iiinova — where your digital self keeps evolving

Parker Singleton @parkersingleton.bsky.social · 06/04/2026

In partnership with metapsy.org we built an interactive dashboard for exploring the data. Users can: • Filter studies by characteristics • Test different analysis parameters • Test moderators • Download figures and reports Dashboard: metapsy.org/sypres/psilo...

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Parker Singleton @parkersingleton.bsky.social · 06/04/2026

Our database includes 200+ effect sizes, encompassing all depression outcomes and timepoints reported by arm in each of the 15 RCTs included. This database can be downloaded from our website (sypres.io) or imported directly into R environments using the metapsyData package.

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Parker Singleton @parkersingleton.bsky.social · 06/04/2026

Here, in our first SYPRES review, psilocybin showed a greater reduction in depression scores compared to control conditions, with a pooled Hedges’ g = -0.90. But important caveats—small studies, blinding challenges, risk of bias, and design heterogeneity, which we discuss in the paper.

Forrest plot of main meta-analytic model.

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Parker Singleton @parkersingleton.bsky.social · 06/04/2026

sypres.io is our new initiative featuring: 📊 Regularly updated meta-analyses 🔍 User-guided sensitivity and moderation analysis 🕵️ Transparent study quality & risk-of-bias assessments ⚙️ Open code & data

Screen shot of psilocybin for depression study page on sypres.io

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Parker Singleton @parkersingleton.bsky.social · 06/04/2026

Psychedelic science has experienced the benefits and drawbacks of hype. However, new results are rarely contextualized in the historical body of evidence in an accessible manner. We need more robust, living, evidence synthesis that is visible and accessible to all stakeholders.

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Parker Singleton @parkersingleton.bsky.social · 18/08/2025

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Parker Singleton @parkersingleton.bsky.social · 18/08/2025

Our database includes 200+ effect sizes, encompassing all depression outcomes and timepoints reported by arm in each of the 12 RCTs included. This database can be downloaded from our website (sypres.io) or imported directly into R environments using the metapsyData package from metapsy.org

screenshot of the GitHub repository readme for the database

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Parker Singleton @parkersingleton.bsky.social · 18/08/2025

Here, psilocybin showed a greater reduction in depression scores compared to control conditions, with a pooled Hedges’ g = -0.91 (k = 9; p = 0.0013, I2 = 58.1%, n = 501). But important caveats—small studies, blinding challenges, risk of bias, and design heterogeneity, which we discuss in the paper.

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Parker Singleton @parkersingleton.bsky.social · 18/08/2025

screenshot of the psilocybin for depression section on sypres.io

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Parker Singleton @parkersingleton.bsky.social · 18/08/2025

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Parker Singleton @parkersingleton.bsky.social · 18/08/2025

🍄 Our new living systematic review and meta-analysis on psilocybin for depression is out. Here's what we found and the open science infrastructure we built to support it 🧪🧵 www.medrxiv.org/content/10.1...

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Parker Singleton @parkersingleton.bsky.social · 03/07/2025

yea, that random structure with a hypervalent nitrogen is definitely 5-meo-dmt

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

Lastly but not leastly, we used PK/PD modeling to recapitulate DMT's impacts on control energy - demonstrating that control models can predict (some) pharmacological effects on brain dynamics.

scheme of simulation paradigm and simulated vs empirical traces overlaid

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

We used dominance analysis to compare 2a's relative importance in explaining these regional metrics compared with other serotonin receptors. See the paper/SI for interesting negative controls using the placebo condition for the last two figures.

dominance analysis radar plot showing 2a most dominate for all 3 regional metrics

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

Three regional metrics correlated spatially with serotonin 2a density (from PET): a) the amount of control energy reduction by DMT, b) each region's temporal coupling between control energy and LZ, and c) each region's temporal coupling between control energy and drug intensity.

three scatterplots of regional CE metrics vs 2a density

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

Over time, control energy reductions by DMT (an fMRI measure) correlated with increases in Lempel-Ziv complexity (from EEG) and subjective drug intensity ratings.

CE over time plotted alongside LZ and intensity

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

On a network level, these reductions were most prominent in the DMN, FPN, and VIS networks. Notably, in these three networks the effect was strongest during DMT's peak effects (first half) for the DMN and FPN, but the reverse was true for VIS. See paper for discussion on arousal.

traces of CE for each network under DMT and PCB

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

Control energy (whole-brain) was reduced under DMT compared with placebo for ~2/3rds of post-injection time-points.

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

We deployed a time-resolved network control analysis of the brain's trajectory through its activational landscape to map control energy in the brain as it changes throughout the scanning sessions.

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Parker Singleton @parkersingleton.bsky.social · 21/04/2025

The serotonergic psychedelic DMT induces a profoundly immersive altered state of consciousness lasting under 20 minutes, allowing the entire experience to be captured during a single scanning session. Here, we analyzed data from N=14 individuals under-going simultaneous EEG-fMRI.

fMRI-EEG recorded for 8 mins before and 20 mins after injection of DMT/placebo

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Parker Singleton @parkersingleton.bsky.social · 10/01/2025

SYPRES (Synthesis of Psychedelic Research Studies) will be an open-access, online, interactive dashboard featuring: 📊 Regularly updated meta-analyses 🔍 User-guided sensitivity and moderation analysis 🕵️ Transparent study quality & risk-of-bias assessments ⚙️ Open code & data

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Parker Singleton @parkersingleton.bsky.social · 10/01/2025

In a media environment where individual studies get featured prominently in the NY Times, lay observers might think psychedelics are proven safe and effective 100 times over (i.e. hype). New results are rarely contextualized in the historical body of evidence in an accessible manner.

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Parker Singleton @parkersingleton.bsky.social · 26/11/2024

someone seriously needs to change this law

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Parker Singleton @parkersingleton.bsky.social · 18/11/2024

Been wanting to do mushroom logs for years. Hurricane Helene provided the right wood at the right time so finally got it going. Hopefully we'll have shiitake abundance in several months. #fungifriends

bag of shiitake mycelium from mushroom mountain

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Parker Singleton @parkersingleton.bsky.social · 10/11/2023

Howdy yall, I help organize webinar called "Machine Learning in Medicine" that all are free to join. In 10 minutes we have Lena Maier-Hein presenting on the importance of scientific rigor in medical imaging AI. Hop in and see what its about! weillcornell.zoom.us/j/92581271707 Passcode: 437063

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Parker Singleton @parkersingleton.bsky.social · 01/11/2023

Me simply not getting desk rejected by frontiers:

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Parker Singleton @parkersingleton.bsky.social · 13/10/2023

Howdy #machinelearning in #medicine folks. Will be hosting this seminar in a half hour - all are free to join. Helen Zhou: "Towards Characterizing and Adapting to Shifts in Medical Data over Time" weillcornell.zoom.us/j/9258127170... 437063

Abstract: As machine learning algorithms in healthcare transition from research into deployment, they face a constantly evolving environment, rife with changing clinical practices, data collection policies, patient populations, and even diseases themselves. Models that performed well in the past are liable to fail in the future, and especially in such high-stakes settings as healthcare, complacency can have consequences. In this talk, we start by empirically characterizing real-world shifts over time in medical data by examining model performance using a deployment-oriented evaluation framework (EMDOT). Inspired by the concept of backtesting, EMDOT simulates possible training procedures that practitioners might have been able to execute at each point in time, and evaluates the resulting models on all future time points. Across six distinct sources of medical data, we find varying levels of performance improvement and degradation, and we inspect surprising jumps in performance over time

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