scholarly journals Distinct types of neural reorganization during long-term learning

2019 ◽  
Vol 121 (4) ◽  
pp. 1329-1341 ◽  
Author(s):  
Xiao Zhou ◽  
Rex N. Tien ◽  
Sadhana Ravikumar ◽  
Steven M. Chase
Keyword(s):  
Neural Activity ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Skill Learning ◽  
Cortical Reorganization ◽  
Computer Interface ◽  
Slow Phase ◽  
Full Time ◽  
Motor Error

What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW & NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.

scholarly journals EEG-based brain-computer interface in chronic tetraplegics to actuate a robotic arm device as assistive technology - clinical survey and long term post trial follow-up

BIOPHILIA ◽  
2011 ◽  
Vol 1 (4) ◽  
pp. 4_28-4_28
Author(s):  
Gelu Onose ◽  
Cristian Grozea ◽  
Aurelian Anghelescu ◽  
Cristina Daia ◽  
Crina Julieta Sinescu ◽  
...  
Keyword(s):  
Assistive Technology ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Clinical Survey ◽  
Post Trial

scholarly journals Plasticity of the Cortical Motor System

2008 ◽  
Vol 20 (1) ◽  
pp. 5-22 ◽  
Author(s):  
Bogdan Sadowski
Keyword(s):  
Motor Cortex ◽  
Motor System ◽  
Primary Motor Cortex ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Skill Learning ◽  
Dynamic Features ◽  
Cortical Motor

Plasticity of the Cortical Motor SystemThe involvement of brain plastic mechanisms in the control of motor functions under normal and pathological conditions is described. These mechanisms are based on a similar principle as the neuronal models of neuronal plasticity - long-term potentiation (LTP), and long-term depression (LTD). In the motor cortex, LTP-like phenomena play a role in strengthening synaptic connections between pyramidal neurons. LTD is important for the elimination of unnecessary inputs to the cortex. The dynamic features of the primary motor cortex activity depend on particular neuronal interconnectivity within this area. The pyramidal cells send horizontal collaterals to adjacent subregions of the primary motor cortex, and so can either excite or inhibit remote pyramidal cells. These connections can expand or shrink depending on actual physiological demands, and play a role in skill learning.

scholarly journals Primary motor cortex has independent representations for ipsilateral and contralateral arm movements but correlated representations for grasping

2019 ◽  
Author(s):  
John E Downey ◽  
Kristin M Quick ◽  
Nathaniel Schwed ◽  
Jeffrey M Weiss ◽  
George F Wittenberg ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Human Subjects ◽  
Brain Computer Interface ◽  
Hand Movement ◽  
Arm Movement ◽  
Computer Interface ◽  
Contralateral Movement ◽  
Contralateral Motor ◽  
Continuous Movements

AbstractMotor commands for the arms and hands generally originate in contralateral motor cortex anatomically. However, ipsilateral primary motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we present the results of bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in left primary motor cortex for a clinical brain-computer interface study. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects’ ability to control both arms through a brain-computer interface system. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected, and poses new questions about the different ways primary motor cortex controls hands and arms.

scholarly journals Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

2021 ◽  
Vol 15 ◽  
Author(s):  
Neethu Robinson ◽  
Tushar Chouhan ◽  
Ernest Mihelj ◽  
Paulina Kratka ◽  
Frédéric Debraine ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
Brain Activation ◽  
Classification Performance ◽  
Open Loop ◽  
Computer Interface ◽  
Single Subject ◽  
User Engagement

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

scholarly journals Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces

2009 ◽  
Vol 27 (1) ◽  
pp. E10 ◽  
Author(s):  
Eric C. Leuthardt ◽  
Zac Freudenberg ◽  
David Bundy ◽  
Jarod Roland
Keyword(s):  
Motor Cortex ◽  
Minimally Invasive ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Future Application ◽  
Electrode Spacing ◽  
Sufficient Information ◽  
Invasive Monitoring ◽  
Seizure Localization

Object There is a growing interest in the use of recording from the surface of the brain, known as electrocorticography (ECoG), as a practical signal platform for brain-computer interface application. The signal has a combination of high signal quality and long-term stability that may be the ideal intermediate modality for future application. The research paradigm for studying ECoG signals uses patients requiring invasive monitoring for seizure localization. The implanted arrays span cortex areas on the order of centimeters. Currently, it is unknown what level of motor information can be discerned from small regions of human cortex with microscale ECoG recording. Methods In this study, a patient requiring invasive monitoring for seizure localization underwent concurrent implantation with a 16-microwire array (1-mm electrode spacing) placed over primary motor cortex. Microscale activity was recorded while the patient performed simple contra- and ipsilateral wrist movements that were monitored in parallel with electromyography. Using various statistical methods, linear and nonlinear relationships between these microcortical changes and recorded electromyography activity were defined. Results Small regions of primary motor cortex (< 5 mm) carry sufficient information to separate multiple aspects of motor movements (that is, wrist flexion/extension and ipsilateral/contralateral movements). Conclusions These findings support the conclusion that small regions of cortex investigated by ECoG recording may provide sufficient information about motor intentions to support brain-computer interface operations in the future. Given the small scale of the cortical region required, the requisite implanted array would be minimally invasive in terms of surgical placement of the electrode array.

Optimizing the Usability of Brain-Computer Interfaces

2018 ◽  
Vol 30 (5) ◽  
pp. 1323-1358 ◽  
Author(s):  
Yin Zhang ◽  
Steve M. Chase
Keyword(s):  
Neural Activity ◽  
Skill Learning ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Biomimetic Interfaces ◽  
Extended Practice ◽  
Definition Of ◽  
Open Question ◽  
The Brain

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

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