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Accumulating evidence suggests that the serum response factor (SRF) cofactor megakaryoblastic leukemia (MKL)/myocardin-related transcription factor (MRTF) has critical roles in many physiological and pathological processes in various cell types. MKL/MRTF molecules comprise MKL1/MRTFA and MKL2/MRTFB, which possess actin-binding motifs at the N-terminus, and SRF-binding domains and a transcriptional activation domain (TAD) at the C-terminus. Several studies have reported that, in association with actin rearrangement, MKL/MRTF translocates from the cytoplasm to the nucleus, where it regulates SRF-mediated gene expression and controls cell motility. Therefore, it is important to elucidate the roles of MKL/MRTF in the nervous system with regard to its structural and functional regulation by extracellular stimuli. We demonstrated that MKL/MRTF is highly expressed in the brain, especially the synapses, and is involved in dendritic complexity and dendritic spine maturation. In addition to the positive regulation of dendritic complexity, we identified several MKL/MRTF isoforms that negatively regulate dendritic complexity in cortical neurons. We found that the MKL/MRTF isoforms were expressed differentially during brain development and the impacts of these isoforms on the immediate early genes including Arc/Arg3.1 , were different. Here, we review the roles of MKL/MRTF in the nervous system, with a special focus on the MKL/MRTF-mediated fine-tuning of neuronal morphology and gene transcription. In the concluding remarks, we briefly discuss the future perspectives and the possible involvement of MKL/MRTF in neurological disorders such as schizophrenia and autism spectrum disorder.

Injuries to spinal motoneurons manifest in a variety of forms, including damage to peripheral axons, neurodegenerative disease, or direct insult centrally. Such injuries produce a variety of negative structural and functional changes in both the directly affected and neighboring motoneurons. Exercise is a relatively simple behavioral intervention that has been demonstrated to protect against, and accelerate recovery from, these negative changes. In this article, we describe how exercise is neuroprotective for motoneurons, accelerating axon regeneration following axotomy and attenuating dendritic atrophy following the death of neighboring motoneurons. In both of these injury models, the positive effects of exercise have been found to be dependent on gonadal hormone action. Here we describe a model in which exercise, hormones, and brain-derived neurotrophic factor might all interact to produce neuroprotective effects on motoneuron structure following neural injury.

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations—the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons. Artificial intelligence has made major progress in recent years thanks to a technique known as deep learning, which works by mimicking the human brain. When computers employ deep learning, they learn by using networks made up of many layers of simulated neurons. Deep learning has opened the door to computers with human – or even super-human – levels of skill in recognizing images, processing speech and controlling vehicles. But many neuroscientists are skeptical about whether the brain itself performs deep learning. The patterns of activity that occur in computer networks during deep learning resemble those seen in human brains. But some features of deep learning seem incompatible with how the brain works. Moreover, neurons in artificial networks are much simpler than our own neurons. For instance, in the region of the brain responsible for thinking and planning, most neurons have complex tree-like shapes. Each cell has ‘roots’ deep inside the brain and ‘branches’ close to the surface. By contrast, simulated neurons have a uniform structure. To find out whether networks made up of more realistic simulated neurons could be used to make deep learning more biologically realistic, Guerguiev et al. designed artificial neurons with two compartments, similar to the ‘roots’ and ‘branches’. The network learned to recognize hand-written digits more easily when it had many layers than when it had only a few. This shows that artificial neurons more like those in the brain can enable deep learning. It even suggests that our own neurons may have evolved their shape to support this process. If confirmed, the link between neuronal shape and deep learning could help us develop better brain-computer interfaces. These allow people to use their brain activity to control devices such as artificial limbs. Despite advances in computing, we are still superior to computers when it comes to learning. Understanding how our own brains show deep learning could thus help us develop better, more human-like artificial intelligence in the future.

Background Corticotropin-releasing hormone (CRH) is an important neuromodulator that is widely distributed in the brain and plays a key role in mediating stress responses and autonomic functions. While the distribution pattern of fluorescently labeled CRH-expressing neurons has been studied in different transgenic mouse lines, a full appreciation of the broad diversity of this population and local neural connectivity can only come from integration of single-cell morphological information as a defining feature. However, the morphologies of single CRH neurons and the local circuits formed by these neurons have not been acquired at brain-wide and dendritic-scale levels. Results We screened the EYFP-expressing CRH-IRES-Cre;Ai32 mouse line to reveal the morphologies of individual CRH neurons throughout the whole mouse brain by using a fluorescence micro-optical sectioning tomography (fMOST) system. Diverse dendritic morphologies and projection fibers of CRH neurons were found in various brain regions. Follow-up reconstructions showed that hypothalamic CRH neurons had the smallest somatic volumes and simplest dendritic branches and that CRH neurons in several brain regions shared a common bipolar morphology. Further investigations of local CRH neurons in the medial prefrontal cortex unveiled somatic depth-dependent morphologies of CRH neurons that exhibited three types of mutual connections: basal dendrites (upper layer) with apical dendrites (layer 3); dendritic-somatic connections (in layer 2/3); and dendritic-dendritic connections (in layer 4). Moreover, hypothalamic CRH neurons were classified into two types according to their somatic locations and characteristics of dendritic varicosities. Rostral-projecting CRH neurons in the anterior parvicellular area had fewer and smaller dendritic varicosities, whereas CRH neurons in the periventricular area had more and larger varicosities that were present within dendrites projecting to the third ventricle. Arborization-dependent dendritic spines of CRH neurons were detected, among which the most sophisticated types were found in the amygdala and the simplest types were found in the hypothalamus. Conclusions By using the CRH-IRES-Cre;Ai32 mouse line and fMOST imaging, we obtained region-specific morphological distributions of CRH neurons at the dendrite level in the whole mouse brain. Taken together, our findings provide comprehensive brain-wide morphological information of stress-related CRH neurons and may facilitate further studies of the CRH neuronal system.

A growing body of evidence suggests that the gut microbiome may contribute to changes in brain morphology. The microbiota-gut-brain axis (MGBA) has been shown to influence neurogenesis, axon myelination, and synapse structure. However, it remains unclear whether the MGBA can influence the morphology and density of inhibitory GABAergic interneurons. The aim of this study was to determine whether antibiotic-induced dysbiosis (AID) is associated with alterations in dendritic morphology of GABAergic inhibitory interneurons in the medial entorhinal cortex (mEC), somatosensory cortex (SSC), motor cortex (MC), and hippocampus (Hp). A cohort of six-month-old GAD-67-EGFP transgenic mice was treated with an antibiotic cocktail for two weeks, resulting in gut dysbiosis as validated by collecting stool samples at baseline and after treatment, then using next-generation sequencing of 16S ribosomal RNA. The results demonstrate that the proposed model effectively exhibited the defining features of gut dysbiosis, including a significant reduction in microbiome diversity, expansion of pathobionts, and loss of beneficial microbes. The AID group showed alterations in density and morphology of GABAergic interneurons in different brain areas. The mean dendritic length and mean dendritic segments of the SSC and Hp were found to be significantly decreased, while no such decrease was observed in the mEC or MC. Furthermore, the density of interneurons was decreased in the mEC, Hp, and SSC areas, while no change was observed in the MC area. The interneuron dysfunction plays a role in the pathogenesis of neurological disease. The findings of this study suggest that AID potentially influences the density and morphology of the interneurons, which may contribute to the development of neurological disorders.

Fractional-order derivatives have been widely used to describe the spiking patterns of neurons, without considering their self-similar dendritic structures. In this study, a self-similar resistor–capacitor network is proposed to relate the spiny dendritic structure with fractional spiking properties. In order to achieve this goal, two types of networks comprising recursively staggered resistors and capacitors were developed to model the functional properties of smooth and spiny dendrites, respectively. Their overall electrotonic properties can be described by fractional order temporal operators derived by Heaviside operational calculus. According to this operator method, spiking patterns of spiny dendrites were controlled by the standard 0.5-order derivative, whereas an exponential modulation term was added in the governing fractional operator of the smooth dendrites. The application of these fractional operators in a leaky integrate-and-fire model reveals that the dendritic spine plays an important role in alternations of the spiking properties, including first-spike latency, firing rate adaptation, and afterhyperpolarization conductance. Further, the multilevel assembly of this network indicates that the fractional spiking behaviors of spiny neurons might originate from their hierarchical substructures, thereby highlighting possible functional consequences of alterations to dendritic self-similarity.

Effect of dendritic and globular (rosette-like) morphology and its parameters on the hardness of Al–7.5% Si castings has been investigated. It was established that Brinell hardness demonstrates a slight increase with the rise of globules’ size and a slight decrease with the rise in dendrite parameter. In the first case, this is apparently due to isothermal holding in the process of obtaining castings, in the second case, with a decrease in the quenching rate of eutectics due to a decrease in the surface area of the dendrites because of coarsening.

The gut microbiota plays a critical role in regulating brain structure and function via the microbiota-gut-brain axis. Antibiotic-induced gut dysbiosis (AIGD) has been linked to neuroanatomical changes and cognitive deficits. However, its impact on neuronal morphology in layer II of the medial entorhinal cortex (mECII), a region central to spatial memory, remains poorly understood. This study examines how AIGD affects dendritic architecture in mECII stellate and pyramidal island cells. Mice received a broad-spectrum oral antibiotic cocktail to induce AIGD. Gut microbiota composition was analyzed using 16S rRNA sequencing. Golgi-stained neurons in mECII were assessed for dendritic complexity via Sholl analysis. Iba1 staining evaluated microglial activation in mECII. Intestinal sections were stained with NeuN and CD8 to assess enteric neuron density and inflammation. Microbial abundance was correlated with dendritic parameters. AIGD resulted in significant dysbiosis, including depletion of butyrate-producing taxa ( , ) and enrichment of proinflammatory bacteria ( , , ). Stellate cells showed marked dendritic atrophy, while pyramidal island cells were unaffected. Dendritic complexity positively correlated with and negatively with . No microglial activation was detected in mECII, but CD8 + T-cell infiltration increased in the gut without changes in NeuN-labeled enteric neurons. These findings suggest AIGD selectively alters mECII stellate cell morphology through peripheral immune signaling or microbial metabolites, independent of local microglial activation. This study highlights the role of gut microbiota in shaping neuronal architecture and supports microbiome-targeted strategies to counteract dysbiosis-associated neuroanatomical changes.

Mistimed exposure to light has been demonstrated to negatively affect multiple aspects of physiology and behavior. Here we analyzed the effects of chronic exposure to abnormal lighting conditions in mice. We exposed mice for one year to either: a standard light/dark cycle, a “light-pollution” condition in which low levels of light were present in the dark phase of the circadian cycle (dim light at night, DLAN), or altered light cycles in which the length of the weekday and weekend light phase differed by 6 hours (“social jetlag”). Mice exhibited several circadian activity phenotypes, as well as changes in motor function, associated particularly with the DLAN condition. Our data suggest that these phenotypes might be due to changes outside the core clock. Dendritic spine changes in other brain regions raise the possibility that these phenotypes are mediated by changes in neuronal coordination outside of the clock. Given the prevalence of artificial light exposure in the modern world, further work is required to establish whether these negative effects are observed in humans as well.