? ? Over 600,000 Americans have severely impaired motor function from disorders including spinal cord injury, amyotrophic lateral sclerosis, pontine stroke, and cerebral palsy. A brain-machine interface (BMI) could enable locked-in or tetraplegic patients to communicate and interact with their environment. Two crucial decisions in designing a BMI are (1) what type of brain signals to use as inputs to a controller and (2) what methods to use to decode those signals. Most BMIs have used either noninvasive scalp EEG recordings or invasive intracortical recordings of single- or multi-neuron spikes as control inputs. A few have used subdural or intracortical local field potentials (LFPs). However, no group has yet systematically compared these signals in motor cortex for use in BMI applications. This proposal's first goal is to assess the relative performance of spikes and field potentials (both intracortical and epidural) as control inputs for a variety of movement-related outputs. Epidural field potentials (EFPs) are intermediate in invasiveness, signal quality, stability and spatial resolution compared with existing scalp, subdural, and intracortical recordings, and thus represent an unexplored middle ground. This proposal's second goal is to evaluate linear and nonlinear techniques-including several novel to BMI applications-for both decoding data and reducing the inherently large dimensionality of data from multiple neural signals. The primary hypotheses of the proposed project are (1) that spikes will perform better in decoding more complex movement-related outputs, but that field potentials may perform similarly on decoding simpler outputs, and (2) that nonlinear decoders and dimensionality-reduction techniques may provide improved accuracy over linear methods.
The specific aims to address these hypotheses are 1) to evaluate single neuron spikes as inputs to decoders of movement- related outputs, 2) to develop a novel epidural multi-electrode recording technique in the macaque monkey, and 3) to evaluate field potential signals as inputs to decoders of movement-related outputs.
Aims 1 and 3 will involve application of dimensionality-reduction algorithms (e.g., independent components analysis, Isomap) and decoding algorithms (system identification, neural networks, support vector machines) to both spikes and field potentials.
Aim 2 will entail using a computer model and spatial spectral analysis to optimize the epidural electrode array design. This project will provide the first comparison of spikes, LFPs and EFPs as inputs for identical BMI output applications. The supervision of Drs. Lee Miller and W. Zev Rymer, with additional guidance from Drs. Simon Levine, Jonathan Wolpaw and Nicholas Hatsopoulos, will provide the principal investigator with expertise in recording and processing both spikes and field potentials for BMI applications using a variety of state-of-the- art techniques. A comprehensive career development plan including clinical and research mentoring, seminars, and courses, will foster the candidate's transition into an independent physician-scientist. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08NS060223-02
Application #
7470575
Study Section
Special Emphasis Panel (ZEB1-OSR-D (M1))
Program Officer
Chen, Daofen
Project Start
2007-08-01
Project End
2012-06-30
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
2
Fiscal Year
2008
Total Cost
$167,832
Indirect Cost
Name
Northwestern University at Chicago
Department
Neurology
Type
Schools of Medicine
DUNS #
005436803
City
Chicago
State
IL
Country
United States
Zip Code
60611
Flint, Robert D; Scheid, Michael R; Wright, Zachary A et al. (2016) Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control. J Neurosci 36:3623-32
Krucoff, Max O; Rahimpour, Shervin; Slutzky, Marc W et al. (2016) Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation. Front Neurosci 10:584
Scheid, Michael R; Flint, Robert D; Wright, Zachary A et al. (2013) Long-term, stable behavior of local field potentials during brain machine interface use. Conf Proc IEEE Eng Med Biol Soc 2013:307-10
Flint, Robert D; Wright, Zachary A; Scheid, Michael R et al. (2013) Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J Neural Eng 10:056005
Flint, Robert D; Lindberg, Eric W; Jordan, Luke R et al. (2012) Accurate decoding of reaching movements from field potentials in the absence of spikes. J Neural Eng 9:046006
Flint, Robert D; Ethier, Christian; Oby, Emily R et al. (2012) Local field potentials allow accurate decoding of muscle activity. J Neurophysiol 108:18-24
Flint, Robert D; Wright, Zachary A; Slutzky, Marc W (2012) Control of a biomimetic brain machine interface with local field potentials: performance and stability of a static decoder over 200 days. Conf Proc IEEE Eng Med Biol Soc 2012:6719-22
Stevenson, Ian H; Cherian, Anil; London, Brian M et al. (2011) Statistical assessment of the stability of neural movement representations. J Neurophysiol 106:764-74
London, Brian M; Torres, Ricardo Ruiz; Slutzky, Marc W et al. (2011) Designing stimulation patterns for an afferent BMI: representation of kinetics in somatosensory cortex. Conf Proc IEEE Eng Med Biol Soc 2011:7521-4
Slutzky, Marc W; Jordan, Luke R; Lindberg, Eric W et al. (2011) Decoding the rat forelimb movement direction from epidural and intracortical field potentials. J Neural Eng 8:036013

Showing the most recent 10 out of 13 publications