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Divergent pallidal pathways underlying distinct Parkinsonian behavioral deficits

Abstract

The basal ganglia regulate a wide range of behaviors, including motor control and cognitive functions, and are profoundly affected in Parkinson’s disease (PD). However, the functional organization of different basal ganglia nuclei has not been fully elucidated at the circuit level. In this study, we investigated the functional roles of distinct parvalbumin-expressing neuronal populations in the external globus pallidus (GPe-PV) and their contributions to different PD-related behaviors. We demonstrate that substantia nigra pars reticulata (SNr)-projecting GPe-PV neurons and parafascicular thalamus (PF)-projecting GPe-PV neurons are associated with locomotion and reversal learning, respectively. In a mouse model of PD, we found that selective manipulation of the SNr-projecting GPe-PV neurons alleviated locomotor deficit, whereas manipulation of the PF-projecting GPe-PV neurons rescued the impaired reversal learning. Our findings establish the behavioral importance of two distinct GPe-PV neuronal populations and, thereby, provide a new framework for understanding the circuit basis of different behavioral deficits in the Parkinsonian state.

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Fig. 1: Distinct subpopulations of GPe-PV neurons project to the SNr and PF.
Fig. 2: Whole-brain mapping of inputs to PVGPe-SNr and PVGPe-PF neurons.
Fig. 3: PVGPe-SNr and PVGPe-PF neurons exhibit distinct electrophysiological properties.
Fig. 4: Activity of PVGPe-SNr neurons bidirectionally modulates locomotion.
Fig. 5: Activation of PVGPe-PF neurons impairs reversal learning.
Fig. 6: PVGPe-SNr and PVGPe-PF neurons exhibit distinct electrophysiological adaptations to dopamine depletion.
Fig. 7: PVGPe-SNr and PVGPe-PF neurons mediate different behavioral deficits in dopamine-depleted mice.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code that supports the findings of this study is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank D. Knowland and C. Santiago for their comments on the manuscript. S. Lilascharoen helped with quantification in Fig. 1 and with illustrations. We thank C. Gremel for essential comments on the reversal learning behavioral experiment. We thank the members of the Lim laboratory for support and discussions. V.L. was supported by the Anandamahidol Foundation Fellowship. This work was supported by grants from the National Institutes of Health (U01NS094342, R01DA049787, R01NS097772, R01MH108594 and U01MH114829).

Author information

Authors and Affiliations

Authors

Contributions

V.L. and B.K.L. conceived and designed the study. V.L. performed all electrophysiological recordings. V.L., E.H.W., S.C.P. and A.N.T. performed stereotaxic surgery. V.L., C.D.Y. and E.H.W. performed behavioral experiments. V.L., S.C.P., A.N.T. and X.-Y.W. designed and generated viruses. V.L., J.H.C., N.D., S.C.P. and A.N.T. performed histology and immunohistochemistry. E.H.W. constructed the fiber photometry recording. V.L., S.C.P. and Y.-G.P. performed SHIELD-MAP tissue clearing and light-sheet imaging. Y.-G.P. and K.C. provided resources for SHIELD-MAP. H.P. assisted with the operant behavior experiment and in situ hybridization. V.L., E.H.W. and B.K.L. analyzed the data and interpreted the results. V.L., E.H.W. and B.K.L. wrote the manuscript with contributions from S.C.P. and A.N.T.

Corresponding author

Correspondence to Byung Kook Lim.

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Competing interests

K.C. is a co-inventor on a patent application owned by MIT covering the SHIELD technology and is a co-founder of LifeCanvas Technologies.

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Peer review information Nature Neuroscience thanks Aryn Gittis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Molecular identity of PVGPe-SNr and PVGPe-PF neurons.

a. mFISH experiments for PVGPe-SNr neurons (top row) and PVGPe-PF neurons (bottom row) with several molecular markers for GPe neurons including parvalbumin (Pvalb), somatostatin (Sst), sodium voltage-gated channel beta subunit 4 (Scn4b), LIM homeobox protein (Lhx6), NK2 homeobox 1 (Nkx2-1) and Forkhead box protein P2 (Foxp2). PVGPe-SNr and PVGPe-PF neurons are labelled by the probe against eGFP. Scale bar in a, 20 mm. b, c, Quantification of GFP-labelled PVGPe-SNr neurons (b, n = 3 mice) and GFP-labelled PVGPe-PF neurons (c, n = 4 mice) expressing specific molecular markers. Fractions show the number of neurons expressing each molecular marker out of total GFP positive neurons in sections.

Extended Data Fig. 2 Controls for fiber photometry recordings.

a, Optic fiber placements in PF and SNr for fiber photometry recordings. Different colors denote fibers from the same mouse (n = 7 mice). b, Representative images from n = 7 mice used in photometry recordings showing mRuby3 and axon-GCaMP6s expression in axons of GPe-PV neurons at implantation sites for optic fibers. Scale bars, 200 μm. c, Z-scored ΔF/F (averaged across all events) representing the activity of PVGPe-SNr axons at randomly chosen time points during treadmill locomotion. Number of events was determined based on the number of locomotion onsets in each recording session (n = 135 events). d, same as in c, but showing the activity of PVGPe-PF axons. e, Z-scored ΔF/F (averaged across 7 mice) representing the activity of PVGPe-SNr axons at randomly chosen time points during different stages of reversal-learning task. Number of events was determined based on the number of trials in each stage of the task. f, same as in e, but showing the activity of PVGPe-PF axons. Shaded areas accompanying the z-scored ΔF/F traces in c-f indicate SEM.

Extended Data Fig. 3 Validation of optogenetic activation and chemogenetic inhibition.

a-b, Fiber tip locations in PF (a) and SNr (b) for data in Figs. 4f-g, 5j-k, 7b and Extended Data Fig. 5, 8b-c. c, Representative traces from cell-attached recording showing inhibition of firing activity in SNr neurons during photostimulation of PVGPe-SNr axons at different frequencies. d, Firing rates of SNr neurons during 5-50 Hz and constant photostimulation of PVGPe-SNr axons (n = 21 cells). Data presented as mean ± SEM. e. Representative ex vivo cell-attached recording from PVGPe-SNr neurons (top) and PVGPe-PF neurons (bottom) expressing hM4Di. Gray bars show the application of CNO during recording. f-g. Summary data showing firing rates before and 2 min after CNO bath application of both PVGPe-SNr neurons (f; n = 3 cells; Paired t-test, t(2) = 9.259; *p = 0.0115) and PVGPe-PF neurons (g; n = 3 cells; Paired t-test, t(2) = 5.459; *p = 0.0320).

Extended Data Fig. 4 Structure of the reversal-learning task and comparison of overall neural activity during trials and inter-trial intervals.

a, Example task structure using gravel and sand as digging media to provided two different contexts. The food reward is paired with sand in the association phase and is later switched to gravel in the reversal-learning phase. b, Timeline of the task showing how the association, early, and late reversal-learning stages are defined for a representative mouse. Check marks and crosses represent correct and incorrect trials, respectively. c, Average speed of animals across different stages of the reversal learning task. Note that no significant difference in locomotion was observed at different stages of the task. (Left: Duration of Trial to Choice, n = 9 mice, One-way ANOVA, F(2, 24) = 0.4743, p = 0.6280; Right: Duration of Choice, n = 9, One-way ANOVA, F(2, 24) = 0.7019, p = 0.5055). All data presented as mean ± SEM. d, Comparison of mean z-scored ΔF/F for PVGPe-SNr axons between trial periods and inter-trial intervals (ITI) during the association phase (left; Wilcoxon sign-rank test, W = -12; p = 0.3750, n = 7 mice) and reversal-learning phase (right; Wilcoxon sign rank test, W = -18; p = 0.1562, n = 7 mice). e, same as in d, but for PVGPe-PF axons during the association phase (left; Wilcoxon sign-rank test, W = -28; *p = 0.0156, n = 7 mice) and reversal-learning phase (right; Wilcoxon sign rank test, W = -28; *p = 0.0156, n = 7 mice).

Extended Data Fig. 5 Activation of PVGPe-PF neurons increased number of regressive errors made during reversal learning.

a-b, Number of errors during the reversal-learning phase made by mice that received photostimulation in PVGPe-SNr neurons (n = 7 mice for eGFP, n = 9 mice for oChIEF). a, Perseverative errors; Unpaired t-test, t(14) = 0.9432, p = 0.3616. b, Regressive errors; Unpaired t-test, t(14) = 0.3002, p = 0.7684. c-d, Number of errors during the reversal-learning phase made by mice that received photostimulation in PVGPe-PF neurons (n = 10 mice for eGFP, n = 8 mice for oChIEF). c, Perseverative errors; Unpaired t-test, t(16) = 0.8109, p = 0.4293. d, Regressive errors; Unpaired t-test, t(16) = 2.951, **p = 0.0094. All data presented as mean ± SEM.

Extended Data Fig. 6 Role of PVGPe-SNr and PVGPe-PF in reversal learning test for discriminated operant response in lever-pressing system.

a, A schematic diagram for behavioral task. After the rule switch, active lever becomes inactive, and vice versa. b-d, Traces of z-scored ΔF/F (averaged across 5 mice) from PVGPe-PF axons during session start (b), from start to lever pressing (c) and during lever pressing (d) at the different behavioral stage. Note that the fiber photometry signals for interval between session start and lever pressing was interpolated because of the difference in interval. e-g, Traces of z-scored ΔF/F (averaged across 4 mice) from PVGPe-SNr axons during session start (e), from start to lever pressing (f) and during lever pressing (g) at the different behavioral stage. Note that the fiber photometry signals for interval between session start and lever pressing was interpolated because of the difference in interval. h-k, Activation of PVGPe-SNr and PVGPe-PF axons did not affect association (h, j) and reversal (i, k) in operant discrimination tasks. l-o, Inhibition of PVGPe-SNr and PVGPe-PF axons did not affect association (l, n) and reversal (m, o) in operant discrimination tasks. Shaded areas accompanying the z-scored ΔF/F traces in b-g indicate SEM. All other data are presented as mean ± SEM.

Extended Data Fig. 7 Quantification of TH immunoreactivity.

a, Representative image of TH immunoreactivity in the striatum of a mouse injected with vehicle (0.02% sodium ascorbate in 0.9% saline). b, Representative image of TH immunoreactivity in the striatum 3 days after bilateral injection of low-dose 6-OHDA (1.25 μg/μl). c, Representative image of TH immunoreactivity in the striatum 10 days after bilateral injection of high-dose 6-OHDA (2.5 μg/μl). d, Quantification of TH immunoreactivity at different stages of dopamine depletion in rescue experiments for reversal learning and locomotion. Data presented as % mean ± SEM of naïve control striatal sections (n = 6 mice for PVGPe-PF: eGFP (vehicle), n = 7 mice for PVGPe-PF: eGFP (OHDA), n = 7 mice for PVGPe-PF: hM4D (OHDA), n = 10 mice for PVGPe-SNr: eGFP (OHDA), and n = 11 mice for PVGPe-SNr: oChIEF (OHDA)). Scale bar, 1 mm (a-c).

Extended Data Fig. 8 Manipulation of PVGPe-PF but not PVGPe-SNr neurons rescues behavioral flexibility deficit in dopamine-depleted mice.

a, Number of errors during the reversal-learning phase made by mice that received chemogenetic inhibition in PVGPe-PF neurons after dopamine depletion (n = 7 mice for eGFP-vehicle, n = 7 mice for eGFP-OHDA, and n = 7 mice for hM4Di-OHDA). Left, perseverative errors; One-way ANOVA, F(2,18) = 0.9771, p = 0.3955. Right, regressive errors; One-way ANOVA, F(2,18) = 5.595, p = 0.0129; Bonferroni’s post hoc test, *p = 0.0312 (eGFP-vehicle vs. eGFP-OHDA) and 0.0267 (eGFP-OHDA vs. hM4Di-OHDA). b, Performance of mice that received photostimulation in PVGPe-SNr neurons after dopamine depletion (n = 6 mice for eGFP-vehicle, n = 4 mice for eGFP-OHDA, and n = 6 mice for oChIEF-OHDA). Left, dopamine depletion did not affect performance in the association phase. One-way ANOVA, F(2,13) = 0.8222, p = 0.4611. Right, activation of PVGPe-SNr neurons during reversal learning did not improved behavioral flexibility in dopamine-depleted mice. One-way ANOVA, F(2,13) = 11.69, p = 0.0012; Bonferroni’s post hoc test, **p = 0.0032 (eGFP-vehicle vs. eGFP-OHDA) and 0.0042 (eGFP-vehicle vs. oChIEF-OHDA). c, Number of errors during the reversal-learning phase made by mice in c. Left, perseverative errors; One-way ANOVA, F(2,13) = 1.308, p = 0.3038. Right, regressive errors; One-way ANOVA, F(2,13) = 0.6464, p = 0.54. All data presented as mean ± SEM.

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Lilascharoen, V., Wang, E.HJ., Do, N. et al. Divergent pallidal pathways underlying distinct Parkinsonian behavioral deficits. Nat Neurosci 24, 504–515 (2021). https://doi.org/10.1038/s41593-021-00810-y

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