Advances in neurobiology最新文献_第10页

Hippocampal Engrams and Contextual Memory. 海马刻痕与情境记忆

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-62983-9_4

Krithika Vasudevan, James E Hassell, Stephen Maren

{"title":"Hippocampal Engrams and Contextual Memory.","authors":"Krithika Vasudevan, James E Hassell, Stephen Maren","doi":"10.1007/978-3-031-62983-9_4","DOIUrl":"10.1007/978-3-031-62983-9_4","url":null,"abstract":"Memories are not formed in a vacuum and often include rich details about the time and place in which events occur. Contextual stimuli promote the retrieval of events that have previously occurred in the encoding context and limit the retrieval of context-inappropriate information. Contexts that are associated with traumatic or harmful events both directly elicit fear and serve as reminders of aversive events associated with trauma. It has long been appreciated that the hippocampus is involved in contextual learning and memory and is central to contextual fear conditioning. However, little is known about the underlying neuronal mechanisms underlying the encoding and retrieval of contextual fear memories. Recent advancements in neuronal labeling methods, including activity-dependent tagging of cellular ensembles encoding memory (\"engrams\"), provide unique insight into the neural substrates of memory in the hippocampus. Moreover, these methods allow for the selective manipulation of memory ensembles. Attenuating or erasing fear memories may have considerable therapeutic value for patients with post-traumatic stress disorder or other trauma- or stressor-related conditions. In this chapter, we review the role of the hippocampus in contextual fear conditioning in rodents and explore recent work implicating hippocampal ensembles in the encoding and retrieval of aversive memories.","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"38 ","pages":"45-66"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141615643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Box-Counting Fractal Analysis: A Primer for the Clinician. 盒式计数分形分析：临床医师入门手册

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_2

Audrey L Karperien, Herbert F Jelinek

引用次数: 0

Clinical Sensitivity of Fractal Neurodynamics. 分形神经动力学的临床敏感性

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_15

Elzbieta Olejarczyk, Milena Cukic, Camillo Porcaro, Filippo Zappasodi, Franca Tecchio

{"title":"Clinical Sensitivity of Fractal Neurodynamics.","authors":"Elzbieta Olejarczyk, Milena Cukic, Camillo Porcaro, Filippo Zappasodi, Franca Tecchio","doi":"10.1007/978-3-031-47606-8_15","DOIUrl":"10.1007/978-3-031-47606-8_15","url":null,"abstract":"Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with \"self-emerging.\" This awareness drives us to identify the measures that best quantify the \"complexity\" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"36 ","pages":"285-312"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140100774","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}

引用次数: 0

Fractal Geometry Meets Computational Intelligence: Future Perspectives. 分形几何学与计算智能：未来展望。

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_48

Lorenzo Livi, Alireza Sadeghian, Antonio Di Ieva

{"title":"Fractal Geometry Meets Computational Intelligence: Future Perspectives.","authors":"Lorenzo Livi, Alireza Sadeghian, Antonio Di Ieva","doi":"10.1007/978-3-031-47606-8_48","DOIUrl":"10.1007/978-3-031-47606-8_48","url":null,"abstract":"Characterizations in terms of fractals are typically employed for systems with complex and multiscale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many interacting subunits. The human brain can be modeled in terms of observable variables together with their spatio-temporal-functional relations. Computational intelligence is a research field bridging many nature-inspired computational methods, such as artificial neural networks, fuzzy systems, and evolutionary and swarm intelligence optimization techniques. Typical problems faced by means of computational intelligence methods include those of recognition, such as classification and prediction. Although historically conceived to operate in some vector space, such methods have been recently extended to the so-called nongeometric spaces, considering labeled graphs as the most general example of such patterns. Here, we suggest that fractal analysis and computational intelligence methods can be exploited together in neuroscience research. Fractal characterizations can be used to (i) assess scale-invariant properties and (ii) offer numeric, feature-based representations to complement the usually more complex pattern structures encountered in neurosciences. Computational intelligence methods could be used to exploit such fractal characterizations, considering also the possibility to perform data-driven analysis of nongeometric input spaces, therby overcoming the intrinsic limits related to Euclidean geometry.","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"36 ","pages":"983-997"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140100789","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}

引用次数: 0

Fractal Resonance: Can Fractal Geometry Be Used to Optimize the Connectivity of Neurons to Artificial Implants? 分形共振：分形几何能否用于优化神经元与人工植入物的连接？

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_44

C Rowland, S Moslehi, J H Smith, B Harland, J Dalrymple-Alford, R P Taylor

{"title":"Fractal Resonance: Can Fractal Geometry Be Used to Optimize the Connectivity of Neurons to Artificial Implants?","authors":"C Rowland, S Moslehi, J H Smith, B Harland, J Dalrymple-Alford, R P Taylor","doi":"10.1007/978-3-031-47606-8_44","DOIUrl":"10.1007/978-3-031-47606-8_44","url":null,"abstract":"In parallel to medical applications, exploring how neurons interact with the artificial interface of implants in the human body can be used to learn about their fundamental behavior. For both fundamental and applied research, it is important to determine the conditions that encourage neurons to maintain their natural behavior during these interactions. Whereas previous biocompatibility studies have focused on the material properties of the neuron-implant interface, here we discuss the concept of fractal resonance - the possibility that favorable connectivity properties might emerge by matching the fractal geometry of the implant surface to that of the neurons.To investigate fractal resonance, we first determine the degree to which neurons are fractal and the impact of this fractality on their functionality. By analyzing three-dimensional images of rat hippocampal neurons, we find that the way their dendrites fork and weave through space is important for generating their fractal-like behavior. By modeling variations in neuron connectivity along with the associated energetic and material costs, we highlight how the neurons' fractal dimension optimizes these constraints. To simulate neuron interactions with implant interfaces, we distort the neuron models away from their natural form by modifying the dendrites' fork and weaving patterns. We find that small deviations can induce large changes in fractal dimension, causing the balance between connectivity and cost to deteriorate rapidly. We propose that implant surfaces should be patterned to match the fractal dimension of the neurons, allowing them to maintain their natural functionality as they interact with the implant.","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"36 ","pages":"877-906"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140100792","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}

引用次数: 0

Fractal Similarity of Pain Brain Networks. 疼痛脑网络的分形相似性

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_32

Camille Fauchon, Hélène Bastuji, Roland Peyron, Luis Garcia-Larrea

{"title":"Fractal Similarity of Pain Brain Networks.","authors":"Camille Fauchon, Hélène Bastuji, Roland Peyron, Luis Garcia-Larrea","doi":"10.1007/978-3-031-47606-8_32","DOIUrl":"10.1007/978-3-031-47606-8_32","url":null,"abstract":"The conscious perception of pain is the result of dynamic interactions of neural activities from local brain regions to distributed brain networks. Mapping out the networks of functional connections between brain regions that form and disperse when an experimental participant received nociceptive stimulations allow to characterize the pattern of network connections related to the pain experience.Although the pattern of intra- and inter-areal connections across the brain are incredibly complex, they appear also largely scale free, with \"fractal\" connectivity properties reproducing at short and long-time scales. Our results combining intracranial recordings and functional imaging in humans during pain indicate striking similarities in the activity and topological representation of networks at different orders of temporality, with reproduction of patterns of activation from the millisecond to the multisecond range. The connectivity analyzed using graph theory on fMRI data was organized in four sets of brain regions matching those identified through iEEG (i.e., sensorimotor, default mode, central executive, and amygdalo-hippocampal).Here, we discuss similarities in brain network organization at different scales or \"orders,\" in participants as they feel pain. Description of this fractal-like organization may provide clues about how our brain regions work together to create the perception of pain and how pain becomes chronic when its organization is altered.","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"36 ","pages":"639-657"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140100793","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}

引用次数: 0

Fractal-Based Analysis of Histological Features of Brain Tumors. 基于分形的脑肿瘤组织学特征分析

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_26

Omar S Al-Kadi, Antonio Di Ieva

{"title":"Fractal-Based Analysis of Histological Features of Brain Tumors.","authors":"Omar S Al-Kadi, Antonio Di Ieva","doi":"10.1007/978-3-031-47606-8_26","DOIUrl":"10.1007/978-3-031-47606-8_26","url":null,"abstract":"The structural complexity of brain tumor tissue represents a major challenge for effective histopathological diagnosis. Tumor vasculature is known to be heterogeneous, and mixtures of patterns are usually present. Therefore, extracting key descriptive features for accurate quantification is not a straightforward task. Several steps are involved in the texture analysis process where tissue heterogeneity contributes to the variability of the results. One of the interesting aspects of the brain lies in its fractal nature. Many regions within the brain tissue yield similar statistical properties at different scales of magnification. Fractal-based analysis of the histological features of brain tumors can reveal the underlying complexity of tissue structure and angiostructure, also providing an indication of tissue abnormality development. It can further be used to quantify the chaotic signature of disease to distinguish between different temporal tumor stages and histopathological grades.Brain meningioma subtype classifications' improvement from histopathological images is the main focus of this chapter. Meningioma tissue texture exhibits a wide range of histological patterns whereby a single slide may show a combination of multiple patterns. Distinctive fractal patterns quantified in a multiresolution manner would be for better spatial relationship representation. Fractal features extracted from textural tissue patterns can be useful in characterizing meningioma tumors in terms of subtype classification, a challenging problem compared to histological grading, and furthermore can provide an objective measure for quantifying subtle features within subtypes that are hard to discriminate.","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"36 ","pages":"501-524"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140100797","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}

引用次数: 0

Fractals in Neuroanatomy and Basic Neurosciences: An Overview. 神经解剖学和基础神经科学中的分形：概述。

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_6

Antonio Di Ieva

引用次数: 0

Fractals, Pattern Recognition, Memetics, and AI: A Personal Journal in the Computational Neurosurgery. 分形、模式识别、记忆学和人工智能：计算神经外科个人期刊》。

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-47606-8_14

Antonio Di Ieva

引用次数: 0

Metabolic Control of Microglia. 小胶质细胞的代谢控制

Advances in neurobiology Pub Date : 2024-01-01 DOI: 10.1007/978-3-031-55529-9_34

Monica Emili Garcia-Segura, Stefano Pluchino, Luca Peruzzotti-Jametti

引用次数: 0