Advances in neurobiology最新文献

Physical exercise is often cited as an important part of an intervention for depression, and there is empirical evidence to support this. However, the mechanism of action through which any potential antidepressant effects are produced is not widely understood. Recent evidence points toward the involvement of endogenous opioids, and especially the mu-opioid system, as a partial mediator of these effects. In this chapter, we discuss the current level of empirical support for physical exercise as either an adjunctive or standalone intervention for depression. We then review the extant evidence for involvement of endogenous opioids in the proposed antidepressant effects of exercise, with a focus specifically on evidence for mu-opioid system involvement.

The function of endogenous opioids spans from initiating behaviors that are critical for survival, to responding to rapidly changing environmental conditions. A network of interconnected systems throughout the body characterizes the endogenous opioid system (EOS). EOS receptors for beta-endorphin, enkephalin, dynorphin, and endomorphin underpin the diverse functions of the EOS across biological systems. This chapter presents a succinct yet comprehensive summary of the structure of the EOS, EOS receptors, and their relationship to other biological systems.

Memories are not formed in a vacuum and often include rich details about the time and place in which events occur. Contextual stimuli promote the retrieval of events that have previously occurred in the encoding context and limit the retrieval of context-inappropriate information. Contexts that are associated with traumatic or harmful events both directly elicit fear and serve as reminders of aversive events associated with trauma. It has long been appreciated that the hippocampus is involved in contextual learning and memory and is central to contextual fear conditioning. However, little is known about the underlying neuronal mechanisms underlying the encoding and retrieval of contextual fear memories. Recent advancements in neuronal labeling methods, including activity-dependent tagging of cellular ensembles encoding memory ("engrams"), provide unique insight into the neural substrates of memory in the hippocampus. Moreover, these methods allow for the selective manipulation of memory ensembles. Attenuating or erasing fear memories may have considerable therapeutic value for patients with post-traumatic stress disorder or other trauma- or stressor-related conditions. In this chapter, we review the role of the hippocampus in contextual fear conditioning in rodents and explore recent work implicating hippocampal ensembles in the encoding and retrieval of aversive memories.

Memory traces for behavioral experiences, such as fear conditioning or taste aversion, are believed to be stored through biophysical and molecular changes in distributed neuronal ensembles across various brain regions. These ensembles are known as engrams, and the cells that constitute them are referred to as engram cells. Recent advancements in techniques for labeling and manipulating neural activity have facilitated the study of engram cells throughout different memory phases, including acquisition, allocation, long-term storage, retrieval, and erasure. In this chapter, we will explore the application of next-generation sequencing methods to engram research, shedding new light on the contribution of transcriptional and epigenetic mechanisms to engram formation and stability.

The medial prefrontal cortex (mPFC) plays a critical role in recalling recent and remote fearful memories. Modern neuroscience techniques, such as projection-specific circuit manipulation and activity-dependent labeling, have illuminated how mPFC memory ensembles are reorganized over time. This chapter discusses the implications of new findings for traditional theories of memory, such as the systems consolidation theory and theories of memory engrams. It also examines the specific contributions of mPFC subregions, like the prelimbic and infralimbic cortices, in fear memory, highlighting how their distinct connections influence memory recall. Further, it elaborates on the cellular and molecular changes within the mPFC that support memory persistence and how these are influenced by interactions with the hippocampus. Ultimately, this chapter provides insights into how lasting memories are dynamically encoded in prefrontal circuits, arguing for a key role of memory ensembles that extend beyond strict definitions of the engram.

This chapter lays out the elementary principles of fractal geometry underpinning much of the rest of this book. It assumes a minimal mathematical background, defines the key principles and terms in context, and outlines the basics of a fractal analysis method known as box counting and how it is used to perform fractal, lacunarity, and multifractal analyses. As a standalone reference, this chapter grounds the reader to be able to understand, evaluate, and apply essential methods to appreciate and heal the exquisitely detailed fractal geometry of the brain.

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

Characterizations in terms of fractals are typically employed for systems with complex and multiscale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many interacting subunits. The human brain can be modeled in terms of observable variables together with their spatio-temporal-functional relations. Computational intelligence is a research field bridging many nature-inspired computational methods, such as artificial neural networks, fuzzy systems, and evolutionary and swarm intelligence optimization techniques. Typical problems faced by means of computational intelligence methods include those of recognition, such as classification and prediction. Although historically conceived to operate in some vector space, such methods have been recently extended to the so-called nongeometric spaces, considering labeled graphs as the most general example of such patterns. Here, we suggest that fractal analysis and computational intelligence methods can be exploited together in neuroscience research. Fractal characterizations can be used to (i) assess scale-invariant properties and (ii) offer numeric, feature-based representations to complement the usually more complex pattern structures encountered in neurosciences. Computational intelligence methods could be used to exploit such fractal characterizations, considering also the possibility to perform data-driven analysis of nongeometric input spaces, therby overcoming the intrinsic limits related to Euclidean geometry.

In parallel to medical applications, exploring how neurons interact with the artificial interface of implants in the human body can be used to learn about their fundamental behavior. For both fundamental and applied research, it is important to determine the conditions that encourage neurons to maintain their natural behavior during these interactions. Whereas previous biocompatibility studies have focused on the material properties of the neuron-implant interface, here we discuss the concept of fractal resonance - the possibility that favorable connectivity properties might emerge by matching the fractal geometry of the implant surface to that of the neurons.To investigate fractal resonance, we first determine the degree to which neurons are fractal and the impact of this fractality on their functionality. By analyzing three-dimensional images of rat hippocampal neurons, we find that the way their dendrites fork and weave through space is important for generating their fractal-like behavior. By modeling variations in neuron connectivity along with the associated energetic and material costs, we highlight how the neurons' fractal dimension optimizes these constraints. To simulate neuron interactions with implant interfaces, we distort the neuron models away from their natural form by modifying the dendrites' fork and weaving patterns. We find that small deviations can induce large changes in fractal dimension, causing the balance between connectivity and cost to deteriorate rapidly. We propose that implant surfaces should be patterned to match the fractal dimension of the neurons, allowing them to maintain their natural functionality as they interact with the implant.

The conscious perception of pain is the result of dynamic interactions of neural activities from local brain regions to distributed brain networks. Mapping out the networks of functional connections between brain regions that form and disperse when an experimental participant received nociceptive stimulations allow to characterize the pattern of network connections related to the pain experience.Although the pattern of intra- and inter-areal connections across the brain are incredibly complex, they appear also largely scale free, with "fractal" connectivity properties reproducing at short and long-time scales. Our results combining intracranial recordings and functional imaging in humans during pain indicate striking similarities in the activity and topological representation of networks at different orders of temporality, with reproduction of patterns of activation from the millisecond to the multisecond range. The connectivity analyzed using graph theory on fMRI data was organized in four sets of brain regions matching those identified through iEEG (i.e., sensorimotor, default mode, central executive, and amygdalo-hippocampal).Here, we discuss similarities in brain network organization at different scales or "orders," in participants as they feel pain. Description of this fractal-like organization may provide clues about how our brain regions work together to create the perception of pain and how pain becomes chronic when its organization is altered.