A comparison of neuroelectrophysiology databases

BIDS Manager-Pipeline: A framework for multi-subject analysis in electrophysiology

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed “FlywheelTools,” a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

Multicentre studies are of utmost importance to confirm hypotheses. The lack of established standards and the ensuing complexity of their data management often hamper their implementation. The Brain Imaging Data Structure (BIDS) is an initiative for organizing and describing neuroimaging and electrophysiological data. Building on BIDS, we have developed two software programs: BIDS Manager and BIDS Uploader. The former has been designed to collect, organise and manage the data and the latter has been conceived to handle their transfer and anonymisation from the partner centres. These two programs aim at facilitating the implementation of multicentre study by providing a standardised framework.

Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical records, medical images). Integration and analysis of such data in research centers is pivotal for elucidating disease mechanisms and improving clinical outcomes. However, data collection in clinics often relies on non-standardized methods, such as paper-based documentation. Moreover, diverse data types are collected in different departments hindering efficient data organization, secure sharing and compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Henceforth, in this manuscript we present a specialized data management system designed to enhance research workflows in Deep Brain Stimulation (DBS), a state-of-the-art neurosurgical procedure employed to treat symptoms of movement and psychiatric disorders. The system leverages REDCap to promote accurate data capture in hospital settings and secure sharing with research institutes, Brain Imaging Data Structure (BIDS) as image storing standard and a DBS-specific SQLite database as comprehensive data store and unified interface to all data types. A self-developed Python tool automates the data flow between these three components, ensuring their full interoperability. The proposed framework has already been successfully employed for capturing and analyzing data of 107 patients from 2 medical institutions. It effectively addresses the challenges of managing, sharing and retrieving diverse data types, fostering advancements in data quality, organization, analysis, and collaboration among medical and research institutions.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets. To solve this critical issue, the neuroimaging community created the Brain Imaging Data Structure (BIDS) that specifies standards for how datasets should be organized to facilitate sharing and reproducibility of science. Currently, BIDS supports dozens of neuroimaging modalities including MRI, EEG, MEG, PET, and many others. In this paper, we present the extension of BIDS for NIRS data alongside tools that may assist researchers in organizing existing and new data with the goal of promoting public disseminations of fNIRS datasets.

The Brain Integrated Resource for Human Anatomy and Intracranial Neurophysiology (B(RAIN)2) is a multi-center data resource that provides standardized imaging and intracranial neurophysiological data from patients who have undergone deep brain stimulation (DBS) implantation with intracranial electroencephalography (iEEG) recordings. The database includes patients’ electrophysiological recordings from the cerebral cortex and deeper brain structures, alongside high-resolution neurological imaging scans taken at preoperative, intraoperative, and postoperative stages. The data are systematically organized using the Brain Imaging Data Structure (BIDS) format for imaging data and iEEG-BIDS for intracranial electrophysiological recordings. This standardized structure facilitates data integration, sharing, and analysis across research institutions. Additionally, the database features detailed metadata, such as diagnostic information and neurological assessment scores, providing a comprehensive profile of each patient’s data.

BackgroundWhile a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, less attention has been paid to reproducibility of brain positron emission tomography (PET) research.MethodsWe examined the current landscape of factors that contribute to reproducible neuroimaging data analysis, comparing brain MRI to brain PET. The factors included scientific standards, analytic plan pre‐registration, data and code sharing, containerized workflows, and standardized processing pipelines. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we conducted one case study in which the brain PET processing pipeline developed for one flagship study (Alzheimer's Disease Neuroimaging Initiative, ADNI) for brain 18F‐florbetapir and 18F‐fluorodeoxyglucose (FDG) analysis was reproduced in our laboratory.ResultsCompared to fMRI, PET research encounters substantial challenges in adopting essential reproducibility practices. While 85% of fMRI studies adhere to the standardized reporting practices, PET is in the early stages of establishing reporting practices, lacking a standardized checklist. Pre‐registration is prevalent for fMRI (57.6%) but notably absent in PET, with no pre‐registered PET studies found on platforms such as the Center for Open Science. The number of shared datasets is higher for fMRI compared to PET, with 92 fMRI datasets and only 9 PET datasets on repositories like OpenNeuro. Moreover, there are very few data repositories for newer PET radiotracers. Containerized software availability also favor fMRI, with platforms like (Brain Imaging Data Structure, BIDS) apps hosting 66% dedicated fMRI tools, while no PET‐specific software is available. Standardized processing pipelines follow the same trend, with fMRI boasting optimized workflows for specific protocols, while PET has few dedicated pipelines and lacks validation studies for existing options. Furthermore, while our case study demonstrated excellent correlation results for both the 18F‐FDG and 18F‐florbetapir methods (Figure 2), the replication of the ADNI PET pipeline posed substantial challenges and implementation delays. These obstacles and delays in execution were due to incomplete documentation and ambiguities in application details.ConclusionPET neuroimaging lags behind its MRI‐related counterparts in achieving robust reproducibility.

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.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

The statistical analysis of functional MRI requires suitable data, which is prepared with pre-processing workflows [1]. These workflows typically include head-motion and distortion corrections, intra-subject registration, spatial normalization, etc. [2]. However, these workflows remain largely subordinated to the features of the input data, the subsequent statistical analysis tool of choice, the investigator's preferences. Moreover settings and details are rarely reported in published literature. FMRIprep is a software instrument for fMRI preparation that aims at the standardization of fMRI preprocessing. The tool is designed around three principles: 1) robustness to the heterogeneity of input data (e.g. acquisition parameters, protocol design, etc.); 2) ease of use, relying on the BIDS standards [3] for the input data and the resulting derivatives; and 3) a "glass-box" philosophy that stresses the need for visually assessing the outcomes before analysis, and the transparency on the workflow design and tooling configuration. [1] Ashburner J et al. (2009), Preparing fMRI data for statistical analysis, fMRI Techniques and Protocols (Neuromethods), Humana Press, doi:10.1007/978-1-60327-919-2_6; [2] Strother S. (2006), Review of Preprocessing Steps for BOLD fMRI, IEEE EMB Mag, 25(2):27-41. doi:10.1109/MEMB.2006.1607667; [3] Gorgolewski KJ et al. (2016), The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 3:160044. doi:10.1038/sdata.2016.44; [4] Poldrack RA et al. (2013), Toward open sharing of task-based fMRI data: the OpenfMRI project. Frontiers in Neuroinformatics 7:12. doi:10.3389/fninf.2013.00012; [5] Gorgolewski KJ et al. (2016), Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Zenodo [Software]. doi:10.5281/zenodo.50186; [6] Gorgolewski KJ et al. (2016), BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods [Internet]. bioRxiv. p. 079145. doi:10.1101/079145;

The brain, body, and behavior dataset (BBBD) provides multimodal recordings from 178 participants across five experiments in which short educational videos were viewed. The dataset contains about 110 hours of electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), respiration, pupil size, gaze position, saccades, blinks, fixations, and head-motion signals, all time-aligned to the video and standardized in the Brain Imaging Data Structure (BIDS) format. Participants watched three to six videos (mean = 28 ± 5 min) under conditions that manipulated attention (attentive versus distracted), learning goals (incidental versus intentional), and motivation (monetary incentive). Demographic data, Adult ADHD Self-Report Scale (ASRS) scores, and digit-span working-memory assessments are included. Technical validation shows expected effects: higher alpha-band power during distraction, a posterior-to-anterior gradient in EEG power, increased blink rate, and reduced saccade rate when attention was diverted. Approximately 97 percent of the continuous signal data are newly released. BBBD enables investigation of how neural, ocular, and physiological dynamics relate to attention and learning during naturalistic video viewing.

Diffusion MRI (dMRI) has become a crucial imaging technique in the field of neuroscience, with a growing number of clinical applications. Although most studies still focus on the brain, there is a growing interest in utilizing dMRI to investigate the healthy or injured spinal cord. The past decade has also seen the development of biophysical models that link MR-based diffusion measures to underlying microscopic tissue characteristics, which necessitates validation through ex vivo dMRI measurements. Building upon 13 years of research and development, we present an open-source, MATLAB-based academic software toolkit dubbed ACID:A Comprehensive Toolbox forImage Processing and Modeling of Brain, Spinal Cord, and Ex VivoDiffusion MRI Data. ACID is an extension to the Statistical Parametric Mapping (SPM) software, designed to process and model dMRI data of the brain, spinal cord, and ex vivo specimens by incorporating state-of-the-art artifact correction tools, diffusion and kurtosis tensor imaging, and biophysical models that enable the estimation of microstructural properties in white matter. Additionally, the software includes an array of linear and nonlinear fitting algorithms for accurate diffusion parameter estimation. By adhering to the Brain Imaging Data Structure (BIDS) data organization principles, ACID facilitates standardized analysis, ensures compatibility with other BIDS-compliant software, and aligns with the growing availability of large databases utilizing the BIDS format. Furthermore, being integrated into the popular SPM framework, ACID benefits from a wide range of segmentation, spatial processing, and statistical analysis tools as well as a large and growing number of SPM extensions. As such, this comprehensive toolbox covers the entire processing chain from raw DICOM data to group-level statistics, all within a single software package.

Automated seizure detection and localization from intracranial EEG requires validated benchmark datasets with expert annotations, yet existing open datasets lack multi-expert consensus annotations and exclude stimulation-induced seizures. We present stereotactic EEG recordings from 83 seizures (46 spontaneous, 37 stimulation-induced) across 32 patients (19 from the University of Pennsylvania, 13 from the Children's Hospital of Philadelphia) with drug-resistant epilepsy. Three board-certified epileptologists independently annotated each seizure for onset time, onset channels, and channels seizing at 10 seconds post-onset using a standardized protocol. Consensus annotations were determined through majority voting. Inter-rater agreement was κ = 0.64 for onset channels and κ = 0.62 for spread channels. Individual rater agreement with consensus was κ = 0.81 for onset and κ = 0.80 for spread. Agreement metrics did not differ between spontaneous and stimulation-induced seizures. All data follow Brain Imaging Data Structure (BIDS) standards and include electrode localizations, patient demographics, and clinical outcomes. This dataset enables the validation of seizure onset and spread detection and localization against human expert performance and supports comparative analysis of seizure networks across spontaneous and stimulation-induced seizures.

TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity