Events in context-The HED framework for the study of brain, experience and behavior.

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN.

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.

The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data.

With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data to individual idiosyncrasies associated with each package. Here we describe PyMVPA BIDS-App, a fast and robust pipeline based on the data organization of the BIDS standard that performs multivariate analyses using powerful functionality of PyMVPA. The app runs flexibly with blocked and event-related fMRI experimental designs, is capable of performing classification as well as representational similarity analysis, and works both within regions of interest or on the whole brain through searchlights. In addition, the app accepts as input both volumetric and surface-based data. Inspections into the intermediate stages of the analyses are available and the readability of final results are facilitated through visualizations. The PyMVPA BIDS-App is designed to be accessible to novice users, while also offering more control to experts through command-line arguments in a highly reproducible environment.

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

Continuous Automated Analysis Workflow for MRS Studies.

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

Infant distractibility from social events mediates the relation between maternal responsiveness and infant language outcomes.

Research demonstrates that contingent and appropriate maternal responsiveness to infant requests and bids for attention leads to better language outcomes. Research also indicates that infants who are less distracted by irrelevant competing stimulation and attend efficiently to audiovisual social events (e.g., faces and voices) show better language outcomes. However, few studies have assessed relations between maternal responsiveness, infant attention to faces and voices, and distractibility, and how together these factors lead to early language outcomes. A newly developed audiovisual protocol, the Multisensory Attention Assessment Protocol (MAAP; Bahrick et al., 2018), allows researchers to examine individual differences in attention to faces and voices and distractibility, and to assess relations with other variables. At 12 months, infants (n = 79) in an ongoing longitudinal study participated in the MAAP to assess intersensory matching of synchronous faces and voices and attention to an irrelevant competing visual distractor event. They also were observed in a brief play interaction to assess infant bids for attention and maternal responsiveness (accept, redirect, or ignore). At 18 months, receptive and expressive language were assessed using the Mullen Scales of Early Learning. Several noteworthy findings emerged: 1) mothers were generally responsive, accepting 74% and redirecting 14% of infant bids, 2) infants who had a greater number of their bids redirected by mothers, and who had better intersensory matching of synchronous faces and voices, showed less attention to the distractor, and 3) infants who showed less attention to the distractor had better receptive language. Findings demonstrate that maternal redirecting of infant attention by mothers who are generally responsive may promote better infant attentional control (lower distractibility) which in turn predicts better receptive language in toddlers.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data.

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

IABC: A Toolbox for Intelligent Analysis of Brain Connectivity.

Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject neuroimaging measures for further analysis. After user inputs functional magnetic resonance imaging (fMRI) data of multiple subjects that are regularly organized (e.g., in Brain Imaging Data Structure (BIDS)) and clicks a few buttons to set parameters, IABC automatically outputs brain functional networks, their related time courses, and functional network connectivity of each subject. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.

Behavioral changes in the housing market before and after the Covid-19 lockdown.

We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding logs data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lockdown on March 12, and the re-opening on April 20. We observe changes on the date of the lockdown in transaction volumes, sell-prediction spreads, exploitative bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that about half of the total fall in prices had already occurred before the lockdown was implemented. The re-opening completely reverses the lockdown effect on prices. We show that voluntary behavioral changes, as well as lockdown and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.

BMAT: An open-source BIDS managing and analysis tool.

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.

Una mirada actual a la caracterización de datos y ficheros de neurociencias / A Current Look at Characterization of Neuroscience Data and Files

Para los neurocientíficos constituye un desafío realizar un seguimiento de los datos y metadatos generados en cada investigación y extraer con precisión toda la información relevante, hecho crucial para interpretar resultados y requisito mínimo para que los investigadores construyan sus investigaciones sobre los hallazgos anteriores. Se debe mantener tanta información como sea posible desde el inicio, incluso si esta pudiera parece ser irrelevante, además de registrar y almacenar los datos con sus metadatos de forma clara y concisa. Un análisis preliminar sobre la literatura especializada arrojó ausencia de una investigación detallada sobre cómo incorporar la gestión de datos y metadatos en las investigaciones clínicas del cerebro, en términos de organizar datos y metadatos completamente en repositorios digitales, recopilar e ingresar estos teniendo en cuenta su completitud, y sacar provecho de dicha recopilación en el proceso de análisis de los datos. Esta investigación tiene como objetivo caracterizar conceptual y técnicamente los datos y metadatos de neurociencias para facilitar el desarrollo de soluciones informáticas para su gestión y procesamiento. Se consultaron diferentes fuentes bibliográficas, así como bases de datos y repositorios tales como: Pubmed, Scielo, Nature, Researchgate, entre otros. El análisis sobre la recopilación, organización, procesamiento y almacenamiento de los datos y metadatos de neurociencias para cada técnica de adquisición de datos (EEG, iEEG, MEG, PET), así como su vínculo a la estructura de datos de imágenes cerebrales (BIDS) permitió obtener una caracterización general de cómo gestionar y procesar la información contenida en los mismos(AU)

For neuroscientists, it is a challenge to keep track of the data and metadata generated in each investigation and accurately extract all the relevant information, a crucial fact to interpret results and a minimum requirement for researchers to build their investigations on previous findings. Keep as much information as possible from the start, even if it may seem irrelevant and record and store the data with its metadata clearly and concisely. A preliminary analysis of the specialized literature revealed an absence of detailed research on how to incorporate data and metadata management in clinical brain research, in terms of organizing data and metadata completely in digital repositories, collecting and inputting them taking into account their completeness. , and take advantage of such collection in the process of data analysis. This research aims to conceptually and technically characterize neuroscience data and metadata to facilitate the development of computer solutions for its management and processing. Different bibliographic sources were consulted, as well as databases and repositories such as: Pubmed, Scielo, Nature, Researchgate, among others. The analysis on the collection, organization, processing and storage of neuroscience data and metadata for each data acquisition technique (EEG, iEEG, MEG, PET), as well as its link to the brain imaging data structure (BIDS) allowed to obtain a general characterization of how to manage and process the information contained in them(AU)

BIDS-structured resting-state electroencephalography (EEG) data extracted from an experimental paradigm.

Electroencephalography (EEG) offers a unique window into the dynamics of the neuronal symphony that powers our brains. Here, we describe a publicly available dataset of EEG recorded from 111 healthy subjects. The data were recorded with 64 electrodes in a resting-state condition, an approach that offers broad-spectred analysis options, including functional connectivity and graph theory. In a subset of the subjects (n = 42), a second EEG recording was performed, 2-3 months after the initial recording, allowing measurement stability to be assessed. Furthermore, in connection with the EEG acquisition, a range of neuropsychological test scores were obtained for each subject. The dataset is comprehensive and organised according to the Brain Imaging Data Structure (BIDS) specification, providing a valuable starting point for both aspiring and experienced researchers in a range of fields, including cognitive neuroscience, data science, machine learning, and clinical neurophysiology.

Bidding on the go: Links between walking, social actions, and caregiver responses in infant siblings of children with autism spectrum disorder.

The development of walking is associated with a shift in how neurotypical infants initiate social interactions. Walking infants are more likely to locate objects in distant places, carry them, and then share those objects by approaching caregivers and using gestures to show or offer their discoveries (i.e., moving bids). The simultaneous organization of the behaviors necessary to generate moving bids requires the coordination of multiple skills-walking, fine motor skills, and gesturing. Infants with an elevated likelihood (EL) for autism spectrum disorder (ASD) exhibit differences and delays in each of these behaviors. This study investigated interconnections between infant walking, social actions, and caregiver responses in 18-month-old EL infants with diverse developmental outcomes (ASD, non-ASD language delay, no diagnosis). We observed 85 infant-caregiver dyads at home during everyday activities for 45 minutes and identified all times when infants walked, instances of walking paired with social action (i.e., approaching the caregiver, approaching while carrying an object, producing a moving bid), and whether caregivers responded to their infants' social actions. There were no group differences in infants' production of social actions. Caregiver responses, however, were more clearly modulated by outcome group. While all caregivers were similarly and highly likely to respond to moving bids, caregivers of EL-ASD infants were substantially more likely to respond when their infants simply approached them (with or without an object in hand). Taken together, this research underscores the complexity of EL infant-caregiver interactions and highlights the role that each partner plays in shaping how they unfold.

Influences of infants' and mothers' contingent vocal responsiveness on young infants' vocal social bids in the Still Face Task.

Infants' social bids in the still face phase of the Still Face Task demonstrate their emerging sense of self agency as these behaviors happen in the absence of the partner's social overtures. The study examined the role of infants' contingent responsiveness to their mothers in social interactions on their social bidding to the mother when she becomes unresponsive. Social bids are non-distress vocalizations or smiles while looking at the unresponsive partner. Infants and their mothers longitudinally engaged in the Still Face Task when infants were one, two, and three months. At two months, infant non-distress vocalizations and smiles and contingent vocal and smiling responsiveness increased in the initial interactive phase and vocal and smile social bids increased in the still face phase. Infant contingent vocal responsiveness predicted infant vocal social bids but infant contingent smiling responsiveness did not predict infant smile social bids. Infant contingent vocal responsiveness was a stronger predictor than infant non-distress vocalizations per se of infant vocal social bids at two and three months. However, maternal contingent vocal responsiveness was the primary predictor of infant vocal social bids at these ages. Maternal contingent responsiveness to infant behavior allows infants to sense their agency in affecting their mothers' behavior. Yet infants are active participants, becoming contingently responsive to their mothers, which facilitates their awareness that they are effective agents in instigating social interaction, as demonstrated by social bids.

Micapipe: A pipeline for multimodal neuroimaging and connectome analysis.

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

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

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.

A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.