{"title":"ALBA Network – towards diversity and equity in brain sciences","authors":"E. Binder","doi":"10.1515/nf-2021-0010","DOIUrl":"https://doi.org/10.1515/nf-2021-0010","url":null,"abstract":"","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"103 - 104"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67145151","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}
Abstract Psychiatric disorders are common and seem to increase in prevalence worldwide. Most scientific approaches for this phenomenon fail to explain why the majority of mental disorders, unlike most somatic diseases, manifest in early adulthood, why individuals are not more resilient, and why some genes increasing the risk for mental disorders have not been selected against. In this article, an evolutionary perspective on mental health and disorder is taken. This perspective suggests that mismatches between ancestral and contemporary environments contribute to the risk for mental disorders. In addition, predictions from attachment theory and life history theory can explain individual differences with regard to the risk of developing a psychiatric or psychosomatic disorder. Insights from evolutionary approaches to psychiatric and psychosomatic disorders may contribute to improve the prevention and treatment of mental disorders.
{"title":"Mental health and biological evolution: implications for psychiatry and psychosomatic medicine","authors":"M. Brüne","doi":"10.1515/nf-2020-0033","DOIUrl":"https://doi.org/10.1515/nf-2020-0033","url":null,"abstract":"Abstract Psychiatric disorders are common and seem to increase in prevalence worldwide. Most scientific approaches for this phenomenon fail to explain why the majority of mental disorders, unlike most somatic diseases, manifest in early adulthood, why individuals are not more resilient, and why some genes increasing the risk for mental disorders have not been selected against. In this article, an evolutionary perspective on mental health and disorder is taken. This perspective suggests that mismatches between ancestral and contemporary environments contribute to the risk for mental disorders. In addition, predictions from attachment theory and life history theory can explain individual differences with regard to the risk of developing a psychiatric or psychosomatic disorder. Insights from evolutionary approaches to psychiatric and psychosomatic disorders may contribute to improve the prevention and treatment of mental disorders.","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"79 - 88"},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46082345","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":"Innovative retinal interfaces for optimized artificial vision – a new DFG funded Research Training Group","authors":"P. Walter, W. Mokwa, S. Ingebrandt","doi":"10.1515/nf-2021-0011","DOIUrl":"https://doi.org/10.1515/nf-2021-0011","url":null,"abstract":"","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"99 - 101"},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45390932","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}
Misa Arizono, S. Bancelin, P. Bethge, Ronan Chéreau, Agata Idziak, V. K. Inavalli, T. Pfeiffer, J. Tønnesen, U. V. Nägerl
Abstract Progress in microscopy technology has a long history of triggering major advances in neuroscience. Super-resolution microscopy (SRM), famous for shattering the diffraction barrier of light microscopy, is no exception. SRM gives access to anatomical designs and dynamics of nanostructures, which are impossible to resolve using conventional light microscopy, from the elaborate anatomy of neurons and glial cells, to the organelles and molecules inside of them. In this review, we will mainly focus on a particular SRM technique (STED microscopy), and explain a series of technical developments we have made over the years to make it practical and viable in the field of neuroscience. We will also highlight several neurobiological findings on the dynamic structure-function relationship of neurons and glia cells, which illustrate the value of live-cell STED microscopy, especially when combined with other modern approaches to investigate the nanoscale behavior of brain cells.
{"title":"Nanoscale imaging of the functional anatomy of the brain","authors":"Misa Arizono, S. Bancelin, P. Bethge, Ronan Chéreau, Agata Idziak, V. K. Inavalli, T. Pfeiffer, J. Tønnesen, U. V. Nägerl","doi":"10.1515/nf-2021-0004","DOIUrl":"https://doi.org/10.1515/nf-2021-0004","url":null,"abstract":"Abstract Progress in microscopy technology has a long history of triggering major advances in neuroscience. Super-resolution microscopy (SRM), famous for shattering the diffraction barrier of light microscopy, is no exception. SRM gives access to anatomical designs and dynamics of nanostructures, which are impossible to resolve using conventional light microscopy, from the elaborate anatomy of neurons and glial cells, to the organelles and molecules inside of them. In this review, we will mainly focus on a particular SRM technique (STED microscopy), and explain a series of technical developments we have made over the years to make it practical and viable in the field of neuroscience. We will also highlight several neurobiological findings on the dynamic structure-function relationship of neurons and glia cells, which illustrate the value of live-cell STED microscopy, especially when combined with other modern approaches to investigate the nanoscale behavior of brain cells.","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"67 - 77"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43189508","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":"Frank W. Stahnisch: A new field in mind. A history of interdisciplinarity in the early brain sciences","authors":"R. Nitsch","doi":"10.1515/NF-2021-0001","DOIUrl":"https://doi.org/10.1515/NF-2021-0001","url":null,"abstract":"","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"175 - 176"},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48654718","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":"Supplement (online only): Proceedings of the 14th Göttingen Meeting of the German Neuroscience Society 2021","authors":"","doi":"10.1515/nf-2021-2003","DOIUrl":"https://doi.org/10.1515/nf-2021-2003","url":null,"abstract":"","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48308195","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":"Editorial","authors":"M. Denker, Alexandra Stein, T. Wachtler","doi":"10.1515/nf-2020-0042","DOIUrl":"https://doi.org/10.1515/nf-2020-0042","url":null,"abstract":"","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"1 - 2"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47613643","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}
C. Klingner, P. Ritter, S. Brodoehl, Christian Gaser, A. Scherag, D. Güllmar, F. Rosenow, U. Ziemann, O. Witte
Abstract In clinical neuroscience, there are considerable difficulties in translating basic research into clinical applications such as diagnostic tools or therapeutic interventions. This gap, known as the “valley of death,” was mainly attributed to the problem of “small numbers” in clinical neuroscience research, i.e. sample sizes that are too small (Hutson et al., 2017). As a possible solution, it has been repeatedly suggested to systematically manage research data to provide long-term storage, accessibility, and federate data. This goal is supported by a current call of the DFG for a national research data infrastructure (NFDI). This article will review current challenges and possible solutions specific to clinical neuroscience and discuss them in the context of other national and international health data initiatives. A successful NFDI consortium will help to overcome not only the “valley of death” but also promises a path to individualized medicine by enabling big data to produce generalizable results based on artificial intelligence and other methods.
在临床神经科学中，将基础研究转化为临床应用(如诊断工具或治疗干预)存在相当大的困难。这一差距被称为“死亡之谷”，主要归因于临床神经科学研究中的“小数量”问题，即样本量太小(Hutson et al.， 2017)。作为一种可能的解决方案，人们一再建议系统地管理研究数据，以提供长期存储、可访问性和联邦数据。这一目标得到了DFG目前对国家研究数据基础设施(NFDI)的呼吁的支持。本文将回顾当前临床神经科学面临的挑战和可能的解决方案，并在其他国家和国际健康数据倡议的背景下讨论它们。一个成功的NFDI联盟不仅有助于克服“死亡之谷”，而且还有望通过大数据产生基于人工智能和其他方法的可推广结果，从而开辟个性化医疗的道路。
{"title":"Research data management in clinical neuroscience: the national research data infrastructure initiative","authors":"C. Klingner, P. Ritter, S. Brodoehl, Christian Gaser, A. Scherag, D. Güllmar, F. Rosenow, U. Ziemann, O. Witte","doi":"10.1515/nf-2020-0039","DOIUrl":"https://doi.org/10.1515/nf-2020-0039","url":null,"abstract":"Abstract In clinical neuroscience, there are considerable difficulties in translating basic research into clinical applications such as diagnostic tools or therapeutic interventions. This gap, known as the “valley of death,” was mainly attributed to the problem of “small numbers” in clinical neuroscience research, i.e. sample sizes that are too small (Hutson et al., 2017). As a possible solution, it has been repeatedly suggested to systematically manage research data to provide long-term storage, accessibility, and federate data. This goal is supported by a current call of the DFG for a national research data infrastructure (NFDI). This article will review current challenges and possible solutions specific to clinical neuroscience and discuss them in the context of other national and international health data initiatives. A successful NFDI consortium will help to overcome not only the “valley of death” but also promises a path to individualized medicine by enabling big data to produce generalizable results based on artificial intelligence and other methods.","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"35 - 43"},"PeriodicalIF":0.0,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45552497","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}
Abstract Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.
{"title":"Reproducibility and efficiency in handling complex neurophysiological data","authors":"M. Denker, S. Grün, T. Wachtler, H. Scherberger","doi":"10.1515/nf-2020-0041","DOIUrl":"https://doi.org/10.1515/nf-2020-0041","url":null,"abstract":"Abstract Preparing a neurophysiological data set with the aim of sharing and publishing is hard. Many of the available tools and services to provide a smooth workflow for data publication are still in their maturing stages and not well integrated. Also, best practices and concrete examples of how to create a rigorous and complete package of an electrophysiology experiment are still lacking. Given the heterogeneity of the field, such unifying guidelines and processes can only be formulated together as a community effort. One of the goals of the NFDI-Neuro consortium initiative is to build such a community for systems and behavioral neuroscience. NFDI-Neuro aims to address the needs of the community to make data management easier and to tackle these challenges in collaboration with various international initiatives (e.g., INCF, EBRAINS). This will give scientists the opportunity to spend more time analyzing the wealth of electrophysiological data they leverage, rather than dealing with data formats and data integrity.","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"27 - 34"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/nf-2020-0041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44988875","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}
T. Wachtler, Pavol Bauer, M. Denker, S. Grün, Michael Hanke, J. Klein, S. Oeltze-Jafra, P. Ritter, S. Rotter, H. Scherberger, Alexandra Stein, O. Witte
Abstract Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.
{"title":"NFDI-Neuro: building a community for neuroscience research data management in Germany","authors":"T. Wachtler, Pavol Bauer, M. Denker, S. Grün, Michael Hanke, J. Klein, S. Oeltze-Jafra, P. Ritter, S. Rotter, H. Scherberger, Alexandra Stein, O. Witte","doi":"10.1515/nf-2020-0036","DOIUrl":"https://doi.org/10.1515/nf-2020-0036","url":null,"abstract":"Abstract Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.","PeriodicalId":56108,"journal":{"name":"Neuroforum","volume":"27 1","pages":"3 - 15"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/nf-2020-0036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44454178","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}
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