Shaoshi Zhang @ZShaoshi
neuroscience, computational models | Computational Brain Imaging Group | Huge fan of Metroidvania and Edward Hopper. Singapore Joined May 2020-
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Honored to share our work, published in @APAPubJournals, emerging from a productive collaboration between @braincircuit and the UCLA Department of Psychiatry. 🧠Using an independent data modality (TMS) and rigorous permutation testing, we demonstrated that treatment sites associated with addiction improvement converge on the same addiction-remission brain circuit derived from lesions by Joutsa et al. 2022. 🧠These convergent maps highlight potential neuromodulation targets, including the frontopolar cortex, superior frontal gyrus, and a subregion of the posterior parietal cortex. 💡Don’t miss our Supplementary Materials, where we provide preliminary evidence that individualized connectivity to the same circuit corresponds to addiction improvement, paving the way for patient-specific, connectivity-guided targeting of this network. My profound gratitude goes to all the authors, in particular, the co-senior authors, my role model, Dr. Mike Fox @foxmdphd, and my wonderful co-mentor on this study, Dr. Nicole Petersen @tweetnicolepete, with whom it was truly a pleasure to work. Please read our work below: doi.org/10.1176/appi.a…
The p-tau217 breakthrough blood test replicated again, predicting Alzheimer's disease in a large cohort mean age 61. The cover of the new issue is telling @TheLancet thelancet.com/journals/lance…
This looks like a straightforward, highly applicable solution to the long-standing problem of valid inference for K-fold CV performance differences. The trade-off is smaller training sets from the split-half step and having to rerun K-fold CV many times.
So we propose SHARP, which involves repeated split-half to generate pairs of independent statistics. There are still 3 unknowns — mean, variance, between-repetition correlation — but the independent pairs provide a 3rd information source to estimate all 3 unknowns. 7/N
New paper in Imaging Neuroscience by Ru Kong, B.T. Thomas Yeo, et al: Network-based near-scalp personalized brain stimulation targets doi.org/10.1162/IMAG.a…
Here's bonus slides on cross-validation tests, separate from our preprint. Covering: 1. paired (sign-flip) permutation test 2. label-swap permutation test 3. sample-level vs fold-averaged stats 4. a common misapplication of the corrected t-test 5. three bootstrap variants 1/N
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
@bttyeo @tianchuzeng @kkli20111 @ZShaoshi @ten_photos This is fantastic! I'm glad to have something to point people to in reviews beyond Demšar, 2006 (and Benavoli 2017 for the Bayesian perspective).
Apparently I was doing cross-validation wrong. Thanks @bttyeo and @ZShaoshi for helping us fix it.
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Biomedical AI may be headed for a replication crisis. (This work below is not about AI-generated reports; it’s about studies of biomedicine that use ML in their methods, and how they are evaluted.)
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Omg I've been commenting about this in manuscript reviews for years. Thank goodness there's actually a paper to cite now!! Thanks @bttyeo !
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Eye opener 👀
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Proud to participate in this study! We should keep rigorous in AI-Biomedical research, we also observe some concerning trends in AI+biomarker studies… Congratulations @tianchuzeng Tian Fang and @ZShaoshi
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Important work. Worth to take a look if you are doing AI in biomedical research.
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Once again, @ten_photos came to the rescue - we prayed to him for a better statistical test for k-shot learning (since the corrected t-test is overly conservative in that scenario), and he answered our prayers with a new test that also covers classical cross-validation.
So we propose SHARP, which involves repeated split-half to generate pairs of independent statistics. There are still 3 unknowns — mean, variance, between-repetition correlation — but the independent pairs provide a 3rd information source to estimate all 3 unknowns. 7/N
@bttyeo @tianchuzeng @kkli20111 @ZShaoshi @ten_photos Can't stress this enough 👇 If you use ML to compare predictive models in your research (neuroscience, genetics, you name it), this paper is a must read! 👀 The majority of work in this space (mine included 🙋) misses critical nuances when reporting comparative stats.
My quibble: This is traditional ML *not* AI in the generative sense it means now till eternity. But yeah this is a thing. Metric chasing brought this on. Reviewers reward higher metric values & not well cross-validated results. We've been told AuC<0.8 not worth submitting. 🙄
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Oh my god, almost no biomedical AI papers had proper validations. This feels like field-wide malpractice.
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
So glad this is finally public. Grateful to my wonderful co-authors for the long journey.
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
It’s incredible to see this study come to fruition! Shout out to the amazing @tianchuzeng and @kkli20111 who spearheaded this work and huge thank you to all other coauthors!
For years, we've known that running a standard t-test on cross-validation folds violates sample independence. We wanted to see how widespread this issue actually is. The result? 97% of the studies used an invalid statistical test. 🧵👇
In a meta-analysis of 210 biomedical AI studies that statistically compared models under cross-validation, 97% used invalid statistical tests. Here's our new preprint doi.org/10.64898/2026.… led by @tianchuzeng @kkli20111 @ZShaoshi @ten_photos 1/N
Lesion network mapping (LNM) has been powerful in linking symptoms and brain functional circuits, but ongoing debates highlight that it is still hard to isolate symptom-specific effects. We came up with a new method, robust LNM (rLNM) — a unified framework combining null models and selective specificity to reveal reliable, symptom-specific networks from background structure. biorxiv.org/content/10.648… @bttyeo @foxmdphd @ndosenbach @club_scan
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