Parkinson's disease (PD) causes impaired movement and cognition. PD can involve profound changes in cortical and subcortical brain activity as measured by electroencephalography or intracranial recordings of local field potentials (LFP). Such signals can adaptively guide deep-brain stimulation (DBS) as part of PD therapy. However, adaptive DBS requires the identification of triggers of neuronal activity dependent on real time monitoring and analysis. Current methods do not always identify PD-related signals and can entail delays. We test an alternative approach based on linear predictive coding (LPC), which fits autoregressive (AR) models to time-series data. Parameters of these AR models can be calculated by fast algorithms in real time. We compare LFPs from the striatum in an animal model of PD with dopamine depletion in the absence and presence of the dopamine precursor levodopa, which is used to treat motor symptoms of PD. We show that in dopamine-depleted mice a first order AR model characterized by a single LPC parameter obtained by LFP sampling at 1 kHz for just 1 min can distinguish between levodopa-treated and saline-treated mice and outperform current methods. This suggests that LPC may be useful in online analysis of neuronal signals to guide DBS in real time and could contribute to DBS-based treatment of PD.

Prefrontal dysfunction is a common feature of brain diseases such as schizophrenia and contributes to deficits in executive functions, including working memory, attention, flexibility, inhibitory control, and timing of behaviors. Currently, few interventions improve prefrontal function. Here, we tested whether stimulating the axons of prefrontal neurons in the striatum could compensate for deficits in temporal processing related to prefrontal dysfunction. We used an interval-timing task that requires working memory for temporal rules and attention to the passage of time. Our previous work showed that inactivation of the medial frontal cortex (MFC) impairs interval timing and attenuates ramping activity, a key form of temporal processing in the dorsomedial striatum (DMS). We found that 20-Hz optogenetic stimulation of MFC axon terminals increased curvature of time-response histograms and improved interval-timing behavior. Furthermore, optogenetic stimulation of terminals modulated time-related ramping of medium spiny neurons in the striatum. These data suggest that corticostriatal stimulation can compensate for deficits caused by MFC inactivation and they imply that frontostriatal projections are sufficient for controlling responses in time.

Prefrontal dysfunction is a common feature of brain diseases such as schizophrenia and contributes to deficits in executive functions, including working memory, attention, flexibility, inhibitory control, and timing of behaviors. Currently, few interventions can compensate for impaired prefrontal function. Here, we tested whether stimulating the axons of prefrontal neurons in the striatum could compensate for deficits in temporal processing related to prefrontal dysfunction. We used an interval-timing task that requires working memory for temporal rules and attention to the passage of time. Our previous work showed that inactivation of the medial frontal cortex (MFC) impairs interval timing and attenuates ramping activity, a key form of temporal processing in the dorsomedial striatum (DMS). We found that 20-Hz optogenetic stimulation of MFC axon terminals in the DMS shifted response times and improved intervaltiming behavior. Furthermore, optogenetic stimulation of terminals modulated time-related ramping of medium spiny neurons in the striatum. These data suggest that corticostriatal stimulation can compensate for deficits caused by MFC inactivation and they imply that frontostriatal projections are sufficient for controlling responses in time.

Brain rhythms are strongly linked with behavior, and abnormal rhythms can signify pathophysiology. For instance, the basal ganglia exhibit a wide range of low-frequency oscillations during movement, but pathological “beta” rhythms at ~ 20 Hz have been observed in Parkinson’s disease (PD) and in PD animal models. All brain rhythms have a frequency, which describes how often they oscillate, and a phase, which describes the precise time that peaks and troughs of brain rhythms occur. Although frequency has been extensively studied, the relevance of phase is unknown, in part because it is difficult to causally manipulate the instantaneous phase of ongoing brain rhythms. Here, we developed a phase-adaptive, real-time, closed-loop algorithm to deliver optogenetic stimulation at a specific phase with millisecond latency. We combined this Phase-Adaptive Brain STimulation (PABST) approach with cell-type-specific optogenetic methods to stimulate basal ganglia networks in dopamine-depleted mice that model motor aspects of human PD. We focused on striatal medium spiny neurons expressing D1-type dopamine receptors because these neurons can facilitate movement. We report three main results. First, we found that our approach delivered PABST within system latencies of 13 ms. Second, we report that closed-loop stimulation powerfully influenced the spike-field coherence of local brain rhythms within the dorsal striatum. Finally, we found that both 4 Hz PABST and 20 Hz PABST improved movement speed, but we found differences between phase only with 4 Hz PABST. These data provide causal evidence that phase is relevant for brain stimulation, which will allow for more precise, targeted, and individualized brain stimulation. Our findings are applicable to a broad range of preclinical brain stimulation approaches and could also inform circuit-specific neuromodulation treatments for human brain disease.

Brain rhythms are strongly linked with behavior, and abnormal rhythms can signify pathophysiology. For instance, the basal ganglia exhibit a wide range of low-frequency oscillations during movement, but pathological “beta” rhythms at ~ 20 Hz have been observed in Parkinson’s disease (PD) and in PD animal models. All brain rhythms have a frequency, which describes how often they oscillate, and a phase, which describes the precise time that peaks and troughs of brain rhythms occur. Although frequency has been extensively studied, the relevance of phase is unknown, in part because it is difficult to causally manipulate the instantaneous phase of ongoing brain rhythms. Here, we developed a phase-adaptive, real-time, closed-loop algorithm to deliver optogenetic stimulation at a specific phase with millisecond latency. We combined this Phase-Adaptive Brain STimulation (PABST) approach with cell-type-specific optogenetic methods to stimulate basal ganglia networks in dopamine-depleted mice that model motor aspects of human PD. We focused on striatal medium spiny neurons expressing D1-type dopamine receptors because these neurons can facilitate movement. We report three main results. First, we found that our approach delivered PABST within system latencies of 13 milliseconds. Second, we report that closed-loop stimulation powerfully influenced the spike-field coherence of local brain rhythms within the dorsal striatum. Finally, we found that 4-Hz, but not 20-Hz, PABST improved movement speed by 41%. These data provide causal evidence that phase is relevant for brain stimulation, which will allow for more precise, targeted, and individualized brain stimulation. Our findings are applicable to a broad range of preclinical brain stimulation approaches and could also inform circuit-specific neuromodulation treatments for human brain disease.