Open data sharing in autism neuroimaging: a systematic review of impact, transparency, and rigor
Poster No:
1884
Submission Type:
Abstract Submission
Authors:
Phoebe Thomson1, Diego Perez1, Shinwon Park2, Page Freeman1, Rowen Gesue1, Teresa George1, Michael Milham1, Adriana Di Martino1
Institutions:
1Child Mind Institute, New York, NY, 2Autism Center, Child Mind Institute, New York, NY
First Author:
Co-Author(s):
Introduction:
The Autism Brain Imaging Data Exchange (ABIDE) initiative provided the first open resource of autism-related neuroimaging data, aggregating over 2100 MRI and related phenotypic information across 24 international institutions (Di Martino et al., 2013; 2017). The goal of ABIDE was to accelerate the pace of discovery and facilitate collaborations. Over a decade since its first release, the present systematic review aims to assess the impact of the ABIDE dataset on autism neuroimaging research, comparing studies that utilize this open dataset to those using other data sources. We focused on quantitatively measuring ABIDE use and impact in the field, studies' investigative scope, and what aspects of autism have been most examined given its heterogeneity.
Methods:
Consistent with PRISMA guidelines, this systematic literature review identified peer-reviewed articles published between January 2013–December 2024 using resting-state functional, structural and diffusion MRI (R-fMRI, sMRI, dMRI) data, as the imaging modalities included in ABIDE. We then categorized articles into those using ABIDE data and those using other data. Article data were extracted and analyzed across three domains: 1) bibliometrics; 2) primary questions and approaches; 3) samples' characteristics. Additional analyses integrating this information allowed us to evaluate the extent of scientists' engagement by assessing leading authors' institutions, and the degree of transparency and rigor of the literature. Summary statistics within, and statistical comparisons between, ABIDE and non-ABIDE articles were conducted; false-discovery rate (FDR) corrections (q<0.05) were applied as appropriate.
Results:
We identified 922 peer-reviewed empirical neuroimaging articles published using ABIDE and 741 using non-ABIDE data (Fig.1A–B). Both ABIDE and non-ABIDE literatures were highly cited (h-index of 78 and 76, respectively), and showed no significant differences in journal impact factor (p=.056; Fig.2A). ABIDE articles were led by institutions across all continents and extended beyond the original data donor institutions (Fig.1C). Across literatures, the most common research questions were around diagnostic group mean comparisons. There was, however, a wider range of research purposes in ABIDE than non-ABIDE articles, and a significantly greater number of studies focusing on novel analytical approaches (e.g., diagnostic classification, neurosubtyping; Fig.2B). ABIDE articles examined significantly larger sample sizes (p<.001; Fig.2C). Though both literatures focused predominantly on childhood and adolescence, ABIDE articles studied older mean sample ages (p=.002; Fig.2D). There were no overall differences in transparency scores between ABIDE and non-ABIDE R-fMRI articles (p=.104; Fig.2E), however patterns varied across criteria. For example, more detailed sample characteristics were provided in non-ABIDE articles, while measures relating to robustness and replication were more represented in ABIDE.
Conclusions:
ABIDE has contributed to 55% of the autism neuroimaging studies published in the last 10 years. It has provided global access to autism imaging data, facilitating a huge increase in publications over time. It has also provided greater statistical power for group mean comparisons and the examination of urgently needed approaches for exploring autism heterogeneity. Further, it contributed to a shift towards more methodological rigor in the field, allowing for analyses focused on robustness and replication. Along with clear benefits, results from this review emphasize several gaps including, for example, greater focus on younger age ranges and clinical measures. Adopting clear reporting standards when using open data is important to understand the representativeness of samples and ensure replicability of neuroimaging findings. Findings motivate and inform future open data efforts and practices for autism and beyond.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI 1
Diffusion MRI
Keywords:
Autism
FUNCTIONAL MRI
Open Data
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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Di Martino, A., O’Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., ... & Milham, M. P. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific data, 4(1), 1-15.