Advances in neurobiology最新文献

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.

The structural complexity of brain tumor tissue represents a major challenge for effective histopathological diagnosis. Tumor vasculature is known to be heterogeneous, and mixtures of patterns are usually present. Therefore, extracting key descriptive features for accurate quantification is not a straightforward task. Several steps are involved in the texture analysis process where tissue heterogeneity contributes to the variability of the results. One of the interesting aspects of the brain lies in its fractal nature. Many regions within the brain tissue yield similar statistical properties at different scales of magnification. Fractal-based analysis of the histological features of brain tumors can reveal the underlying complexity of tissue structure and angiostructure, also providing an indication of tissue abnormality development. It can further be used to quantify the chaotic signature of disease to distinguish between different temporal tumor stages and histopathological grades.Brain meningioma subtype classifications' improvement from histopathological images is the main focus of this chapter. Meningioma tissue texture exhibits a wide range of histological patterns whereby a single slide may show a combination of multiple patterns. Distinctive fractal patterns quantified in a multiresolution manner would be for better spatial relationship representation. Fractal features extracted from textural tissue patterns can be useful in characterizing meningioma tumors in terms of subtype classification, a challenging problem compared to histological grading, and furthermore can provide an objective measure for quantifying subtle features within subtypes that are hard to discriminate.

The introduction of fractal geometry to the neurosciences has been a major paradigm shift over the last decades as it has helped overcome approximations and limitations that occur when Euclidean and reductionist approaches are used to analyze neurons or the entire brain. Fractal geometry allows for quantitative analysis and description of the geometric complexity of the brain, from its single units to the neuronal networks.As illustrated in the second section of this book, fractal analysis provides a quantitative tool for the study of the morphology of brain cells (i.e., neurons and microglia) and its components (e.g., dendritic trees, synapses), as well as the brain structure itself (cortex, functional modules, neuronal networks). The self-similar logic which generates and shapes the different hierarchical systems of the brain and even some structures related to its "container," that is, the cranial sutures on the skull, is widely discussed in the following chapters, with a link between the applications of fractal analysis to the neuroanatomy and basic neurosciences to the clinical applications discussed in the third section.

In this chapter, the personal journey of the author in many countries, including Italy, Germany, Austria, the United Kingdom, Switzerland, the United States, Canada, and Australia, is summarized, aimed to merge different translational fields (such as neurosurgery and the clinical neurosciences in general, biomedical engineering, mathematics, computer science, and cognitive sciences) and lay the foundations of a new field defined computational neurosurgery, with fractals, pattern recognition, memetics, and artificial intelligence as the common key words of the journey.

When neurons are recruited to form the memory engram, they are driven to activate the expression of a series of immediate-early genes (IEGs). While these IEGs have been used relatively indiscriminately to identify the so-called engram neurons, recent research has demonstrated that different IEG ensembles can be physically and functionally distinct within the memory engram. This inherent heterogeneity of the memory engram is driven by the diversity in the functions and distributions of different IEGs. This process, which we call molecular sorting, is analogous to sorting the entire population of engram neurons into different sub-engrams molecularly defined by different IEGs. In this chapter, we will describe the molecular sorting process by systematically reviewing published work on engram ensemble cells defined by the following four major IEGs: Fos, Npas4, Arc, and Egr1. By comparing and contrasting these likely different components of the memory engram, we hope to gain a better understanding of the logic and significance behind the molecular sorting process for memory functions.

Microglia, immune sentinels of the central nervous system (CNS), play a critical role in maintaining its health and integrity. This chapter delves into the concept of immunometabolism, exploring how microglial metabolism shapes their diverse immune functions. It examines the impact of cell metabolism on microglia during various CNS states, including homeostasis, development, aging, and inflammation. Particularly in CNS inflammation, the chapter discusses how metabolic rewiring in microglia can initiate, resolve, or perpetuate inflammatory responses. The potential of targeting microglial metabolism as a therapeutic strategy for chronic CNS disorders with prominent innate immune cell activation is also explored.