Lijun An
LightNews LightNews
banner
anlijuncn.bsky.social
Lijun An
@anlijuncn.bsky.social
190 followers 67 following 15 posts
Postdoc@Jacob Vogel ⬅️ PhD@Thomas Yeo. Neurodegenerative Disease, Brain Imaging, Machine Learning, Multi-omics

https://anlijun.cn
Posts Replies Media Videos
Pinned
anlijuncn.bsky.social
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
Reposted by Lijun An
hansonmark.bsky.social
We wrote the Strain on scientific publishing to highlight the problems of time & trust. With a fantastic group of co-authors, we present The Drain of Scientific Publishing:

a 🧵 1/n

Drain: arxiv.org/abs/2511.04820
Strain: direct.mit.edu/qss/article/...
Oligopoly: direct.mit.edu/qss/article/...
A table showing profit margins of major publishers. A snippet of text related to this table is below. 1. The four-fold drain 1.1 Money Currently, academic publishing is dominated by profit-oriented, multinational companies for whom scientific knowledge is a commodity to be sold back to the academic community who created it. The dominant four are Elsevier, Springer Nature, Wiley and Taylor & Francis, which collectively generated over US$7.1 billion in revenue from journal publishing in 2024 alone, and over US$12 billion in profits between 2019 and 2024 (Table 1A). Their profit margins have always been over 30% in the last five years, and for the largest publisher (Elsevier) always over 37%. Against many comparators, across many sectors, scientific publishing is one of the most consistently profitable industries (Table S1). These financial arrangements make a substantial difference to science budgets. In 2024, 46% of Elsevier revenues and 53% of Taylor & Francis revenues were generated in North America, meaning that North American researchers were charged over US$2.27 billion by just two for-profit publishers. The Canadian research councils and the US National Science Foundation were allocated US$9.3 billion in that year. A figure detailing the drain on researcher time. 1. The four-fold drain 1.2 Time The number of papers published each year is growing faster than the scientific workforce, with the number of papers per researcher almost doubling between 1996 and 2022 (Figure 1A). This reflects the fact that publishers’ commercial desire to publish (sell) more material has aligned well with the competitive prestige culture in which publications help secure jobs, grants, promotions, and awards. To the extent that this growth is driven by a pressure for profit, rather than scholarly imperatives, it distorts the way researchers spend their time. The publishing system depends on unpaid reviewer labour, estimated to be over 130 million unpaid hours annually in 2020 alone (9). Researchers have complained about the demands of peer-review for decades, but the scale of the problem is now worse, with editors reporting widespread difficulties recruiting reviewers. The growth in publications involves not only the authors’ time, but that of academic editors and reviewers who are dealing with so many review demands. Even more seriously, the imperative to produce ever more articles reshapes the nature of scientific inquiry. Evidence across multiple fields shows that more papers result in ‘ossification’, not new ideas (10). It may seem paradoxical that more papers can slow progress until one considers how it affects researchers’ time. While rewards remain tied to volume, prestige, and impact of publications, researchers will be nudged away from riskier, local, interdisciplinary, and long-term work. The result is a treadmill of constant activity with limited progress whereas core scholarly practices – such as reading, reflecting and engaging with others’ contributions – is de-prioritized. What looks like productivity often masks intellectual exhaustion built on a demoralizing, narrowing scientific vision. A table of profit margins across industries. The section of text related to this table is below: 1. The four-fold drain 1.1 Money Currently, academic publishing is dominated by profit-oriented, multinational companies for whom scientific knowledge is a commodity to be sold back to the academic community who created it. The dominant four are Elsevier, Springer Nature, Wiley and Taylor & Francis, which collectively generated over US$7.1 billion in revenue from journal publishing in 2024 alone, and over US$12 billion in profits between 2019 and 2024 (Table 1A). Their profit margins have always been over 30% in the last five years, and for the largest publisher (Elsevier) always over 37%. Against many comparators, across many sectors, scientific publishing is one of the most consistently profitable industries (Table S1). These financial arrangements make a substantial difference to science budgets. In 2024, 46% of Elsevier revenues and 53% of Taylor & Francis revenues were generated in North America, meaning that North American researchers were charged over US$2.27 billion by just two for-profit publishers. The Canadian research councils and the US National Science Foundation were allocated US$9.3 billion in that year. The costs of inaction are plain: wasted public funds, lost researcher time, compromised scientific integrity and eroded public trust. Today, the system rewards commercial publishers first, and science second. Without bold action from the funders we risk continuing to pour resources into a system that prioritizes profit over the advancement of scientific knowledge.
November 11, 2025 at 11:52 AM
Reposted by Lijun An
bttyeo.bsky.social
🚨 Predicting Alzheimer's Progression 🚨 A thread 🧵

1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨

📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...
November 20, 2024 at 2:30 AM
Reposted by Lijun An
bttyeo.bsky.social
Preprint is now published! doi.org/10.1002/hbm....

Thanks to co-authors @chen-zhang.bsky.social @anlijuncn.bsky.social @csabaorban.bsky.social
November 4, 2025 at 12:35 AM
anlijuncn.bsky.social
Check out lab's latest preprint for MRI super resolution!!!
jwvogel.bsky.social 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
July 22, 2025 at 5:52 AM
Reposted by Lijun An
jwvogel.bsky.social
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!👇
July 17, 2025 at 5:51 AM
Reposted by Lijun An
bttyeo.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...
July 17, 2025 at 1:36 AM
anlijuncn.bsky.social
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
July 15, 2025 at 8:08 PM
anlijuncn.bsky.social
Check out Xiaoyu’s fantastic work!!
xiaoyucaly.bsky.social 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
May 2, 2025 at 9:51 AM
Reposted by Lijun An
xiaoyucaly.bsky.social
🧵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.
April 24, 2025 at 7:42 PM
Reposted by Lijun An
shaoshiz.bsky.social
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
bttyeo.bsky.social 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
April 11, 2025 at 5:27 AM
Reposted by Lijun An
longonga.bsky.social
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
April 4, 2025 at 5:48 AM
Reposted by Lijun An
bttyeo.bsky.social
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
April 11, 2025 at 1:35 AM
Reposted by Lijun An
bttyeo.bsky.social
Updated preprint for those who might be interested: doi.org/10.1101/2024...
March 26, 2025 at 4:50 AM
anlijuncn.bsky.social
Excellent work by Ruby!!
March 26, 2025 at 7:24 AM
Reposted by Lijun An
hcp4715.bsky.social
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
December 11, 2024 at 12:19 PM
Reposted by Lijun An
esfinn.bsky.social
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
December 11, 2024 at 3:30 PM
Reposted by Lijun An
bttyeo.bsky.social
🚨 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...
November 20, 2024 at 3:06 AM