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<p>In recent Datascience and AI studies, multimodality has become a key principle for achieving more accurate and dependable results. Managing multimodal data requires specialized platforms and structures to handle diverse data types from single subjects. The established BIDS (<i>Brain Imaging Data Structure</i>) standard faces limitations in supporting multimodal data, as each dataset is assigned to a single study with unique subject identities, preventing integration of multimodal data from the same individual across multiple studies. To address this limitation, this paper introduces FAIR m-BIDS (<i>FAIR Multimodal Brain Imaging Data Structure</i>), extending conventional BIDS by shifting granularity from dataset level to individual data entities. Each brain data file receives an independent <i>GUId-Key (Global Unique Identifier Key</i>), enabling researchers to select and integrate data items from different modalities and studies into customized multimodal datasets. The proposed structure enhances FAIR principles through improved findability, accessibility, interoperability, and reusability. Global identifiers enable tracking anonymized subject data across multiple datasets and modalities, while maintaining compatibility with conventional BIDS standards for advanced AI and neuroscience research applications.</p>

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FAIR m-BIDS: Advancing brain data utilization through multimodal and FAIR principles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Seyed Mohammad Mirhosseini,
  • Hoda Naseri,
  • Bahaareh Siahlou,
  • Mehran Panahi Arasi,
  • Saeid Monazami Eslami,
  • Ali Asghar Safaei

摘要

In recent Datascience and AI studies, multimodality has become a key principle for achieving more accurate and dependable results. Managing multimodal data requires specialized platforms and structures to handle diverse data types from single subjects. The established BIDS (Brain Imaging Data Structure) standard faces limitations in supporting multimodal data, as each dataset is assigned to a single study with unique subject identities, preventing integration of multimodal data from the same individual across multiple studies. To address this limitation, this paper introduces FAIR m-BIDS (FAIR Multimodal Brain Imaging Data Structure), extending conventional BIDS by shifting granularity from dataset level to individual data entities. Each brain data file receives an independent GUId-Key (Global Unique Identifier Key), enabling researchers to select and integrate data items from different modalities and studies into customized multimodal datasets. The proposed structure enhances FAIR principles through improved findability, accessibility, interoperability, and reusability. Global identifiers enable tracking anonymized subject data across multiple datasets and modalities, while maintaining compatibility with conventional BIDS standards for advanced AI and neuroscience research applications.