The convergence of neuroimaging and artificial intelligence (AI) is a fast-expanding field of study with enormous potential for advances in computational neuroscience. Neuroimaging techniques such as Magnetic Resonance Imaging (MRI), functional MRI (fMRI), Electroencephalography (EEG), and Magnetoencephalography (MEG) create massive volumes of complex data from which valuable insights can be derived. AI provides the computational power and knowledge required to evaluate and comprehend such enormous and complex datasets, allowing for a greater understanding of the human brain's structure, function, and interconnection.
Deep learning algorithms, machine learning, and predictive modeling are examples of innovative computational neuroscience techniques that exploit the capabilities of AI. These techniques have the potential to transform our understanding of the brain and pave the way for new treatments and therapies for neurological disorders such as schizophrenia, Parkinson's disease, depression, Alzheimer's disease, epilepsy, and multiple sclerosis.
Investigating the interface of neuroimaging and AI promises to be an intriguing field of research that will continue to attract academics from a variety of disciplines, including neurology, computer science, and engineering. Innovative techniques that combine neuroimaging and artificial intelligence have the potential to alter our understanding of the human brain and revolutionize healthcare.
The purpose of this study area is to investigate the relationship between neuroimaging and AI in the context of computational neuroscience. The primary issue addressed by this study is the difficulty in processing and understanding vast amounts of complicated data generated by neuroimaging techniques such as MRI, fMRI, EEG, and MEG.
Deep learning algorithms, machine learning, and predictive modeling, among other breakthroughs in AI, have shown promise in solving this difficulty by providing the computational power and skill required to evaluate and make sense of such vast datasets. Researchers can gain a greater understanding of the anatomy, function, and connectivity of the human brain by utilizing these cutting-edge approaches.
This research's ultimate goal is to create new treatments and therapies for neurological illnesses such as schizophrenia, Parkinson's disease, depression, Alzheimer's disease, epilepsy, and multiple sclerosis. This goal can be met by investigating creative methodologies that combine neuroimaging with AI, as well as bringing together academics from many disciplines, including neurology, computer science, and engineering. Working together, these scientists can make important advances in our understanding of the human brain and pave the road for better healthcare outcomes.
The scope of this Research Topic is broad and multidisciplinary, inviting contributions from a range of fields including neuroscience, computer science, and engineering. We welcome articles that investigate novel computational neuroscience strategies at the confluence of neuroimaging and artificial intelligence (AI).
Among the specific topics of interest include, but are not limited to:
• The application of deep learning techniques to neuroimaging data.
• Analysis of neuroimaging data using machine learning and predictive modeling.
• Neuroimaging data analysis using neural network designs.
• AI-assisted diagnosis and therapy of neurological illnesses utilizing neuroimaging data.
• Enhanced brain mapping with the combination of AI and many neuroimaging modalities.
• Ethics in the use of artificial intelligence with neuroimaging data.
We invite original research papers, reviews, mini-reviews, viewpoints, and commentary on the Research Topic's theme. We urge authors to submit publications that highlight the use of novel approaches to neuroimaging data to acquire insights into brain structure, function, and connectivity. Contributions that investigate the potential of AI and neuroimaging to improve the diagnosis and treatment of neurological illnesses are also encouraged.
The convergence of neuroimaging and artificial intelligence (AI) is a fast-expanding field of study with enormous potential for advances in computational neuroscience. Neuroimaging techniques such as Magnetic Resonance Imaging (MRI), functional MRI (fMRI), Electroencephalography (EEG), and Magnetoencephalography (MEG) create massive volumes of complex data from which valuable insights can be derived. AI provides the computational power and knowledge required to evaluate and comprehend such enormous and complex datasets, allowing for a greater understanding of the human brain's structure, function, and interconnection.
Deep learning algorithms, machine learning, and predictive modeling are examples of innovative computational neuroscience techniques that exploit the capabilities of AI. These techniques have the potential to transform our understanding of the brain and pave the way for new treatments and therapies for neurological disorders such as schizophrenia, Parkinson's disease, depression, Alzheimer's disease, epilepsy, and multiple sclerosis.
Investigating the interface of neuroimaging and AI promises to be an intriguing field of research that will continue to attract academics from a variety of disciplines, including neurology, computer science, and engineering. Innovative techniques that combine neuroimaging and artificial intelligence have the potential to alter our understanding of the human brain and revolutionize healthcare.
The purpose of this study area is to investigate the relationship between neuroimaging and AI in the context of computational neuroscience. The primary issue addressed by this study is the difficulty in processing and understanding vast amounts of complicated data generated by neuroimaging techniques such as MRI, fMRI, EEG, and MEG.
Deep learning algorithms, machine learning, and predictive modeling, among other breakthroughs in AI, have shown promise in solving this difficulty by providing the computational power and skill required to evaluate and make sense of such vast datasets. Researchers can gain a greater understanding of the anatomy, function, and connectivity of the human brain by utilizing these cutting-edge approaches.
This research's ultimate goal is to create new treatments and therapies for neurological illnesses such as schizophrenia, Parkinson's disease, depression, Alzheimer's disease, epilepsy, and multiple sclerosis. This goal can be met by investigating creative methodologies that combine neuroimaging with AI, as well as bringing together academics from many disciplines, including neurology, computer science, and engineering. Working together, these scientists can make important advances in our understanding of the human brain and pave the road for better healthcare outcomes.
The scope of this Research Topic is broad and multidisciplinary, inviting contributions from a range of fields including neuroscience, computer science, and engineering. We welcome articles that investigate novel computational neuroscience strategies at the confluence of neuroimaging and artificial intelligence (AI).
Among the specific topics of interest include, but are not limited to:
• The application of deep learning techniques to neuroimaging data.
• Analysis of neuroimaging data using machine learning and predictive modeling.
• Neuroimaging data analysis using neural network designs.
• AI-assisted diagnosis and therapy of neurological illnesses utilizing neuroimaging data.
• Enhanced brain mapping with the combination of AI and many neuroimaging modalities.
• Ethics in the use of artificial intelligence with neuroimaging data.
We invite original research papers, reviews, mini-reviews, viewpoints, and commentary on the Research Topic's theme. We urge authors to submit publications that highlight the use of novel approaches to neuroimaging data to acquire insights into brain structure, function, and connectivity. Contributions that investigate the potential of AI and neuroimaging to improve the diagnosis and treatment of neurological illnesses are also encouraged.
