Advances in neurobiology最新文献

The concept of fractal was popularized by Mandelbrot as a tool to tame the geometrical structure of objects with infinite hierarchical depth. The key aspect of fractals is the use of simple parsimonious rules and initial conditions, which when applied recursively can generate unbounded complexity. Fractals are structures ubiquitous in nature, being present in coast lines, bacteria colonies, trees, and physiological time series. However, within the field of cognitive science, the core question is not which phenomena can generate fractal structures, but whether human or animal minds can represent recursive processes, and if so in which domains. In this chapter, we will explore the cognitive and neural mechanisms underlying the representation of recursive hierarchical embedding. Language is the domain in which this capacity is best studied. Humans can generate an infinite array of hierarchically structured sentences, and this capacity distinguishes us from other species. However, recent research suggests that humans can represent similar structures in the domains of music, vision, and action and has provided additional cues as to how these capacities are cognitively implemented. Using a comparative approach, we will map the commonalities and differences across domains and offer a roadmap to understand the neurobiological implementation of fractal cognition.

The fractal dimension is a morphometric measure that has been used to investigate the changes of brain shape complexity in aging and neurodegenerative diseases. This chapter reviews fractal dimension studies in aging and neurodegenerative disorders in the literature. Research has shown that the fractal dimension of the left cerebral hemisphere increases until adolescence and then decreases with aging, while the fractal dimension of the right hemisphere continues to increase until adulthood. Studies in neurodegenerative diseases demonstrated a decline in the fractal dimension of the gray matter and white matter in Alzheimer's disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia. In multiple sclerosis, the white matter fractal dimension decreases, but conversely, the fractal dimension of the gray matter increases at specific stages of disease. There is also a decline in the gray matter fractal dimension in frontotemporal dementia and multiple system atrophy of the cerebellar type and in the white matter fractal dimension in epilepsy and stroke. Region-specific changes in fractal dimension have also been found in Huntington's disease and Parkinson's disease. Associations were found between the fractal dimension and clinical scores, showing the potential of the fractal dimension as a marker to monitor brain shape changes in normal or pathological processes and predict cognitive or motor function.

People are continually exposed to the rich complexity generated by the repetition of fractal patterns at different size scales. Fractals are prevalent in natural scenery and also in patterns generated by artists and mathematicians. In this chapter, we will investigate the powerful significance of fractals for the human senses. In particular, we propose that fractals with mid-range complexity play a unique role in our visual experiences because the visual system has adapted to these prevalent natural patterns. This adaptation is evident at multiple stages of the visual system, ranging from data acquisition by the eye to processing of this data in the higher visual areas of the brain. Based on these results, we will discuss a fluency model in which the visual system processes mid-complexity fractals with relative ease. This fluency optimizes the observer's capabilities (such as enhanced attention and pattern recognition) and generates an aesthetic experience accompanied by a reduction in the observer's physiological stress levels. In addition to reviewing people's responses to viewing fractals, we will compare these responses to recent research focused on fractal sounds and fractal surface textures. We will extend our fractal fluency model to allow for stimuli across multiple senses.

Morphometrics have been able to distinguish important features of glioblastoma from magnetic resonance imaging (MRI). Using morphometrics computed on segmentations of various imaging abnormalities, we show that the average and range of lacunarity and fractal dimension values across MRI slices can be prognostic for survival. We look at the repeatability of these metrics to multiple segmentations and how they are impacted by image resolution. We speak to the challenges to overcome before these metrics are included in clinical care, and the insight that they may provide.

Self-similar stochastic processes and broad probability distributions are ubiquitous in nature and in many man-made systems. The brain is a particularly interesting example of (natural) complex system where those features play a pivotal role. In fact, the controversial yet experimentally validated "criticality hypothesis" explaining the functioning of the brain implies the presence of scaling laws for correlations. Recently, we have analyzed a collection of rest tremor velocity signals recorded from patients affected by Parkinson's disease, with the aim of determining and hence exploiting the presence of scaling laws. Our results show that multiple scaling laws are required in order to describe the dynamics of such signals, stressing the complexity of the underlying generating mechanism. We successively extracted numeric features by using the multifractal detrended fluctuation analysis procedure. We found that such features can be effective for discriminating classes of signals recorded in different experimental conditions. Notably, we show that the use of medication (L-DOPA) can be recognized with high accuracy.

This chapter (part one of a trilogy) summarizes the neurobiological foundations of endogenous opioids in the regulation of energy balance and eating behavior, dysregulation of which translates to maladaptive dietary responses in individuals with obesity and eating disorders, including anorexia, bulimia, and binge eating disorder. Knowledge of these neurobiological foundations is vital to researchers' and clinicians' understanding of pathophysiology as well as the science-based development of multidisciplinary diagnoses and treatments for obesity and eating disorders. We highlight mechanisms of endogenous opioids in both homeostatic and hedonic feeding behavior, review research on the dysregulation of food reward that plays a role in a wide array of obesity and disordered eating, and the clinical implications of neurobiological responses to food for current science-based treatments for obesity and eating disorders.

This chapter deals with the methodical challenges confronting researchers of the fractal phenomenon known as pink or 1/f noise. This chapter introduces concepts and statistical techniques for identifying fractal patterns in empirical time series. It defines some basic statistical terms, describes two essential characteristics of pink noise (self-similarity and long memory), and outlines four parameters representing the theoretical properties of fractal processes: the Hurst coefficient (H), the scaling exponent (α), the power exponent (β), and the fractional differencing parameter (d) of the ARFIMA (autoregressive fractionally integrated moving average) method. Then, it compares and evaluates different approaches to estimating fractal parameters from observed data and outlines the advantages, disadvantages, and constraints of some popular estimators. The final section of this chapter answers the questions: Which strategy is appropriate for the identification of fractal noise in empirical settings and how can it be applied to the data?

Microglia, which are the resident innate immune cells of the central nervous system (CNS), have emerged as critical for maintaining health by not only ensuring proper development, activity, and plasticity of neurones and glial cells but also maintaining and restoring homeostasis when faced with various challenges across the lifespan. This chapter is dedicated to the current understanding of microglia, including their beneficial versus detrimental roles, which are highly complex, rely on various microglial states, and intimately depend on their spatiotemporal context. Microglia are first contextualized within the perspective of finding therapeutic strategies to cure diseases in the twenty-first century-the overall functions of neuroglia with relation one to another and to neurones, and their shared CNS environment. A historical framework is provided, and the main principles of glial neuropathology are enunciated. The current view of microglial nomenclature is then covered, notably by discussing the rejected concepts of microglial activation, their polarisation into M1 and M2 phenotypes, and neuroinflammation. The transformation of the microglial population through the addition, migration, and elimination of individual members, as well as their dynamic metamorphosis between a wide variety of structural and functional states, based on the experienced physiological and pathological stimuli, is subsequently discussed. Lastly, the perspective of microglia as a cell type endowed with a health status determining their outcomes on adaptive CNS plasticity as well as disease pathology is proposed for twenty-first-century approaches to disease prevention and treatment.

Microglia are the resident immune cells of the brain. As such, they rapidly detect changes in normal brain homeostasis and accurately respond by fine-tuning in a tightly regulated manner their morphology, gene expression, and functional behavior. Depending on the nature of these changes, microglia can thicken and retract their processes, proliferate and migrate, release numerous signaling factors and compounds influencing neuronal physiology (e.g., cytokines and trophic factors), in addition to secreting proteases able to transform the extracellular matrix, and phagocytosing various types of cellular debris, etc. Because microglia also transform rapidly (on a time scale of minutes) during experimental procedures, studying these very special cells requires methods that are specifically non-invasive. The development of such methods has provided unprecedented insights into the roles of microglia during normal physiological conditions. In particular, transcranial two-photon in vivo imaging revealed that presumably "resting" microglia continuously survey the brain parenchyma with their highly motile processes, in addition to modulating their structural and functional interactions with neuronal circuits along the changes in neuronal activity and behavioral experience occurring throughout the lifespan. In this chapter, we will describe how surveillant microglia interact with synaptic elements and modulate the number, maturation, function, and plasticity of synapses in the healthy developing, mature, and aging brain, with consequences on neuronal activity, learning and memory, and the behavioral outcome.

Aging is the greatest risk factor for neurodegenerative diseases. Microglia are the resident immune cells in the central nervous system (CNS), playing key roles in its normal functioning, and as mediators for age-dependent changes of the CNS, condition at which they generate a hostile environment for neurons. Transforming Growth Factor β1 (TGFβ1) is a regulatory cytokine involved in immuneregulation and neuroprotection, affecting glial cell inflammatory activation, neuronal survival, and function. TGFβ1 signaling undergoes age-dependent changes affecting the regulation of microglial cells and can contribute to the pathophysiology of neurodegenerative diseases. This chapter focuses on assessing the role of age-related changes on the regulation of microglial cells and their impact on neuroinflammation and neuronal function, for understanding age-dependent changes of the nervous system.