iiinova — where your digital self keeps evolving

Steven Meisler @stevenmeisler.com · 01/10/2026

We also asked whether each metric preserves a participant-specific spatial “fingerprint” across sessions. Most metrics had extremely high discriminability, even when local ICCs were more variable.

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Steven Meisler @stevenmeisler.com · 01/10/2026

Next: test–retest reliability. dMRI-derived metrics were consistently among the most reliable measures, with many median ICCs approaching or exceeding 0.9. MP2RAGE R1 and T1w/T2w also performed well. QSM, R2*, and ihMT (especially in gray matter) were more variable.

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Steven Meisler @stevenmeisler.com · 01/10/2026

How similar are different myelin-sensitive measures to each other? Intermetric correlations were generally stronger in white matter and strongest among measures from the same acquisition/modeling family. QSM measures were particularly distinct from most of the other metrics.

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Steven Meisler @stevenmeisler.com · 01/10/2026

First question: how well do these metrics differentiate gray and white matter? The answer: a lot! MP2RAGE R1 showed the strongest differentiation, followed by several dMRI, T1w/T2w, and ihMT measures. QSM measures showed much weaker and more heterogeneous differences.

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Steven Meisler @stevenmeisler.com · 01/10/2026

MIRROR includes 2 MRI sessions in 22 adults, with: - diffusion MRI (dMRI) - ihMT - MP2RAGE - QSM - T1w/T2w - Relaxometry We derive 27 myelin-sensitive metrics for primary analyses (93 metrics in the supplement!).

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Steven Meisler @stevenmeisler.com · 01/10/2026

There are many MRI approaches for studying myelin. But which ones are reliable, how do they relate to each other, and how do they behave in gray vs white matter? In our new preprint, we introduce Myelin Imaging Reliability and ReprOducibility Resource (MIRROR) 🧠🪞 📄🔗 www.biorxiv.org/content/10.6...

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

Do these developmental effects replicate across scanners? Before harmonization, only modestly. After harmonization: near-perfect correspondence across vendors.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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…

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Steven Meisler @stevenmeisler.com · 19/04/2026

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.

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Steven Meisler @stevenmeisler.com · 19/04/2026

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...

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Steven Meisler @stevenmeisler.com · 17/11/2024

Applying to be the Traeger Endowed Chair of Pellet Smoking

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