Associative Learning: How nitric oxide helps update memories
To find food and avoid danger in changing environments, animals need to be able to learn to associate specific sensory stimuli, such as odors, with reward or punishment. Such memories must also adapt to reflect new circumstances. How is such memory flexibility achieved?
An excellent model system for studying these questions is the fruit fly Drosophila melanogaster, which can learn to associate certain odors with reward or punishment. Odor-associated memories are formed in a part of the fly brain called the mushroom body, which contains about 2000 neurons called Kenyon cells, which are connected to other neurons called mushroom body output neurons (MBONs; Amin and Lin, 2019). A small percentage of Kenyon cells (~5–10%) is activated by a given odor, which triggers a behavioral response (via the MBONs), such as approaching the odor or avoiding it.
The connections between the Kenyon cells and the MBONs can be altered by subpopulations of dopaminergic neurons that carry reward or punishment information (Hige et al., 2015). When a fly smells an odor at the same time as it experiences reward or punishment, certain dopaminergic neurons are activated at the same time as an odor-specific set of Kenyon cells. This simultaneous activation alters Kenyon cell-to-MBON connections, leading to an altered odor-induced behavior (positive or negative memory).
Now, in eLife, Gerald Rubin and colleagues at Janelia Research Campus, Columbia University, and the University of Virginia – including Yoshinori Aso as first author – report that some dopaminergic neurons release nitric oxide as well as dopamine, and that this nitric oxide acts in the ‘opposite direction’ to the dopamine, and also on a slower timescale (Aso et al., 2019). These opposing signals ensure that odor-associated memories are forgotten after time passes, allowing for memory flexibility.
Different types of dopaminergic neurons innervate different ‘compartments’ in the mushroom body (Figure 1A). Previous work showed that activating a dopaminergic neuron that responds to punishment (reward) while the fly smells an odor can induce a negative (positive) artificial memory, so the fly later avoids (approaches) that particular odor (Aso and Rubin, 2016; Aso et al., 2012; Aso et al., 2010). This memory can form and decay quickly or slowly depending on what dopaminergic neuron induced it.
One would expect that if a fly cannot produce dopamine (because an enzyme needed to make it is knocked out), these memories would not form. However, Aso et al. encountered something surprising when they performed experiments on flies that lacked dopamine: activating a dopaminergic neuron called PPL1-γ1pedc (which normally induces negative memories) induced a positive memory, while activating PAM-γ5/PAM-β’2a (which normally induce positive memories) induced a negative memory. These 'inverted memory' results are consistent with previous results that dopamine-less flies form positive memories when they are trained to associate an odor to a negative experience like an electric shock (Riemensperger et al., 2011). Aso et al. went further by pinning down this inverted memory to specific neurons. Thus, dopaminergic neurons can induce memory formation without dopamine, suggesting that they might use another neurotransmitter as a co-transmitter.
But what co-transmitter? Using RNA-seq, Aso et al. looked at gene expression in different dopaminergic neuron subtypes to see what the subtypes inducing inverted memories express that others do not. This analysis pointed to nitric oxide synthase, the enzyme that produces nitric oxide (NO). This gas has several roles in the body, including acting as a neurotransmitter in mammalian long term memory (Harooni et al., 2009). Moreover, the major downstream signaling molecule for NO, soluble guanylate cyclase, is expressed in Kenyon cells, suggesting that NO might diffuse across cell membranes from dopaminergic neurons to Kenyon cells (Figure 1B).
Aso et al. showed that both NO production in dopaminergic neurons and soluble guanylate cyclase signaling in Kenyon cells are required for inverted memory. When either is disrupted, activating the dopaminergic neurons described above in dopamine-less flies no longer produces inverted memories. However, when dopamine levels were restored in flies lacking both NO and dopamine, normal memory formation took place, indicating NO is not required for normal memory (as opposed to inverted memory).
Why would these dopaminergic neurons simultaneously induce two opposite memories? The inverted memory induced by NO forms more slowly than the normal memory induced by dopamine, suggesting that the inverted memory could cancel out the normal memory after some time passes (Figure 1C). Consistent with this hypothesis, normal memories in NO-deficient flies last much longer than in wild-type flies, suggesting that the inverted memory is actually a kind of active forgetting. Indeed, NO-deficient flies struggle to update memories based on new information.
Aso et al. used computational modeling to understand how the dopamine-induced normal memory and the NO-induced inverted memory interact with each other. The results suggest that dopamine and NO could modulate three classical parameters of synaptic strength (quantal size, release probability, and number of release sites), which give the mean postsynaptic response when multiplied together. It will be interesting to test which, if any, of these parameters are altered during learning.
The mushroom body is architecturally similar to the vertebrate cerebellum (Farris, 2011), and this study highlights further molecular parallels. In the cerebellum, NO can induce a change in the connection between granule cells (akin to Kenyon cells) and Purkinje cells (akin to MBONs) in the opposite direction to the change that happens during normal learning (Lev-Ram et al., 2002). Whether due to conservation from an ancestral brain structure or convergent evolution to a useful architecture, these parallels underscore the usefulness of the mushroom body as a model system to understand memory dynamics. It will be interesting in the future to explore how opposing co-transmitters modulate memory timescales in other systems, and whether such systems are involved in human learning disabilities or dementia.
References
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Neuronal mechanisms underlying innate and learned olfactory processing in DrosophilaCurrent Opinion in Insect Science 36:9–17.https://doi.org/10.1016/j.cois.2019.06.003
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Are mushroom bodies cerebellum-like structures?Arthropod Structure & Development 40:368–379.https://doi.org/10.1016/j.asd.2011.02.004
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- Version of Record published: January 8, 2020 (version 1)
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© 2020, Green and Lin
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The presence of global synchronization of vasomotion induced by oscillating visual stimuli was identified in the mouse brain. Endogenous autofluorescence was used and the vessel ‘shadow’ was quantified to evaluate the magnitude of the frequency-locked vasomotion. This method allows vasomotion to be easily quantified in non-transgenic wild-type mice using either the wide-field macro-zoom microscopy or the deep-brain fiber photometry methods. Vertical stripes horizontally oscillating at a low temporal frequency (0.25 Hz) were presented to the awake mouse, and oscillatory vasomotion locked to the temporal frequency of the visual stimulation was induced not only in the primary visual cortex but across a wide surface area of the cortex and the cerebellum. The visually induced vasomotion adapted to a wide range of stimulation parameters. Repeated trials of the visual stimulus presentations resulted in the plastic entrainment of vasomotion. Horizontally oscillating visual stimulus is known to induce horizontal optokinetic response (HOKR). The amplitude of the eye movement is known to increase with repeated training sessions, and the flocculus region of the cerebellum is known to be essential for this learning to occur. Here, we show a strong correlation between the average HOKR performance gain and the vasomotion entrainment magnitude in the cerebellar flocculus. Therefore, the plasticity of vasomotion and neuronal circuits appeared to occur in parallel. Efficient energy delivery by the entrained vasomotion may contribute to meeting the energy demand for increased coordinated neuronal activity and the subsequent neuronal circuit reorganization.
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Background:
Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine’s molecular mechanisms connect to its neural and behavioral effects.
Methods:
We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets.
Results:
We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine’s data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level.
Conclusions:
These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry.
Funding:
This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1–190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016–0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 – 2056) (FXV).
Clinical trial number: