Vulnerable periods during the development of the nervous system are sensitive to environmental insults because they are dependent on the temporal and regional emergence of critical developmental processes (i.e., proliferation, migration, differentiation, synaptogenesis, myelination, and apoptosis). Evidence from numerous sources demonstrates that neural development extends from the embryonic period through adolescence. In general, the sequence of events is comparable among species, although the time scales are considerably different. Developmental exposure of animals or humans to numerous agents (e.g., X-ray irradiation, methylazoxymethanol, ethanol, lead, methyl mercury, or chlorpyrifos) demonstrates that interference with one or more of these developmental processes can lead to developmental neurotoxicity. Different behavioral domains (e.g., sensory, motor, and various cognitive functions) are subserved by different brain areas. Although there are important differences between the rodent and human brain, analogous structures can be identified. Moreover, the ontogeny of specific behaviors can be used to draw inferences regarding the maturation of specific brain structures or neural circuits in rodents and primates, including humans. Furthermore, various clinical disorders in humans (e.g., schizophrenia, dyslexia, epilepsy, and autism) may also be the result of interference with normal ontogeny of developmental processes in the nervous system. Of critical concern is the possibility that developmental exposure to neurotoxicants may result in an acceleration of age-related decline in function. This concern is compounded by the fact that developmental neurotoxicity that results in small effects can have a profound societal impact when amortized across the entire population and across the life span of humans.

Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models.

The journal of neuroscience : the official journal of the Society for Neuroscience.

Background and Purpose—Injury-induced cortical reorganization is a widely recognized phenomenon. In contrast, there is almost no information on treatment-induced plastic changes in the human brain. The aim of the present study was to evaluate reorganization in the motor cortex of stroke patients that was induced with an efficacious rehabilitation treatment. Methods—We used focal transcranial magnetic stimulation to map the cortical motor output area of a hand muscle on both sides in 13 stroke patients in the chronic stage of their illness before and after a 12-day-period of constraint-induced movement therapy. Results—Before treatment, the cortical representation area of the affected hand muscle was significantly smaller than the contralateral side. After treatment, the muscle output area size in the affected hemisphere was significantly enlarged, corresponding to a greatly improved motor performance of the paretic limb. Shifts of the center of the output map in the affected hemisphere suggested the recru...

https://www.ahajournals.org/doi/pdf/10.1161/01.STR.31.6.1210

Treatment-Induced Cortical Reorganization After Stroke in Humans

One fundamental function of primary motor cortex (MI) is to control voluntary movements. Recent evidence suggests that this role emerges from distributed networks rather than discrete representations and that in adult mammals these networks are capable of modification. Neuronal recordings and activation patterns revealed with neuroimaging methods have shown considerable plasticity of MI representations and cell properties following pathological or traumatic changes and in relation to everyday experience, including motor-skill learning and cognitive motor actions. The intrinsic horizontal neuronal connections in MI are a strong candidate substrate for map reorganization: They interconnect large regions of MI, they show activity-dependent plasticity, and they modify in association with skill learning. These findings suggest that MI cortex is not simply a static motor control structure. It also contains a dynamic substrate that participates in motor learning and possibly in cognitive events as well.

https://www.uta.edu/rfmems/Telemetry/Implant%20Sensor/47185_Plasticity%20and%20primary%20motor%20cortex.pdf

Plasticity and primary motor cortex.

In contrast to peripheral nerves, central axons do not regenerate. Partial injuries to the spinal cord, however, are followed by functional recovery. We investigated the anatomical basis of this recovery and found that after incomplete spinal cord injury in rats, transected hindlimb corticospinal tract (CST) axons sprouted into the cervical gray matter to contact short and long propriospinal neurons (PSNs). Over 12 weeks, contacts with long PSNs that bridged the lesion were maintained, whereas contacts with short PSNs that did not bridge the lesion were lost. In turn, long PSNs arborize on lumbar motor neurons, creating a new intraspinal circuit relaying cortical input to its original spinal targets. We confirmed the functionality of this circuit by electrophysiological and behavioral testing before and after CST re-lesion. Retrograde transynaptic tracing confirmed its integrity, and revealed changes of cortical representation. Hence, after incomplete spinal cord injury, spontaneous extensive remodeling occurs, based on axonal sprout formation and removal. Such remodeling may be crucial for rehabilitation in humans.

The injured spinal cord spontaneously forms a new intraspinal circuit in adult rats.

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

Brain–muscle interface: The next-generation BMI

The Brain-Computer Interfaces are quite useful for persons with neurodegenerative disorders. This paper presents such an interface which is useful and intuitive for arbitrary pick and place tasks. We have developed a higher-level interface in which the user has to select the location of an object on his/her computer screen, which needs to be picked up or the location where the object needs to be placed. After providing the location, the robot directly reaches to pick the object or to place the object at the selected location. To select these locations, we have utilized a 2-class SSVEP paradigm. Using the SSVEP and eye-blinks, the user is able to perform pick and place task at arbitrary locations.

A Brain-Computer Interface for arbitrary Pick and Place Tasks

The evolution of opposable thumb has enabled fine grasping ability and precision grip, therefore the ability to finely manipulate the objects and refined tool use. Since tactile inputs to an opposable thumb are often spatially and temporally out of sync with inputs from the fingers, we hypothesized that inputs from the opposable thumb would be processed in an independent module in the primary somatosensory cortex (area 3b). Here we show that in area 3b of macaque monkeys, most neurons in the thumb representation do not respond to tactile stimulation of other digits and receive few intrinsic cortical inputs from other digits. However, neurons in the representations of other 4 digits respond to touch on any of the 4 digits and interconnect significantly more. The thumb inputs are thus processed in an independent module, whereas there is a significantly more interdigital information exchange between the other digits. This cortical organization reflects behavioral use of a hand with an opposable thumb.

Neeraj Jain

Papers

Brain–muscle interface: The next-generation BMI

A Brain-Computer Interface for arbitrary Pick and Place Tasks

Somatosensory cortex of macaque monkeys is designed for opposable thumb.