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Lijun An @anlijuncn.bsky.social · Jul 22

Check out lab's latest preprint for MRI super resolution!!!

Jake Vogel @jwvogel.bsky.social · Jul 22

‼️NEW PREPRINT‼️

What if you could take a normal 3T T1w MRI and make it look like it was acquired from a 7T scanner?

That's exactly what we do using AI in our new preprint!

Link: arxiv.org/abs/2507.13782

#neuroskyence #neurosky #compneuro #AI #datascience #neurology #mrisky #neuroimaging

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Reposted by Lijun An

Jake Vogel @jwvogel.bsky.social · Jul 17

Incredibly excited for this new work from our lab. We test the potential of AI-based neurodegenerative disease diagnostics using plasma proteomics data from n>17,000 people, led by the brilliant and indefatigable @anlijuncn.bsky.social Check it out!👇

Lijun An @anlijuncn.bsky.social · Jul 15

Can AI reveal risk & co-pathology of multiple neurodegenerative diseases from a single blood sample? We explored AI-based diagnostic power on high rank plasma proteomics (N=17,170). www.medrxiv.org/content/10.1... #neuroskyence #neurosky #Alzheimer #compneuro #AI #datascience #neurology

Benchmarking the AI-based diagnostic potential of plasma proteomics for neurodegenerative disease in 17,170 people

Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathologi...

www.medrxiv.org

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Reposted by Lijun An

Thomas Yeo @bttyeo.bsky.social · Jul 17

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...

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Lijun An @anlijuncn.bsky.social · Jul 15

dsdsd

Lijun An @anlijuncn.bsky.social · Jul 15

[10/10] Finally, thanks to all our best collaborators: Bart Smets, Rowan Saloner, Shinya Tasaki, Ying Xu, Varsha Krish, Farhad Imam, Danielle van Westen, Christopher D. Whelan!

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Lijun An @anlijuncn.bsky.social · Jul 15

[9/10] And massive thanks to the coolest ‪@biofinder.bsky.social‬ team—Shorena Janelidze, Erik Stomrud, Sebastian Palmqvist, ‪@rikossenkoppele.bsky.social‬, Niklas Mattsson-Carlgren, and Oskar Hansson —for sharing the awesome data and supporting this project every step of the way.

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Lijun An @anlijuncn.bsky.social · Jul 15

[8/10] Endless thanks to ‪@jwvogel.bsky.social‬ for guiding and supporting this work from day one. To our amazing team, especially Alexa Pichet Binette, Ines Hristovska, Gabriele Vilkaite, and @xiaoyucaly.bsky.social‬, for their enormous support.

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Lijun An @anlijuncn.bsky.social · Jul 15

[7/10] ProtAIDe-Dx is a step toward the development of scalable, minimally invasive, and multi-disease diagnostic tools for neurodegenerative diseases. We hope our work can establish a benchmark for AI-driven proteomics tools, paving the way for precision medicine in these diseases.

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Lijun An @anlijuncn.bsky.social · Jul 15

[6/10] We built a proof-of-concept diagnostic report with ProtAIDe-Dx, visualizing diagnostic probabilities across conditions and highlighting proteins that contributed to the individual’s prediction and traits linked to those proteins, enabling a biologically interpretable diagnosis.[Figure 5]

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Lijun An @anlijuncn.bsky.social · Jul 15

[5/10] Could ProtAIDe-Dx aid real-world clinical application? In a memory clinic cohort, ProtAIDe-Dx significantly improved differential diagnosis (especially PD and CVD) when combined with accessible clinical biomarkers (MMSE, MRI, plasma p-tau217 & NfL). (Figure 4D).

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Lijun An @anlijuncn.bsky.social · Jul 15

[4/10] ProtAIDe-Dx identified a novel and compact set of top predictive proteins. Notably, OMG and Histone H1-2 (H1-2) distinguished controls from all neurodegenerative conditions, and ubiquitin (UBB) was a discriminative protein for FTD. [Figure 3A].

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Lijun An @anlijuncn.bsky.social · Jul 15

[3/10] The models’ diagnostic probabilities highlighted subgroups of patients with proteomic patterns more similar to a different condition, e.g., AD patients resembling stroke patients, indicating possible misdiagnosis, potential co-pathology, or may even indicate distinct etiologies. [Figure 2A].

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Lijun An @anlijuncn.bsky.social · Jul 15

[2/10] We developed a multi-task model, ProtAIDe-Dx, to provide simultaneous probabilistic diagnosis across six conditions associated with dementia in aging (Control, AD, PD, FTD, ALS, and Stroke/TIA). CVed balanced ranged from 69%-96% and AUCs > 79% across all conditions. [Figure 1B]

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Lijun An @anlijuncn.bsky.social · Jul 15

[1/10] For the first time, we can answer this question with the world's largest neurodegeneration proteomics consortium, GNPC @neuroproteome.bsky.social #GNPC #Neurodegeneration #Proteomics #OpenScience #ADRD #Biomarkers #Parkinsons #ALS #FTD #NatureMedicine #NatureAging #SomaScan #Plasma #CSF

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Lijun An @anlijuncn.bsky.social · Jul 15

Can AI reveal risk & co-pathology of multiple neurodegenerative diseases from a single blood sample? We explored AI-based diagnostic power on high rank plasma proteomics (N=17,170). www.medrxiv.org/content/10.1... #neuroskyence #neurosky #Alzheimer #compneuro #AI #datascience #neurology

Benchmarking the AI-based diagnostic potential of plasma proteomics for neurodegenerative disease in 17,170 people

Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathologi...

www.medrxiv.org

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Lijun An @anlijuncn.bsky.social · May 2

Check out Xiaoyu’s fantastic work!!

Yu Xiao (肖燏) @xiaoyucaly.bsky.social · Apr 24

🧠New preprint! What drives tau, the pathological protein in AD, to spread?
We found that WHERE tau appears and HOW MUCH accumulates are governed by different mechanisms. Check it out: www.biorxiv.org/content/10.1... 

#MedSky #neuroskyence #neurosky #alzsky #compneuro #MRI #neuroimaging #neurology

www.biorxiv.org

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Reposted by Lijun An

Yu Xiao (肖燏) @xiaoyucaly.bsky.social · Apr 24

🧵15/ Huge thanks to our amazing team and coauthors!

Endless thanks to @jwvogel.bsky.social for guiding and supporting this work from day one. To our amazing team DeMON lab, especially @anlijuncn.bsky.social for enormous support.

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Reposted by Lijun An

Shaoshi Zhang @shaoshiz.bsky.social · Apr 11

Check our latest preprint led by the amazing @tianchu.bsky.social and @tianfang.bsky.social where we speed up the tedious parameter optimization process for biophysical modelling

Thomas Yeo @bttyeo.bsky.social · Apr 11

While the world burns, we cook up a new preprint! doi.org/10.1101/2025...

Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N

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Reposted by Lijun An

Lianglong Sun @longonga.bsky.social · Apr 4

Happy to share that our article “Human lifespan changes in the brain’s functional connectome” is now published online at Nature Neuroscience @natureneuro.bsky.social !

Many thanks to all collaborators & data contributors, and the editor team & reviewers!

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

Human lifespan changes in the brain’s functional connectome - Nature Neuroscience

Sun et al. report human lifespan changes in the brain’s functional connectome in 33,250 individuals, which highlights critical growth milestones and distinct maturation patterns and offers a normative...

www.nature.com

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Reposted by Lijun An

Thomas Yeo @bttyeo.bsky.social · Apr 11

While the world burns, we cook up a new preprint! doi.org/10.1101/2025...

Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N

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Reposted by Lijun An

Thomas Yeo @bttyeo.bsky.social · Mar 26

Updated preprint for those who might be interested: doi.org/10.1101/2024...

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Lijun An @anlijuncn.bsky.social · Mar 26

Excellent work by Ruby!!

Thomas Yeo @bttyeo.bsky.social · Mar 26

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

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.

www.nature.com

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Reposted by Lijun An

Hu Chuan-Peng @hcp4715.bsky.social · Dec 11

a cool new study "established in India & Tanzania, with appropriate training, structured teams, & daily automated analysis & feedback, non-specialists can reliably collect EEG data alongside various survey & assessments w/ consistently high throughput & quality. "
www.biorxiv.org/content/10.1...

EEG data quality in large scale field studies in India and Tanzania

There is a growing imperative to understand the neurophysiological impact of our rapidly changing and diverse technological, social, chemical, and physical environments. To untangle the multidimension...

www.biorxiv.org

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Reposted by Lijun An

Emily Finn @esfinn.bsky.social · Dec 11

This paper sets up a bit of a straw man in that I don't think most people who use movies and stories as fMRI stimuli assume that all movies will (or should) evoke the same response. In a naturalistic neuroimaging expt, the movie *is* the task...

Between-movie variability severely limits generalizability of “naturalistic” neuroimaging

“Naturalistic imaging” paradigms, where participants watch movies during fMRI, have gained popularity over the past two decades. Many movie-watching studies measure inter-subject correlation (ISC), wh...

www.biorxiv.org

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Reposted by Lijun An

Thomas Yeo @bttyeo.bsky.social · Nov 20

🚨 Brain Age vs Direct Models in Alzheimer’s disease (AD) 🚨 A thread 🧵

1/ Brain age is a powerful indicator of general brain health, trained on massive datasets. But does this translate to better prediction for specific outcomes, like AD?

Preprint by @twktan.bsky.social : doi.org/10.1101/2024...

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