In the United States, over 5 million people are living with paralysis, with stroke and spinal cord injury (SCI) as the leading causes (1). Intracortical brain–computer interfaces (BCIs) have been used to decode intended movements from neural activity recorded in the primary motor cortex (2–5) and subsequently used to restore intention-driven movement in tetraplegia (6). To enable dexterous movements and decrease reliance on visual feedback, direct stimulation of the sensory cortex has recently been used to provide somatosensory feedback (7, 8). Cortical stimulation is also used for pain management [i.e., motor cortex stimulation (MCS)], which may be valuable for people with paralysis who are known to have chronic pain (9). Cortical stimulation of somatosensory or motor areas may also be used in the future to enhance neural sprouting to improve therapeutic neuroplasticity (10–13). Despite the growth in the field of implanted neural interfaces, there is limited discussion determining the optimal planning and targeting of the implants.

Preparing for epilepsy or other respective brain surgeries in able-bodied patients typically entail pre-surgical mapping that relies on intact function, such as active movement. However, for people with impaired motor and sensory function, standard mapping procedures are not always possible or optimal. Because this patient population is unable to move or perceive external somatosensory stimuli, mapping approaches for BCIs have typically been based on anatomy. Some studies have adapted standard pre-surgical mapping methods to help localize motor and sensory areas (3, 8, 14, 15).

For typical pre-surgical mapping, the goal is to determine eloquent areas to be avoided during surgery. The common methods are task-based fMRI, evoked field mapping with magnetoencephalography (MEG), or transcranial magnetic stimulation (TMS). Task-based fMRI identifies areas where blood flow changes when performing a task or processing a stimuli. This technique provides a 3D map of activation, but the signal has a multiple-seconds time scale leading to broad areas of activation (i.e., high sensitivity, but low specificity). Another caveat to fMRI is the necessity for strong magnetic RF fields that are often incompatible with implants that a person with impairments often have (e.g., spinal bracing and peripheral nerve stimulation devices). Transcranial magnetic stimulation stimulates the brain transcutaneously to find cortical areas that initiate eloquent responses. Though TMS mapping is currently uncommon clinically, its most common application is for mapping motor function by sending magnetic pulse through the skull and activating the descending fibers of the motor cortex to induce a motor twitch at the hand. However, for many people with paralysis, the connections from their brain to the hand are disrupted (i.e., spinal cord), impeding the TMS mapping. Furthermore, externally activating the brain with TMS induces activation of the white matter tracts related to function, though the target for most cortical BCIs is gray matter. Transcranial magnetic stimulation has been used in paralyzed populations but not pre-surgical planning (16–19).

Contrary to fMRI and TMS, MEG directly maps the neural activity internally generated by the brain. Magnetoencephalography can also be performed safely for people who have metallic implants. Like all somatosensory and motor mapping, MEG mapping relies on the patient to have intact function. However, because cortical responses are expected to be weaker and have more complex patterns in people with known sensorimotor dysfunction (20, 21), traditional analysis methods are not ideal. For example, the standard dipole pole models used in traditional magnetic source imaging [i.e., equivalent current dipole (ECD)] map single points to represent the brain activity during events, but are less accurate in cases of low signal-to-noise ratios and complex, widespread activation patterns (22–24). Instead, distributed source models (DSMs) overcome these limitations by modeling multiple dipoles across the brain surface to explain the recorded magnetic field. Distributed source models map the amplitudes of the currents across the cortical surface that could have generated the recorded magnetic field.

Another limitation of traditional MEG mapping is the focus on early components of the evoked response. In particular, somatosensory evoked fields (SSEFs) typically evaluate the activity 20 ms after electrical stimulation. This component of the response (i.e., N20m) originates from pyramidal cells in the posterior wall of the central sulcus (cortical area 3b). However, N20 response has a lower amplitude in patients with SCI compared to healthy controls likely due to afferent disruption (21). Later components of the evoked field (e.g., P100m) have stronger responses and map to areas that process more advanced information such as shapes and textures (25). Furthermore, unlike the early components that correspond to the input signals, these later components correspond to the conscious experience that are the targets for BCIs (26).

Optimized methods to map sensorimotor activity on the cortex are needed to guide brain interface devices of the future. These advanced mapping techniques are especially important for patients with sensorimotor impairment who have the most to gain from BCI technology. We present a method and results for mapping somatosensory and motor function in an individual with paralysis and an existing implanted BCI device. This approach may be valuable for guiding implants in other applications, such as cortical stimulation for the treatment of pain, and to improve implant targeting to help reduce the craniotomy size.

Evoked activity was identified and mapped for all tasks, even though the motor and sensory capacity of the participant was impaired. Figure 3 shows the peak of the source maps and the broader organization. Table 1 shows the times and parameters used for source mapping.

Figure 1 shows the evoked sensory field waveforms used in the analysis. Somatosensory activity peaks were found near the P35m peak for all body parts. This peak was clear for normal face sensation, but weak for the impaired sensations at the thumb and index finger. However, the source maps matched what was expected based on anatomy. A late component of the evoked field was observed for the thumb task at 107 ms. This activity was also mapped and localized as expected based on anatomy. This later component was not seen on the index finger task, likely because the participant could not perceive the sensation and this late component is related to perception (33, 34). However, it should be noted that an earlier component (30 ms) for the index finger had an expected physical location based on anatomy and somatotopy.

Figure 2 shows the evoked motor field waveforms used in the analysis. Due to the complexity of the motor tasks, it was challenging to determine the timing for the motor maps. Despite this, the motor activity mapped well to the expected based on somatotopy and anatomy. Typical motor evoke analysis maps the activity before a physical trigger to represent the initiation of the movement. However, this was not possible given the participant's impairment and the tasks performed. Therefore, the first obvious peak activity in the ROI was mapped. These times were 104 ms after experimenter trigger for the thumb (D1) and 67 ms after experimenter trigger for the index finger (D2). These delays fit the expected delays that would occur during a predictable action observation task. As the participant could perform overt wrist extension with limited mobility, the wrist task involved the release of a button to trigger the data by the participant himself. For this wrist task, two components were seen: one just before the trigger (−7 ms) and just after (58 ms). We hypothesize that the initial activity (−7 ms) corresponded to the initiation of the movement where he raised his finger holding down a button while the later component (58 ms) corresponded to the actual execution of the wrist movement. The earlier component mapped to the expected anatomical finger area of the motor cortex (i.e., the inferior part of the hand knob) and overlapped with the index and thumb motor maps. The later component was lateral to the finger areas and the earlier wrist component.

The evoked fields from impaired somatosensory and motor tasks were successfully mapped for an individual with paralysis and existing implanted BCI hardware. In general, these maps fit the expected anatomy and somatotopic organization (see Figure 3). These maps included both somatosensory and motor function of the index finger despite the participant being unable to elicit any movement or perceive any sensation in that digit. Though the response was modest for the index finger, being able to map body parts without any intact function is encouraging for BCI applications in patients with severe paralysis. Overall, these results add to the limited existing literature that demonstrates that MEG can be used for somatosensory and motor mapping in paralyzed populations. Mapping with MEG is especially critical since mapping with fMRI is often not possible due to implanted metallic objects in this patient population (e.g., fixation and pain stimulators).

This is the first demonstration of non-invasive sensorimotor mapping in an individual with an existing BCI implant. As BCI technology improves, patients will want to take advantage of new hardware, but it will be important that we can map intact brain activity in those with existing hardware to guide placement of upgrades. It is unlikely that BCI hardware will be MRI compatible in the near future; therefore, MEG may play an important role in these cases.

Several key aspects of the methods presented contributed to successful mapping. Having over 700 trials to average across for each impaired task was likely necessary to elucidate the weak evoked fields. To optimize the time with the participant and maximize the number of trials, we had to focus the data collection to a few tasks. To do this, we had to have a deep understanding of the participant's limited intact function and the goal of the mapping (e.g., targeting gyri of the hand area). Another key was noise reduction. Using filtering, such as tSSS, can greatly reduce artifacts, but we still needed to remove 84 sensors due to noise from the previous implant. Also, restricting the analysis to the implant target ROI was necessary since mapping the global peak activity would not have been in the ROI. This is likely due to the artifacts present and/or the weak evoked responses. Mapping more intact functions as intra-subject controls (i.e., wrist movement and face stimulation) confirmed that our data and analysis were appropriate and gave us confidence in the results.

Task design was also important in eliciting the most brain activity associated with impaired function. To map motor function of the completely paralyzed index finger and thumb, our task required the participant to imagine and attempt the movement simultaneously while watching an experimenter perform the movement. Our previous work with participants who were paralyzed due to SCI demonstrated that motor-related MEG signals are stronger when attempting to move over just imagining the movement (20). Having the participant watch the movement performed also taps into the action observation and mirror neuron systems (35–40).

Unfortunately, the two somatosensory peaks for the thumb (42 and 107 ms) did not align. The earlier peak was more noisy and right next to the index finger map while the later peak was more pronounced but more lateral. Considering the participant's limited residual sensation and the noisiness of the early component, we are hesitant to rely on the early component for implant targeting. The early component was included in this analysis to demonstrate that a response existed during the ideal time range of for a clinical evoked study. It is interesting to speculate, however, that this spatial difference could represent neuroplasticity and remapping/unmasking of thumb representation in the previous index finger representation area. It is possible that the thumb representation expanded medially into the finger area since the participant had only sensation in the proximal part of his thumb. This would suggest that the area between the 42 and 107-ms components may all be thumb related. However, the choice to target early or late components for a sensory restoration device implant is not clear. On one hand, the later components may map areas where conscious perception occurs and electrically activating those areas may induce near-normal perceived sensation (33, 34). However, stimulating the area of the earlier component may tap into pre-perception areas of the afferent neural stream. For this case, we are more interested in the later component since it has a clearer waveform that localizes to the expected anatomy, though for us, our implant plan includes three arrays that can cover the two thumb areas and another area of interest such as index finger.

The MEG mapping described here will inform surgical planning and will help minimize the extents of the craniotomy. Awake intraoperative cortical stimulation is also under consideration to further confirm final placement of the electrode arrays. The MEG mapping approach reported here may also be useful in placement of other implant types such as subdural or depth electrocorticography electrodes, which have also been demonstrated in evoking focal tactile percepts in the hand (41, 42).

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Northwell Health Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

CB and SC designed the study. SC, JC, JL, and CB conducted the tests. SF analyzed the data, wrote the paper, and helped design the experiments. JE reviewed and analyzed images. All authors reviewed and edited the paper.

CB has ownership interests in Neuvotion, Inc. and Sanguistat, LLC and is an inventor on multiple patents in the related field of neuroprosthetics. JE is a member of the Medical Advisory Board of Compumedics LLC. Compumedics Neuroscan makes EEG/MEG hardware and software, but they were not used in this study. The remaining 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.

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.