
DOI: https://doi.org/10.1016/j.neuron.2017.02.008
,
PMID: 28231460
de Lafuente V
, Romo R. 2005. Neuronal correlates of subjective sensory experience.
Nature Neuroscience
8
:
1698–1703.
DOI: https://doi.org/10.1038/nn1587
,
PMID: 16286929
Flesher SN
, Collinger JL, Foldes ST, Weiss JM, Downey JE, Tyler-Kabara EC, Bensmaia SJ, Schwartz AB,
Boninger ML, Gaunt RA. 2016. Intracortical microstimulation of human somatosensory cortex.
Science
Translational Medicine
8
:361ra141.
DOI: https://doi.org/10.1126/scitranslmed.aaf8083
,
PMID: 27738096
Florence SL
, Taub HB, Kaas JH. 1998. Large-scale sprouting of cortical connections after peripheral injury in
adult macaque monkeys.
Science
282
:1117–1121.
DOI: https://doi.org/10.1126/science.282.5391.1117
,
PMID:
9804549
Friedman RM
, Chen LM, Roe AW. 2004. Modality maps within primate somatosensory cortex.
PNAS
101
:12724–
12729.
DOI: https://doi.org/10.1073/pnas.0404884101
,
PMID: 15308779
Geyer S
, Schleicher A, Zilles K. 1999. Areas 3a, 3b, and 1 of human primary somatosensory cortex.
NeuroImage
10
:63–83.
DOI: https://doi.org/10.1006/nimg.1999.0440
,
PMID: 10385582
Graczyk EL
, Schiefer MA, Saal HP, Delhaye BP, Bensmaia SJ, Tyler DJ. 2016. The neural basis of perceived
intensity in natural and artificial touch.
Science Translational Medicine
8
:362ra142.
DOI: https://doi.org/10.
1126/scitranslmed.aaf5187
,
PMID: 27797958
Johnson LA
, Wander JD, Sarma D, Su DK, Fetz EE, Ojemann JG. 2013. Direct electrical stimulation of the
somatosensory cortex in humans using electrocorticography electrodes: a qualitative and quantitative report.
Journal of Neural Engineering
10
:036021.
DOI: https://doi.org/10.1088/1741-2560/10/3/036021
,
PMID: 23665776
Armenta Salas
etal
. eLife 2018;7:e32904.
DOI: https://doi.org/10.7554/eLife.32904
10 of 11
Short report
Neuroscience
Kaas JH
, Merzenich MM, Killackey HP. 1983. The reorganization of somatosensory cortex following peripheral
nerve damage in adult and developing mammals.
Annual Review of Neuroscience
6
:325–356.
DOI: https://doi.
org/10.1146/annurev.ne.06.030183.001545
,
PMID: 6340591
Kaas JH
, Nelson RJ, Sur M, Lin CS, Merzenich MM. 1979. Multiple representations of the body within the
primary somatosensory cortex of primates.
Science
204
:521–523.
DOI: https://doi.org/10.1126/science.107591
,
PMID: 107591
Kaas JH
. 1983. What, if anything, is SI? Organization of first somatosensory area of cortex.
Physiological Reviews
63
:206–231.
DOI: https://doi.org/10.1152/physrev.1983.63.1.206
,
PMID: 6401864
Kastner S
, De Weerd P, Desimone R, Ungerleider LG. 1998. Mechanisms of directed attention in the human
extrastriate cortex as revealed by functional MRI.
Science
282
:108–111.
DOI: https://doi.org/10.1126/science.
282.5386.108
,
PMID: 9756472
Kim S
, Callier T, Tabot GA, Gaunt RA, Tenore FV, Bensmaia SJ. 2015a. Behavioral assessment of sensitivity to
intracortical microstimulation of primate somatosensory cortex.
PNAS
112
:15202–15207.
DOI: https://doi.org/
10.1073/pnas.1509265112
,
PMID: 26504211
Kim SS
, Gomez-Ramirez M, Thakur PH, Hsiao SS. 2015b. Multimodal interactions between proprioceptive and
cutaneous signals in primary somatosensory cortex.
Neuron
86
:555–566.
DOI: https://doi.org/10.1016/j.neuron.
2015.03.020
,
PMID: 25864632
Klaes C
, Shi Y, Kellis S, Minxha J, Revechkis B, Andersen RA. 2014. A cognitive neuroprosthetic that uses cortical
stimulation for somatosensory feedback.
Journal of Neural Engineering
11
:056024.
DOI: https://doi.org/10.
1088/1741-2560/11/5/056024
,
PMID: 25242377
McKenna TM
, Whitsel BL, Dreyer DA. 1982. Anterior parietal cortical topographic organization in macaque
monkey: a reevaluation.
Journal of Neurophysiology
48
:289–317.
DOI: https://doi.org/10.1152/jn.1982.48.2.
289
,
PMID: 7119852
Moore CI
, Stern CE, Dunbar C, Kostyk SK, Gehi A, Corkin S. 2000. Referred phantom sensations and cortical
reorganization after spinal cord injury in humans.
PNAS
97
:14703–14708.
DOI: https://doi.org/10.1073/pnas.
250348997
,
PMID: 11114177
O’Doherty JE
, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MA. 2011. Active tactile
exploration using a brain-machine-brain interface.
Nature
479
:228–231.
DOI: https://doi.org/10.1038/
nature10489
,
PMID: 21976021
Romo R
, Herna ́ndez A, Zainos A, Brody CD, Lemus L. 2000. Sensing without touching.
Neuron
26
:273–278.
DOI: https://doi.org/10.1016/S0896-6273(00)81156-3
Romo R
, Herna ́ndez A, Zainos A, Salinas E. 1998. Somatosensory discrimination based on cortical
microstimulation.
Nature
392
:387–390.
DOI: https://doi.org/10.1038/32891
,
PMID: 9537321
Sainburg RL
, Poizner H, Ghez C. 1993. Loss of proprioception produces deficits in interjoint coordination.
Journal of Neurophysiology
70
:2136–2147.
DOI: https://doi.org/10.1152/jn.1993.70.5.2136
,
PMID: 8294975
Staines WR
, Graham SJ, Black SE, McIlroy WE. 2002. Task-relevant modulation of contralateral and ipsilateral
primary somatosensory cortex and the role of a prefrontal-cortical sensory gating system.
NeuroImage
15
:190–
199.
DOI: https://doi.org/10.1006/nimg.2001.0953
,
PMID: 11771988
Tabot GA
, Dammann JF, Berg JA, Tenore FV, Boback JL, Vogelstein RJ, Bensmaia SJ. 2013. Restoring the sense
of touch with a prosthetic hand through a brain interface.
PNAS
110
:18279–18284.
DOI: https://doi.org/10.
1073/pnas.1221113110
,
PMID: 24127595
Tomlinson T
, Miller LE. 2016. Toward a proprioceptive neural interface that mimics natural cortical activity.
Advances in Experimental Medicine and Biology
957
:367–388.
DOI: https://doi.org/10.1007/978-3-319-47313-
0_20
,
PMID: 28035576
Yau JM
, Kim SS, Thakur PH, Bensmaia SJ. 2016. Feeling form: the neural basis of haptic shape perception.
Journal of Neurophysiology
115
:631–642.
DOI: https://doi.org/10.1152/jn.00598.2015
,
PMID: 26581869
Armenta Salas
etal
. eLife 2018;7:e32904.
DOI: https://doi.org/10.7554/eLife.32904
11 of 11
Short report
Neuroscience