Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA.
IEEE Trans Neural Syst Rehabil Eng. 2011 Oct;19(5):501-13. doi: 10.1109/TNSRE.2011.2163145. Epub 2011 Aug 30.
A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulse-rates. In studying the neural coding in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches.
为了实现假肢控制的神经接口开发，需要解决一个主要问题，即需要躯体感觉反馈。在这里，我们研究了两种可能的策略：刺激背根神经节（DRG）或初级体感皮层（S1）。在每种方法中，我们必须确定一个模型，该模型能够反映神经放电对肢体状态的表示。然后，该模型可用于设计刺激，人为地激活神经系统，将有关肢体状态的信息传达给受试者。使用基于被动肢体运动期间记录的自然刺激模式来激活 DRG 神经元，会在 S1 中引发类似于原始运动的活动。我们还发现，S1 神经群体可以在广泛的刺激脉冲率范围内准确区分不同的 DRG 刺激模式。在研究 S1 中的神经编码时，我们还使用猴子的多电极记录解码了主动肢体运动的运动学。具有本体感觉和皮肤感觉的神经元对此解码的贡献相同。有些神经元对肢体状态的最近过去最具信息量，但其他许多神经元似乎对即将发生的运动做出了信号，这表明它们也受到传出副本信号的调制。最后，我们表明，猴子能够检测到通过植入 2 区的大量电极进行的刺激。我们讨论了用于传达时变肢体状态信息的适当刺激范式的设计，以及中枢和外周方法的相对优点和局限性。
