Objective: To expose the problems and inherent limitations of neuroscience-based brain research on mental disorders. Method: Discussion of the theory underlying brain research on mental disorders, followed by a system...Objective: To expose the problems and inherent limitations of neuroscience-based brain research on mental disorders. Method: Discussion of the theory underlying brain research on mental disorders, followed by a systematic evaluation of typical studies. Results: The fundamental problem is that brain researchers fail to differentiate between biological mental disorders in which brain processes cause the disorder (notably schizophrenia, bipolar disorder, and melancholic depression) and learned mental disorders in which brain processes mediate but do not cause the disorder (which is the case with reactive depression, reactive anxiety, OCD, and PTSD). Researchers have been unsuccessful in identifying mechanisms in the brain that cause biological mental disorders, and will never be able to locate the innumerable specific neural connections that mediate learned mental disorders. Moreover, the author’s review of typical studies in this field shows that they have serious problems with theory, measurement, and data analysis, and that their findings cannot be trusted. Conclusions: Neuroscience-based brain research on mental disorders, unlike other neurological research, has been an expensive failure and it is not worth continuing.展开更多
Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can ...Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.展开更多
Mapping brain activity has received growing worldwide interest because it is expected to improve disease treatment and allow for the development of important neuromorphic computational methods.MEMS and microsystems ar...Mapping brain activity has received growing worldwide interest because it is expected to improve disease treatment and allow for the development of important neuromorphic computational methods.MEMS and microsystems are expected to continue to offer new and exciting solutions to meet the need for high-density,high-fidelity neural interfaces.Herein,the state-of-the-art in recording and stimulation tools for brain research is reviewed,and some of the most significant technology trends shaping the field of neurotechnology are discussed.展开更多
A major basic research projectin the field of neurosciencewas launched on November26 last year at the Shanghai-basedInstitute of Neuroscience of the Chi-nese Academy of Sciences(CAS).
Objective To explore the efficacy of gamma knife radiosurgery for brain metastases. Methods 112 cases with brain metastases were treated by gamma knife. Among them,most cases were performed with surgery combined with ...Objective To explore the efficacy of gamma knife radiosurgery for brain metastases. Methods 112 cases with brain metastases were treated by gamma knife. Among them,most cases were performed with surgery combined with whole brain radiation therapy and chemotherapy. Results 85 cases were followed-up展开更多
通过全面综合分析Web of Science数据库中相关文献,本文系统梳理了21世纪以来俄语认知神经科学研究的发展动态。研究发现,该领域研究的核心主要集中于语言理解和产生的大脑机制,其研究主要借助事件相关电位技术、眼动追踪技术和功能磁...通过全面综合分析Web of Science数据库中相关文献,本文系统梳理了21世纪以来俄语认知神经科学研究的发展动态。研究发现,该领域研究的核心主要集中于语言理解和产生的大脑机制,其研究主要借助事件相关电位技术、眼动追踪技术和功能磁共振成像技术。在多学科融合的趋势下,俄语认知神经研究有望通过拓展研究领域、整合多模态方法以及深入挖掘俄语特点等方面取得更深入和全面的成果。该研究为俄语认知神经科学领域的未来发展提供了有益的参考。展开更多
文摘Objective: To expose the problems and inherent limitations of neuroscience-based brain research on mental disorders. Method: Discussion of the theory underlying brain research on mental disorders, followed by a systematic evaluation of typical studies. Results: The fundamental problem is that brain researchers fail to differentiate between biological mental disorders in which brain processes cause the disorder (notably schizophrenia, bipolar disorder, and melancholic depression) and learned mental disorders in which brain processes mediate but do not cause the disorder (which is the case with reactive depression, reactive anxiety, OCD, and PTSD). Researchers have been unsuccessful in identifying mechanisms in the brain that cause biological mental disorders, and will never be able to locate the innumerable specific neural connections that mediate learned mental disorders. Moreover, the author’s review of typical studies in this field shows that they have serious problems with theory, measurement, and data analysis, and that their findings cannot be trusted. Conclusions: Neuroscience-based brain research on mental disorders, unlike other neurological research, has been an expensive failure and it is not worth continuing.
文摘Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.
基金We gratefully acknowledge funding from the NIH(U01-NS090526-01,R21-EB-019221-01)the NSF(1545858).
文摘Mapping brain activity has received growing worldwide interest because it is expected to improve disease treatment and allow for the development of important neuromorphic computational methods.MEMS and microsystems are expected to continue to offer new and exciting solutions to meet the need for high-density,high-fidelity neural interfaces.Herein,the state-of-the-art in recording and stimulation tools for brain research is reviewed,and some of the most significant technology trends shaping the field of neurotechnology are discussed.
文摘A major basic research projectin the field of neurosciencewas launched on November26 last year at the Shanghai-basedInstitute of Neuroscience of the Chi-nese Academy of Sciences(CAS).
文摘Objective To explore the efficacy of gamma knife radiosurgery for brain metastases. Methods 112 cases with brain metastases were treated by gamma knife. Among them,most cases were performed with surgery combined with whole brain radiation therapy and chemotherapy. Results 85 cases were followed-up
文摘通过全面综合分析Web of Science数据库中相关文献,本文系统梳理了21世纪以来俄语认知神经科学研究的发展动态。研究发现,该领域研究的核心主要集中于语言理解和产生的大脑机制,其研究主要借助事件相关电位技术、眼动追踪技术和功能磁共振成像技术。在多学科融合的趋势下,俄语认知神经研究有望通过拓展研究领域、整合多模态方法以及深入挖掘俄语特点等方面取得更深入和全面的成果。该研究为俄语认知神经科学领域的未来发展提供了有益的参考。
