Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke

ABSTRACT

Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; <4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation.

AUTHORS

• Dhakshin S. Ramanathan

• Ling Guo

• Tanuj Gulati

• April K. Hishinuma

• Seok-Joon Won

• Robert T. Knight

• Edward F. Chang

• Raymond A. Swanson

• Karunesh Ganguly

Date: 2018

DOI: 10.1038/s41591-018-0058-y

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Detecting event-related changes of multivariate phase coupling in dynamic brain networks

Authors:

• Ryan T. Canolty

• Charles F. Cadieu

• Kilian Koepsell

• Karunesh Ganguly

• Robert T. Knight

• Jose M. Carmena

Date: 2012

PubMed: 22236706

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Abstract:

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation, and regulate the efficacy of communication between cortical regions and distinct spatiotemporal scales. In this view, anatomically-connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks. Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently-developed probabilistic model - Phase Coupling Estimation (PCE; Cadieu and Koepsell, 2010) - can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly-employed phase-locking value (PLV). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and non-human primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

Cortical representation of ipsilateral arm movements in monkey and man

Authors:

• Karunesh Ganguly

• Lavi Secundo

• Gireeja Ranade

• Amy Orsborn

• Edward F. Chang

• Dragan F. Dimitrov

• Johnathan D. Wallis

• Nicholas M. Barbaro

• Robert T. Knight

• Jose M. Carmena

Date: 2009

DOI: 10.1523/JNEUROSCI.2471-09.2009

PubMed: 19828809

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Abstract:

A fundamental organizational principle of the primate motor system is cortical control of contralateral limb movements. Motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-outreaching movements, we found thatthe ensemble spiking activity in M1 could continuously representipsilateral limb position. Interestingly, this representation was more correlated with joint angles than hand position. Using bilateral electromyography recordings, we excluded the possibility that postural or mirror movements could exclusively account for these findings. In addition, linear methods could decode limb position from cortical field potentials in both monkeys. We also found that M1 spiking activity could control a biomimetic brain–machine interface reflecting ipsilateral kinematics. Finally, we recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain–machine interface.