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@storrisi.bsky.social

{f,s,d}MRI Research Scientist @ SFVA + UCSF

84 Posts150 Follower79 Folgt
@storrisi.bsky.social · last wk.

wanted to learn @nilearn.bsky.social GLM-ing at NeuroHackademy, so as part of a group project I analyzed the open dataset "Study Forrest" localizers at the group level (n=15), visualized with alpha thresholding (also nilearn). here's the body localizer, corrected at p<0.001 via permutation testing:

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Blaise Frederick@blaisefrederick.bsky.social · last mo.

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

Alexander Rauscher 🖇️@rauschermri.bsky.social · 2mo ago

You can track respiration during fMRI using the phase images of your EPI scan; the fMRI respiration tracking toolbox got an update. github.com/alexjaffray/...

GitHub - alexjaffray/fMRI-phase-toolbox: Toolbox for processing and analysis of spatiotemporal image phase fluctuations during fMRI scans.

Toolbox for processing and analysis of spatiotemporal image phase fluctuations during fMRI scans. - alexjaffray/fMRI-phase-toolbox

github.com

Marc L Seal @parkvillegeek.bsky.social · 3mo ago

Did you know? State-of-the-art preprocessing diffusion MRI data can improve tractography | Brain Structure and Function | Springer Nature Link link.springer.com/article/10.1...

Did you know? State-of-the-art preprocessing diffusion MRI data can improve tractography - Brain Structure and Function

Brain Structure and Function - Diffusion MRI fiber tractography is sensitive to noise and artifacts in diffusion-weighted images, and these challenges can propagate into fiber-orientation...

link.springer.com

Josh Johansen@jojolab.bsky.social · 3mo ago

I’m very sad to share that Howard Fields has died. He made seminal discoveries in pain modulation, placebo, and addiction. He was also a wonderful mentor and friend who taught me how to think about the brain and the joy of scientific discovery. www.iasp-pain.org/publications...

In Memory of Howard L. Fields - International Association for the Study of Pain (IASP)

IASP is deeply saddened to hear of the recent death of Honorary IASP Member, Prof Howard Fields, and we offer our condolences to his family, friends and colleagues. We are […]

iasp-pain.org

The Journal of Pain@thejournalofpain.bsky.social · 3mo ago

Original: Drs. Strigo, Simmons, @storrisi.bsky.social, @benalter.bsky.social, et al. identified distinct neural phenotypes of pain anticipation in chronic low back pain (cLBP) and examined their clinical and physiological correlates in 43 cLBP patients. FULL:

Brain mechanisms of pain anticipation in chronic low back pain: Insights into phenotypical responses to mind-body therapy

Pain perception and emotional processing share common predictive mechanisms influenced by anticipation, expectation, and uncertainty. This study identifies distinct neural phenotypes of pain…

jpain.org

Dan Quintana@dsquintana.bsky.social · 3mo ago

Selecting an effect size for power analysis is hard. Many researchers fall back on Cohen's thresholds, but they have no empirical basis and vary wildly by field. Our new paper offers a better option: field-specific effect size distributions built from meta-analytic data doi.org/10.3758/s134...

Abstract Effect sizes are useful for understanding the magnitude of study results and for planning new studies via power analysis. However, despite their wide usage, effect sizes are often misinterpreted. This is mostly due to an over-reliance on general effect size benchmarks that were not intended for broad application across diverse research fields. Inaccurate effect size interpretations can lead to incorrect conclusions about the magnitude of study results and incorrect sample size estimates, thereby increasing the likelihood of false-positive results. This article introduces the ESDist R package, which is designed to calculate empirically derived effect-size benchmarks or a range of reliably detectable empirical effect sizes for a specific research question or field of interest by computing effect size distributions (ESDs). This package can be used on data that can be easily extracted from pre-existing meta-analyses to help researchers more accurately plan new studies or to better understand how an individual study might relate to other studies in their field. ESDist includes a set of features that make it easy to use in a priori power analysis. Moreover, the package includes a feature for estimating effect size benchmarks that account for publication bias and are weighted by effect sizes' variances, which addresses existing limitations of using ESDs for study planning or interpretation.
@storrisi.bsky.social · 4mo ago

so i guess dcm2bids died because they made breaking changes multiple times? codebase hasn't been updated in years. what are people using now?