Handbook of clinical neurology最新文献

{"title":"Neuroglia in the healthy brain.","authors":"Alexei Verkhratsky, Elly M Hol, Lot D de Witte, Eleanora Aronica","doi":"10.1016/B978-0-443-19104-6.00008-5","DOIUrl":"https://doi.org/10.1016/B978-0-443-19104-6.00008-5","url":null,"abstract":"<p><p>The nervous tissue is composed of neurons and neuroglia, which by working in a tightly coordinated manner, define the function of the nervous system. Neuroglia, defined as homeostatic and defensive cells of the nervous system, are highly heterogeneous in form and function and are endowed with a remarkable plasticity that allows life-long adaptation to environmental challenges. Neuroglia of the peripheral nervous system are represented by myelinating, nonmyelinating, perisynaptic, and cutaneous Schwann cells, satellite glia of sensory and sympathetic ganglia and enteric glia of the enteric nervous system. Neuroglia of the central nervous system (CNS) are classified into macroglia and microglia. Macroglia in turn are represented by astroglia and oligodendroglia. Astroglia represent an extended class of homeostatic glial cells, which include astrocytes (protoplasmic, fibrous, velate, and marginal), radial astrocytes (Bergmann glial cells, glia-like nervous stem cells, and tanycytes), and ependymoglia. The oligodendroglial lineage is mainly responsible for myelination and support of central axons and is represented by oligodendrocytes and oligodendrocyte precursor cells. Microglia are the cells of nonneural, myeloid origin that invade the neural tube early in embryonic development. These cells are tissue macrophages adapted to the nervous system requirements. Microglia contribute to physiology of the nervous tissue and to the innate immunity and defense of the CNS.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"209 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroglia and the microbiota-gut-brain axis.","authors":"Hugo J Blair, Lorena Morales, John F Cryan, María R Aburto","doi":"10.1016/B978-0-443-19104-6.00001-2","DOIUrl":"https://doi.org/10.1016/B978-0-443-19104-6.00001-2","url":null,"abstract":"<p><p>Glial cells are key players in the regulation of nervous system functioning in both the central and enteric nervous systems. Glial cells are dynamic and respond to environmental cues to modulate their activity. Increasing evidence suggests that these signals include those originating from the gut microbiota, the community of microorganisms, including bacteria, viruses, archaea, and protozoa, that inhabit the gut. The gut microbiota and the brain communicate in a bidirectional manner across multiple signaling pathways and interfaces that together comprise the microbiota-gut-brain axis. Here, we detail the role of glial cells, including astrocytes, microglia, and oligodendrocytes in the central nervous system, and glial cells in the enteric nervous system along this gut-brain axis. We review what is known regarding the modulation of glia by microbial signals, in particular by microbial metabolites which signal to the brain through systemic circulation and via the vagus nerve. In addition, we highlight what is yet to be discovered regarding the role of other gut microbiota signaling pathways in glial cell modulation and the challenges of research in this area.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"209 ","pages":"171-196"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell omics and heterogeneity of neuroglial cells.","authors":"Sylvie C Lahaie, Naama Brezner, Keith K Murai","doi":"10.1016/B978-0-443-19104-6.00013-9","DOIUrl":"https://doi.org/10.1016/B978-0-443-19104-6.00013-9","url":null,"abstract":"<p><p>Our bodies contain a rich diversity of cell types with unique physiologic properties. Interestingly, cells within our bodies contain the same DNA content, yet they can vary dramatically with respect to their molecular, structural, and functional properties. The need to better understand cellular complexity and diversity in biologic systems has led to a technical revolution in the field through the development of sophisticated single-cell \"omic\" approaches. This allows the investigation of the genome, epigenome, transcriptome, and proteome of individual cells derived from complex samples or tissues, such as nervous system tissue. These methods are allowing scientists to detect distinct cell populations and cellular states in different species (including rodent and human) and molecular transitions of cell populations across the lifespan. Recent studies have revealed that astrocytes, oligodendrocytes, and microglia exhibit greater molecular and functional heterogeneity than originally thought and innovative single-cell technologies have allowed a more comprehensive and less biased view of this cellular diversity. The chapter begins by providing a primer of single-cell transcriptomic and spatial transcriptomic approaches that have been particularly influential in uncovering single-cell diversity of neuroglial cells in the brain. It then takes a closer look at how these technologies have been pivotal in defining neuroglial cell subtypes and for determining their spatial relationships within the CNS. Then, it concludes with discussion of how the recent technical advances and discoveries have provoked new questions about the origin, organization, and functional purpose of diverse neuroglial cell subtypes.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"209 ","pages":"265-275"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurologic prognostication in coma and disorders of consciousness.","authors":"Shubham Biyani, Henry Chang, Vishank A Shah","doi":"10.1016/B978-0-443-13408-1.00017-8","DOIUrl":"https://doi.org/10.1016/B978-0-443-13408-1.00017-8","url":null,"abstract":"<p><p>Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"207 ","pages":"237-264"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Irregular sleep-wake rhythm disorder: From the pathophysiologic perspective to the treatment.","authors":"Aleksandar Videnovic, Alice Cai","doi":"10.1016/B978-0-323-90918-1.00006-X","DOIUrl":"https://doi.org/10.1016/B978-0-323-90918-1.00006-X","url":null,"abstract":"<p><p>Irregular sleep-wake rhythm disorder (ISWRD) is an intrinsic circadian rhythm disorder caused by loss of the brain's circadian regulation, through changes of the input and/or output to the suprachiasmatic nucleus (SCN), or of the SCN itself. Although there are limited prevalence data for this rare disease, ISWRD is associated with neurodegenerative disorders, including the Alzheimer disease (AD) and the Parkinson disease (PD), which will become increasingly prevalent in an aging population. It additionally presents in childhood developmental disorders, psychiatric disorders, and traumatic brain injury (TBI). Patients present with unpredictable, short sleep periods over a 24-h period, with significant day-to-day and weekly variability. Symptoms manifest as insomnia and excessive daytime sleepiness. Sleep logs and actigraphy monitoring capture rest-activity patterns required for diagnosis. Treatment aims to enhance external circadian cues through timed light therapy, behavioral activity regimens, and melatonin, but efficacy remains quite limited. Pathophysiology of ISWRD in association with various diseases and their specific management are discussed. There is a need for further investigation of disease pathophysiology, development of widely applicable tools for diagnosis, and development of treatments.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"206 ","pages":"71-87"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shift work sleep disorder.","authors":"Claudia R C Moreno","doi":"10.1016/B978-0-323-90918-1.00015-0","DOIUrl":"https://doi.org/10.1016/B978-0-323-90918-1.00015-0","url":null,"abstract":"<p><p>Shift work sleep disorder (SWSD) is a circadian rhythm sleep-wake disorders affecting individuals who work in nonstandard hours, particularly night shifts. It manifests as difficulty sleeping during the day and staying awake during work hours, leading to health issues. SWSD is not universally experienced by all shift workers, with about 30% affected. Diagnosing SWSD involves monitoring sleep patterns and differentiating it from other disorders such as sleep apnea. Prevention and treatment include collective measures such as optimizing shift schedules and individual strategies such as sleep/circadian hygiene, light therapy, melatonin use, and, if necessary, prescription medications. Despite these interventions, the primary recommendation is to switch to daytime work, although this may not be feasible for all workers.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"206 ","pages":"89-92"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In vitro models for human neuroglia.","authors":"Bas Lendemeijer, Femke M S de Vrij","doi":"10.1016/B978-0-443-19104-6.00015-2","DOIUrl":"https://doi.org/10.1016/B978-0-443-19104-6.00015-2","url":null,"abstract":"<p><p>Neuroglia are a heterogenous population of cells in the nervous system. In the central nervous system, this group is classified into astrocytes, oligodendrocytes, and microglia. Neuroglia in the peripheral nervous system are divided into Schwann cells and enteric glia. These groups of cells display considerable differences in their developmental origin, morphology, function, and regional abundance. Compared to animal models, human neuroglia differ in their transcriptomic profile, morphology, and function. Investigating the physiology of healthy or diseased human neuroglia in vivo is challenging due to the inaccessibility of the tissue. Therefore, researchers have developed numerous in vitro models attempting to replicate the natural tissue environment. Earlier models made use of postmortem, postsurgical, or fetal tissue to establish human neuroglial cells in vitro, either as a pure population of the desired cell type or as organotypic slice cultures. Advancements in human stem cell differentiation techniques have greatly enhanced the possibilities for creating in vitro models of human neuroglia. This chapter provides an overview of the current models used to study the functioning and development of human neuroglia in vitro, both in health and disease.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"209 ","pages":"213-227"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroglia in eating disorders (obesity, Prader-Willi syndrome and anorexia nervosa).","authors":"Felipe Correa-da-Silva, Chun-Xia Yi","doi":"10.1016/B978-0-443-19102-2.00019-3","DOIUrl":"https://doi.org/10.1016/B978-0-443-19102-2.00019-3","url":null,"abstract":"<p><p>The hypothalamus is widely recognized as one of the most extensively studied brain regions involved in the central regulation of energy homeostasis. Within the hypothalamus, peptidergic neurons play a crucial role in monitoring peripheral concentrations of metabolites and hormones, and they finely adjust the sensing of these factors, leading to the activation of either anorexigenic (appetite-suppressing) or orexigenic (appetite-stimulating) pathways. While cortical innervation of the hypothalamus does influence these processes, it is generally considered of secondary importance. Eating-related disorders, such as obesity and anorexia nervosa, are strongly associated with imbalances in energy intake and expenditure. The phenotypes of these disorders can be attributed to dysfunctions in the hypothalamus. Traditionally, it has been believed that hypothalamic dysfunction in these disorders primarily stems from defects in neural pathways. However, recent evidence challenges this perception, highlighting the active participation of neuroglial cells in shaping both physiologic and behavioral characteristics. This review aims to provide an overview of the latest insights into glial biology in three specific eating disorders: obesity, Prader-Willi syndrome, and anorexia. In these disorders, neural dysfunction coincides with glial malfunction, suggesting that neuroglia actively contribute to the development and progression of various neurologic disorders. These findings underscore the importance of glial cells and open up potential new avenues for therapeutic interventions.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"210 ","pages":"313-324"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain asymmetries in figurative language comprehension.","authors":"Costanza Papagno","doi":"10.1016/B978-0-443-15646-5.00013-0","DOIUrl":"https://doi.org/10.1016/B978-0-443-15646-5.00013-0","url":null,"abstract":"<p><p>This chapter reviews the literature concerning the neural basis of three types of figurative expressions, namely, idioms, metaphors, and irony. Besides these three forms of language, which are the most investigated, many other types exist, differing in their linguistic structure and, consequently, in the corresponding comprehension processes. After defining the most common figurative expressions and showing how they differ in terms of linguistic properties, the chapter presents early studies that focused on the role of the right hemisphere (RH) in figurative language comprehension in general, without a clear distinction among different forms. Later literature shows how evidence has been accumulated, suggesting that both hemispheres are involved in figurative language processing. Therefore, a sharp distinction between the left hemisphere (LH) and the RH, the first involved in literal language and the second in figurative language, is not tenable. Idioms, metaphors, and irony will be considered separately, demonstrating that different expressions rely on the LH and RH to a different degree.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"208 ","pages":"289-299"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain laterality of numbers and calculation: Complex networks and their development.","authors":"Carlo Semenza, Silvia Benavides-Varela, Elena Salillas","doi":"10.1016/B978-0-443-15646-5.00017-8","DOIUrl":"https://doi.org/10.1016/B978-0-443-15646-5.00017-8","url":null,"abstract":"<p><p>This chapter reviews notions about the lateralization of numbers and calculation in the brain, including its developmental pattern. Such notions have changed dramatically in recent decades. What was once considered a function almost exclusively located in the left hemisphere has been found to be sustained by complex brain networks encompassing both hemispheres. Depending on the specific task, however, each hemisphere has its own role. Much of this progress was determined by the convergency of investigations conducted with different methods. Contrary to traditional wisdom, the right hemisphere is not involved in arithmetic just as far as generic spatial aspects are concerned. Very specific arithmetic functions like remembering the spatial templates for complex operations, or processing of zero in complex numbers, are indeed sustained in specific right-sided areas. The system used in the typical adult appears to be the result of a complex pattern of development. The numerical brain clearly evolved from less mature to more advanced brain networks because of growth and education. Children seem to be equipped with the ability to represent the number nonverbally from a very early age. The bilateral processing of number-related tasks is however a late acquisition.</p>","PeriodicalId":12907,"journal":{"name":"Handbook of clinical neurology","volume":"208 ","pages":"461-480"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143614606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}