New dataset out in Data in Brief 🧠 We're releasing an open, test-retest neuroimaging dataset built for directly comparing single-echo vs. multi-echo fMRI, head-to-head, in the same people. 1/

A single-echo/multi-echo BOLD fMRI test-retest dataset

Functional MRI (fMRI) remains one of the primary tools for non-invasive studies of brain activity and organization in humans. Recently, multi-echo ima…

sciencedirect.com

Dr. Tianye Zhai’s careful and exhaustive new paper evaluating the effects of numerous fMRI denoising pipeline components, alone and in combination, just came out in HBM - check it out! onlinelibrary.wiley.com/doi/10.1002/...

Benchmarking fMRI Denoising Pipelines

This paper benchmarked fMRI denoising pipelines on task- and resting-state, single- and multi-echo fMRI datasets, in a modern 1-step regression framework, with pre-whitening and time-shifted physiolo...

onlinelibrary.wiley.com

We are currently recruiting a data analyst and a postdoctoral fellow to lead projects related to perinatal neuroimaging and reproductive psychiatry. Apply at the links below!

DGS: Apply Now & Register

ALT: DGS: Apply Now & Register

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Know someone interested in fMRI methods, software development, statistics, machine learning, and neuroimaging? Please share and encourage them to apply.

Paul Taylor@afni-pt.bsky.social · 2mo ago

Job ad (2/2): data scientist in the AFNI group (SSCC, NIMH, NIH, USA). FMRI/MRI methods dev, software design, statistics, analysis, data viz, quality control, machine learning and more! Please see: discuss.afni.nimh.nih.gov/t/job-ad-dat... I am happy to chat at OHBM+Brainhack. Please share!

Congrats to our own @markiewicz.bsky.social for winning the OHBM Open Science Award!

Job ad (1/2): Postdoc fellowship in the AFNI group (SSCC, NIMH, NIH, USA). FMRI/MRI methods dev, software design, statistics, analysis, data viz, quality control, machine learning and more! Please see: discuss.afni.nimh.nih.gov/t/job-ad-pos... I am happy to chat at OHBM+Brainhack. Please share!

Job ad (2/2): data scientist in the AFNI group (SSCC, NIMH, NIH, USA). FMRI/MRI methods dev, software design, statistics, analysis, data viz, quality control, machine learning and more! Please see: discuss.afni.nimh.nih.gov/t/job-ad-dat... I am happy to chat at OHBM+Brainhack. Please share!

We're suffering from success, our hard drive filled up from all the meta-analyses being run. If you can only view the results on neurovault or if you got a weird error today, that's likely why. Thankfully we have some easy fixes! github.com/neurostuff/n... Keep on meta-analyzing! #meta-analysis

Postdoc opening w/ a great team inc close collab Dan Wolf! fMRI + computational modeling in NIH-funded clinical high risk for psychosis study. U. Maryland in collaboration with UPenn + Mt. Sinai. Full posting here: umb.taleo.net/careersection/jobdetail.ftl?job=260000DJ

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

Community Review: BEP028 - Provenance · bids-standard bids-specification · Discussion #2405

We are pleased to announce the community review period for BIDS Extension Proposal (BEP) 028! BEP028 extends the BIDS standard to describe the provenance of BIDS datasets. It introduces new organiz...

github.com

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

Community Review: BEP032 - Microelectrode Electrophysiology · bids-standard bids-specification · Discussion #2406

We are pleased to announce the community review period for BIDS Extension Proposal (BEP) 032! BEP032 extends the BIDS standard to describe microelectrode electrophysiology data. The draft specifica...

github.com

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!

Noemí Rubau@noemirubau.bsky.social · 4mo ago

Imagine we could characterize neurobiological vulnerability to perinatal mood disorders (PMADs)…even before conception🤯? At the TReNDS Lab, we’re building a multimodal dataset following individuals at elevated risk for PMADs from preconception through postpartum.🧠🤰Check out our preprint! 🧵👇

The PMADS Project: A Longitudinal Multimodal Cohort Study to Understand Risk for Perinatal Mood and Anxiety Disorders https://www.biorxiv.org/content/10.64898/2026.04.10.717834v1

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

Welcome! You are invited to join a meeting: Neurosynth Compose Town Hall. After registering, you will receive a confirmation email about joining the meeting.

Welcome! You are invited to join a meeting: Neurosynth Compose Town Hall. After registering, you will receive a confirmation email about joining the meeting.

utexas.zoom.us

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

a man is standing in a kitchen with his hands on his chest and asking for help .

Alt: Tom Cruise is standing in a kitchen with his hands on his chest and asking for help, saying "Help me, Help you"

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