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.

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 cutaneous 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 circuit, and the difficulty of writing information into this circuit with sufficient integrity, have posed significant 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 electrode arrays. Non-human primates (NHPs) successfully incorporated ICMS information to perform 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 perception 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 subset 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 frequency, of the electrical stimulus differentiates the modality (i.e., cutaneous or proprioceptive) of the elicited percept at some stimulation sites.

Figure 1

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In Experiment 1, over an eight-week period, electrical stimuli were tested across a range of current amplitudes between 20–100 µA, 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 current 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 correlation coefficient), pooling all electrode responses over all days. Furthermore, there was no significant 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 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 surface of digits and a finger pad). In agreement with previous reports, stimulation could produce percepts 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 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 forearm (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 cortex 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)

Figure 2

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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 cutaneous (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 descriptors 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 consideration the complexity of these sensory experiences (Darie et al., 2017).

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 parameters 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 proprioceptive 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 mechanisms 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 elicited 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, χ²(1,378)=4.41, proprioceptive 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, χ²(1,378)=1.39) and, furthermore, we found no significant difference between the impedance of proprioceptive- and cutaneous-only (p=0.922, χ²(1,155)=0.01) or mixed-response electrodes (p=0.372, χ²(1,221)=0.8). To account for potential bias from an uneven distribution of responses across amplitudes, 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 proprioceptive 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).

Figure 3

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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 μA) 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, χ²(1323)= 18.17, Figure 3E). Similar to the main mapping task, we did not find any significant effect on modality due to electrode impedance (p=0.305, χ²(1323)=0.8). Furthermore, there was no significance when testing the effect of frequency in eliciting proprioceptive or cutaneous responses (p=0.22, χ²(1323)= 1.48).

This amplitude-specific effect on sensation modality is perhaps surprising given the more commonly 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-modulating S1 firing activity (Kim et al., 2015b), we are unaware of any reported effect of amplitude or frequency 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 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 μA and 4.0 ± 2.1 a.u. for 100 μA, with a slope of 0.02 (1st-order polynomial, least squares fitting). As subjective measures of intensity are most likely sensitive to day-to-day variability, in post-hoc analysis we also normalized intensity values within each session (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 stimulation), medium (sensation persists throughout the stimulation but not for the full length of the stimulation) 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 duration was not recorded for 22 responses of the 381 responses. For Experiment 2 this trend was replicated (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, χ²(1323)=4.53) or frequency of stimulation (p=0.2, χ²(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 opportunity 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 movement-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.

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Author details

1. Michelle Armenta Salas

   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

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Luke Bashford and Spencer Kellis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0634-2891

2. Luke Bashford

   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

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Michelle Armenta Salas and Spencer Kellis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4391-2491

3. Spencer Kellis

   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

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Michelle Armenta Salas and Luke Bashford

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5158-1058

4. Matiar Jafari

   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. UCLA-Caltech Medical Scientist Training Program, Los Angeles, United States

   Contribution

   Software, Investigation, Methodology

   Competing interests

   No competing interests declared

5. HyeongChan Jo

   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

   Contribution

   Methodology

   Competing interests

   No competing interests declared

6. Daniel Kramer

   1. USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, United States
   2. Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, United States

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

7. Kathleen Shanfield

   Rancho Los Amigos National Rehabilitation Center, Downey, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

8. Kelsie Pejsa

   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

   Contribution

   Resources, Project administration

   Competing interests

   No competing interests declared

9. Brian Lee

   1. USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, United States
   2. Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, United States

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

10. Charles Y Liu

    1. USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, United States
    2. Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, United States
    3. Rancho Los Amigos National Rehabilitation Center, Downey, United States

    Contribution

    Conceptualization, Resources, Funding acquisition, Methodology

    Competing interests

    No competing interests declared

11. Richard A Andersen

    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

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing—review and editing

    For correspondence

    andersen@vis.caltech.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7947-0472

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