NIRS-BIDS: Brain Imaging Data Structure Extended to Near-Infrared Spectroscopy

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.

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

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.

MP27-19 A NEW CLINICAL PROTOCOL FOR DEMONSTRATION OF CORTICAL BRAIN ACTIVITY MAPPING DURING VOLUNTARY UROFLOW AND PELVIC FLOOR MUSCLE ACTIVITY USING FUNCTIONAL NEAR INFRA-RED SPECTROSCOPY (FNIRS)

Functional near-infrared (fNIR) spectroscopy is a promising new technology that has demonstrated utility in the study of normal human cognition. We utilized fNIR spectroscopy to examine the effect of social anxiety and performance on hemodynamic activity in the dorsolateral prefrontal cortex (DLPFC). Socially phobic participants and non-clinical participants with varying levels of social anxiety completed a public speaking task in front of a small virtual audience while the DLPFC was being monitored by the fNIR device. The relationship between anxiety and both blood volume (BV) and deoxygenated hemoglobin (Hb) varied significantly as a function of speech performance, such that individuals with low social anxiety who performed well showed an increase in DLPFC activation relative to those who did not perform well. This result suggests that effortful thinking and/or efficient top-down inhibitory control may have been required to complete an impromptu speech task with good performance. In contrast, good performers who were highly socially anxious showed lower DLPFC activation relative to good performers who were low in social anxiety, suggesting autopilot thinking or less-effortful thinking. In poor performers, slight increases in DLPFC activation were observed from low to highly anxious individuals, which may reflect a shift from effortless thinking to heightened self-focused attention. Heightened self-focused attention, poor inhibitory control resulting in excessive fear or anxiety, or low motivation may lower performance. These results suggest that there can be different underlying mechanisms in the brain that affect the level of speech performance in individuals with varying degrees of social anxiety. This study highlights the utility of the fNIR device in the assessment of changes in DLPFC in response to exposure to realistic phobic stimuli, and further supports the potential utility of this technology in the study of the neurophysiology of anxiety disorders.

Functional near infrared spectroscopy (fNIRS) is non-invasive brain imaging techniques that detects the cortical activity by measuring the change in the concentration of oxy-hemoglobin and de-oxy hemoglobin. It uses near infrared light of two wave lengths, 760 nm and 830 nm. NIRS is emerging neuro imaging modality with high temporal resolution. The advantage of NIRS system over other neuro imaging modalities is low cost, portable, safe and somehow results in short period of time. The scalp remains intact throughout the experiment. In this study we present a method for identification of brain activity by using fNIRS data. The general linear model has been used in study with predicted blood oxygen level dependent (BOLD) response signal and its delayed versions. The normalized least mean square (NLMS) algorithm has been used for identification of unknown parameters in the model recursively. A one way t-test has been performed for the significance of results.

While cochlear implants (CIs) have transformed the lives of hundreds of thousands of profoundly deaf individuals worldwide, the ability of individual patients to understand speech through their CI varies widely. The longitudinal study reported in this thesis aimed to investigate the role of cortical function and plasticity in helping to predict and explain variability in individual CI outcome. Speech-evoked cortical activations, measured using functional near-infrared spectroscopy (fNIRS), and behavioural measures of speech perception were acquired in adult CI recipients before implantation through to six months after CI activation. As anticipated, cochlear implantation enabled the significant recovery of auditory speech perception over the first six months of CI use, yet individual performance varied widely across CI users. fNIRS enabled the measurement of speech-evoked cortical activations from bilateral superior temporal cortex (STC) that were free from CI-generated artefacts. Cortical activation to visual speech measured before implantation was able to significantly predict future CI outcome measured following six months of CI use. This pre-implant measure of brain activity provided unique predictive value above that of well-established clinical characteristics, including the age-at-onset and duration of deafness. When examining changes in cortical activation from before to after implantation, a greater increase in STC activation to visual speech was found to be related to better CI outcome. These visual-related changes also predicted changes in STC activation to auditory speech. These results highlight the importance of visual speech cues and coupling between the auditory and visual modalities during the recovery of auditory function with a CI. Together, the findings demonstrate the potential of fNIRS as a brain-imaging tool that is uniquely well-suited for use in cochlear implantation. Future applications of this technology could help to explain individual variability in CI success and to deliver more accurate clinical prognoses for CI candidates.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

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.

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 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.

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 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;