Lijun An's Avatar

Lijun An

@anlijuncn.bsky.social

Postdoc@Jacob Vogel ⬅️ PhD@Thomas Yeo. Neurodegenerative Disease, Brain Imaging, Machine Learning, Multi-omics https://anlijun.cn

185 Followers  |  66 Following  |  15 Posts  |  Joined: 20.11.2024  |  1.8067

Latest posts by anlijuncn.bsky.social on Bluesky

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

22.07.2025 05:52 — 👍 3    🔁 0    💬 0    📌 0

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!👇

17.07.2025 05:51 — 👍 2    🔁 2    💬 0    📌 0
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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...

17.07.2025 01:36 — 👍 172    🔁 82    💬 3    📌 16

dsdsd

15.07.2025 20:07 — 👍 0    🔁 0    💬 0    📌 0

[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!

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0

[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.

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0

[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.

15.07.2025 20:07 — 👍 1    🔁 0    💬 1    📌 0

[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.

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0
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[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]

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0
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[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).

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0
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[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].

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0
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[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].

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0
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[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]

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0

[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

15.07.2025 20:07 — 👍 0    🔁 0    💬 1    📌 0
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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...

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

15.07.2025 20:07 — 👍 9    🔁 5    💬 1    📌 2

Check out Xiaoyu’s fantastic work!!

02.05.2025 09:51 — 👍 2    🔁 1    💬 0    📌 0
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🧵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.

24.04.2025 19:41 — 👍 2    🔁 1    💬 1    📌 0

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

11.04.2025 05:27 — 👍 1    🔁 1    💬 0    📌 0
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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...

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...

04.04.2025 05:48 — 👍 36    🔁 19    💬 3    📌 1
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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

11.04.2025 01:35 — 👍 102    🔁 38    💬 1    📌 4

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

26.03.2025 04:50 — 👍 34    🔁 17    💬 0    📌 1

Excellent work by Ruby!!

26.03.2025 07:24 — 👍 2    🔁 0    💬 0    📌 0
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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...

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...

11.12.2024 12:19 — 👍 2    🔁 1    💬 0    📌 0
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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...

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...

11.12.2024 15:30 — 👍 70    🔁 7    💬 9    📌 0
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🚨 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...

20.11.2024 03:06 — 👍 46    🔁 20    💬 2    📌 1

@anlijuncn is following 20 prominent accounts