Songbird subthalamic neurons project to dopaminergic midbrain and exhibit singing-related activity

2021 ◽  
Author(s):  
Anindita Das ◽  
Jesse H. Goldberg
Keyword(s):  
Brain Regions ◽  
Skill Learning ◽  
Song Learning ◽  
Dopamine Neurons ◽  
Neural Recordings ◽  
Performance Benchmarks ◽  
Performance Error ◽  
Song Production ◽  
And Performance ◽  
Subthalamic Neurons

Skill learning requires motor output to be evaluated against internal performance benchmarks. In songbirds, ventral tegmental area (VTA) dopamine neurons (DA) signal performance errors important for learning, but it remains unclear which brain regions project to VTA and how these inputs may contribute to DA error signaling. Here we find that the songbird subthalamic nucleus (STN) projects to VTA and that STN micro-stimulation can excite VTA neurons. We also discover that STN receives inputs from motor cortical, auditory cortical and ventral pallidal brain regions previously implicated in song evaluation. In the first neural recordings from songbird STN, we discover that the activity of most STN neurons is associated with body movements and not singing, but a small fraction of neurons exhibits precise song timing and performance error signals. Our results place the STN in a pathway important for song learning, but not song production, and expand the territories of songbird brain potentially associated with song learning.

scholarly journals Songbird subthalamic neurons signal song timing and error and project to dopaminergic midbrain

2021 ◽  
Author(s):  
Anindita Das ◽  
Jesse H. Goldberg
Keyword(s):  
Ventral Tegmental Area ◽  
Brain Regions ◽  
Skill Learning ◽  
Song Learning ◽  
Dopamine Neurons ◽  
Neural Recordings ◽  
Performance Benchmarks ◽  
Performance Error ◽  
Ventral Tegmental ◽  
And Performance

AbstractSkill learning requires motor output to be evaluated against internal performance benchmarks. In songbirds, ventral tegmental area (VTA) dopamine neurons (DA) signal performance errors important for learning, but it remains unclear which brain regions project VTA and how these inputs may implement the sensorimotor comparisons necessary for error computation. Here we find that the songbird subthalamic nucleus (STN) projects to VTA and that STN microstimulation can excite VTA neurons. We also discover that STN receives inputs from auditory cortical and ventral pallidal brain regions previously implicated in song evaluation. In the first neural recordings from songbird STN, we discover that the activity of most STN neurons is associated with body movements and not singing, but a small fraction of neurons exhibits precise song timing and performance error signals consistent with performance evaluation. Together our results implicate the STN-VTA projection as an evolutionarily conserved pathway important for motor learning and expand the territories of songbird brain associated with song learning.New & NoteworthySongbird subthalamic (STN) neurons exhibit song-timing and performance error signals and are interconnected with auditory pallium, ventral pallidum and ventral tegmental area, three areas important for song learning.

scholarly journals Dopamine Neurons Evaluate Natural Fluctuations in Performance Quality

2021 ◽  
Author(s):  
Alison Duffy ◽  
Kenneth W Latimer ◽  
Jesse H. Goldberg ◽  
Adrienne L. Fairhall ◽  
Vikram Gadagkar
Keyword(s):  
Basal Ganglia ◽  
Motor Skills ◽  
Dopamine Neurons ◽  
Performance Quality ◽  
Performance Benchmarks ◽  
Ongoing Behavior ◽  
Performance Error ◽  
First Time ◽  
Spiking Activity

Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown if dopamine also signals the quality of natural performance fluctuations. Here we recorded dopamine neurons in singing birds and examined how spontaneous dopamine spiking activity correlated with natural fluctuations in ongoing song. Antidromically identified basal ganglia-projecting dopamine neurons correlated with recent, and not future, song variations, consistent with a role in evaluation, not production. Furthermore, dopamine spiking was suppressed following the production of outlying vocal variations, consistent with a role for active song maintenance. These data show for the first time that spontaneous dopamine spiking can evaluate natural behavioral fluctuations unperturbed by experimental events such as cues or rewards.

scholarly journals Dopamine neurons change their tuning according to courtship context in singing birds

2019 ◽  
Author(s):  
Vikram Gadagkar ◽  
Pavel A. Puzerey ◽  
Jesse H. Goldberg
Keyword(s):  
Social Context ◽  
Courtship Song ◽  
Dopamine Neurons ◽  
Evaluation Systems ◽  
Self Evaluation ◽  
Opportunities For Learning ◽  
Performance Error ◽  
And Performance

Attending to mistakes while practicing alone provides opportunities for learning1, 2, but self-evaluation during audience-directed performance could distract from ongoing execution3. It remains unknown how animals switch between practice and performance modes, and how evaluation systems process errors across distinct performance contexts. We recorded from striatal-projecting dopamine (DA) neurons as male songbirds transitioned from singing alone to singing female-directed courtship song. In the presence of the female, singing-related performance error signals were reduced or gated off and DA neurons were instead phasically activated by female vocalizations. Mesostriatal DA neurons can thus dynamically change their tuning with changes in social context.

scholarly journals T-SNE visualization of large-scale neural recordings

2016 ◽  
Author(s):  
George Dimitriadis ◽  
Joana Neto ◽  
Adam R. Kampff
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Data Sets ◽  
Neural Recordings ◽  
Sorting Problem ◽  
Feature Spaces ◽  
High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

scholarly journals A pH-eQTL interaction at the RIT2-SYT4 Parkinson's disease risk locus in the substantia nigra

2020 ◽  
Author(s):  
Sejal Patel ◽  
Derek Howard ◽  
Leon French
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Human Brain ◽  
Disease Risk ◽  
Brain Regions ◽  
Dopamine Neurons ◽  
Postmortem Human Brain ◽  
Increased Risk

BACKGROUND: Parkinson's disease (PD) causes severe motor and cognitive disabilities that result from the progressive loss of dopamine neurons in the substantia nigra. The rs12456492 variant in the RIT2 gene has been repeatedly associated with increased risk for Parkinson's disease. From a transcriptomic perspective, a meta-analysis found that RIT2 gene expression is correlated with pH in the human brain. OBJECTIVE: To assess pH associations at the RIT2-SYT4 locus. METHODS: Linear models to examine two datasets that assayed rs12456492, gene expression, and pH in the postmortem human brain. RESULTS: Using the BrainEAC dataset, we replicate the positive correlation between RIT2 gene expression and pH in the human brain. Furthermore, we found that the relationship between expression and pH is influenced by rs12456492. When tested across ten brain regions, this interaction is specifically found in the substantia nigra. A similar association was found for the co-localized SYT4 gene. In addition, SYT4 associations are stronger in a combined model with both genes, and the SYT4 interaction appears to be specific to males. In the GTEx dataset, the pH associations involving rs12456492 and expression of either SYT4 and RIT2 was not seen. This null finding may be due to the short postmortem intervals (PMI) of the GTEx tissue samples. In the BrainEAC data, we tested the effect of PMI and only observed the interactions in the longer PMI samples. CONCLUSIONS: These previously unknown associations suggest novel mechanistic roles for rs12456492, RIT2, and SYT4 in the regulation of pH in the substantia nigra.

scholarly journals Mimicry is associated with procedural learning, not social bonding, neural systems in autism

2020 ◽  
Author(s):  
Bahar Tunçgenç ◽  
Carolyn Koch ◽  
Amira Herstic ◽  
Inge-Marie Eigsti ◽  
Stewart Mostofsky
Keyword(s):  
Social Bonding ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Skill Learning ◽  
Autism Spectrum Conditions ◽  
Neural Systems ◽  
Procedural Skill ◽  
Communication Difficulties ◽  
Learning Functions ◽  
The Brain

AbstractMimicry facilitates social bonding throughout the lifespan. Mimicry impairments in autism spectrum conditions (ASC) are widely reported, including differentiation of the brain networks associated with its social bonding and learning functions. This study examined associations between volumes of brain regions associated with social bonding versus procedural skill learning, and mimicry of gestures during a naturalistic interaction in ASC and neurotypical (NT) children. Consistent with predictions, results revealed reduced mimicry in ASC relative to the NT children. Mimicry frequency was negatively associated with autism symptom severity. Mimicry was predicted predominantly by the volume of procedural skill learning regions in ASC, and by bonding regions in NT. Further, bonding regions contributed significantly less to mimicry in ASC than in NT, while the contribution of learning regions was not different across groups. These findings suggest that associating mimicry with skill learning, rather than social bonding, may partially explain observed communication difficulties in ASC.

scholarly journals Ibogaine Modifies GDNF, BDNF and NGF Expression in Brain Regions Involved in Mesocorticolimbic and Nigral Dopaminergic Circuits

2018 ◽  
Author(s):  
Soledad Marton ◽  
Bruno González ◽  
Sebastián Rodríguez ◽  
Ernesto Miquel ◽  
Laura Martínez Palma ◽  
...  
Keyword(s):  
Neurotrophic Factor ◽  
Dopaminergic Neurons ◽  
Brain Regions ◽  
Dopamine Neurons ◽  
Acute Administration ◽  
Dependent Manner ◽  
Self Administration ◽  
Seeking Behavior ◽  
A Site ◽  
Dose Dependent

<p>Ibogaine is a psychedelic alkaloid which has been subject of intense scientific research due to its reported ability to attenuate drug-seeking behavior. Recent work suggested that ibogaine effects on alcohol self-administration in rats was related to the release of Glial Cell Derived Neurotrophic Factor (GDNF) in the Ventral Tegmental Area (VTA), a mesencephalic region which hosts soma of dopamine neurons. It is well known that neurotrophic factors (NFs) mediate the neuroadaptations induced in the mesocorticolimbic dopaminergic system by repeated exposure to drugs. Although previous reports have shown ibogaine´s ability to induce GDNF expression in rat midbrain, there are no studies addressing its effect on the expression of GDNF, Brain Derived Neurotrophic Factor (BDNF) or Nerve Growth Factor (NGF) in distinct regions containing dopaminergic neurons. In this work, we examined the effect of ibogaine acute administration on the expression of these NFs in the VTA, Prefrontal Cortex (PFC), Nucleus Accumbens (NAcc) and the Substantia Nigra (SN). Thus, rats were i.p. treated with ibogaine 20 mg/kg (I<sub>20</sub>), 40 mg/kg (I<sub>40</sub>) or vehicle, and NFs expression was analyzed after 3 and 24 hours. Only at 24 h an increase of the expression for the three NFs were observed in a site and dose dependent manner. Results for GDNF showed that only I<sub>40</sub> selectively upregulated its expression in the VTA and SN. Both doses of ibogaine elicited a large increase in the expression of BDNF in the NAcc, SN and PFC, while a significant effect was found in the VTA only for I<sub>40</sub>. Finally, NGF was found to be upregulated in all regions after I<sub>40</sub>, while a selective upregulation was found in PFC and VTA for the I<sub>20</sub> treatment. An increase in the content of mature GDNF was observed in the VTA but no significant increase in the mature BDNF protein content was found in all the studied areas. Interestingly, an increase in the content of proBDNF was detected in the NAcc for both treatments. Further research is needed to understand the neurochemical bases of these changes, and to confirm their contribution to the anti-addictive properties of ibogaine. </p>

scholarly journals Dopamine neurons encode performance error in singing birds

Science ◽  
2016 ◽  
Vol 354 (6317) ◽  
pp. 1278-1282 ◽  
Author(s):  
V. Gadagkar ◽  
P. A. Puzerey ◽  
R. Chen ◽  
E. Baird-Daniel ◽  
A. R. Farhang ◽  
...  
Keyword(s):  
Dopamine Neurons ◽  
Performance Error
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