About this Research Topic
The intersection of artificial intelligence (AI) and neuroimaging represents a rapidly evolving field that seeks to unlock deeper insights into brain structure and function. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG), provide rich datasets that capture brain activity and connectivity. AI technologies - particularly machine learning and deep learning - offer powerful tools to analyze these complex datasets and to model neurocognitive processes. This fusion has already led to significant breakthroughs in understanding brain disorders, identifying biomarkers for neurological diseases, and predicting cognitive functions. As the field progresses, there's a growing need to explore and consolidate these advances, driving the development of new AI-based neuroimaging applications.
The goal of this Research Topic is to address the challenges and opportunities that arise when AI is applied to neuroimaging data. Recent advances in AI technologies, such as deep learning and large language models, have shown promise in decoding neural patterns, improving disease diagnostics, and serving as models of biological neural processing. However, these advances also raise questions about model transparency, data privacy, and ethical considerations.
This Research Topic seeks to:
• Showcase innovative uses of AI in neuroimaging which improve our understanding of brain function.
• Highlight the use of Artificial Neural Networks as models of biological neural processing.
• Showcase novel clinical or engineering applications that apply AI and machine learning to electrophysiological data.
• Address ethical, legal, and societal challenges in AI-based neuroimaging.
• Highlight the importance of reproducibility and validation in AI-based studies.
We invite researchers and practitioners to submit original research articles, reviews, and perspective pieces that align with the following themes:
• Novel analysis techniques utilizing machine learning or deep learning to neuroimaging data (EEG, fMRI, MRI, MEG, ECoG), to better understand brain function.
• The use of Artificial Neural Networks as models of biological neural processing, as validated against neuroimaging data.
• Novel clinical or engineering applications that apply machine learning or deep learning to neuroimaging data, such as auditory attention decoding and imagined speech decoding.
• Ethical considerations, data privacy, societal challenges in AI-based neuroimaging.
• Reproducibility and standardization in AI-neuroimaging research.
Manuscripts should adhere to Frontiers guidelines for submissions and emphasize robust methodology, clear data interpretation, and relevance to the field of neuroimaging. We encourage interdisciplinary collaboration and welcome submissions from researchers at any stage of their careers. All submissions will undergo a rigorous peer-review process to ensure high-quality contributions to this emerging field.
The goal of this Research Topic is to address the challenges and opportunities that arise when AI is applied to neuroimaging data. Recent advances in AI technologies, such as deep learning and large language models, have shown promise in decoding neural patterns, improving disease diagnostics, and serving as models of biological neural processing. However, these advances also raise questions about model transparency, data privacy, and ethical considerations.
This Research Topic seeks to:
• Showcase innovative uses of AI in neuroimaging which improve our understanding of brain function.
• Highlight the use of Artificial Neural Networks as models of biological neural processing.
• Showcase novel clinical or engineering applications that apply AI and machine learning to electrophysiological data.
• Address ethical, legal, and societal challenges in AI-based neuroimaging.
• Highlight the importance of reproducibility and validation in AI-based studies.
We invite researchers and practitioners to submit original research articles, reviews, and perspective pieces that align with the following themes:
• Novel analysis techniques utilizing machine learning or deep learning to neuroimaging data (EEG, fMRI, MRI, MEG, ECoG), to better understand brain function.
• The use of Artificial Neural Networks as models of biological neural processing, as validated against neuroimaging data.
• Novel clinical or engineering applications that apply machine learning or deep learning to neuroimaging data, such as auditory attention decoding and imagined speech decoding.
• Ethical considerations, data privacy, societal challenges in AI-based neuroimaging.
• Reproducibility and standardization in AI-neuroimaging research.
Manuscripts should adhere to Frontiers guidelines for submissions and emphasize robust methodology, clear data interpretation, and relevance to the field of neuroimaging. We encourage interdisciplinary collaboration and welcome submissions from researchers at any stage of their careers. All submissions will undergo a rigorous peer-review process to ensure high-quality contributions to this emerging field.
Keywords: Neuroimaging, AI, Artificial Intelligence, Machine Learning, ML
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
