Huge thanks to the incredible team of 50 co-authors who made this possible! Special thanks to our lab replicator + dMRI guru @cieslakmatt.bsky.social, the UMN team led by @drdamienfair.bsky.social, and my incredibly supportive postdoc advisor @ted-satterthwaite.bsky.social! Preprint: bit.ly/3QMLG0K
Takeaway #4:
These data are openly available!
To lower barriers, we release ABCC on NBDC/LASSO:
- >24,000 processed dMRI scans
- advanced microstructural metrics
- tractography + tabular summaries
Together, this is a major new resource for reproducible developmental neuroimaging.
Takeaway #3:
Image quality is more complicated than it seems.
Some commonly used QC covariates can bias developmental inference. If you are using advanced measures of microstructure (e.g., ICVF) it may not be necessary to include an image quality covariate in analyses.
Takeaway #2:
Not all dMRI measures of microstructure are the same!
Measures from advanced multi-shell models are more sensitive to development, more consistent with each other, and more robust to noise than tensor-derived measures. These should be prioritized in analyses!
Takeaway #1:
Harmonization is essential in multisite dMRI data!
Scanner effects are large and spatially structured; they can distort developmental patterns and reduce generalizability unless addressed carefully.
In ABCD, acquisition batch, age, and quality are partially aligned with one another. Given this collinearity, we found that including these age- and batch-aligned quality covariates can attenuate true developmental effects without mitigating noise in the data.
We compared automated QC metrics vs extensive manual ratings (😮💨) To our surprise (and joy!), dMRI contrast explains more microstructural variance than expert ratings.
Automated QC can outperform visual inspection at scale – saving hours of manual inspection.
How sensitive are dMRI metrics to quality?
FA was most susceptible to image quality. Notably, FA was most related to dMRI contrast, and NOT motion.
In contrast, advanced metrics like ICVF were overall much more robust to image quality – another plus of moving beyond the tensor.
Different scanners tell the same story; but what about different diffusion metrics?
Advanced metrics like ICVF, RTOP, and MKT, exhibited high convergence (ρ ≥ 0.93) in their spatial patterns of development.
However, tensor metrics like FA and MD were less consistent.
Do these developmental effects replicate across scanners?
Before harmonization, only modestly.
After harmonization: near-perfect correspondence across vendors.
With our harmonized data, we asked: which metrics best capture development? Advanced dMRI metrics (ICVF, MKT, and RTOP) showed much stronger age effects (>3x!!!) than traditional tensor metrics (FA and MD).
Bottom line?: Metric choice strongly impacts sensitivity to development.
Given these scanner differences, harmonization is essential! In unharmonized data, acquisition batch explained up to 70% of microstructural variance. We used cutting-edge longitudinal nonlinear harmonization, which eliminated these effects while preserving developmental effects.
ABCD has > 60 scanner batches (unique devices / software versions)!!! There were large differences in quality between scanner vendors (GE, Siemens, and Philips). In GE, image quality was related to software version, which itself was related to age! More on that later…
Data are distributed in the ABCD-BIDS Community Collection (ABCC). Derivatives include preprocessed images, over 30 microstructural maps from 4 software tools, 60+ white matter bundles, tidy tabular summaries of bundle-wise measures, and 40 automated image quality metrics.
We are pleased to announce that BIDS Extension Proposal 28 - Provenance is open for community review!
github.com/bids-standar...
The review period is open from April 20 - May 1.
#bids #neuroimaging #provenance
We are pleased to announce that BIDS Extension Proposal 32 - Microelectrode Electrophysiology is open for community review!
github.com/bids-standar...
The review period is open from April 20 - May 1.
#bids #neuroimaging #electrophysiology
Diffusion MRI (dMRI) is a powerful tool to study white matter maturation. In our new preprint, we process and distribute a new resource of >24,000 ABCD dMRI scans using open source tools! We then evaluate how methods shape inferences about development.
🔗 www.biorxiv.org/content/10.6...
Introducing...the PMADS Study!! Check out our preprint and below thread describing our multimodal study protocol integrating MRIs at 3T and 7T, biofluid collection, and clinical and cognitive assessments to characterize risk for perinatal mood and anxiety disorders. Congrats @noemirubau.bsky.social!
The PMADS Project: A Longitudinal Multimodal Cohort Study to Understand Risk for Perinatal Mood and Anxiety Disorders www.biorxiv.org/content/10.64898/2026.04...
We've got a Town Hall coming up April 23rd 10:00am (CT)! Join the action to hear about our updates to the Coordinate Based Meta-analysis Algorithms and our work on Image Based Meta-Analysis on the platform!
utexas.zoom.us/meeting/regi...
#neuroscience #meta-analysis #openscience
A systematic meta-analysis is hard work—and curation is often the hardest part. Y'all know this better than anyone, and we want to make it easier.
What’s one specific piece of information you consistently had to find or extract from every paper?
Tell us about it: tally.so/r/QKVbQG
#neuroscience
Applications for NeuroHackademy 2026 are now open! This is a two-week NIH-funded summer experience that combines neuroimaging and data science in a summer school / experiential hackathon: neurohackademy.org/apply/
@uwpsychology.bsky.social @uwescience.bsky.social
Statistical Machine Learning post doc positions, w/ Oxford BDI-Novartis Collaboration for AI in Medicine, working on building causal predictive models for MS using a massive clinical trials MRI dataset. Join us on this unique collaboration!
my.corehr.com/pls/uoxrecru...
my.corehr.com/pls/uoxrecru...
New paper in Imaging Neuroscience by James D. Kent, Alejandro de la Vega, et al:
Neurosynth Compose: A web-based platform for flexible and reproducible neuroimaging meta-analysis
doi.org/10.1162/IMAG...
You can check this out now: compose.neurosynth.org
We're continuing to add even more features to streamline and automate meta-analysis-- making it a key part of every scientist's toolkit!
Anatomical White Matter Tracts Span the Cortical Hierarchy to Support Cognitive Diversity www.biorxiv.org/content/10.64898/2025.12...
Big things coming in 2026 🚀:
1️⃣ More support for image-based meta-analysis 🖼️
2️⃣ Categorical and semantic search for studies (e.g., filter by number of participants, task descriptions) 🔍
3️⃣ Explicit filters for PET/VBM 🧠
4️⃣ A searchable gallery of already run meta-analyses (a la O.G. NeuroSynth) 📚
As the year comes to a close, we are taking stock of what we've done and what we are looking forward to in 2026.
(One thing we are looking forward to is our January 22nd TownHall! Register here: bit.ly/ns-townhall).
Keep reading for some fun stats!
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Connect with #BIDS @bidsstandard.bsky.social at #ebrainsSummit2025 summit with INCF in Brussels this week.
Come learn more about the Brain Imaging Data Standard and join the community - BIDS Bounties available!
Cc @incforg.bsky.social
