Brain-computer interfaces based on intracortical recordings of neural activity for restoration of movement and communication of people with paralysis

Paralysis has a severe impact on a patient's quality of life and entails a high emotional burden and life-long social and financial costs (`One Degree of Separation, Paralysis and Spinal Cord Injury in the United States' 2009; “Towards concerted efforts for treating and curing spinal cord injury” 2002; Arno, Levine, and Memmott 1999). Restoring movement and independence for people with paralysis remains a challenging clinical problem, currently with no viable solution. Recent demonstrations of intracortical brain-computer interfaces, neuroprosthetic devices that create a link between a person and a computer based on invasive recordings of a person's brain activity, have brought hope for their potential to restore movement and communication (Ajiboye et al. 2017; Pandarinath et al. 2017; Gilja et al. 2015; Jarosiewicz et al. 2015; Hochberg et al. 2012; Wodlinger et al. 2015; Collinger et al. 2013; Bouton et al. 2016; Aflalo et al. 2015). While the intracortical brain-computer interfaces have steadily improved over the last decade, the recent success in linking brain activity with the newly developed techniques for spinal cord stimulation look to revolutionize locomotor rehabilitation (Moraud et al. 2016; Wenger et al. 2016; Wenger et al. 2014; van den Brand et al. 2012; Rejc et al. 2016; Angeli et al. 2014; Harkema et al. 2011). Specifically, in a recent study a brain-spine interface - a neuroprostheses using gait states decoded from intracortically recorded neuronal activity to control spinal cord stimulation - restored weight-bearing locomotion of the paralyzed leg as early as six days post-injury in rhesus macaques (Capogrosso et al. 2016). The talk will discuss our progress towards enhancing the capabilities of brain-spine interfaces and demonstrating their use to alleviate motor deficits in other neurological disorders. In parallel, there is an ongoing search for identifying neural features and designing decoding algorithms with the aim to deliver both stable and accurate brain-computer interface control over clinically relevant periods of several months (Jarosiewicz et al. 2015; Vansteensel et al. 2016). The talk will also present our progress in developing techniques to identify stable neural features from intracortical neural recordings of people with tetraplegia and locked-in syndrome. The talk will show the use of these techniques to deliver stable long-term control of neural interfaces. This abstract is based on join work with Flavio Raschella, Giuseppe Schiavone, Matthew Perich, Marco Capogrosso, David Borton, Anish A. Sarma, Fabien Wagner, Eduardo Martin Moraud, Christopher Hitz, Jean-Baptiste Mignardot, Daniel Bacher, John D. Simeral, Jad Saab, Chethan Pandarinath, Brittany L. Sorice, Christine Blabe, Erin M. Oakley, Kathryn R. Tringale, Nicolas Buse, Jerome Gandar, Quentin Barraud, David Xing, Elodie Rey, Simone Duis, Yang Jianzhong, Wai Kin D. Ko, Qin Li, Chuan Qin, Emad Eskandar, Sydney S. Cash, Jaimie M. Henderson, Peter Detemple, Tim Denison, Silvestro Micera, Erwan Bezard, Jocelyne Bloch, Krishna V. Shenoy, John P. Donoghue, Leigh R. Hochberg and Grégoire Courtine.