From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control / 神经科学通报·英文版

In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.

Implantable Neural Probes for Brain-Machine Interfaces – Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.

Detection of movement intention using EEG in a human-robot interaction environment

Introduction : This paper presents a detection method for upper limb movement intention as part of a brain-machine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm’s flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.

Brain–muscle interface: The next-generation BMI.

Spinal cord injuries result in loss of movements below the site of injury because connections between the brain and the muscles are cut. Treatment strategies have focused on restoring connectivity by the application of drugs, or cell or tissue transplants. Brain–machine interface (BMI) devices, on the other hand, aim to improve the quality of life of the patients by using technology to record neural signals directly from the brain and using these signals to control robotic devices, which substitute for the paralysed body part by performing functions such as locomotion and feeding (Jain 2010). BMI devices, which have been successfully demonstrated in rats, monkeys and humans (Chapin et al. 1999; Wessberg et al. 2000; Hochberg et al. 2006), are based on a discovery made nearly three decades ago by Georgopoulos and colleagues. They found that in the primary motor cortex direction of movements is coded in the activity of neurons (Georgopoulos et al. 1983). The firing rate of a neuron coding for the direction of the arm movement is maximum for movement in a particular direction, and decreases as the movement direction shifts away. Neurons in the premotor cortex show a similar directional tuning, except that they discharge before the actual movement takes place, during the movement planning phase. BMI devices record activity of ensembles of neurons, analyse it using mathematical algorithms to predict the intended movement and use the output to generate command signals that control the robotic devices (figure 1A). BMI technology has recently added two new tools to its arsenal, which have the potential to overcome certain technical challenges and make it easier to implement. The first advancement is the use of an individual’s ability to modulate neural activity at will. Practitioners of Indian meditative yoga can control their brain rhythms (Khare and Nigam 2000). Interestingly, control can be achieved at the level of a single neuron. Fetz (1969) showed that monkeys could learn to modulate the firing rate of individual neurons in the motor cortex to obtain rewards, an ability that the Fetz group recently used in a BMI device (Moritz et al. 2008). Previous BMI devices have generally relied on recordings from neurons that actually participate in generating specific movements. In these devices the neuronal activity recorded when the animal is physically doing the task is used to optimize a mathematical algorithm, which is subsequently used to control the robot for mimicking the arm movement. This sequence of optimization is not possible in patients with paralysis, because the devices will be introduced post-injury; no pre-injury recordings of the neuronal activity will obviously be available. Voluntary control over the activity of neurons makes it unnecessary to know a priori the exact contribution of a neuron in the movement generation in order to get a signal suitable for controlling a robotic device. The intra-cortical electrodes can provide stable recordings for many years (Jain et al. 2001; Rajan and Jain, unpublished observations), but cannot be moved easily once placed. Moreover, electrodes often lose the ability to record from the same sets of neurons. This, combined with widespread reorganization of the brain following spinal cord injuries (Jain et al. 1997; Tandon et al. 2009; Kambi et al. 2011), can be especially problematic if recordings from specific neurons were essential for BMI devices. The ability to modulate neuronal activity also provides greater flexibility to the scientists in choosing a site for placement of intra-cortical microelectrodes. Finally, the patients can possibly generate multiple patterns of activities, allowing use of recordings from the same groups of neurons to control different movements, such as feeding and walking, which are normally controlled by different neurons in the brain. The second important technological advancement made by Fetz and colleagues (Moritz et al. 2008) gets rid of the robot as the effector device. Instead of using the brain activity to control a robotic arm, they converted the brain signals into electrical signals, which were used to stimulate the muscles of the paralysed arm. In their study, they first trained monkeys to make rotational movements of the wrist to control a cursor and move it towards a target that appeared on a computer screen, and recorded neural activity from neurons in the motor cortex that controlled the flexor and the extensor muscles of the wrist. In the second step of the training, the position of the cursor was represented as a function of the firing rates of the neurons, which were also available to the monkeys as a visual feedback. The monkeys rapidly learnt to maintain the neuronal activity at a particular level to control the cursor. Post training, muscles of the wrist were reversibly paralysed by injecting a local anesthetic into the peripheral nerves innervating the arm, thus blocking neuronal activity from reaching the arm. In the final testing step, the neuronal activities were converted into proportional electrical currents and used to directly stimulate the paralysed muscles of the arm. Monkeys learnt to precisely control the cursor by increasing or decreasing the neuronal activity, which changed the amount of electrical current delivered to the muscles, and generated the appropriate levels of wrist torque. Moreover, monkeys could independently control the activity of a pair of neurons to specifically stimulate antagonistic pairs of muscles, thus effectively restoring movement of the paralysed arm (figure 1B). Although one could assume that activity of neurons associated with the wrist movement would be more accurate at generating wrist torques, the monkeys were able to control wrist torques by controlling activity of neurons irrespective of their association with the wrist movements. As compared with a BMI device controlling a robotic arm, restoring control of movement in the paralysed arm will reduce the hardware that the patients need to carry around. Use of the natural arm will also make the device less obtrusive and aesthetically more acceptable. In their study, Moritz et al. (2008) were able to produce large ballistic movements by muscle stimulation. It is not clear if a similar device can achieve fine control of complex finger movements as this would require rapid, simultaneous or sequential control of multiple neurons. A detailed understanding of the neural control of individual muscles and the role of sensory feedback in muscle control is necessary.

Future developments in brain-machine interface research

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Odor-Dependent Hemodynamic Responses Measured with NIRS in the Main Olfactory Bulb of Anesthetized Rats

In this study, we characterize the hemodynamic changes in the main olfactory bulb of anesthetized Sprague-Dawley (SD) rats with near-infrared spectroscopy (NIRS, ISS Imagent) during presentation of two different odorants. Odorants were presented for 10 seconds with clean air via an automatic odor stimulator. Odorants are: (i) plain air as a reference (Blank), (ii) 2-Heptanone (HEP), (iii) Isopropylbenzene (IB). Our results indicated that a plain air did not cause any change in the concentrations of oxygenated (Delta[HbO2]) and deoxygenated hemoglobin (Delta[Hbr]), but HEP and IB induced strong changes. Furthermore, these odor-specific changes had regional differences within the MOB. Our results suggest that NIRS technology might be a useful tool to identify of various odorants in a non-invasive manner using animals which has a superb olfactory system.

In vivo Performance Evaluation of Implantable Wireless Neural Signal Transmission System for Brain Machine Interface

A brain-machine interface (BMI) has recently been introduced to research a reliable control of machine from the brain information processing through single neural spikes in motor brain areas for paralyzed individuals. Small, wireless, and implantable BMI system should be developed to decode movement information for classifications of neural activities in the brain. In this paper, we have developed a totally implantable wireless neural signal transmission system (TiWiNets) combined with advanced digital signal processing capable of implementing a high performance BMI system. It consisted of a preamplifier with only 2 operational amplifiers (op-amps) for each channel, wireless bluetooth module (BM), a Labview-based monitor program, and 16 bit-RISC microcontroller. Digital finite impulse response (FIR) band-pass filter based on windowed sinc method was designed to transmit neural signals corresponding to the frequency range of 400 Hz to 1.5 kHz via wireless BM, measuring over -48 dB attenuated in the other frequencies. Less than +/-2% error by inputting a sine wave at pass-band frequencies for FIR algorithm test was obtained between simulated and measured FIR results. Because of the powerful digital FIR design, the total dimension could be dramatically reduced to 23x27x4 mm including wireless BM except for battery. The power isolation was built to avoid the effect of radio-frequency interference on the system as well as to protect brain cells from system damage due to excessive power dissipation or external electric leakage. In vivo performance was evaluated in terms of long-term stability and FIR algorithm for 4 months after implantation. Four TiWiNets were implanted into experimental animals' brains, and single neural signals were recorded and analyzed in real time successfully except for one due to silicon- coated problem. They could control remote target machine by classify neural spike trains based on decoding technology. Thus, we concluded that our study could fulfill in vivo needs to study various single neuron-movement relationships in diverse fields of BMI.

Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n=34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.