NIBS-BIDS: extending the brain imaging data structure to non-invasive brain stimulation

Non-invasive brain stimulation (NIBS) refers to a collection of methods for indirectly stimulating neural tissue from across the scalp, including transcranial magnetic stimulation, transcranial electrical stimulation, and transcranial ultrasound stimulation. NIBS techniques are used in a wide range of basic and clinical applications, ranging from investigating the physiology of the motor cortex to treatment of psychiatric and neurological disorders. Despite the widespread use of NIBS, a major challenge facing the field is the low reproducibility of outcomes associated with stimulation, including both neural and behavioural responses. Compounding the issue of reproducibility, there is no widely adopted standard for organising and describing the data collected in NIBS experiments, limiting the direct comparisons of outcomes across the NIBS field.
In response to similar reproducibility issues, the neuroimaging community introduced the brain imaging data structure (BIDS) nearly a decade ago, a standardised format for organising and describing neuroimaging data. The BIDS standard includes recommended file formats, data organisation guidelines, and methods for capturing metadata associated with neuroimaging experiments. Originally designed for data related to magnetic resonance imaging, the BIDS specification has expanded to include data from electroencephalography, magnetoencephalography, positron emission tomography and many other neuroimaging modalities. BIDS-formatted data is also accepted in widely used open-access data repositories, such as openneuro.org, which includes >1,000 neuroimaging datasets.
In this talk, I will overview recent efforts to extend the BIDS specification to include data associated with NIBS experiments. NIBS is unique from other data modalities included in BIDS in that it represents interventions which are often coupled with other neuroimaging or behavioural outcomes, but does not represent a data recording modality itself. As such, a flexible framework for integrating NIBS-related metadata (e.g., position of the coil/electrode/transducer, stimulation parameters etc.) with other data modalities has been developed. Furthermore, so-called ‘offline’ NIBS protocols are not associated with any neuroimaging data files, requiring the development of unique file formats for describing these interventions. Incorporating NIBS within the BIDS framework will provide an avenue to increase data sharing practices across the field and allow the development of automated analysis pipelines and quality assurance protocols. By improving open science practices, we hope to directly address the reproducibility challenge hampering the NIBS field.