Inferring spikes from calcium imaging in dopamine neurons

Standardizing a method for functional assessment of neural networks in brain organoids

During the last decade brain organoids have emerged as an attractive model system, allowing stem cells to be differentiated into complex 3D models, recapitulating many aspects of human brain development. Whilst many studies have analysed anatomical and cytoarchitectural characteristics of organoids, their functional characterisation has been limited, and highly variable between studies. Standardised, consistent methods for recording functional activity are critical to providing a functional understanding of neuronal networks at the synaptic and network level that can yield useful information about functional network phenotypes in disease and healthy states. In this study we outline a detailed methodology for calcium imaging and Multi-Electrode Array (MEA) recordings in brain organoids. To illustrate the utility of these functional interrogation techniques in uncovering induced differences in neural network activity we applied various stimulating media protocols. We demonstrate overlapping information from the two modalities, with comparable numbers of active cells in the four treatment groups and an increase in synchronous behaviour in BrainPhys treated groups. Further development of analysis pipelines to reveal network level changes in brain organoids will enrich our understanding of network formation and perturbation in these structures, and aid in the future development of drugs that target neurological disorders at the network level.

Natural phasic inhibition of dopamine neurons signals cognitive rigidity

When animals unexpectedly fail, their dopamine neurons undergo phasic inhibition that canonically drives extinction learning-a cognitive-flexibility mechanism for discarding outdated strategies. However, the existing evidence equates natural and artificial phasic inhibition, despite their spatiotemporal differences. Addressing this gap, we targeted a GABAA-receptor antagonist precisely to dopamine neurons, yielding three unexpected findings. First, this intervention blocked natural phasic inhibition selectively, leaving tonic activity unaffected. Second, blocking natural phasic inhibition accelerated extinction learning-opposite to canonical mechanisms. Third, our approach selectively benefitted perseverative mice, restoring rapid extinction without affecting new reward learning. Our findings reveal that extinction learning is rapid by default and slowed by natural phasic inhibition-challenging foundational learning theories, while delineating a synaptic mechanism and therapeutic target for cognitive rigidity.

A machine-learning tool to identify bistable states from calcium imaging data

Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.

Engineering, applications, and future perspectives of GPCR-based genetically encoded fluorescent indicators for neuromodulators

This review explores the evolving landscape of G-protein-coupled receptor (GPCR)-based genetically encoded fluorescent indicators (GEFIs), with a focus on their development, structural components, engineering strategies, and applications. We highlight the unique features of this indicator class, emphasizing the importance of both the sensing domain (GPCR structure and activation mechanism) and the reporting domain (circularly permuted fluorescent protein (cpFP) structure and fluorescence modulation). Further, we discuss indicator engineering approaches, including the selection of suitable cpFPs and expression systems. Additionally, we showcase the diversity and flexibility of their application by presenting a summary of studies where such indicators were used. Along with all the advantages, we also focus on the current limitations as well as common misconceptions that arise when using these indicators. Finally, we discuss future directions in indicator engineering, including strategies for screening with increased throughput, optimization of the ligand-binding properties, structural insights, and spectral diversity.

Elevated parkinsonism pathological markers in dopaminergic neurons with developmental exposure to atrazine

Atrazine (ATZ) is one of the most used herbicides in the US and a known endocrine disruptor. ATZ is frequently detected in drinking water, especially in Midwestern regions of the United States, exceeding the EPA regulation of maximum contamination level (MCL) of 3 ppb. Epidemiology studies have suggested an association between ATZ exposure and neurodegeneration. Less, however, is known about the neurotoxic mechanism of ATZ, particularly for exposures at a developmental stage. Here, we exposed floor plate progenitors (FPPs) derived from human induced pluripotent stem cells (hiPSCs) to low concentrations of ATZ at 0.3 and 3 ppb for two days followed by differentiation into dopaminergic (DA) neurons in ATZ-free medium. We then examined the morphology, activity, pathological protein aggregation, and transcriptomic changes of differentiated DA neurons. We observed significant decrease in the complexity of neurite network, increase of neuronal activity, and elevated tau- and α-synuclein (aSyn) pathologies after ATZ exposure. The ATZ-induced neuronal changes observed here align with pathological characteristics in Parkinson's disease (PD). Transcriptomic analysis further corroborates our findings; and collectively provides a strong evidence base that low-concentration ATZ exposure during development can elicit increased risk of neurodegeneration.

Neural circuit models for evidence accumulation through choice-selective sequences

Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.

Estrogenic control of reward prediction errors and reinforcement learning

Gonadal hormones act throughout the brain 1 , and nearly all neuropsychiatric disorders vary in symptom severity with hormonal fluctuations over the reproductive cycle, gestation, and perimenopause 2-4 . Yet the mechanisms by which hormones influence mental and cognitive processes are unclear. Exogenous estrogenic hormones modulate dopamine signaling in the nucleus accumbens core (NAcc) 5,6 , which instantiates reward prediction errors (RPEs) for reinforcement learning 7-16 . Here we show that endogenous estrogenic hormones enhance RPEs and sensitivity to previous rewards by regulating expression of dopamine reuptake proteins in the NAcc. We trained rats to perform a temporal wagering task with different reward states; rats adjusted how quickly they initiated trials across states, balancing effort against expected rewards. Dopamine release in the NAcc reflected RPEs that predicted and causally in-fluenced subsequent initiation times. When fertile, females more quickly adjusted their initiation times to match reward states due to enhanced dopaminergic RPEs in the NAcc. Proteomics revealed reduced expression of dopamine transporters in fertile stages of the reproductive cycle. Finally, genetic suppression of midbrain estrogen receptors eliminated hormonal modulation of behavior. Estrogenic hormones therefore control the rate of reinforcement learning by regulating RPEs via dopamine reuptake, providing a mechanism by which hormones influence neural dynamics for motivation and learning.

Deep-brain optical recording of neural dynamics during behavior

In vivo fluorescence recording techniques have produced landmark discoveries in neuroscience, providing insight into how single cell and circuit-level computations mediate sensory processing and generate complex behaviors. While much attention has been given to recording from cortical brain regions, deep-brain fluorescence recording is more complex because it requires additional measures to gain optical access to harder to reach brain nuclei. Here we discuss detailed considerations and tradeoffs regarding deep-brain fluorescence recording techniques and provide a comprehensive guide for all major steps involved, from project planning to data analysis. The goal is to impart guidance for new and experienced investigators seeking to use in vivo deep fluorescence optical recordings in awake, behaving rodent models.

Cell type specificity for circuit output in the midbrain dopaminergic system

Midbrain dopaminergic neurons are a relatively small group of neurons in the mammalian brain controlling a wide range of behaviors. In recent years, increasingly sophisticated tracing, imaging, transcriptomic, and machine learning approaches have provided substantial insights into the anatomical, molecular, and functional heterogeneity of dopaminergic neurons. Despite this wealth of new knowledge, it remains unclear whether and how the diverse features defining dopaminergic subclasses converge to delineate functional ensembles within the dopaminergic system. Here, we review recent studies investigating various aspects of dopaminergic heterogeneity and discuss how development, behavior, and disease influence subtype characteristics. We then outline what further approaches could be pursued to gain a more inclusive picture of dopaminergic diversity, which could be crucial to understanding the functional architecture of this system.

Functional brain region-specific neural spheroids for modeling neurological diseases and therapeutics screening

3D spheroids have emerged as powerful drug discovery tools given their high-throughput screening (HTS) compatibility. Here, we describe a method for generating functional neural spheroids by cell-aggregation of differentiated human induced pluripotent stem cell (hiPSC)-derived neurons and astrocytes at cell type compositions mimicking specific regions of the human brain. Recordings of intracellular calcium oscillations were used as functional assays, and the utility of this spheroids system was shown through disease modeling, drug testing, and formation of assembloids to model neurocircuitry. As a proof of concept, we generated spheroids incorporating neurons with Alzheimer's disease-associated alleles, as well as opioid use disorder modeling spheroids induced by chronic treatment of a mu-opioid receptor agonist. We reversed baseline functional deficits in each pilot disease model with clinically approved treatments and showed that assembloid activity can be chemogenetically manipulated. Here, we lay the groundwork for brain region-specific neural spheroids as a robust functional assay platform for HTS studies.

A computational model to uncover the biophysical underpinnings of neural firing heterogeneity in dissociated hippocampal cultures

Calcium (Ca2+ ) imaging reveals a variety of correlated firing in cultures of dissociated hippocampal neurons, pinpointing the non-synaptic paracrine release of glutamate as a possible mediator for such firing patterns, although the biophysical underpinnings remain unknown. An intriguing possibility is that extracellular glutamate could bind metabotropic receptors linked with inositol trisphosphate (IP3 ) mediated release of Ca2+ from the endoplasmic reticulum of individual neurons, thereby modulating neural activity in combination with sarco/endoplasmic reticulum Ca2+ transport ATPase (SERCA) and voltage-gated Ca2+ channels (VGCC). However, the possibility that such release may occur in different neuronal compartments and can be inherently stochastic poses challenges in the characterization of such interplay between various Ca2+ channels. Here we deploy biophysical modeling in association with Monte Carlo parameter sampling to characterize such interplay and successfully predict experimentally observed Ca2+ patterns. The results show that the neurotransmitter level at the plasma membrane is the extrinsic source of heterogeneity in somatic Ca2+ transients. Our analysis, in particular, identifies the origin of such heterogeneity to an intrinsic differentiation of hippocampal neurons in terms of multiple cellular properties pertaining to intracellular Ca2+ signaling, such as VGCC, IP3 receptor, and SERCA expression. In the future, the biophysical model and parameter estimation approach used in this study can be upgraded to predict the response of a system of interconnected neurons.

Artificial Vision: The High-Frequency Electrical Stimulation of the Blind Mouse Retina Decay Spike Generation and Electrogenically Clamped Intracellular Ca2+ at Elevated Levels

BACKGROUND

The electrical stimulation (stim) of retinal neurons enables blind patients to experience limited artificial vision. A rapid response outage of the stimulated ganglion cells (GCs) allows for a low visual sensation rate. Hence, to elucidate the underlying mechanism, we investigated different stim parameters and the role of the neuromodulator calcium (Ca2+).

METHODS

Subretinal stim was applied on retinal explants (blind rd1 mouse) using multielectrode arrays (MEAs) or single metal electrodes, and the GC activity was recorded using Ca2+ imaging or MEA, respectively. Stim parameters, including voltage, phase polarity, and frequency, were investigated using specific blockers.

RESULTS

At lower stim frequencies (<5 Hz), GCs responded synaptically according to the stim pulses (stim: biphasic, cathodic-first, -1.6/+1.5 V). In contrast, higher stim frequencies (≥5 Hz) also activated GCs directly and induced a rapid GC spike response outage (<500 ms, MEA recordings), while in Ca2+ imaging at the same frequencies, increased intracellular Ca2+ levels were observed.

CONCLUSIONS

Our study elucidated the mechanisms involved in stim-dependent GC spike response outage: sustained high-frequency stim-induced spike outage, accompanied by electrogenically clamped intracellular Ca2+ levels at elevated levels. These findings will guide future studies optimizing stim paradigms for electrical implant applications for interfacing neurons.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Quantifying uncertainty in spikes estimated from calcium imaging data

In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike-i.e., that there was no increase in calcium-at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample $p$-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the $\texttt{spikefinder}$ challenge.

Arginine-vasopressin-expressing neurons in the murine suprachiasmatic nucleus exhibit a circadian rhythm in network coherence in vivo

The suprachiasmatic nucleus (SCN) is composed of functionally distinct subpopulations of GABAergic neurons which form a neural network responsible for synchronizing most physiological and behavioral circadian rhythms in mammals. To date, little is known regarding which aspects of SCN rhythmicity are generated by individual SCN neurons, and which aspects result from neuronal interaction within a network. Here, we utilize in vivo miniaturized microscopy to measure fluorescent GCaMP-reported calcium dynamics in arginine vasopressin (AVP)-expressing neurons in the intact SCN of awake, behaving mice. We report that SCN AVP neurons exhibit periodic, slow calcium waves which we demonstrate, using in vivo electrical recordings, likely reflect burst firing. Further, we observe substantial heterogeneity of function in that AVP neurons exhibit unstable rhythms, and relatively weak rhythmicity at the population level. Network analysis reveals that correlated cellular behavior, or coherence, among neuron pairs also exhibited stochastic rhythms with about 33% of pairs rhythmic at any time. Unlike single-cell variables, coherence exhibited a strong rhythm at the population level with time of maximal coherence among AVP neuronal pairs at CT/ZT 6 and 9, coinciding with the timing of maximal neuronal activity for the SCN as a whole. These results demonstrate robust circadian variation in the coordination between stochastically rhythmic neurons and that interactions between AVP neurons in the SCN may be more influential than single-cell activity in the regulation of circadian rhythms. Furthermore, they demonstrate that cells in this circuit, like those in many other circuits, exhibit profound heterogenicity of function over time and space.

Mesoaccumbal Dopamine Heterogeneity: What Do Dopamine Firing and Release Have to Do with It?

Ventral tegmental area (VTA) dopamine (DA) neurons are often thought to uniformly encode reward prediction errors. Conversely, DA release in the nucleus accumbens (NAc), the prominent projection target of these neurons, has been implicated in reinforcement learning, motivation, aversion, and incentive salience. This contrast between heterogeneous functions of DA release versus a homogeneous role for DA neuron activity raises numerous questions regarding how VTA DA activity translates into NAc DA release. Further complicating this issue is increasing evidence that distinct VTA DA projections into defined NAc subregions mediate diverse behavioral functions. Here, we evaluate evidence for heterogeneity within the mesoaccumbal DA system and argue that frameworks of DA function must incorporate the precise topographic organization of VTA DA neurons to clarify their contribution to health and disease.

Cholinergic interneurons mediate cocaine extinction in male mice through plasticity across medium spiny neuron subtypes

Cholinergic interneurons (ChINs) in the nucleus accumbens (NAc) have been implicated in the extinction of drug associations, as well as related plasticity in medium spiny neurons (MSNs). However, since most previous work relied on artificial manipulations, whether endogenous acetylcholine signaling relates to drug associations is unclear. Moreover, despite great interest in the opposing effects of dopamine on MSN subtypes, whether ChIN-mediated effects vary by MSN subtype is also unclear. Here, we find that high endogenous acetylcholine event frequency correlates with greater extinction of cocaine-context associations across male mice. Additionally, extinction is associated with a weakening of glutamatergic synapses across MSN subtypes. Manipulating ChIN activity bidirectionally controls both the rate of extinction and the associated plasticity at MSNs. Our findings indicate that NAc ChINs mediate drug-context extinction by reducing glutamatergic synaptic strength across MSN subtypes, and that natural variation in acetylcholine signaling may contribute to individual differences in extinction.

Fleming et al. show that individual differences in nucleus accumbens (NAc) acetylcholine signaling correlate with extinction of a cocaine-context association. Manipulations of NAc cholinergic interneuron activity support a model where acetylcholine release weakens glutamatergic presynaptic strength at NAc D1R and D2R medium spiny neurons, promoting cocaine-context extinction.

Local accumbens in vivo imaging during deep brain stimulation reveals a strategy-dependent amelioration of hedonic feeding

Impulsive overeating is a common, disabling feature of eating disorders. Calcium imaging using fiber photometry has emerged as an in vivo methodology to measure neuronal population activity immune to electrical stimulation artifact from deep brain stimulation (DBS). Thus, when used simultaneously, calcium imaging can elucidate poorly understood DBS mechanisms. We show that nucleus accumbens D1 medial spiny calcium signaling increases in preparation of hedonic feeding of high-fat food. Further, responsive, over continuous, DBS strategies effectively disrupt this activity leading to decreased consumption. Implementation of this methodology to better understand mechanisms of these and other forms of neuromodulation for various indications may help advance the field to identify novel therapeutic targets with applications extending beyond obesity.

Impulsive overeating is a common, disabling feature of eating disorders. Both continuous deep brain stimulation (DBS) and responsive DBS, which limits current delivery to pathological brain states, have emerged as potential therapies. We used in vivo fiber photometry in wild-type, Drd1-cre, and A2a-cre mice to 1) assay subtype-specific medium spiny neuron (MSN) activity of the nucleus accumbens (NAc) during hedonic feeding of high-fat food, and 2) examine DBS strategy-specific effects on NAc activity. D1, but not D2, NAc GCaMP activity increased immediately prior to high-fat food approach. Responsive DBS triggered a GCaMP surge throughout the stimulation period and durably reduced high-fat intake. However, with continuous DBS, this surge decayed, and high-fat intake reemerged. Our results argue for a stimulation strategy-dependent modulation of D1 MSNs with a more sustained decrease in consumption with responsive DBS. This study illustrates the important role in vivo imaging can play in understanding effects of such novel therapies.