Data-Driven Brain Imaging and Signal Analysis: Methods from Acquisition to Clinical Insight

The human brain operates through electrical activity and complex structural connections, which can be captured using various imaging and signal-recording technologies. As neurological and mental health disorders remain a growing global concern, methodological and data-driven approaches to study brain function are increasingly vital. Researchers are combining detailed imaging and physiological signals with computational tools such as artificial intelligence (AI) and machine learning (ML) to improve early detection, understand disease progression, and guide future treatment strategies.

This Research Topic highlights methods, techniques, and advances in analyzing and integrating multimodal brain data, with a focus on signals and images acquired via electroencephalography (EEG), magnetoencephalography (MEG), magnetic resonance imaging (MRI - including functional MRI, structural MRI, and other modalities), and positron emission tomography (PET). We aim to showcase how AI and ML can improve diagnostic accuracy (for example, identifying biomarkers and subtypes), develop predictive models, and integrate data from multiple sources (for example, combining EEG and MRI data).

We welcome contributions that apply AI, ML, and computational methods to analyze and interpret brain data. Submissions may address, but are not limited to, the following areas:

• Using AI/ML to enhance diagnosis and prognosis
• Real-time brain activity processing and interpretation
• Developing predictive models for diagnosis, outcomes, and treatment strategies
• Multimodal data fusion for deeper insights
• Innovations in data cleaning, preprocessing, annotation, or standardization
• Open-source tools, datasets, and reproducible workflows

Novel methods, or applications of recently developed methods to new domains, are particularly encouraged.

Overall, our goal is to encourage robust, scalable, and accessible data-driven approaches for brain signal and image analysis. Through open, collaborative practices, we aim to translate complex brain data into practical tools and insights for precise, personalized care.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Neuroscience, medical imaging, neural signals, data-driven analysis, machine learning, brain disorders, neuroinformatics

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.