Phase-adaptive Brain Stimulation of Striatal D1 Medium Spiny Neurons

doi:10.21203/rs.3.rs-1390088/v1

Download PDF

Research Article

Phase-adaptive Brain Stimulation of Striatal D1 Medium Spiny Neurons

https://doi.org/10.21203/rs.3.rs-1390088/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

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.

basal ganglia

D1-MSNs

striatum

L-DOPA-induced dyskinesia

Parkinson's disease

medium-spiny-neurons

Neuronal activity involves electrical potentials that oscillate over time ¹. Brain rhythms describe voltage oscillations in the time domain. These oscillations are characterized by a frequency, how often they oscillate (Hz), and a phase (radians), the precise time that peaks and troughs of oscillations occur. For instance, brain rhythms with the same frequency and opposite phase might have different voltage depending on the instantaneous phase ². The frequency of brain rhythms has been extensively studied ^3,4. However, the phase of brain rhythms may be particularly relevant for brain stimulation in cases where pulses might arrive at the peak of a phase, when voltage is high, or at the trough of a phase, when voltage is low ^5,6. These stimulation pulses might have very different effects dependent on the phase of oscillating brain rhythms. With the advent of clinical brain stimulation for Parkinson’s disease (PD) and other neuropsychiatric diseases ^7–9, determining the significance of the phase is critical to future efforts of developing brain stimulation therapies for human brain disease.

We explored oscillation frequency and phase in forebrain networks in the dorsal striatum, a key brain structure for motor control. The dorsal striatum has marked movement-related low-frequency oscillations ^10–13. In addition, dorsal striatal beta rhythms at ~ 20 Hz are linked with movement suppression ^12,14,15. Critically, these beta rhythms are pathologically increased in PD ^16,17 and depend on the neurotransmitter, dopamine, which is a key treatment in PD ^14,18,19. Closed-loop brain stimulation, which has been shown to improve motor symptoms of human PD, can be triggered by the amplitude of beta rhythms ^20,21. However, despite insights that the phase of different rhythms can be coupled ^22,23, it is unclear precisely what role phase is playing in dorsal striatal brain rhythms. A key barrier lies in the difficulty to detect the phase of brain rhythms in real-time, and the even more difficult challenge to deliver brain stimulation to manipulate these rhythms with closed-loop stimulation. Indeed, causally studying the phase of brain rhythms requires perturbing brain networks at temporally precise moments on the scale of milliseconds to target specific phases (peak, or 0 radians, or trough, or pi radians) of these ongoing rhythms.

Here we developed techniques and algorithms for real-time stimulation with millisecond system latencies between recordings and stimulation. This system, which enabled us to test the hypothesis that Phase-Adaptive Brain STimulation (PABST) of striatal brain rhythms impacts movement. We tested this idea in dopamine-depleted mice in which we delivered closed-loop optogenetic stimulation of D1-dopamine expressing striatal medium spiny neurons (D1 MSNs). We report three results: 1) Our PABST system has sufficiently low latency (13 milliseconds) to deliver laser pulses in phase with ongoing rhythms; 2) Phase-specific striatal stimulation powerfully affects spike-field coherence in the dorsal striatum, but not local field potentials (LFPs); and 3) 4-Hz PABST improved movement velocity, whereas 20-Hz PABST did not. These data suggest that phase is important for 4-Hz striatal oscillations. These findings, although in a limited context, are relevant for understanding forebrain brain rhythms, for refining preclinical brain stimulation efforts, and for delivering optimized and individualized clinical brain stimulation.

Mice: Six D1 dopamine receptor (D1DR)-Cre mice (Drd1a-cre+; derived from Gensat strain EY262; aged 3 months; 25–32 g) were used in this study. Mice genetics were verified by genotyping, using primers for the D1-Cre recombinase transgene (D1-Cre-F: 5′-AGG GGC TGG GTG GTG AGT GAT TG; D1-Cre-R: CGC CGC ATA ACC AGT GAA ACA GC-3′). All procedures were approved by the Institutional Animal Care and Use Committee (#0062039) at the University of Iowa, in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and the ARRIVE guidelines.

Dopamine-depletion and ChR2 expression

As in our previous work, we used an adeno-associated vector (AAV) construct with floxed inverted channelrhodopsin (AAV-ChR2), along with mCherry (University of North Carolina at Chapel Hill (UNC) Vector Core; AAV5-EF1a-DIO-hChR2(H134R)-mCherry) to stimulate neurons expressing D1DRs ^24,25. We injected mice with AAV-ChR2 (0.5 µL of approximately ≥ 1x10¹³ vg/ml) into the dorsal striatum (AP + 0.5/ML -1.5/DV -3.0) and immediately placed an optical fiber cannula (200 µm core, 0.22 NA, Doric Lenses) at the same coordinates as the injection site. We also unilaterally injected the neurotoxin, 6-hydroxydopamine (6-OHDA; 2.5 µl with 2 ug/ul), into the dorsal striatum in two injection locations (AP + 0.4/ML -1.8/DV -3.5 and AP + 1.1/ML -2.0/DV -3.5) to deplete dopamine; of note, this manipulation reliably kills nigrostriatal dopamine terminals and produces reliable impairments in striatal field potentials ^10,26.

Phase-adaptive brain stimulation (PABST)

For PABST, we implanted 16-channel microwire optrodes (Microprobes) in the dorsal striatum at AP + 0.5/ML -1.5/DV -3.0 (Fig. 1). We recorded LFPs across 16 channels using an Open Ephys system. The raw signal was amplified with a total gain of 198X, high-pass filtered at 0.1 Hz, and recorded with 16-bit resolution at a 30k-Hz sampling rate.

To estimate phase, LFPs were streamed from the Open Ephys system to a notebook computer (Dell) via a zeroMQ data messaging system. Real-time analysis was performed in MATLAB; data were buffered over ~ 10 milliseconds. Wavelet-based frequency and phase estimation were performed using a complex Morelet. Instantaneous frequency and phase were calculated using wavelets and predicted for 50 milliseconds in the future using linear estimation.

During closed-loop experiments, D1DR + mice with optical cannula were connected to the optical patch cable through a zirconia ferrule (Doric Lenses) without anesthesia. Light was generated using a 473-nm diode-pumped solid-state (DPSS) laser source (OEM Laser Systems), and an optical rotary joint (Doric Lenses) was used to facilitate animal rotation. Before each experiment, the power output of the laser was adjusted to 8 mW at the fiber tip. Power measurements verified that the laser reached 90% power within 0.74 milliseconds of TTL triggers and maintained 8 mW with < 5% error.

Motion tracking

We captured motion using a 3-D motion-tracking system (OptiTrack); we have previously used this system to track mouse movement in detail ^10,26. Briefly, we implanted two 4-mm infrared-reflective spheres attached to the recording headstage in the anterior-posterior dimension. Four infrared cameras recorded the X (right-left), Y (forward-back), and Z (up-down) coordinates of the mouse’s head at 120 frames/per second (frames/s) to track head position ¹⁰. Automated computer tracking data were synchronized with a video camera at 30 frames/s and neurophysiological recording hardware.

Histology: ChR2 expression and electrode localization were confirmed by immunohistochemistry. Briefly, mice were deeply anesthetized with ketamine (100 mg/kg) and xylazine (10 mg/kg) and intracardially perfused with ice-cold 4% paraformaldehyde. Brains were post-fixed in 4% paraformaldehyde overnight and immersed in 30% sucrose until the brains sank. Brains were sectioned (50 µm) with a cryostat (Leica) and stored in PBS. Immunostaining procedures were performed with free-floating brain sections. Primary antibodies to Cre (mouse anti-Cre; Millipore-MAB 3120; 1:500), D1 receptor (rat anti-D1DR; Sigma-D2944; 1:200), and tyrosine hydroxylase (rabbit anti-TH; Millipore -MAB152; 1:500) were incubated overnight at 4° C. Sections were visualized with Alexa Fluor fluorescent secondary antibodies (goat anti-mouse IgG Alexa 633, goat anti-rat IgG Alexa 568, goat anti-rabbit IgG Alexa 488, and goat anti-mouse IgG Alexa 350; Thermo Fisher Scientific; 1:1,000) and matched with the host primary by incubating 2 hours at room temperature. Images were captured on an Olympus VS120 microscope.

Statistics

All procedures were reviewed by the Biostatistics, Epidemiology, and Research Design Core within the University of Iowa Institute for Clinical and Translational Science. To avoid making any distributional assumptions about the data, continuous measures were reported as medians and inter-quartile ranges. Testing for significant differences followed the non-parametric Wilcoxon rank sum test.

We tested the hypothesis that the phase-adaptive brain stimulation (PABST) of dorsal striatal brain rhythms impacts behavior. We developed a real-time, closed-loop algorithm to calculate the ongoing instantaneous phase of brain rhythms from striatal LFPs to effect PABST. We streamed striatal LFPs to notebook computers for real-time preprocessing, filtering, and phase calculation in MATLAB. The sources of latency in these calculations were: A) headstage to Open Ephys acquisition system latency: <0.1 millisecond (Figure 1A); B) input buffer latencies (required for time-frequency analyses): 10.00 (9.87–10.10) milliseconds (all values median (Q1-Q3; Figure 1B)); C) calculation latency: 2.05 (1.86–2.32) milliseconds (Figure 1C); and D) Open Ephys to MATLAB latency: 0.21 (0.20–0.24)) milliseconds (Figure 1D). During optogenetic brain stimulation, the 473-nm laser achieved 90% power at 0.74 milliseconds (Figure 1E). Although it is difficult to simultaneously measure latencies and deliver stimulation, we estimated the total median latency as 12.98 milliseconds. Because this latency is less than the 50 millisecond period of 20-Hz rhythms, these data show that this system latency is compatible with closed-loop PABST targeting dorsal striatal beta rhythms.

We estimated the phase-accuracy of PABST in targeting 4 Hz and 20 Hz dorsal striatal brain rhythms. Because all real-time brain stimulation systems have some lag, they must predict the phase at some point in the future, even if it is a few milliseconds (Figure 2A). We estimated the phase of striatal LFPs (Figure 2B–D) and delivered real-time, closed-loop stimulation pulses targeting 4-Hz and 20-Hz rhythms (Figure 2E). We found that at 4 Hz the system produced an average phase difference of -0.01 (-0.90–0.82; Figure 2F), and at 20 Hz the system produced an average phase difference of 0.10 (-0.93–1.11; Figure 2G). These data indicate that we can deliver phase-adaptive brain stimulation to striatal LFPs at 4 Hz and 20 Hz.

We harnessed PABST to deliver closed-loop, real-time brain stimulation in the dorsal striatum. We tracked the position of mice at ~10-millisecond accuracy using an infrared tracking system (Figure 3A). We implanted recording electrodes in the mouse dorsal striatum (Figure 3B) and depleted dopamine using the neurotoxin 6-OHDA, which produces reliable impairments in movement and changes striatal oscillations (Figure 3C) ^10,26. We expressed ChR2+ in D1 MSNs. With this approach, we were able to record from striatal neuronal ensembles and LFPs in dopamine-depleted animals as they moved freely and were able to deliver real-time phase-responsive optogenetic stimulation of D1DR+ neurons.

Next, we examined how PABST influenced striatal brain networks. We recorded LFPs and neuronal activity in six mice, during 30-second epochs without stimulation, with closed-loop stimulation targeting either 4-Hz or 20-Hz peaks. We found that there was no reliable difference in striatal power spectra between peak vs. trough stimulation, although 20-Hz peak stimulation was marginally significant (p = 0.090; Cohen’s d = 1.3; Figure 4A–D). However, we found consistent differences in spike-field coherence with 4-Hz and 20-Hz peak vs. trough stimulation (Spike-field coherence, 4-Hz peak: p = 0.005; Cohen’s d = 6.2, Figure 4E) and (Spike-field coherence, 20-Hz peak: p = 0.002; Cohen’s d = 2.5, Figure 4F). These data suggest that in the dorsal striatum, 4-Hz peak stimulation increased spike-field coherence, whereas 20-Hz peak stimulation decreased spike-field coherence.

Finally, we examined whether PABST influences movement velocity, as has been shown previously for striatal D1-MSN stimulation ^27,28. We found that stimulation of D1-MSNs in-phase with 4-Hz oscillations at troughs, but not peaks, dramatically increased movement speed (Figure 5A). Of note, these movements were quite distinct from high-velocity dyskinetic movement observed with high-dose levodopa or with high-intensity optogenetics ^10,29. We compared velocity changes in six mice for peak vs. trough PABST stimulation relative to epochs with no stimulation (Figure 5A). Strikingly, we found that 4-Hz trough PABST reliably increased velocity relative to 4-Hz peak PABST (peak 0.93 (0.86–1.02) vs. trough 1.30 (1.24–2.09); p = 0.002; Cohen’s d = 1.7; 41% increase over peak stimulation; Figure 5B). We did not observe reliable results for 20-Hz peak vs. trough PABST (peak: 1.27 (1.20–1.32) vs. trough 1.46 (1.25–2.09; p = 0.130; Figure 5C). Taken together, these data suggest that the phase of 4-Hz PABST is relevant to D1-MSN stimulation of movement velocity. These data provide insight into the nature of D1- MSN firing relative to striatal brain rhythms and could be useful for future brain stimulation technologies.

We interrogated striatal brain rhythm phase using adaptive brain stimulation. We used PABST to deliver optogenetic pulses at the peak phase or trough phase of ongoing striatal brain rhythms. First, we found that the latency of PABST was 13 milliseconds, and that we could deliver phase-accurate stimulation at the peak/trough with 4-Hz and 20-Hz rhythms. Second, PABST targeting D1-MSNs powerfully modulated spike-field coherence of striatal brain rhythms, but not the overall power of striatal brain rhythms. Finally, 4-Hz trough PABST increased movement velocity by 41% compared to 4-Hz peak PABST. These data directly test the hypothesis that the phase of ongoing brain rhythms is relevant to brain stimulation and provide insight into brain rhythms in the dorsal striatum.

Neural systems are characterized by diverse and complex rhythms ¹. Our work demonstrates a novel, causal role for dorsal striatal brain rhythm phase in movements, albeit in a highly limited context—optogenetic D1-MSN stimulation of the dorsal striatum in dopamine-depleted mice. In the dorsal striatum, bursts of low-frequency rhythms can be powerfully affected by movement ^13,30. Because these rhythms are abnormal in basal ganglia disorders in preclinical models of movement disorders such as PD ^10,11,15,31, and because these rhythms synchronize striatal oscillations ³², understanding the details of these oscillations is highly relevant for human disease. PABST is a new technology, combining off-the-shelf equipment to deliver phase-responsive stimulation with millisecond precision to dynamic striatal networks. This technology could be used, along with other similar techniques ³³ to probe a variety of neuronal oscillations in the context of behavior and disease. Indeed, most preclinical and all clinical brain stimulation are delivered without consideration of these underlying brain rhythms and phase. Closed-loop technologies may be critical to adjusting brain stimulation to ongoing dynamics, so considering brain rhythm frequency and phase might help maximize the efficacy of brain stimulation for a wide range of applications ^6,34.

We found that 4-Hz trough PABST of D1-MSNs is effective for improving movement velocity. In the context of D1-MSNs, our work is consistent with prior work that has shown that D1-MSN stimulation promotes movement ^27,35. Our work advances prior stimulation approaches by showing that highly precise 4-Hz trough PABST, but not 20 Hz peak PABST, improved movement velocity in dopamine-depleted animals. This work could be important not only for a basic understanding of basal ganglia networks, but also for targeted brain stimulation interventions to improve movement disorders ^8,36. Current adaptive brain stimulation approaches target the power in frequency-specific bands, whereas we target the phase ²⁰. While our work is currently a proof-of-concept in preclinical models, PABST has the potential to be more powerful while delivering fewer pulses, and thus more efficient, even when considering the computational requirements of PABST. Future work will investigate PABST’s potential to treat movement disorders.

D1-MSNs have been strongly implicated in dyskinesias ^37,37. Stimulation of these neurons with higher stimulation frequencies (10–20 Hz) has also been shown to promote dyskinesias ^10,38. We did not observe dyskinesias with 20-Hz stimulation in the present study, but dyskinesias may occur in animals receiving high-dose levodopa or stimulation PABST. We have observed 4-Hz spike-field coherence around dyskinetic movements ¹⁰. Because 4-Hz trough PABST can decrease spike-field coherence, it might be particularly effective in dyskinesias.

Our work has several limitations. First, all real-time, closed-loop systems involve finite computation time and must account for latency by predicting dynamic brain conditions in the future. Our phase-latency is 13 milliseconds. It is possible that with higher fidelity models of future brain state, phase accuracy will improve, enabling more effective brain stimulation ³⁹. Second, it is striking that we did not see differences in LFP power but did see differences in spike-field coherence. These data suggest that PABST affected local neuronal populations, but not wider brain networks. It is possible that with stimulation of more D1-MSNs, or another circuit element, we could achieve more powerful effects. Finally, our OptiTrack technology is measures head movement, but it is possible that there are other fine movements that our system does not capture ⁴⁰.

In summary, our work provides causal evidence of the relevance of phase to brain stimulation. Our phase-adaptive technology delivers low-latency stimulation which powerfully affects striatal spike-field coherence and increases movement velocity by 40% in dopamine-depleted mice. Future work will refine these methods and further refine PABST to deliver highly optimized and personalized brain stimulation with maximal efficacy.

Author Contributions

YK and NSN designed the experiments. YK, DJ, MK, LT, and SLA collected the data. YK, DJ, and NSN wrote the code and checked the analysis. YK and NSN wrote the manuscript. All authors reviewed the manuscript.

Data Availability

All code and raw data are available at https://narayanan.lab.uiowa.edu.

Acknowledgements

This work was funded by MH116043 and NS120987.

Buzsaki, G. Rhythms of the Brain: (Oxford University Press, 2011).
Cohen, M. X. Analyzing Neural Time Series Data: Theory and Practice (Issues in Clinical and Cognitive Neuropsychology). (The MIT Press, 2014).
Lindsley, D. B. Psychological phenomena and the electroencephalogram. Electroencephalogr Clin Neurophysiol 4, 443–56 (1952).
Steriade, M. Grouping of brain rhythms in corticothalamic systems. Neuroscience 137, 1087–1106 (2006).
Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).
Grosenick, L., Marshel, J. H. & Deisseroth, K. Closed-loop and activity-guided optogenetic control. Neuron 86, 106–139 (2015).
Mayberg, H. S. et al. Deep Brain Stimulation for Treatment-Resistant Depression. Neuron 45, 651–660 (2005).
Perlmutter, J. S. & Mink, J. W. Deep brain stimulation. Annu. Rev. Neurosci. 29, 229–257 (2006).
Sheth, S. A. et al. Limbic system surgery for treatment-refractory obsessive-compulsive disorder: a prospective long-term follow-up of 64 patients: Clinical article. J. Neurosurg. 118, 491–497 (2013).
Alberico, S. L., Kim, Y.-C., Lence, T. & Narayanan, N. S. Axial levodopa-induced dyskinesias and neuronal activity in the dorsal striatum. Neuroscience 343, 240–249 (2017).
Emmons, E. B., Ruggiero, R. N., Kelley, R. M., Parker, K. L. & Narayanan, N. S. Corticostriatal Field Potentials Are Modulated at Delta and Theta Frequencies during Interval-Timing Task in Rodents. Front. Psychol. 7, (2016).
Feingold, J., Gibson, D. J., DePasquale, B. & Graybiel, A. M. Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks. Proc. Natl. Acad. Sci. 112, 13687–13692 (2015).
Tort, A. B. L. et al. Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task. Proc. Natl. Acad. Sci. 105, 20517–20522 (2008).
Avila, I. et al. Beta frequency synchronization in basal ganglia output during rest and walk in a hemiparkinsonian rat. Exp. Neurol. 221, 307–319 (2010).
Singh, A. & Papa, S. M. Striatal Oscillations in Parkinsonian Non-Human Primates. Neuroscience 449, 116–122 (2020).
Giannicola, G. et al. The effects of levodopa and ongoing deep brain stimulation on subthalamic beta oscillations in Parkinson’s disease. Exp. Neurol. 226, 120–127 (2010).
Singh, A. et al. Timing variability and midfrontal ~4 Hz rhythms correlate with cognition in Parkinson’s disease. NPJ Park. Dis. 7, 14 (2021).
Jenkinson, N. & Brown, P. New insights into the relationship between dopamine, beta oscillations and motor function. Trends Neurosci. 34, 611–618 (2011).
Lemaire, N. et al. Effects of dopamine depletion on LFP oscillations in striatum are task- and learning-dependent and selectively reversed by L-DOPA. Proc. Natl. Acad. Sci. 109, 18126–18131 (2012).
Little, S. et al. Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 87, 717–721 (2016).
Little, S. et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol. 74, 449–457 (2013).
de Hemptinne, C. et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson’s disease. Nat. Neurosci. 18, 779–786 (2015).
de Hemptinne, C. et al. Exaggerated phase-amplitude coupling in the primary motor cortex in Parkinson disease. Proc. Natl. Acad. Sci. U. S. A. 110, 4780–4785 (2013).
Kim, Y.-C. et al. Optogenetic Stimulation of Frontal D1 Neurons Compensates for Impaired Temporal Control of Action in Dopamine-Depleted Mice. Curr. Biol. CB 27, 39–47 (2017).
Kim, Y.-C. & Narayanan, N. S. Prefrontal D1 Dopamine-Receptor Neurons and Delta Resonance in Interval Timing. Cereb. Cortex N. Y. N 1991 (2018) doi:10.1093/cercor/bhy083.
Anjum, M. F. et al. Linear Predictive Approaches Separate Field Potentials in Animal Model of Parkinson’s Disease. Front. Neurosci. 14, 394 (2020).
Kravitz, A. V. et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature 466, 622–626 (2010).
Kravitz, A. V. & Kreitzer, A. C. Optogenetic manipulation of neural circuitry in vivo. Curr. Opin. Neurobiol. 21, 433–439 (2011).
F Hernandez, L., Castela, I., Ruiz-DeDiego, I., Obeso, J. A. & Moratalla, R. Striatal activation by optogenetics induces dyskinesias in the 6-hydroxydopamine rat model of Parkinson disease. Mov. Disord. Off. J. Mov. Disord. Soc. 32, 530–537 (2017).
Courtemanche, R., Fujii, N. & Graybiel, A. M. Synchronous, Focally Modulated β-Band Oscillations Characterize Local Field Potential Activity in the Striatum of Awake Behaving Monkeys. J. Neurosci. 23, 11741–11752 (2003).
Singh, A. et al. Human striatal recordings reveal abnormal discharge of projection neurons in Parkinson’s disease. Proc. Natl. Acad. Sci. U. S. A. 113, 9629–9634 (2016).
Berke, J. D., Okatan, M., Skurski, J. & Eichenbaum, H. B. Oscillatory Entrainment of Striatal Neurons in Freely Moving Rats. Neuron 43, 883–896 (2004).
Schatza, M. J., Blackwood, E. B., Nagrale, S. S. & Widge, A. S. Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE). 2021.06.21.449019 https://www.biorxiv.org/content/10.1101/2021.06.21.449019v1 (2021) doi:10.1101/2021.06.21.449019.
Srinivasan, S. S., Maimon, B. E., Diaz, M., Song, H. & Herr, H. M. Closed-loop functional optogenetic stimulation. Nat. Commun. 9, 5303 (2018).
Freeze, B. S., Kravitz, A. V., Hammack, N., Berke, J. D. & Kreitzer, A. C. Control of basal ganglia output by direct and indirect pathway projection neurons. J. Neurosci. Off. J. Soc. Neurosci. 33, 18531–18539 (2013).
Limousin, P. et al. Electrical stimulation of the subthalamic nucleus in advanced Parkinson’s disease. N. Engl. J. Med. 339, 1105–1111 (1998).
Aubert, I. et al. Increased D1 dopamine receptor signaling in levodopa-induced dyskinesia. Ann. Neurol. 57, 17–26 (2005).
Perez, X. A., Zhang, D., Bordia, T. & Quik, M. Striatal D1 medium spiny neuron activation induces dyskinesias in parkinsonian mice. Mov. Disord. 32, 538–548 (2017).
Mitra, P. Observed Brain Dynamics. (Oxford University Press, 2007).
Bova, A. et al. Precisely timed dopamine signals establish distinct kinematic representations of skilled movements. eLife 9, e61591 (2020).

No competing interests reported.

Download PDF

Editorial decision: Major revision
27 Apr, 2022
Reviews received at journal
06 Apr, 2022
Reviewers agreed at journal
04 Apr, 2022
Reviewers invited by journal
25 Mar, 2022
Editor assigned by journal
25 Mar, 2022
Editor invited by journal
07 Mar, 2022
Submission checks completed at journal
07 Mar, 2022
First submitted to journal
23 Feb, 2022

You are reading this latest preprint version

Phase-adaptive Brain Stimulation of Striatal D1 Medium Spiny Neurons

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1