A Unified Framework for Dopamine Signals across Timescales

Kim, HyungGoo R.; Malik, Athar N.; Mikhael, John G.; Bech, Pol; Tsutsui-Kimura, Iku; Sun, Fangmiao; Zhang, Yajun; Li, Yulong; Watabe-Uchida, Mitsuko; Gershman, Samuel J.; Uchida, Naoshige

doi:10.1016/j.cell.2020.11.013

Cited by 220 publications

(311 citation statements)

References 68 publications

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“…These changes in tonic firing rates are consistent with a recent electrophysiology study that showed that many VTA DA neurons exhibit upward or downward ramps in tonic firing rate during reward approach in virtual reality (54). Thus, we show that spike inference can produce the subtle, slow-timescale changes in tonic firing rates that have been reported in DA neurons as animals approach rewards.…”

Section: Resultssupporting

confidence: 90%

Inferring spikes from calcium imaging in dopamine neurons

Fleming

Jewell

Engelhard

et al. 2020

Preprint

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Calcium imaging has led to discoveries about neural correlates of behavior in subcortical neurons, including dopamine (DA) neurons. However, spike inference methods have not been tested in most populations of subcortical neurons. To address this gap, we simultaneously performed calcium imaging and electrophysiology in DA neurons in brain slices, and applied a recently developed spike inference algorithm to the GCaMP fluorescence. This revealed that individual spikes can be inferred accurately in this population. Next, we inferred spikes in vivo from calcium imaging from these neurons during Pavlovian conditioning, as well as during navigation in virtual reality. In both cases, we quantitatively recapitulated previous in vivo electrophysiological observations. Our work provides a validated approach to infer spikes from calcium imaging in DA neurons, and implies that aspects of both tonic and phasic spike patterns can be recovered.

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Section: Resultssupporting

confidence: 90%

Inferring spikes from calcium imaging in dopamine neurons

Fleming

Jewell

Engelhard

et al. 2020

Preprint

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“…The ATS system features five independent ports that can deliver either water reward or air-puff punishment with high temporal precision 1 , 11 . Since the system uses TTL pulses to synchronize optogenetic stimulation with the behavior controller, it is possible to precisely deliver photostimulation in any given task phase and part of the trial, allowing event or state specific manipulations 55 – 59 . To validate this application of our system, we showed that optogenetic stimulation of basal forebrain cholinergic neurons can interfere with quick learning of the 5CSRTT in the ATS, demonstrating feasibility of automated optogenetic manipulation experiments.…”

Section: Discussionmentioning

confidence: 99%

Efficient training of mice on the 5-choice serial reaction time task in an automated rodent training system

Birtalan

Bánhidi

Sanders

et al. 2020

Sci Rep

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Experiments aiming to understand sensory-motor systems, cognition and behavior necessitate training animals to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial. Human labor not only limits the amount of training per day, but also introduces several sources of variability and may increase animal stress. Here we present an automated training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test sensory detection, sustained attention and impulsivity. We found that full automation without human intervention allowed rapid, cost-efficient training, and decreased stress as measured by corticosterone levels. Training breaks introduced only a transient drop in performance, and mice readily generalized across training systems when transferred from automated to manual protocols. We further validated our automated training system with wireless optogenetics and pharmacology experiments, expanding the breadth of experimental needs our system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral training, with methods and principles transferrable to a range of rodent tasks.

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“…In Table 1 you will find a summary of currently available DA biosensors as well as their main properties, which are further detailed in Section 5 . DA biosensors have already generated key findings in the basic understanding of reward behavior [ 101 , 102 , 103 , 104 , 105 , 106 , 107 ], thirst regulation [ 108 ], feeding behavior [ 109 ], addiction [ 38 , 110 , 111 ], aversive learning [ 112 ], depressive-like behavior [ 98 ], sleep-wake cycle [ 113 ] or to dissect neuromodulator mechanisms in disease models [ 114 , 115 ] using a variety of in vivo imaging modalities shown in Figure 1 . DA biosensors can also be used to understand DA release dynamics in vitro or ex vivo [ 58 , 59 , 60 , 61 ], as shown for example in Reference [ 116 ] where dLight1 was used to understand the metabolic demands and bioenergetic roles of the mitochondria in governing phasic DA release.…”

Section: Catalogue Of Gpcr Biosensors For Dopaminementioning

confidence: 99%

“…In Supplementary Table S2 we present an overview of previously published in vivo applications of DA biosensors from the dLight, RdLight and GRAB-DA families in rodent animal models [ 38 , 58 , 60 , 61 , 98 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 224 , 226 , 227 , 228 ]. The large majority of experiments was performed in either the dorsal striatum or the NAc where the DA concentration reaches its maximal levels.…”

Section: Practical Considerations For Sensor Choice: One Sensor Domentioning

confidence: 99%

“…Moreover, DA transients can also encode slower variables such as spontaneous movements across several seconds [ 58 , 94 ]. DA transients can also ramp up over several seconds in freely moving animals approaching rewards or running to distant goals following cues [ 94 , 101 , 105 , 216 ]. Here fast on/off-kinetics are useful to identify more precisely the boundaries of the transient; however slow off-kinetics may allow to detect smaller fluctuations in DA, that could otherwise be occluded.…”

Section: Practical Considerations For Sensor Choice: One Sensor Domentioning

confidence: 99%

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GPCR-Based Dopamine Sensors—A Detailed Guide to Inform Sensor Choice for In Vivo Imaging

Labouesse

Cola

Patriarchi

2020

IJMS

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Understanding how dopamine (DA) encodes behavior depends on technologies that can reliably monitor DA release in freely-behaving animals. Recently, red and green genetically encoded sensors for DA (dLight, GRAB-DA) were developed and now provide the ability to track release dynamics at a subsecond resolution, with submicromolar affinity and high molecular specificity. Combined with rapid developments in in vivo imaging, these sensors have the potential to transform the field of DA sensing and DA-based drug discovery. When implementing these tools in the laboratory, it is important to consider there is not a ‘one-size-fits-all’ sensor. Sensor properties, most importantly their affinity and dynamic range, must be carefully chosen to match local DA levels. Molecular specificity, sensor kinetics, spectral properties, brightness, sensor scaffold and pharmacology can further influence sensor choice depending on the experimental question. In this review, we use DA as an example; we briefly summarize old and new techniques to monitor DA release, including DA biosensors. We then outline a map of DA heterogeneity across the brain and provide a guide for optimal sensor choice and implementation based on local DA levels and other experimental parameters. Altogether this review should act as a tool to guide DA sensor choice for end-users.

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A Unified Framework for Dopamine Signals across Timescales

Cited by 220 publications

References 68 publications

Inferring spikes from calcium imaging in dopamine neurons

Inferring spikes from calcium imaging in dopamine neurons

Efficient training of mice on the 5-choice serial reaction time task in an automated rodent training system

GPCR-Based Dopamine Sensors—A Detailed Guide to Inform Sensor Choice for In Vivo Imaging

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