Spatiotemporal Imaging of Glutamate-Induced Biophotonic Activities and Transmission in Neural Circuits

The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE), may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions) in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min). The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide), but only partly by an action potential inhibitor (TTX), an anesthetic (procaine), or the removal of intracellular and extracellular Ca²⁺. We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of neural information.     

Modulation of Neural Circuits in Crustacea

Recent research has shown that neuromodulators play importantroles in shaping simple behaviors. They act at many differentsites within the animal in a coordinated fashion, modulatingthe motor circuits in the central nervous system, altering motoneuronexcitability, and modulating muscle response to motoneuron input.Within the central circuits that co-ordinate simple movements,neuromodulators play a dramatic sculpting role, changing thecells that participate in the circuit, altering their intrinsicproperties, and affecting the strength of synaptic interactionsthat form the "wiring diagram" of the circuit. As a result,they are able to shape a family of related circuits out of asingle anatomically identified network, each driving a uniquevariant on the basic motor theme. Examples of these actionsfrom the Crustacea are described in this paper, focussing onthe modulation of posture in the lobster, and on modulationof rhythmic motor programs for stomach movements in the stomatogastricganglion of lobsters and crabs.     

Improved Estimation and Interpretation of Correlations in Neural Circuits

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150–350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective. Because of its superior performance, this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.     

Explicit Logic Circuits Discriminate Neural States

The magnitude and apparent complexity of the brain''s connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron''s response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems.     

Trade-offs and Noise Tolerance in Signal Detection by Genetic Circuits

Genetic circuits can implement elaborated tasks of amplitude or frequency signal detection. What type of constraints could circuits experience in the performance of these tasks, and how are they affected by molecular noise? Here, we consider a simple detection process–a signal acting on a two-component module–to analyze these issues. We show that the presence of a feedback interaction in the detection module imposes a trade-off on amplitude and frequency detection, whose intensity depends on feedback strength. A direct interaction between the signal and the output species, in a type of feed-forward loop architecture, greatly modifies these trade-offs. Indeed, we observe that coherent feed-forward loops can act simultaneously as good frequency and amplitude noise-tolerant detectors. Alternatively, incoherent feed-forward loop structures can work as high-pass filters improving high frequency detection, and reaching noise tolerance by means of noise filtering. Analysis of experimental data from several specific coherent and incoherent feed-forward loops shows that these properties can be realized in a natural context. Overall, our results emphasize the limits imposed by circuit structure on its characteristic stimulus response, the functional plasticity of coherent feed-forward loops, and the seemingly paradoxical advantage of improving signal detection with noisy circuit components.     

GABAA Receptor-Mediated Tonic Depolarization in Developing Neural Circuits

The activation of GABA_A receptors (the type A receptors for γ-aminobutyric acid) produces two distinct forms of responses, phasic (i.e., transient) and tonic (i.e., persistent), that are mediated by synaptic and extrasynaptic GABA_A receptors, respectively. During development, the intracellular chloride levels are high so activation of these receptors causes a net outward flow of anions that leads to neuronal depolarization rather than hyperpolarization. Therefore, in developing neural circuits, tonic activation of GABA_A receptors may provide persistent depolarization. Recently, it became evident that GABA_A receptor-mediated tonic depolarization alters the structure of patterned spontaneous activity, a feature that is common in developing neural circuits and is important for neural circuit refinement. Thus, this persistent depolarization may lead to a long-lasting increase in intracellular calcium level that modulates network properties via calcium-dependent signaling cascades. This article highlights the features of GABA_A receptor-mediated tonic depolarization, summarizes the principles for discovery, reviews the current findings in diverse developing circuits, examines the underlying molecular mechanisms and modulation systems, and discusses their functional specializations for each developing neural circuit.     

Long-Range Signal Transmission in Autocrine Relays

Intracellular signaling induced by peptide growth factors can stimulate secretion of these molecules into the extracellular medium. In autocrine and paracrine networks, this can establish a positive feedback loop between ligand binding and ligand release. When coupled to intercellular communication by autocrine ligands, this positive feedback can generate constant-speed traveling waves. To demonstrate that, we propose a mechanistic model of autocrine relay systems. The model is relevant to the physiology of epithelial layers and to a number of in vitro experimental formats. Using asymptotic and numerical tools, we find that traveling waves in autocrine relays exist and have a number of unusual properties, such as an optimal ligand binding strength necessary for the maximal speed of propagation. We compare our results to recent observations of autocrine and paracrine systems and discuss the steps toward experimental tests of our predictions.     

Human Stem Cell-Derived Neurons Repair Circuits and Restore Neural Function

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Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.     

Microbiota-Generated Metabolites Promote Metabolic Benefits via Gut-Brain Neural Circuits

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Amigo Adhesion Protein Regulates Development of Neural Circuits in Zebrafish Brain

The Amigo protein family consists of three transmembrane proteins characterized by six leucine-rich repeat domains and one immunoglobulin-like domain in their extracellular moieties. Previous in vitro studies have suggested a role as homophilic adhesion molecules in brain neurons, but the in vivo functions remain unknown. Here we have cloned all three zebrafish amigos and show that amigo1 is the predominant family member expressed during nervous system development in zebrafish. Knockdown of amigo1 expression using morpholino oligonucleotides impairs the formation of fasciculated tracts in early fiber scaffolds of brain. A similar defect in fiber tract development is caused by mRNA-mediated expression of the Amigo1 ectodomain that inhibits adhesion mediated by the full-length protein. Analysis of differentiated neural circuits reveals defects in the catecholaminergic system. At the behavioral level, the disturbed formation of neural circuitry is reflected in enhanced locomotor activity and in the inability of the larvae to perform normal escape responses. We suggest that Amigo1 is essential for the development of neural circuits of zebrafish, where its mechanism involves homophilic interactions within the developing fiber tracts and regulation of the Kv2.1 potassium channel to form functional neural circuitry that controls locomotion.     

Excitability Changes in Intracortical Neural Circuits Induced by Differentially Controlled Walking Patterns

Our previous single-pulse transcranial magnetic stimulation (TMS) study revealed that excitability in the motor cortex can be altered by conscious control of walking relative to less conscious normal walking. However, substantial elements and underlying mechanisms for inducing walking-related cortical plasticity are still unknown. Hence, in this study we aimed to examine the characteristics of electromyographic (EMG) recordings obtained during different walking conditions, namely, symmetrical walking (SW), asymmetrical walking 1 (AW1), and asymmetrical walking 2 (AW2), with left to right stance duration ratios of 1:1, 1:2, and 2:1, respectively. Furthermore, we investigated the influence of three types of walking control on subsequent changes in the intracortical neural circuits. Prior to each type of 7-min walking task, EMG analyses of the left tibialis anterior (TA) and soleus (SOL) muscles during walking were performed following approximately 3 min of preparative walking. Paired-pulse TMS was used to measure short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) in the left TA and SOL at baseline, immediately after the 7-min walking task, and 30 min post-task. EMG activity in the TA was significantly increased during AW1 and AW2 compared to during SW, whereas a significant difference in EMG activity of the SOL was observed only between AW1 and AW2. As for intracortical excitability, there was a significant alteration in SICI in the TA between SW and AW1, but not between SW and AW2. For the same amount of walking exercise, we found that the different methods used to control walking patterns induced different excitability changes in SICI. Our research shows that activation patterns associated with controlled leg muscles can alter post-exercise excitability in intracortical circuits. Therefore, how leg muscles are activated in a clinical setting could influence the outcome of walking in patients with stroke.