Neuroimaging techniques, including Magnetic Resonance Imaging (MRI), are widely used in research and medical settings to investigate brain structure and function. However, these techniques generate large amounts of highly sensitive data that raise significant privacy concerns. Privacy issues can arise when data sharing is necessary for research purposes, as it may reveal personal information about individuals, including their health status and cognitive abilities. Therefore, it is crucial to develop privacy-preserving methods for analyzing neuroimaging data to balance data sharing and privacy preservation concerns.The goal of this Research Topic is to explore and develop privacy-preserving methods for neuroimaging analysis that balance data sharing and privacy preservation concerns. This topic aims to address privacy concerns related to the collection, storage, processing, and sharing of neuroimaging data in research and medical settings. The main goal is to stimulate innovative research in the field of neuroimaging analysis and privacy, leading to the development of efficient, reliable, and secure methods for protecting the privacy of neuroimaging data while enabling scientific advances.In this context, Artificial Intelligence methods such as machine and deep learning are vital for protecting privacy in neuroimaging analysis. They enable efficient data analysis, develop anonymization and de-identification techniques, and apply differential privacy mechanisms. AI could contribute to secure data sharing through frameworks such as federated learning. Ethical considerations are addressed by incorporating ethical principles into AI models. These technologies could strike a balance between sharing valuable data for research while safeguarding the privacy and confidentiality of individuals in neuroimaging studies. However, AI characteristics such as explainability, interpretability, and trust must be considered while balancing data sharing and privacy preservation. They ensure transparency, understanding of sensitive information handling, identification of privacy risks, and inspire confidence in responsible and ethical practices.This Research Topic invites contributions related to Privacy-Preserving Neuroimaging Analysis. Authors are encouraged to submit Original Research, Reviews, and Perspective articles, addressing the following themes: (i) the identification of privacy risks related to neuroimaging analysis, (ii) the development of novel privacy-preserving techniques for neuroimaging analysis, (iii) the evaluation and comparison of different privacy-preserving techniques,(iv) the ethical and legal implications related to the use of privacy-preserving neuroimaging analysis,(v) AI methods for privacy preservation in neuroimaging analysis while ensuring interpretability, explainability, trust, and other AI characteristics, and(vi) case studies and practical applications in neuroimaging research and medical settings.
Neuroimaging techniques, including Magnetic Resonance Imaging (MRI), are widely used in research and medical settings to investigate brain structure and function. However, these techniques generate large amounts of highly sensitive data that raise significant privacy concerns. Privacy issues can arise when data sharing is necessary for research purposes, as it may reveal personal information about individuals, including their health status and cognitive abilities. Therefore, it is crucial to develop privacy-preserving methods for analyzing neuroimaging data to balance data sharing and privacy preservation concerns.The goal of this Research Topic is to explore and develop privacy-preserving methods for neuroimaging analysis that balance data sharing and privacy preservation concerns. This topic aims to address privacy concerns related to the collection, storage, processing, and sharing of neuroimaging data in research and medical settings. The main goal is to stimulate innovative research in the field of neuroimaging analysis and privacy, leading to the development of efficient, reliable, and secure methods for protecting the privacy of neuroimaging data while enabling scientific advances.In this context, Artificial Intelligence methods such as machine and deep learning are vital for protecting privacy in neuroimaging analysis. They enable efficient data analysis, develop anonymization and de-identification techniques, and apply differential privacy mechanisms. AI could contribute to secure data sharing through frameworks such as federated learning. Ethical considerations are addressed by incorporating ethical principles into AI models. These technologies could strike a balance between sharing valuable data for research while safeguarding the privacy and confidentiality of individuals in neuroimaging studies. However, AI characteristics such as explainability, interpretability, and trust must be considered while balancing data sharing and privacy preservation. They ensure transparency, understanding of sensitive information handling, identification of privacy risks, and inspire confidence in responsible and ethical practices.This Research Topic invites contributions related to Privacy-Preserving Neuroimaging Analysis. Authors are encouraged to submit Original Research, Reviews, and Perspective articles, addressing the following themes: (i) the identification of privacy risks related to neuroimaging analysis, (ii) the development of novel privacy-preserving techniques for neuroimaging analysis, (iii) the evaluation and comparison of different privacy-preserving techniques,(iv) the ethical and legal implications related to the use of privacy-preserving neuroimaging analysis,(v) AI methods for privacy preservation in neuroimaging analysis while ensuring interpretability, explainability, trust, and other AI characteristics, and(vi) case studies and practical applications in neuroimaging research and medical settings.
