Neuroinformatics Applications of Data Science and Artificial Intelligence.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-10-01 DOI:10.1007/s12021-024-09692-4
Ivo D Dinov
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引用次数: 0

Abstract

Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.

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数据科学和人工智能的神经信息学应用。
利用庞大的神经成像和电生理学数据集,人工智能算法正在揭示各种模式,为了解大脑结构和功能提供前所未有的洞察力。神经信息学是神经科学与人工智能的融合,正在推动脑机接口、人工智能驱动的认知增强以及用于治疗神经系统疾病的个性化神经调控等技术的发展。这些发展为改善认知功能、恢复运动能力和创建人机协作系统带来了潜力。展望未来,神经科学与人工智能的融合必将改变认知建模、决策和心理健康干预。这种融合与对核聚变能源的追求如出一辙,都是出于开启深刻理解源泉的需要。随着 STEM 学科继续推动大脑基础模型的核心发展,神经信息学有望引领增强智能、个性化医疗保健和有效决策系统方面的创新。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T. Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease. A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.
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