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?

While our understanding of long-term disability after traumatic brain injury (TBI) has habitually focused on cognitive and sensorimotor functioning, it is increasingly appreciated that changes in social function for survivors of a brain injury are common and have a profound impact on one's quality of life. In this chapter, we highlight the consequences of TBI on social behavior, taking into account evidence from studies of patient populations as well as from preclinical animal models. After first considering the protracted nature of the development of social behavior across the lifespan, including the neurobiological networks that underlie social functioning, we discuss how TBI results in social behavior impairments and how these manifest. We focus particularly on how age-at-injury influences TBI-induced social impairments, with most of the evidence suggesting age-dependent vulnerability after injury at a younger age. In addition, we explore how biological sex is a key determinant of social behavior impairments after TBI, while gender in humans may also influence the nature and extent of social outcomes. Finally, we identify key knowledge gaps and emphasize the need for further research in the field.

Myeloid cells are fundamental constituents of the brain tumor microenvironment. In this chapter, we describe the state-of-the-art knowledge on the role of microglial cells in the cross-talk with the most common and aggressive brain tumor, glioblastoma. We report in vitro and in vivo studies related to glioblastoma patients and glioma models to outline the symbiotic interactions that microglia develop with tumoral cells, highlighting the heterogeneity of microglial functions in shaping the brain tumor microenvironment.

Neuropathic pain (NP) is pain resulting from lesions or disease of the somatosensory system. A cardinal feature of NP is tactile allodynia (a painful response to normally innocuous stimulation). In 2003, a breakthrough strategy for inducing NP was proposed in which microglia of the spinal dorsal horn (SDH) are activated after peripheral nerve injury (PNI) to overexpress P2X4 receptor (P2X4R) and play an important role in inducing tactile allodynia. In 2005, it was reported that stimulation of microglial P2X4Rs evokes the release of brain-derived neurotrophic factor (BDNF), which causes a depolarizing shift of the anion reversal potential (E_anion) of secondary sensory neurons. These findings and other facts suggest the mechanism by which innocuous touch stimuli cause severe pain and the important role of microglia in the mechanism.