Showing papers by "Neeraj Jain published in 2011"

Reorganization of the primary motor cortex of adult macaque monkeys after sensory loss resulting from partial spinal cord injuries.

Abstract: Long-term injuries to the dorsal columns of the spinal cord at cervical levels result in large-scale somatotopic reorganization of the somatosensory areas of the cortex and the ventroposterior nucleus of the thalamus. As a result of this reorganization, intact inputs from the face expand into the deafferented hand representations. Dorsal column injuries also result in permanent deficits in the use of digits for precision grip and a loss of fractionated movements of the digits. We determined whether the chronic loss of sensory inputs and the behavioral deficits caused by lesions of the dorsal columns in adult macaque monkeys affect organization of the motor cortex. The results show that, in the primary motor cortex, intracortical microstimulation evokes extension–flexion movements of the thumb at significantly fewer sites compared with the normal monkeys. There is a corresponding increase in the adduction–abduction movements. Furthermore, there is a significant increase in the thresholds of the currents required to evoke movements of the digits. Thus, long-term sensory loss in adult monkeys does not change the overall topography of the movement representation in the motor cortex but results in changes in the details of movement representations.

Brain–muscle interface: The next-generation BMI

Abstract: 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