A newly developed artificial neural connection device allows new cortical sites to swiftly regain the control of a paralyzed hand

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Restoration of lost motor function after stroke is typically accomplished after strenuous rehabilitation therapy lasting for over months.

However, new research published by a group led by Yukio Nishimura, the project leader of the Neural Prosthesis Project, Tokyo Metropolitan Institute of Medical Science showed that an artificial neural connection (ANC) successfully allowed a new cortical site, previously unassociated with hand movements, to regain control of a paralyzed hand in a matter of minutes.

In this research, experimental animals regained voluntary control of a paralyzed hand about ten minutes after establishment of an ANC. Animals engaged in learning with a functional ANC showed variable levels of input signals provided by the cerebral cortex as hand movement improved.

Specifically, the activated area of the cortex became more focused as control of hand movements improved.

Through this training of various areas of the cerebral cortex, the research team successfully imparted a new ability to control paralyzed hands via an ANC, even if those areas were not involved in hand control prior to the stroke.

Examples of such regions include areas of the cortex that controls the movement of other body parts such as the face or shoulder, and even the somatosensory cortex, which is responsible for tactile and proprioception processing and is normally not associated with motor control.

This finding suggests that an ANC can impart new motor control functions to any cortical region.

This research will contribute to the development of innovative therapies that will help stroke patients regain lost motor function by imparting this function to regions of the cerebral cortex previously not associated with hand movement.

This research will contribute to the development of innovative therapies that will help stroke patients regain lost motor function by imparting this function to regions of the cerebral cortex previously not associated with hand movement.

It is expected that these therapies will have practical clinical applications beyond restoring motor function and lead to the development of novel techniques to further integrate human brains with computers.

The researchers will continue to investigate whether extended use of an ANC will enhance the activity of spared neural networks and facilitate functional recovery so that patients will be able to regain voluntary control of paralyzed body parts even if they discontinue using the ANC


Brain-computer interface (BCI) technology aims to provide functional restoration to individuals with movement disorders such as spinal cord injury or amyotrophic lateral sclerosis. Electrocorticography (ECoG) records brain activity with electrodes placed on the cortical surface, and has emerged as a promising neural recording modality for BCIs. ECoG recordings from individuals undergoing clinical presurgical brain mapping have been used to investigate human motor [Arroyo et al., 1993Miller et al., 2007aCrone et al., 1993Crone et al., 1998bCrone et al., 1998a], sensory [Chestek et al., 2013Wahnoun et al., 2015Sun et al., 2015Branco et al., 2017], language [Crone et al., 2001Mainy et al., 2007Kellis et al., 2010Wang et al., 2011Pei et al., 2011], and other cognitive functions [Edwards et al., 2005Trautner et al., 2006Lachaux et al., 2005Tallon-Baudry et al., 2005Jung et al., 2008Ray et al., 2008].

In particular, movement-related information, including individual finger movements, hand posture [Wang et al., 2009Miller et al., 2009bKubánek et al., 2009Degenhart et al., 2011aChestek et al., 2013Collinger et al., 2014Flint et al., 2017], and arm movement trajectories can be extracted from ECoG activity [Shimoda et al., 2012Chao et al., 2010Bundy et al., 2016].

Such work has enabled the study of ECoG-based BCI systems, including real-time one- and two-dimensional computer cursor control by individuals undergoing clinical ECoG monitoring [Leuthardt et al., 2004Leuthardt et al., 2006Felton et al., 2007Schalk et al., 2008]. Additionally, closed-loop control of prosthetic limbs using ECoG has also been demonstrated in both able-bodied subjects [Hotson et al., 2016] and an individual with sensorimotor impairment [Yanagisawa et al., 2012].

While these studies have provided evidence highlighting the potential of ECoG-based BCIs, a number of questions remain about the feasibility of clinical ECoG BCI systems. Key among these is how best to use ECoG to control high degree-of-freedom (DoF) effectors.

In particular, it is unclear what the most effective behavioral strategy is to generate the multiple independent control signals necessary for high-DoF control. Previous BCI studies using single/multi-unit activity (SU/MUA) recorded with intracortical microelectrodes have shown that human subjects can control computer cursors or robotic arms simply by attempting to make the intended movement [Hochberg et al., 2006Collinger et al., 2012Hochberg et al., 2012]. This direct mapping approach requires the encoding of the intended movement kinematics in the neural activity being recorded, and potentially enables intuitive and natural control.

In contrast, current EEG and ECoG recording technology is not capable of recording cortical activity at the same spatial resolution as penetrating microelectrodes, and as a consequence, these recordings likely contain less information about detailed movement kinematics than SU/MUA. Hence, most EEG and ECoG BCI studies have used an abstract somatotopic remapping approach in which subjects associate attempted movements with desired BCI control signals, e.g., tongue and hand movements for moving the cursor in the vertical and horizontal directions, respectively [Leuthardt et al., 2004].

Such an approach allows any cortical activity capable of being robustly modulated to be re-purposed for continuous BCI control. Apart from several notable demonstrations of prosthetic hand control [Yanagisawa et al., 2012Hotson et al., 2016], most ECoG BCI studies have relied upon this approach.

However, it is currently unclear how effective the somatotopic remapping approach is for high degree-of-freedom control.

In particular, it is possible that as the number of independent control signals needed for BCI control is increased, the spatial resolution of ECoG may place a limit on what can be extracted from motor cortex. In this case, it may be necessary to use the activity of multiple cortical areas to achieve successful high-DoF BCI control using ECoG [Branco et al., 2017].

Though primary motor cortex has been the focus of most BCI studies using ECoG and intracortical microelectrodes, there are reasons to believe that somatosensory cortex may also be capable of supporting robust BCI control.

Activation of both pre and post-central gyri is often observed in individuals with chronic spinal cord injury during attempted movement [Cramer et al., 2005Shoham et al., 2001Hotz-Boendermaker et al., 2008] and in able-bodied individuals during motor imagery in the absence of overt movement [Miller et al., 2010Christensen et al., 2007Lacourse et al., 2005Porro et al., 1996]. Such somatosensory cortical activity may represent efferent copies of motor control signals [Christensen et al., 2007Crapse and Sommer, 2008Gritsenko et al., 2007], or reflect engagement of sensory imagery [Hotz-Boendermaker et al., 2008].

Furthermore, our previous work has presented evidence suggesting that somatosensory cortex can support ECoG-based BCI control [Wang et al., 2013]. However, a detailed examination of the ability of individuals with paralysis to voluntarily modulate somatosensory cortex in order to control a BCI device has not been conducted.

Ultimately, the utility of BCI systems for functional restoration is determined by how reliably they can be controlled by individuals with movement deficits such as upper-limb paralysis.

The majority of ECoG-based studies have been conducted in able-bodied individuals undergoing clinical ECoG monitoring for epilepsy treatment or in able-bodied non-human primates [Rouse and Moran, 2009Chao et al., 2010Williams et al., 2013].

These studies have provided critical theoretical and practical foundations for ECoG-based BCI applications. However, individuals with movement disorders may face unique challenges obtaining BCI control.

Chronic paralysis may result in cortical reorganization and neuronal loss due to degenerative processes in the case of amyotrophic lateral sclerosis (ALS), or loss of connection to spinal and/or peripheral neural networks in the case of spinal cord injury (SCI) or brachial plexus injury [Turner et al., 2001Wrigley et al., 2009Verstraete et al., 2010].

These anatomical and neurophysiological changes can negatively impact an individual’s ability to modulate motor and somatosensory cortical activities for BCI control [Yanagisawa et al., 2012]. Thus, it is vital to conduct ECoG BCI studies in individuals with arm paralysis to identify important clinical and translational challenges and guide the research and development of ECoG BCI systems.

Previously, we have presented work demonstrating three-dimensional ECoG-based BCI control by an individual with SCI [Wang et al., 2013]. Here, we expand upon this by including results from two additional subjects with upper-limb paralysis: one with ALS and another with brachial plexus injury. High-density ECoG grids were implanted over sensorimotor cortical areas of each participant for one month. We evaluated ECoG signal modulation during different attempted movements and show that subjects can successfully employ a somatotopic remapping strategy in order to control two- and three-dimensional (2D and 3D) computer cursor movements using ECoG signals recorded from the sensorimotor cortex.

Furthermore, we find that somatosensory cortex can be activated by attempted arm and hand movements of individuals with upper-limb paralysis, and that this activity can be leveraged for BCI control. These results demonstrate the potential of ECoG-based BCI systems to provide functional restoration for individuals with various movement disorders.


Source:
Tokyo Metropolitan Institute of Medical Science

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