Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience.  https://www.selleckchem.com/ALK.html  Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.A fundamental interest in circuit analysis is to parse out the synaptic inputs underlying a behavioral experience. Toward this aim, we have devised an unbiased strategy that specifically labels the afferent inputs that are activated by a defined stimulus in an activity-dependent manner. We validated this strategy in four brain circuits receiving known sensory inputs. This strategy, as demonstrated here, accurately identifies these inputs.Though it is well known that chronic infections of Toxoplasma gondii (T. gondii) can induce mental and behavioral disorders in the host, little is known about the role of long non-coding RNAs (lncRNAs) in this pathological process. In this study, we employed an advanced lncRNAs and mRNAs integration chip (Affymetrix HTA 2.0) to detect the expression of both lncRNAs and mRNAs in T. gondii Chinese 1 strain infected mouse brain. As a result, for the first time, the downregulation of lncRNA-11496 (NONMMUGO11496) was identified as the responsible factor for this pathological process. We showed that dysregulation of lncRNA-11496 affected proliferation, differentiation and apoptosis of mouse microglia. Furthermore, we proved that Mef2c (Myocyte-specific enhancer factor 2C), a member of the MEF2 subfamily, is the target gene of lncRNA-11496. In a more detailed study, we confirmed that lncRNA-11496 positively regulated the expression of Mef2c by binding to histone deacetylase 2 (HDAC2). Importantly, Mef2c itself could coordinate neuronal differentiation, survival, as well as synapse formation. Thus, our current study provides the first evidence in terms of the modulatory action of lncRNAs in chronic toxoplasmosis in T. gondii infected mouse brain, providing a solid scientific basis for using lncRNA-11496 as a therapeutic target to treat T. gondii induced neurological disorder.The striatum, the main input structure of the basal ganglia, is critical for action selection and adaptive motor control. To understand the neuronal mechanisms underlying these functions, an analysis of microcircuits that compose the striatum is necessary. Among these, cholinergic interneurons (ChIs) provide intrinsic striatal innervation whose dysfunction is implicated in neuropsychiatric diseases, such as Parkinson's disease and Tourette syndrome. The ability to experimentally manipulate the activity of ChIs is critical to gain insights into their contribution to the normal function of the striatum and the emergence of behavioral abnormalities in pathological states. In this study, we generated and tested CAV-pChAT-GFP, a replication-defective canine adenovirus type 2 (CAV-2) vector carrying the green fluorescent protein (GFP) sequence under the control of the human choline acetyltransferase (ChAT) promoter. We first tested the potential specificity of CAV-pChAT-GFP to label striatal ChIs in a rat before performing experiments on two macaque monkeys. In the vector-injected rat and monkey striatum, we found that GFP expression preferentially colocalized with ChAT-immunoreactivity throughout the striatum, including those from local circuit interneurons. CAV-2 vectors containing transgene driven by the ChAT promoter provide a powerful tool for investigating ChI contributions to circuit function and behavior in nonhuman primates.Dopamine, alongside other neuromodulators, defines brain and neuronal states, inter alia through regulation of global and local mRNA translation. Yet, the signaling pathways underlying the effects of dopamine on mRNA translation and psychiatric disorders are not clear. In order to examine the molecular pathways downstream of dopamine receptors, we used genetic, pharmacologic, biochemical, and imaging methods, and found that activation of dopamine receptor D1 but not D2 leads to rapid dephosphorylation of eEF2 at Thr56 but not eIF2α in cortical primary neuronal culture in a time-dependent manner. NMDA receptor, mTOR, and ERK pathways are upstream of the D1 receptor-dependent eEF2 dephosphorylation and essential for it. Furthermore, D1 receptor activation resulted in a major reduction in dendritic eEF2 phosphorylation levels. D1-dependent eEF2 dephosphorylation results in an increase of BDNF and synapsin2b expression which was followed by a small yet significant increase in general protein synthesis. These results reveal the role of dopamine D1 receptor in the regulation of eEF2 pathway translation in neurons and present eEF2 as a promising therapeutic target for addiction and depression as well as other psychiatric disorders.