Advances in neurobiology最新文献

Modulation of microglia function for treatment of neurodegenerative and neuropsychiatric disorders is an emerging field of neuroscience drug development. This is largely attributed to human genetic association studies combined with biological evidence indicating that the innate immune system acts as a causal contributor superimposed on the reactive component of neuronal loss in neurological dysfunction. The identification of disease risk gene variants that encode immune-modulatory proteins in microglia provides tools to evaluate how microglia cellular function or dysfunction affect neuronal health. The development of clinical stage therapeutic compounds that modify myeloid cell function enables us to investigate how modulating microglia function could become a transformational approach to mitigate neurological disorders. Improving our ability to boost microglia-promoting homeostatic and reparative functions hopefully will translate into achieving a better outcome for patients affected by neurological diseases. In this chapter, we aim to provide an overview of the microglial emerging therapies and targets being studied in current clinical trials.

New in vitro models provide an exciting opportunity to study live human microglia. Previously, a major limitation in understanding human microglia in health and disease has been their limited availability. Here, we provide an overview of methods to obtain human stem cell or blood monocyte-derived microglia-like cells that provide a nearly unlimited source of live human microglia for research. We address how understanding microglial ontogeny can help modeling microglial identity and function in a dish with increased accuracy. Moreover, we categorize stem cell-derived differentiation methods into embryoid body based, growth factor driven, and coculture-driven approaches, and review novel viral approaches to reprogram stem cells directly into microglia-like cells. Furthermore, we review typical readouts used in the field to verify microglial identity and characterize functional microglial phenotypes. We provide an overview of methods used to study microglia in environments more closely resembling the (developing) human CNS, such as cocultures and brain organoid systems with incorporated or innately developing microglia. We highlight how microglia-like cells can be utilized to reveal molecular and functional mechanisms in human disease context, focusing on Alzheimer's disease and other neurodegenerative diseases as well as neurodevelopmental diseases. Finally, we provide a critical overview of challenges and future opportunities to more accurately model human microglia in a dish and conclude that novel in vitro microglia-like cells provide an exciting potential to bring preclinical research of microglia to a new era.

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique able to depict the magnetic susceptibility produced by different substances, such as deoxyhemoglobin, calcium, and iron. The main application of SWI in clinical neuroimaging is detecting microbleedings and venous vasculature. Quantitative analyses of SWI have been developed over the last few years, aimed to offer new parameters, which could be used as neuroimaging biomarkers. Each technique has shown pros and cons, but no gold standard exists yet. The fractal dimension (FD) has been investigated as a novel potential objective parameter for monitoring intratumoral space-filling properties of SWI patterns. We showed that SWI patterns found in different tumors or different glioma grades can be represented by a gradient in the fractal dimension, thereby enabling each tumor to be assigned a specific SWI fingerprint. Such results were especially relevant in the differentiation of low-grade versus high-grade gliomas, as well as from high-grade gliomas versus lymphomas.Therefore, FD has been suggested as a potential image biomarker to analyze intrinsic neoplastic architecture in order to improve the differential diagnosis within clinical neuroimaging, determine appropriate therapy, and improve outcome in patients.These promising preliminary findings could be extended into the field of neurotraumatology, by means of the application of computational fractal-based analysis for the qualitative and quantitative imaging of microbleedings in traumatic brain injury patients. In consideration of some evidences showing that SWI signals are correlated with trauma clinical severity, FD might offer some objective prognostic biomarkers.In conclusion, fractal-based morphometrics of SWI could be further investigated to be used in a complementary way with other techniques, in order to form a holistic understanding of the temporal evolution of brain tumors and follow-up response to treatment, with several further applications in other fields, such as neurotraumatology and cerebrovascular neurosurgery as well.

Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.

MATLAB is one of the software platforms most widely used for scientific computation. MATLAB includes a large set of functions, packages, and toolboxes that make it simple and fast to obtain complex mathematical and statistical computations for many applications. In this chapter, we review some tools available in MATLAB for performing fractal analyses on typical neuroscientific data in a practical way. We provide detailed examples of how to calculate the fractal dimension of 1D, 2D, and 3D data in MATLAB. Furthermore, we review other software packages for fractal analysis.

The neuronal ongoing electrical activity in the brain network, the neurodynamics, reflects the structure and functionality of generating neuronal pools. The activity of neurons due to their excitatory and inhibitory projections is associated with specific brain functions. Here, the purpose was to investigate if the local ongoing electrical activity exhibits its characteristic spectral and fractal features in wakefulness and sleep across and within subjects. Moreover, we aimed to show that measures typical of complex systems catch physiological features missed by linear spectral analyses. For this study, we concentrated on the evaluation of the power spectral density (PSD) and Higuchi fractal dimension (HFD) measures. Relevant clinical impact of the specific features of neurodynamics identification stands primarily in the potential of classifying cortical parcels according to their neurodynamics as well as enhancing the effectiveness of neuromodulation interventions to cure symptoms secondary to neuronal activity unbalances.

Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging.

Research has shown that relying only on self-reports for diagnosing psychiatric disorders does not yield accurate results at all times. The advances of technology as well as artificial intelligence and other machine learning algorithms have allowed the introduction of point of care testing (POCT) including EEG characterization and correlations with possible psychopathology. Nonlinear methods of EEG analysis have significant advantages over linear methods. Empirical mode decomposition (EMD) is a reliable nonlinear method of EEG pre-processing. In this chapter, we compare two existing EEG complexity measures - Higuchi fractal dimension (HFD) and sample entropy (SE), with our newly proposed method using Higuchi fractal dimension from the Hilbert Huang transform (HFD-HHT). We present an example using the three complexity measures on a 2-minute EEG recorded from a healthy 20-year-old male after signal pre-processing. Furthermore, we showed the usefulness of these complexity measures in the classification of major depressive disorder (MDD) with healthy controls. Our study is in line with previous research and has shown an increase in HFD and SE values in the full, alpha and beta frequency bands suggestive of an increase in EEG irregularity. Moreover, the HFD-HHT values decreased in those three bands for majority of electrodes which is suggestive of a decrease in irregularity in the frequency-time domain. We conclude that all three complexity measures can be vital features useful for EEG analysis which could be incorporated in POCT systems.

Placebo and nocebo effects have been well documented for nearly two centuries. However, research has only relatively recently begun to explicate the neurobiological underpinnings of these phenomena. Similarly, research on the broader social implications of placebo/nocebo effects, especially within healthcare delivery settings, is in a nascent stage. Biological and psychosocial outcomes of placebo/nocebo effects are of equal relevance. A common pathway for such outcomes is the endogenous opioid system. This chapter describes the history of placebo/nocebo in medicine; delineates the current state of the literature related to placebo/nocebo in relation to pain modulation; summarizes research findings related to human performance in sports and exercise; discusses the implications of placebo/nocebo effects among diverse patient populations; and describes placebo/nocebo influences in research related to psychopharmacology, including the relevance of endogenous opioids to new lines of research on antidepressant pharmacotherapies.

Endogenous opioids and their associated receptors form a system that maintains survival by positively reinforcing behaviors that are vital to life. Cancer and cancer treatment side effects capitalize on this system pathogenically, leading to maladaptive biological responses (e.g., inflammation), as well as cognitive and emotional consequences, most notably depression. Psychologists who treat people with cancer frequently find depression to be a primary target for intervention. However, in people with cancer, the etiology of depression is unique and complex. This complexity necessitates that psycho-oncologists have a fundamental working knowledge of the biological substrates that underlie depression/cancer comorbidity. Building on other chapters in this volume pertaining to cancer and endogenous opioids, this chapter focuses on the clinical applications of basic scientific findings.