A5058615831- Functional Brain Connectivity Studies
- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Scientific Computing and Data Management
- Cell Image Analysis Techniques
- Tendon Structure and Treatment
- Health, Environment, Cognitive Aging
- Time Series Analysis and Forecasting
- Biomedical Text Mining and Ontologies
- Research Data Management Practices
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Advanced Biosensing Techniques and Applications
- Advanced Data Storage Technologies
- Systems Engineering Methodologies and Applications
- Advanced Memory and Neural Computing
- Context-Aware Activity Recognition Systems
- Meta-analysis and systematic reviews
- Machine Learning in Healthcare
- Global Health and Epidemiology
- Advancements in Photolithography Techniques
- Distributed and Parallel Computing Systems
Stanford University
2017-2024
University of Pennsylvania
2021
Research Square (United States)
1971
Western Infirmary
1971
Royal College of Nursing
1971
Barnes Hospital
1971
The sharing of research data is essential to ensure reproducibility and maximize the impact public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative archive that provides ability openly share from broad range brain imaging types following FAIR principles for sharing. We highlight importance Brain Imaging Data Structure standard enabling effective curation, sharing, reuse data. presently shares more than 600 datasets including 20,000 participants, comprising...
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory tools available step. The complexity these has snowballed with rapid advances in MR acquisition image processing techniques. We introduce fMRIPrep , an analysis-agnostic tool that addresses the challenge robust reproducible task-based resting fMRI data....
The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing aggregation, but also simplify application development new methods software working with data. Here, we present an extension BIDS include positron emission tomography (PET) data, known as PET-BIDS, share several open-access datasets curated following PET-BIDS along tools conversion, validation analysis datasets.
The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. visual assessment of every imaging scan a necessary quality control step, yet arduous and time-consuming. A sizeable body evidence shows that images low source variability may comparable to the effect size under study. We present MRIQC Web-API, an open crowdsourced database collects image metrics extracted from MR...
Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting acquisition parameters complicates quantification, analysis, and sharing ASL data, particularly studies across multiple sites, platforms, methods. There strong need standardization data storage, including metadata. Recently, ASL-BIDS, the BIDS extension ASL, was developed released in 1.5.0. This manuscript provides an overview...
We present an extension to the Brain Imaging Data Structure (BIDS) for motion data. Motion data is frequently recorded alongside human brain imaging and electrophysiological The goal of Motion-BIDS make interoperable across different laboratories with other modalities in behavioral research. To this end, standardizes format metadata structure. It describes how document experimental details, considering diversity hardware software systems This promotes findable, accessible, interoperable,...
Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets.Historically, have dealt this complexity idiosyncratically, every lab or individual implementing their own preprocessing and analysis procedures.The resulting lack of field-wide standards has severely limited reproducibility data sharing reuse.
Image-based meta-analysis (IBMA) is a powerful method for synthesizing results from various fMRI studies. However, challenges related to data accessibility and the lack of available tools methods have limited its widespread use. This study examined current state NeuroVault repository developed comprehensive framework selecting analyzing neuroimaging statistical maps within it. By systematically assessing quality NeuroVault's implementing novel selection techniques, we demonstrated...
1 Abstract A recent stream of alarmist publications has questioned the validity published neuroimaging findings. As a consequence, fMRI teams worldwide have been encouraged to increase their sample sizes reach higher power and thus positive predictive value However, an often-overlooked factor influencing is experimental design: by choosing appropriate design, statistical study can be increased within subjects. By optimizing order timing stimuli, gained at no extra cost. To facilitate design...
Abstract The sharing of research data is essential to ensure reproducibility and maximize the impact public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative archive that provides ability openly share from broad range brain imaging types following FAIR principles for sharing. We highlight importance Brain Imaging Data Structure (BIDS) standard enabling effective curation, sharing, reuse data. presently shares more than 600 datasets including 20,000...
Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification patterns evoked by tasks performed during scanning. Despite long history this technique, idiosyncrasies each dataset have led use ad-hoc preprocessing protocols customized for nearly every different study. This approach time-consuming, error-prone, and unsuitable combining datasets from many sources. Here we...
Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from lab to real world. Here, we present Neuroscout, an end-to-end platform for analysis naturalistic fMRI data designed facilitate adoption robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models automatically annotate stimuli dozens studies using stimuli—such as movies...
Abstract Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they interested in. Brain imaging genetics is at the intersection of two disciplines, each dedicated dictionaries and ontologies facilitating search analysis. Here, we present Imaging Data Structure extension, consisting metadata files for human brain to which linked, describe succinctly genomic transcriptomic associated may be in different databases. This...
ABSTRACT The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing aggregation, but also simplify application development new methods software working with data. Here, we present an extension BIDS include positron emission tomography (PET) (PET-BIDS). We describe PET-BIDS in detail share several open-access datasets curated following PET-BIDS. Additionally, highlight tools which are...
The current neuroimaging workflow has matured into a large chain of processing and analysis steps involving number experts, across imaging modalities applications. development fast adoption fMRIPrep [1] have revealed that neuroscientists need tools simplify their research workflow, provide visual reports checkpoints, engender trust in the tool itself. Here we present NiPreps (NeuroImaging Preprocessing toolS) framework, which extends fMRIPrep's approach principles to new modalities. vision...
Echo-Planar Imaging (EPI) allows very fast acquisition of whole-brain data, which enables standard functional & diffusion MRI (f/dMRI). However, EPI is notably sensitive to variations in the base B0 field. Small deviations parts-per-million from nominal caused by steps magnetic susceptibility (tissue interfaces) introduce misplacements registered location voxels up some cm settings along phase-encoding direction (PE), apparent as local geometrical distortions imaged specimen. In...
Abstract The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. visual assessment of every imaging scan a necessary quality control step, yet arduous and time-consuming. A sizeable body evidence shows that images low source variability may comparable to the effect size under study. We present MRIQC Web-API, an open crowdsourced database collects image metrics extracted from MR...