Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm

AbstractElectrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI).Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand.The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3s of the HFB response. These results demonstrate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

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Context-dependent relationship in high-resolution micro-ECoG studies during finger movements

Journal of Neurosurgery ◽

10.3171/2019.1.jns181840 ◽

2020 ◽

Vol 132 (5) ◽

pp. 1358-1366

Author(s):

Chao-Hung Kuo ◽

Timothy M. Blakely ◽

Jeremiah D. Wander ◽

Devapratim Sarma ◽

Jing Wu ◽

...

Keyword(s):

High Resolution ◽

Sensorimotor Cortex ◽

Index Finger ◽

Hand Movement ◽

Cortical Activation ◽

Hand Movements ◽

Finger Movements ◽

Thumb Movement ◽

Neurosurgical Patients ◽

The Relationship

OBJECTIVEThe activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.METHODSThree neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.RESULTSIn all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).CONCLUSIONSHG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

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Mirrored, imagined and executed movements differentially activate sensorimotor cortex in amputees with and without phantom limb pain

Pain ◽

10.1016/j.pain.2010.02.020 ◽

2010 ◽

Vol 149 (2) ◽

pp. 296-304 ◽

Cited By ~ 132

Author(s):

Martin Diers ◽

Christoph Christmann ◽

Caroline Koeppe ◽

Matthias Ruf ◽

Herta Flor

Keyword(s):

Sensorimotor Cortex ◽

Phantom Limb Pain ◽

Phantom Limb ◽

Limb Pain

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S268 NEURAL REPRESENTATION OF HAND MOVEMENT IN A VIRTUAL REALITY ENVIRONMENT FOR THE TREATMENT OF PHANTOM LIMB PAIN

European Journal of Pain Supplements ◽

10.1016/s1754-3207(11)70833-1 ◽

2011 ◽

Vol 5 (S1) ◽

pp. 241-242

Author(s):

S. Kamping ◽

M. Diers ◽

F. Bach ◽

H. Maaß ◽

H. Çakmak ◽

...

Keyword(s):

Virtual Reality ◽

Hand Movement ◽

Phantom Limb Pain ◽

Neural Representation ◽

Phantom Limb ◽

Virtual Reality Environment ◽

Limb Pain

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Package Architecture and Component Design for an Implantable Peripheral Nerve Stimulation and Recording System for Advanced Prosthetics

International Symposium on Microelectronics ◽

10.4071/isom-2016-wa12 ◽

2016 ◽

Vol 2016 (1) ◽

pp. 000144-000150

Author(s):

Caroline K. Bjune ◽

John R. Lachapelle ◽

Andrew Czarnecki ◽

Alexander L. Kindle ◽

John R. Burns ◽

...

Keyword(s):

Peripheral Nerve ◽

Nerve Stimulation ◽

Sensory Feedback ◽

Hand Movement ◽

Phantom Limb Pain ◽

Sensory Stimulation ◽

Peripheral Nerve Stimulation ◽

Phantom Limb ◽

Component Design ◽

Nerve Electrodes

Abstract One of the limitation of current prosthetics is the ability to provide sensory feedback to the human user. Due to this constraint, approximately 60–80 percent of amputees experience a phenomenon known as phantom limb pain, an ongoing painful sensations that to the individual, seems to be coming from the part of the limb that is no longer there. The lack of sensory feedback also limits the intuitive control of the user's hand movement, i.e. sense of grip or position. To address these limitations, we created am implantable system that could provide peripheral nerve stimulation, recording and motor control. The architecture of our Sensory-Stimulation Lead (SSL) system consist of multiple satellites connected to Draper's custom designed nerve electrodes. In this phase of the design, the implanted system is connected to a controller via percutaneous connections. The active electronics of the satellite is enclosed in a hermetic package approximately 14mm in diameter and less than 5mm thick. A custom ceramic feedthrough substrate provides the electrical connections of the internal electronics board to both the nerve electrodes and percutaneous leads. In this paper, we will describe the various packaging components of the system and the design, fabrication, and assembly considerations that drove our technology choices.

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Enhancing Gesture Decoding Performance Using Signals from Posterior Parietal Cortex: A Stereo-Electroencephalograhy (SEEG) Study

10.1101/849752 ◽

2019 ◽

Author(s):

Meng Wang ◽

Guangye Li ◽

Shize Jiang ◽

Zixuan Wei ◽

Jie Hu ◽

...

Keyword(s):

Parietal Cortex ◽

Sensorimotor Cortex ◽

Posterior Parietal Cortex ◽

Hand Movement ◽

Functional Independence ◽

Support Vector ◽

Visuomotor Task ◽

Primary Sensorimotor Cortex ◽

Posterior Parietal ◽

Decoding Accuracy

AbstractObjectiveHand movement is a crucial function for humans’ daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI.ApproachStereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r2. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding.Main ResultsWe find that a majority(L: >60%, R: >40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used.SignificanceThis is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.

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