Advances in neurobiology最新文献

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.

Auditory-verbal hallucinations (AVH) are debilitating symptoms experienced by those diagnosed with psychosis as well as many other neurological and psychiatric disorders. Critical to supporting individuals with AVH is identifying biomarkers that serve to track changes in brain states that put individuals at risk for developing or worsening of symptoms. There has been substantial literature identifying neural areas to track over time that may prove to be effective clinical tools. The efficacy of these tools has been bolstered when considering them under mechanistic accounts of AVH. In this chapter, we explore the literature that connects mechanistic theories and structurally based models of AVH and the potential biomarkers derived from this research.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

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.

From the morphological point of view, the nervous system exhibits a fractal, self-similar geometry at various levels of observations, from single cells up to cell networks. From the functional point of view, it is characterized by a hierarchical organization in which self-similar structures (networks) of different miniaturizations are nested within each other. In particular, neuronal networks, interconnected to form neuronal systems, are formed by neurons, which operate thanks to their molecular networks, mainly having proteins as components that via protein-protein interactions can be assembled in multimeric complexes working as micro-devices. On this basis, the term "self-similarity logic" was introduced to describe a nested organization where, at the various levels, almost the same rules (logic) to perform operations are used. Self-similarity and self-similarity logic both appear to be intimately linked to the biophysical evidence for the nervous system being a pattern-forming system that can flexibly switch from one coherent state to another. Thus, they can represent the key concepts to describe its complexity and its concerted, holistic behavior.

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.