Phase-dependent closed-loop modulation of neural oscillations in vivo
McNamara CG., Rothwell M., SHAROTT A.
Normal brain function is associated with an assortment of oscillations of various frequencies, each reflecting the timing of separate computational processes and levels of synchronization within and between brain areas. Stimulation accurately delivered on a specified phase of a given oscillation provides the opportunity to target individual aspects of brain function. To achieve this, we have developed a highly responsive system to produce a continuous online phase-estimate. In addition to stable oscillations, the system accurately tracks the early cycles of short, transient oscillations and can operate across the frequency range of most established neuronal oscillations (4 to 250 Hz). Here we demonstrate bidirectional modulation of the pathologically elevated parkinsonian beta-band oscillation (around 35 Hz) in 6-OHDA hemi-lesioned rats. Beta phase, monitored using a single channel electrocorticogram above secondary motor cortex, was used to drive electrical stimulation of the globus pallidus on one of eight phases spanning the oscillation cycle. Stimulation of the early ascending phase suppressed the oscillation whereas stimulation of the early descending phase was amplifying. By implementing a rule that prevented stimulation when the phase estimate was unstable, we achieved a system that could adapt stimulation rate and pattern to respond to the changes produced in the target oscillation. This allowed the electronic system to create and maintain a state of equilibrium with the biological system resulting in continuous stable modulation of the target oscillation over time. These results demonstrate the feasibility of phase locked stimulation as a more refined strategy for remediation of pathological beta oscillations in the treatment of the motor symptoms of Parkinson’s disease. Furthermore, they establish the utility of our algorithm and allow for the potential to assess the contribution of rhythmic activity in neuronal computation across a number of brain systems.Competing Interest StatementCGM and AS are inventors on a pending patent application related to the subject matter of this paper.