Combined Single- Neuron Recording and Microdialysis in the Brain of Freely Behaving Animals (Rod...

Author(s):  
Nandor Ludvig
2021 ◽  
Author(s):  
Oscar A. Mendez ◽  
Emiliano Flores Machado ◽  
Jing Lu ◽  
Anita A. Koshy

AbstractToxoplasma gondii is an intracellular parasite that causes a long-term latent infection of neurons. Using a custom MATLAB-based mapping program in combination with a mouse model that allows us to permanently mark neurons injected with parasite proteins, we found that Toxoplasma-injected neurons (TINs) are heterogeneously distributed in the brain, primarily localizing to the cortex followed by the striatum. Using immunofluorescence co-localization assays, we determined that cortical TINs are commonly (>50%) excitatory neurons (FoxP2+) and that striatal TINs are often (>65%) medium spiny neurons (MSNs) (FoxP2+). As MSNs have highly characterized electrophysiology, we used ex vivo slices from infected mice to perform single neuron patch-clamping on striatal TINs and neighboring uninfected MSNs (bystander MSNs). These studies demonstrated that TINs have highly abnormal electrophysiology, while the electrophysiology of bystander MSNs was akin to that of MSNs from uninfected mice. Collectively, these data offer new neuroanatomic and electrophysiologic insights into CNS toxoplasmosis.


eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Gilad D Evrony ◽  
Eunjung Lee ◽  
Peter J Park ◽  
Christopher A Walsh

Whether somatic mutations contribute functional diversity to brain cells is a long-standing question. Single-neuron genomics enables direct measurement of somatic mutation rates in human brain and promises to answer this question. A recent study (<xref ref-type="bibr" rid="bib65">Upton et al., 2015</xref>) reported high rates of somatic LINE-1 element (L1) retrotransposition in the hippocampus and cerebral cortex that would have major implications for normal brain function, and suggested that these events preferentially impact genes important for neuronal function. We identify aspects of the single-cell sequencing approach, bioinformatic analysis, and validation methods that led to thousands of artifacts being interpreted as somatic mutation events. Our reanalysis supports a mutation frequency of approximately 0.2 events per cell, which is about fifty-fold lower than reported, confirming that L1 elements mobilize in some human neurons but indicating that L1 mosaicism is not ubiquitous. Through consideration of the challenges identified, we provide a foundation and framework for designing single-cell genomics studies.


Science ◽  
2018 ◽  
Vol 360 (6396) ◽  
pp. 1447-1451 ◽  
Author(s):  
Guosong Hong ◽  
Tian-Ming Fu ◽  
Mu Qiao ◽  
Robert D. Viveros ◽  
Xiao Yang ◽  
...  

The retina, which processes visual information and sends it to the brain, is an excellent model for studying neural circuitry. It has been probed extensively ex vivo but has been refractory to chronic in vivo electrophysiology. We report a nonsurgical method to achieve chronically stable in vivo recordings from single retinal ganglion cells (RGCs) in awake mice. We developed a noncoaxial intravitreal injection scheme in which injected mesh electronics unrolls inside the eye and conformally coats the highly curved retina without compromising normal eye functions. The method allows 16-channel recordings from multiple types of RGCs with stable responses to visual stimuli for at least 2 weeks, and reveals circadian rhythms in RGC responses over multiple day/night cycles.


2020 ◽  
Vol 21 (21) ◽  
pp. 8048
Author(s):  
Marie A. Labouesse ◽  
Reto B. Cola ◽  
Tommaso Patriarchi

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.


2004 ◽  
Vol 10 (7) ◽  
pp. 1027-1029
Author(s):  
M. Kinsbourne

In an uncertain world, people and other animals make their living by predicting which of alternative courses of action is likely to yield the best return. For humans the return might take many forms, such as material, financial, social, or esthetic, but the underlying currency involved for any species is “inclusive fitness,” the rate at which an animal's genes are propagated. Professor Glimcher demonstrates that Economics methods are applicable to decision-making under conditions of uncertainty, both at the behavioral and the neuronal level. This approach has been called neuro-economics, although “econometrics” characterizes it more precisely. Econometrics is the application of statistical and mathematical methods in the field of economics to test and quantify economic theories and the solution to economic problems. Specifically, individuals' decision-making benefits from knowing how likely a response is to be reinforced, and knowing the reinforcement's value. Even single neurons are sensitive to these variables. Glimcher reaches beyond the heavily studied neural substrate for sensation and response to predictive neural circuitry that factors in the prior probability of reward, and its expected value. Indeed, he and his colleagues have identified neurons in monkey's inferior parietal lobule whose firing rates reflect both probability and value.


2003 ◽  
Vol 89 (2) ◽  
pp. 1067-1077 ◽  
Author(s):  
Ikuo Tanibuchi ◽  
Patricia S. Goldman-Rakic

The mediodorsal nucleus (MD) is the thalamic gateway to the prefrontal cortex, an area of the brain associated with spatial and object working memory functions. We have recorded single-neuron activities from the MD nucleus in monkeys trained to perform spatial tasks with peripheral visual stimuli and a nonspatial task with foveally presented pictures of objects and faces—tasks identical to those we have previously used to map regional specializations in the dorso- and ventro-lateral prefrontal cortex, respectively. We found that MD neurons exhibited categorical specificity—either responding selectively to locations in the spatial tasks or preferentially to specific representations of faces and objects in the nonspatial task. Spatially tuned neurons were located in parts of the MD connected with the dorsolateral prefrontal cortex while neurons responding to the identity of stimuli mainly occupied more ventral positions in the nucleus that has its connections with the inferior prefrontal convexity. Neuronal responses to auditory stimuli were also examined, and vocalization sensitive neurons were found in more posterior portions of the MD. We conclude that MD neurons are dissociable by their spatial and nonspatial coding properties in line with their cortical connections and that the principle of information segregation in cortico-cortical pathways extends to the “association” nuclei of the thalamus.


2009 ◽  
Vol 21 (5) ◽  
pp. 1259-1276 ◽  
Author(s):  
Timothée Masquelier ◽  
Rudy Guyonneau ◽  
Simon J. Thorpe

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.


Cortex ◽  
2014 ◽  
Vol 60 ◽  
pp. 3-9 ◽  
Author(s):  
Elisa Frisaldi ◽  
Elisa Carlino ◽  
Michele Lanotte ◽  
Leonardo Lopiano ◽  
Fabrizio Benedetti

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