The many-to-many problem of endophenotypes in psychiatry - a biological perspective - Molecular Psychiatry Molecular Psychiatry - The many-to-many problem of endophenotypes in psychiatry - a biological perspective

Despite decades of research there are still no reliable biomarkers for any of the major psychiatric diagnoses. In this now published perspective in Molecular Psychiatry we consolidate evidence from genetics, exposome and neuroimaging research on the potential reasons underlying these failures.

Anatomic & functional brain atlases: connecting functional flexibility with fixed infrastructure - Brain Structure and Function Brain Structure and Function - Recent advancements in imaging across scales has propelled the development of anatomic brain atlases reflecting a wide range of cellular properties. It’s...

It's important to note that when discussing brain structure-function relationships across scales, that the definition of function changes with scale. Keep it straight! @sbe.bsky.social link.springer.com/article/10.1...

🚨 New preprint alert! 🚨 Transdiagnostic latent factor models of psychopathology are widely assumed to improve brain-behaviour associations. So we decided to test this directly and found that they don't. A short 🧵

Link: www.biorxiv.org/content/10.6...

1/10

👉Up next, we are excited to welcome @sbe.bsky.social for our next CCP Speaker Series talk! Join us THIS FRIDAY, December 5th, at 1pm EST

RSVP HERE for more details: docs.google.com/forms/d/e/1F...

✨ New preprint ✨

Introducing cuBNM, a toolbox we developed to run brain simulations *much* more efficiently on GPUs 🎉🚀

Preprint: www.biorxiv.org/content/10.1...
Documentation: cubnm.readthedocs.io/en/stable/
GitHub: github.com/amnsbr/cubnm

1/n 🧵

How is self-reported sleep-disordered breathing linked with biomarkers of Alzheimer’s disease? Sleep-disordered breathing (SDB) is prevalent in Alzheimer’s disease (AD). Here, we assessed how self-reported SDB is linked with AD biomarkers, inclu…

✨ SHINY NEW PAPER ✨

Sleep-disordered breathing is associated with higher Aβ burden, lower rFDG, and worse cognition in Alzheimer's Disease.

@sbe.bsky.social

www.sciencedirect.com/science/article/pii/S0197458025001034

Excited to see this work published in @natcomms.nature.com! 🧠📊 In-vivo evidence of E/I alterations underlying functional changes in individuals with autism in the large ABIDE1 dataset AND replication in the independent ABIDE2 dataset.

New preprint from stellar IRTG PhD student Amelie Rauland + team on white matter bundle reconstruction! Shows that WM bundles can be reliably extracted from simple 32-direction dMRI & features predict cognition - huge potential for legacy and clinical data. Thread 👇

www.biorxiv.org/cgi/content/...

The end of a PhD journey 🎓 What a ride - full of lovely people, exciting science, coding tunnels, coffee-brainstorms & amazing conferences! Whole-hearted thank you to all who shared the journey & celebrated with me - especially @sofievalk.bsky.social, @sbe.bsky.social &@mps-cognition.bsky.social 💐🥳❤️

1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social

AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements...

doi.org/10.1038/s415...

Amelie Rauland (all star via IRTG student w/ @sbe.bsky.social ) will be presenting "Benchmarking Reconstruction Methods for Bundle Segmentation in Single-Shell dMRI" on Friday and Saturday, Poster #1283

Mehrere Liniendiagramme, die die durchschnittliche tägliche Nutzungsdauer von Smartphones in verschiedenen Kommunikationskategorien zeigen. In Türkis sind jeweils die Daten der Kontrollgruppe mit typischer neurologischer Entwicklung (TD) abgebildet, und in rosa die Daten der Erwachsenen mit Autismus-Spektrum-Störung (ASD). In der Gesamtkommunikation zeigt sich kein Unterschied zwischen den Gruppen (unterste Grafik). Die TD Gruppe nutzt jedoch mehr verbale Kommunikation als die ASD Gruppe (oberste Grafik), während die ASD Gruppe mehr schriftliche Kommunikation nutzt (zweite Grafik).

#ABCD-J #Studienhighlights: Eine kürzlich erschienene Studie zeigt mittels #JTrack Social Unterschiede in Kommunikationsmustern zwischen neurotypischen Erwachsenen und Erwachsenen mit #Autismus-Spektrum-Störung auf. Neugierig geworden? Das ganze Highlight gibt es hier: www.abcd-j.de/de/showcases...

Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling Individualized simulations reveal a decrease in excitation-inhibition ratio in association areas throughout adolescence.

🧠⚖️📉 How does the cortical excitation-inhibition ratio mature during adolescence?
We asked this in our new paper just out in #ScienceAdvances ✨
“Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling”
📄 www.science.org/doi/full/10....
🧵⤵️

⭐ new paper

Maturation of cortical excitation-inhibition (EI) in adolescence

now in Science Advances 💥

A true tour de force of multimodal MRI, connectomics & GPU-powered 🤖!! biophysical modelling

by hiball superheroes 🦸‍♂️🦸‍♀️🦸‍♂️
@amnsbr.bsky.social @sofievalk.bsky.social @sbe.bsky.social

Our #metaanalysis (n = 231) in JAMAPsych reveals consistent brain abnormalities across #sleep disorders involving affective and cognitive hubs in sgACC, amygdala/hippocampus

Short-term sleep deprivation shows a distinct picture, affecting the thalamus

jamanetwork.com/journals/jam...

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results - Nature Communications Here, the authors present the Network Correspondence Toolbox, which enables researchers to examine and report spatial correspondence between their neuroimaging results and widely used brain atlases.

It's finally here! Use the Network Correspondence Toolbox to help contextualize your neuroimaging findings 🧠

Connectivity profile and function of uniquely human cortical areas Determining the brain specializations unique to humans requires directly comparative anatomical information from other primates, especially our closest relatives. Human ( Homo sapiens ) (m/f), chimpan...

Always thought we should use connectivity blueprints to investigate differences between human, chimpanzee, and macaque brains? www.jneurosci.org/content/earl... For Katherine Bryant’s latest masterpiece.

Analysis Methods: 11:00 – 11:30
Harmonize: Leakage in data harmonization
JuSpace: cross-modal brain map correlation

Applied Concepts: 11:30 – 12:00
Causal modeling for Confounds in ML
Subtypes of major depression

NOW, Join if you're interessted
Topic: DGKN Hackroom 2025 — Tool Talks
Time: Mar 14, 2025 10:30 - 12h
fz-juelich-de.zoom.us/j/6792251876...

Data Management: 10:30 – 11:00
Datalad: GPDR conform collaboration
Jtrack: remote wearable assessment

Real world evidence for altered communication patterns in individuals with autism spectrum disorder - npj Digital Medicine npj Digital Medicine - Real world evidence for altered communication patterns in individuals with autism spectrum disorder

Now out in NPJ Digital Medicine our manuscript on real-world evidence for altered communication preferences in autism: nature.com/articles/s41...
@sbe.bsky.social
@leoschilbach.bsky.social

Neurosalience #S5E10 with Simon Eickhoff - From Big Data to Biomarkers YouTube video by Organization for Human Brain Mapping

A new #Neurosalience episode is out! 🧠

From Big Data to Biomarkers
with Peter Bandettini @fmri-today.bsky.social and Simon Eickhoff @sbe.bsky.social

youtu.be/z91SiENsgF8

Dazu integrieren wir – das Institut für Neurowissenschaften und Medizin: Gehirn und Verhalten (INM-7) @fz-juelich.de, und unsere klinischen Partner an den Unikliniken Aachen, Bonn, Köln, und Düsseldorf – verschiedene technische Lösungen zu einer digitalen Plattform und vernetzen uns.

Thanks to everyone's feedback, we have updated our calculator to optimize sample size N & scan time T for fMRI studies: leonoqr.github.io/ORSP_Calcula...

The first new feature is that users can explore how different N & T leads to different accuracy, e.g., N=1000 & T=30min => 81% max accuracy. 🧵

Fully agreed. This adds another layer of complexity

We likely need to differentiate three aspects here
- "True" changes of the trait
- State-changes due to, e.g., sleep
- Measurement error

All three can and will superimpose

In my view, the take-home message is slightly different from the BWAS paper. The latter argued you need large N.

This paper shows that if your target is unreliable, even very large numbers can’t save you

Also, association vs prediction

How measurement noise limits the accuracy of brain-behaviour predictions - Nature Communications Our ability to identify associations between behaviour and brain imaging is important for uncovering markers of cognition and disease. Here, the authors illustrate the importance of the reliability of...

The limiting factor for MRI based prediction of behavioral traits may be neither the imaging nor the pipeline or learning algorith but rather the reliability of the target phenotypes

Thought provoking work my Martin Gell et al:

www.nature.com/articles/s41...

This is what happens if preprints are treated as „scientific literature“

Don’t get me wrong, I am all for preprinting. But unfortunately, it is often ignored that anybody can preprint anything

We created a #neuroinformagical (obvs) starter pack! Let us know who else to add!
#openscience #datasharing #neuroskyence 🧪

go.bsky.app/LRqnaMP