Advances in neurobiology最新文献

Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.

Arteriovenous malformations (AVMs) are cerebrovascular lesions consisting of a pathologic tangle of the vessels characterized by a core termed the nidus, which is the "nest" where the fistulous connections occur. AVMs can cause headache, stroke, and/or seizures. Their treatment can be challenging requiring surgery, endovascular embolization, and/or radiosurgery as well. AVMs' morphology varies greatly among patients, and there is still a lack of standardization of angioarchitectural parameters, which can be used as morphometric parameters as well as potential clinical biomarkers (e.g., related to prognosis).In search of new diagnostic and prognostic neuroimaging biomarkers of AVMs, computational fractal-based models have been proposed for describing and quantifying the angioarchitecture of the nidus. In fact, the fractal dimension (FD) can be used to quantify AVMs' branching pattern. Higher FD values are related to AVMs characterized by an increased number and tortuosity of the intranidal vessels or to an increasing angioarchitectural complexity as a whole. Moreover, FD has been investigated in relation to the outcome after Gamma Knife radiosurgery, and an inverse relationship between FD and AVM obliteration was found.Taken altogether, FD is able to quantify in a single and objective value what neuroradiologists describe in qualitative and/or semiquantitative way, thus confirming FD as a reliable morphometric neuroimaging biomarker of AVMs and as a potential surrogate imaging biomarker. Moreover, computational fractal-based techniques are under investigation for the automatic segmentation and extraction of the edges of the nidus in neuroimaging, which can be relevant for surgery and/or radiosurgery planning.

The dynamics of tumor growth is a very complex process, generally accompanied by numerous chromosomal aberrations that determine its genetic and dynamical heterogeneity. Consequently, the tumor interface exhibits a non-regular and heterogeneous behavior often described by a single fractal dimension. A more suitable approach is to consider the tumor interface as a multifractal object that can be described by a set of generalized fractal dimensions. In the present work, detrended fluctuation and multifractal analysis are used to characterize the complexity of glioblastoma.

Temporomandibular joint disorders include a variety of clinical syndromes that are difficult to manage if associated with debilitating severe jaw pain. Thus, seeking additional experimental therapies for temporomandibular joint pain reduction is warranted. Targeted enkephalin gene therapy approaches provide clear promise for pain control. The studies detailed here indicate significant analgesia and protection of joint tissue are provided after injection of an overexpression viral vector gene therapy near the joint. The viral vector gene therapy described provides overexpression of naturally occurring opioid peptides after its uptake by trigeminal nerve endings. The viral vectors act as independent "minipump" sources for the opioid peptide synthesis in the neuronal cytoplasm producing the intended biological function, reduction of pain, and tissue repair. The antinociceptive effects provided with this delivery method of opioid expression persist for over 4 weeks. This is coincident with the expected time frame for the duration of the transgene overproduction of the endogenous opioid peptide before its diminution due to dormancy of the virus. These experimental studies establish a basis for the use of replication-defective herpes simplex type 1-based gene therapy for severe chronic inflammatory temporomandibular joint destruction and pain. As innovative means of significantly reducing joint inflammation and preserving tissue architecture, gene therapies may extend their clinical usefulness for patients with temporomandibular joint disorders.

The opioid system involves opioid receptors (OPRs) and endogenous opioid peptides.This chapter will focus on the distribution of OPRs in the cardiovascular system, the expression pattern in the heart, the activation by opioid peptides, and the effects of OPRs activation with potential relevance in cardiovascular performance. In the heart, OPRs are co-expressed with beta adrenergic receptors (β-ARs) in the G-protein-coupled receptor (GPCR) superfamily, functionally cross-talk with β-Ars and modify catecholamine-induced effects. They are involved in cardiac contractility, energy metabolism, myocyte survival or death, vascular resistance. The effects of the opioid system in the regulation of systemic circulation at both the central and peripheral level are presented. The pathways are discussed under physiological (i.e., aging) and pathological conditions (atherosclerosis, heart failure, essential hypertension, ischemic stress). Stimulation of OPRs not only inhibits cardiac excitation-contraction coupling, but also protects the heart against hypoxic and ischemic injury. An enhanced sensitivity to opioids of endocrine organs and neuronal systems is operative in hypertensive patients. The opioid system can be pharmacologically engaged to selectively mimic these responses via cardiac and nervous signaling. The clinical opportunities for the use of cardioprotective effects of opioids require future investigations to provide more specific details of the impact on cardiac performance and electrophysiological properties.

The endogenous opioid system, which consists of opioid receptors and their ligands, is widely expressed in the nervous system and also found in the immune system. As a part of the body's defense machinery, the immune system is heavily regulated by endogenous opioid peptides. Many types of immune cells, including macrophages, dendritic cells, neutrophils, and lymphocytes are influenced by endogenous opioids, which affect cell activation, differentiation, proliferation, apoptosis, phagocytosis, and cytokine production. Additionally, immune cells also synthesize and secrete endogenous opioid peptides and participate peripheral analgesia. This chapter is structured into two sections. Part one focuses on immunoregulatory functions of central endogenous opioids; and part two describes how opioid peptide-containing immune cells participate in local analgesia.

In this chapter, we highlight the advantages, progress, and pending challenges of developing electroencephalography (EEG) and event-related potential (ERP) biomarkers for use in autism spectrum disorder (ASD). We describe reasons why global efforts towards precision treatment in ASD are utilizing EEG indices to quantify biological mechanisms. We overview common sensory processing and attention biomarkers and provide translational examples examining the genetic etiology of autism across animal models and human subgroups. We describe human-specific social biomarkers related to face perception, a complex social cognitive process that may prove informative of autistic social behaviors. Lastly, we discuss outstanding considerations for quantifying EEG biomarkers, the challenges associated with rigor and reproducibility, contexts of future use, and propose opportunities for combinatory multidimensional biomarkers.

Drug development in psychiatry has been hampered by the lack of reliable ways to determine the neurobiological effects of the assets tested, difficulties in identifying patient subsets more amenable to benefit from a given asset, and issues with executing trials in a manner that would convincingly provide answers. An emerging idea in many companies is to validate tools to address changes in neural circuits by pharmacological tools as a key piece in quantifying the effects of our drugs. Here, we review past, present, and emerging approaches to capture the outcome of the modulation of brain circuits. The field is now ripe for implementing these approaches in drug development.

Magnetoencephalography (MEG) is a neuroimaging technique that has excellent temporal as well as good spatial resolution for measuring neural activity and has been extensively employed in cognitive neuroscience. However, MEG has only been more recently applied to investigations of brain networks and biomarkers in psychiatry. Besides providing new insights into the pathophysiology of major psychiatry syndromes, especially in schizophrenia, a major objective of current research is the identification of biomarkers that could inform early intervention and novel treatments. This chapter will provide a state-of-the-art overview of MEG as applied to schizophrenia, autism spectrum disorders, and Alzheimer's disease, summarizing methodological approaches and studies investigating alterations during resting-state and task-related paradigms. In addition, we will highlight future methodological developments and their potential for applications of MEG in psychiatry.

Neuropsychiatric disability is related to reduced ability to change in response to clinical interventions, e.g., plasticity. Study of biomarkers and interventional strategies for plasticity, however, are sparse. In this chapter, we focus on the serial frequency discrimination task (SFDT), which is sensitive to impairments in early auditory processing (EAP) and auditory learning and has been most thoroughly studied in dyslexia and schizophrenia. In the SFDT, participants are presented with repeated paired tones ("reference" and "test") and indicate which tone is higher in pitch. Plasticity during the SFDT is critically dependent upon interactions between prefrontal "cognitive control" regions, and lower-level perceptual and motor regions that may be detected using both fMRI and time-frequency event-related potential (TF-ERP) approaches. Additionally, interactions between the cortex and striatum give insights into glutamate/dopamine interaction mechanisms. The SFDT task has been utilized in the development of N-methyl-D-aspartate receptor (NMDAR) targeted medications, which significantly modulate sensory and premotor neurophysiological activity. Deficits in pitch processing play a critical role in impaired neuro- and social cognitive function in schizophrenia and may contribute to similar impairments in dyslexia. Thus, the SFDT may be ideal for development of treatments aimed at amelioration of neuro- and social cognitive deficits across neuropsychiatric disorders.