Neuroscientist最新文献

As a monovalent cation channel, the transient receptor potential melastatin 4 (TRPM4) channel is a unique member of the transient receptor potential family. Abnormal TRPM4 activity has been identified in various neurologic disorders, such as stroke, spinal cord injury, traumatic brain injury, multiple sclerosis, amyotrophic lateral sclerosis, pathologic pain, and epilepsy. Following brain hypoxia/ischemia and inflammation, TRPM4 up-regulation and enhanced activity contribute to the cell death of neurons, vascular endothelial cells, and astrocytes. Enhanced ionic influx via TRPM4 leads to cell volume increase and oncosis. Depolarization of membrane potential following TRPM4 activation and interaction between TRPM4 and N-methyl-d-aspartate receptors exacerbate excitotoxicity during hypoxia. Importantly, TRPM4 expression and activity remain low in healthy neurons, making it an ideal drug target. Current approaches to inhibit or modulate the TRPM4 channel have various limitations that hamper the interpretation of TRPM4 physiology in the nervous system and potentially hinder their translation into therapy. In this review, we discuss the pathophysiologic roles of TRPM4 and the different inhibitors that modulate TRPM4 activity for potential treatment of neurologic diseases.

The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain-machine interfaces and their implications for mental health in older adults.

Microglia serve as vital innate immune cells in the central nervous system, playing crucial roles in the generation and development of brain neurons, as well as mediating a series of immune and inflammatory responses. The morphologic transitions of microglia are closely linked to their function. With the advent of single-cell sequencing technology, the diversity of microglial subtypes is increasingly recognized. The intricate interactions between microglia and neuronal networks have significant implications for psychiatric disorders and neurodegenerative diseases. A deeper investigation of microglia in neurologic diseases such as Alzheimer disease, depression, and epilepsy can provide valuable insights in understanding the pathogenesis of diseases and exploring novel therapeutic strategies, thereby addressing issues related to central nervous system disorders.

The beginnings of cybernetics were marked by the publication of two papers in 1943. In the first one, Rosenblueth, Wiener, and Bigelow claimed that purposeful behavior is a circular process controlled by negative feedback. In the second seminal paper, McCulloch and Pitts proposed that neurons are interconnected working as logical operators. Both articles raised human-machine analogies and mathematically formulated cognitive mechanisms. These ideas ignited the interest of von Neumann, who was developing the first stored-program computer. Thus, after a preliminary meeting in 1945, a series of meetings were held between 1946 and 1953. The role of the Spanish neurophysiologist Rafael Lorente de Nó in the beginnings of cybernetics is attested not only by his participation in the core members of these Macy conferences but also for his previous description of reverberating circuits formed by a closed chain of internuncial neurons. This was the first neurobiologic demonstration of a feedback loop. Most researchers considered the central nervous system as a mere reflex organ until then; nevertheless, he demonstrated a self-sustained central activity in the nervous system, supporting the idea of self-regulating mechanisms as a key concept not just in machines but also in the brain.

Neural activities in local circuits exhibit complex and multilevel dynamic features. Individual neurons spike irregularly, which is believed to originate from receiving balanced amounts of excitatory and inhibitory inputs, known as the excitation-inhibition balance. The spatial-temporal cascades of clustered neuronal spikes occur in variable sizes and durations, manifested as neural avalanches with scale-free features. These may be explained by the neural criticality hypothesis, which posits that neural systems operate around the transition between distinct dynamic states. Here, we summarize the experimental evidence for and the underlying theory of excitation-inhibition balance and neural criticality. Furthermore, we review recent studies of excitatory-inhibitory networks with synaptic kinetics as a simple solution to reconcile these two apparently distinct theories in a single circuit model. This provides a more unified understanding of multilevel neural activities in local circuits, from spontaneous to stimulus-response dynamics.

Pioneering investigations in the mid-19th century revealed that the perception of tactile cues presented to the surface of the skin improves with training, which is referred to as tactile learning. Surprisingly, tactile learning also occurs for body parts and skin locations that are not physically involved in the training. For example, after training of a finger, tactile learning transfers to adjacent untrained fingers. This suggests that the transfer of tactile learning follows a somatotopic pattern and involves brain regions such as the primary somatosensory cortex (S1), in which the trained and untrained body parts and skin locations are represented close to each other. However, other results showed that transfer occurs between body parts that are not represented close to each other in S1-for example, between the hand and the foot. These and similar findings have led to the suggestion of additional cortical mechanisms to explain the transfer of tactile learning. Here, different mechanisms are reviewed, and the extent to which they can explain the transfer of tactile learning is discussed. What all of these mechanisms have in common is that they assume a representational or functional relationship between the trained and untrained body parts and skin locations. However, none of these mechanisms alone can explain the complex pattern of transfer results, and it is likely that different mechanisms interact to enable transfer, perhaps in concert with higher somatosensory and decision-making areas.